title: A polar model for fast object tracking in 360-degree camera images

abstract: The task of fast object tracking in polar images using emerging high-resolution 360-degree camera technology is presented in this paper. In this approach, when an arbitrary object has been selected in the first frame, the proposed method searches for the object in the next frames. This task is challenging when the video contains complexity which cannot be handled by common tracking methods. The main contribution of this paper uses polar object selection and color binary features to facilitate robust object tracking in 360-degree images. Using the proposed polar object selection method, each object is represented by a polar component and high performance of the tracking algorithm in terms of precision and speed is achieved. We evaluate the applicability of our approach on a new dataset containing more than 30000 frames of 360-degree images wherein high performance in challenging real-world scenarios is demonstrated. The proposed algorithm outperforms the related methods.


Dynamic Walking 2014 A Lower Extremity Soft Robotic Exoskeleton


title: Distributed contour trees

abstract: Topological techniques provide robust tools for data analysis. They are used, for example, for feature extraction, for data de-noising, and for comparison of data sets. This chapter concerns contour trees, a topological descriptor that records the connectivity of the isosurfaces of scalar functions. These trees are fundamental to analysis and visualization of physical phenomena modeled by real-valued measurements.


title: Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design

abstract: Deep convolutional networks have witnessed unprecedented success in various machine learning applications. Formal understanding on what makes these networks so successful is gradually unfolding, but for the most part there are still significant mysteries to unravel. The inductive bias, which reflects prior knowledge embedded in the network architecture, is one of them. In this work, we establish a fundamental connection between the fields of quantum physics and deep learning. We use this connection for asserting novel theoretical observations regarding the role that the number of channels in each layer of the convolutional network fulfills in the overall inductive bias. Specifically, we show an equivalence between the function realized by a deep convolutional arithmetic circuit (ConvAC) and a quantum many-body wave function, which relies on their common underlying tensorial structure. This facilitates the use of quantum entanglement measures as well-defined quantifiers of a deep network's expressive ability to model intricate correlation structures of its inputs. Most importantly, the construction of a deep ConvAC in terms of a Tensor Network is made available. This description enables us to carry a graph-theoretic analysis of a convolutional network, with which we demonstrate a direct control over the inductive bias of the deep network via its channel numbers, that are related to the min-cut in the underlying graph. This result is relevant to any practitioner designing a network for a specific task. We theoretically analyze ConvACs, and empirically validate our findings on more common ConvNets which involve ReLU activations and max pooling. Beyond the results described above, the description of a deep convolutional network in well-defined graph-theoretic tools and the formal connection to quantum entanglement, are two interdisciplinary bridges that are brought forth by this work.


ECDKG: A Distributed Key Generation Protocol Based on Elliptic Curve Discrete Logarithm ∗


title: Secure agreement protocols: reliable and atomic group multicast in rampart

abstract: Reliable and atomic group multicast have been proposed as fundamental communication paradigms to support secure distributed computing in systems in which processes may behave maliciously. These protocols enable messages to be multicast to a group of processes, while ensuring that all honest group members deliver the same messages and, in the case of atomic multicast, deliver these messages in the same order. We present new reliable and atomic group multicast protocols for asynchronous distributed systems. We also describe their implementation as part of Rampart, a toolkit for building high-integrity distributed services, i.e., services that remain correct and available despite the corruption of some component servers by an attacker. To our knowledge, Rampart is the first system to demonstrate reliable and atomic group multicast in asynchronous systems subject to process corruptions.


title: Sparse-MVRVMs Tree for Fast and Accurate Head Pose Estimation in the Wild

abstract: Head pose estimation is an important problem in the field of computer vision and facial analysis. We model the problem of head pose estimation as a regression problem, where the three rotation angles (yaw, pitch, roll) are functions of the face appearance. We make use of that fact and learn the appearance of the face using a tree cascade of sparse Multi-Variate Relevance Vector Machines (MVRVM). Our method is fast and suitable for real-time applications as it is not computationally expensive. Our method learns the face appearance to estimate the head rotation angles. We evaluated our approach on two challenging datasets, the YouTube Faces and the Point and Shoot Challenging (PaSC) dataset. We achieved results of head pose estimation (yaw, pitch, roll) with mean error less than 5\(\circ \) and with error tolerance less than ±4 on the PaSC dataset. In terms of speed, one prediction takes around 6 milliseconds, which is suitable for real-time applications and also with high frame rate.


title: Design of a Class D Audio Amplifier IC Using Sliding Mode Control and Negative Feedback

abstract: In recent years, class D audio amplifiers are becoming the most feasible solution for low-voltage low- power applications due to their high efficiency property; however, to obtain good linearity for high fidelity systems is still a challenge. This work does not require the triangular carrier signal used in conventional class D audio amplifiers. It is shown that by making use of the sliding mode (SM) control technique along with an extra local feedback loop, the design parameters of a class D audio amplifier can be selected according to the linearity requirements. These techniques are applied in the design of a class D audio amplifier to yield a single-chip low distortion audio amplifier with efficiency above 90% and THD as low as 0.08%. Experimental IC results, using a commercial 0.5- mum CMOS technology verified the theoretical results.


title: G-Store: High-Performance Graph Store for Trillion-Edge Processing

abstract: High-performance graph processing brings great benefits to a wide range of scientific applications, e.g., biology networks, recommendation systems, and social networks, where such graphs have grown to terabytes of data with billions of vertices and trillions of edges. Subsequently, storage performance plays a critical role in designing a high-performance computer system for graph analytics. In this paper, we present G-Store, a new graph store that incorporates three techniques to accelerate the I/O and computation of graph algorithms. First, G-Store develops a space-efficient tile format for graph data, which takes advantage of the symmetry present in graphs as well as a new smallest number of bits representation. Second, G-Store utilizes tile-based physical grouping on disks so that multi-core CPUs can achieve high cache and memory performance and fully utilize the throughput from an array of solid-state disks. Third, G-Store employs a novel slide-cache-rewind strategy to pipeline graph I/O and computing. With a modest amount of memory, G-Store utilizes a proactive caching strategy in the system so that all fetched graph data are fully utilized before evicted from memory. We evaluate G-Store on a number of graphs against two state-of-the-art graph engines and show that G-Store achieves 2 to 8× saving in storage and outperforms both by 2 to 32×. G-Store is able to run different algorithms on trillion-edge graphs within tens of minutes, setting a new milestone in semi-external graph processing system.


title: A system for video surveillance and monitoring

abstract: Under the three-year Video Surveillance and Monitoring (VSAM) project (1997‐1999), the Robotics Institute at Carnegie Mellon University (CMU) and the Sarnoff Corporation developed a system for autonomous Video Surveillance and Monitoring. The technical approach uses multiple, cooperative video sensors to provide continuous coverage of people and vehicles in a cluttered environment. This final report presents an overview of the system, and of the technical accomplishments that have been achieved.


title: New Compact CMOS Li-Ion Battery Charger Using Charge-Pump Technique for Portable Applications

abstract: This paper presents a new compact CMOS Li-Ion battery charger for portable applications that uses a charge-pump technique. The proposed charger features a small chip size and a simple circuit structure. Additionally, it provides basic functions with voltage/current detection, end-of-charge detection, and charging speed control. The charger operates in dual-mode and is supported in the trickle/large constant-current mode to constant-voltage mode with different charging rates. This charger is implemented using a TSMC 0.35-mum CMOS process with a 5-V power supply. The output voltage is almost 4.2 V, and the maximum charging current reaches 700 mA. It has 67.89% power efficiency, 837-mW chip power dissipation, and only 1.455times1.348 mm2 in chip area including pads


title: Sparse Matrix Multiplication on CAM Based Accelerator

abstract: Sparse matrix multiplication is an important component of linear algebra computations. In this paper, an architecture based on Content Addressable Memory (CAM) and Resistive Content Addressable Memory (ReCAM) is proposed for accelerating sparse matrix by sparse vector and matrix multiplication in CSR format. Using functional simulation, we show that the proposed ReCAM-based accelerator exhibits two orders of magnitude higher power efficiency as compared to existing sparse matrix-vector multiplication implementations.


title: Go with the Flow: When Listeners Use Music as Technology

abstract: Music has been shown to have a profound effect on lis-teners' internal states as evidenced by neuroscience research. Listeners report selecting and listening to music with specific intent, thereby using music as a tool to achieve desired psychological effects within a given context. In light of these observations, we argue that music information retrieval research must revisit the dominant assumption that listening to music is only an end unto itself. Instead, researchers should embrace the idea that music is also a technology used by listeners to achieve a specific desired internal state, given a particular set of circumstances and a desired goal. This paper focuses on listening to music in isolation (i.e., when the user listens to music by themselves with headphones) and surveys research from the fields of social psychology and neuro-science to build a case for a new line of research in music information retrieval on the ability of music to produce flow states in listeners. We argue that interdisciplinary collaboration is necessary in order to develop the understanding and techniques necessary to allow listeners to exploit the full potential of music as psychological technology.


title: Urdu ligature recognition using multi-level agglomerative hierarchical clustering

abstract: Optical character recognition (OCR) system holds great significance in human-machine interaction. OCR has been the subject of intensive research especially for Latin, Chinese and Japanese script. Comparatively, little work has been done for Urdu OCR, due to the complexities and segmentation errors associated with its cursive script. This paper proposes an Urdu OCR system which aims at ligature-level recognition of Urdu text. This ligature based recognition approach overcomes the character-levelsegmentation problems associated with cursive scripts. A newly developed OCR algorithm is introduced that uses a semi-supervised multi-level clustering for categorization of the ligatures. Classification is performed using four machine learning techniques i.e. decision trees, linear discriminant analysis, naive Bayes and k-nearest neighbor (K-NN). The system was implemented and the results show 62, 61, 73 and 90% accuracy for decision tree, linear discriminant analysis, naive Bayes and K-NN respectively.


title: Revisiting Reuse in Main Memory Database Systems

abstract: Reusing intermediates in databases to speed-up analytical query processing was studied in prior work. Existing solutions require intermediate results of individual operators to be materialized using materialization operators. However, inserting such materialization operations into a query plan not only incurs additional execution costs but also often eliminates important cache- and register-locality opportunities, resulting in even higher performance penalties. This paper studies a novel reuse model for intermediates, which caches internal physical data structures materialized during query processing (due to pipeline breakers) and externalizes them so that they become reusable for upcoming operations. We focus on hash tables, the most commonly used internal data structure in main memory databases to perform join and aggregation operations. As queries arrive, our reuse-aware optimizer reasons about the reuse opportunities for hash tables, employing cost models that take into account hash table statistics together with the CPU and data movement costs within the cache hierarchy. Experimental results, based on our prototype implementation, demonstrate performance gains of 2x for typical analytical workloads with no additional overhead for materializing intermediates.


title: Emotions in robot psychology

abstract: In his famous thought experiments on synthetic vehicles, Valentino Braitenberg stipulated that simple stimulus-response reactions in an organism could evoke the appearance of complex behavior, which, to the unsuspecting human observer, may even appear to be driven by emotions such as fear, aggression, and even love (Braitenberg, Vehikel. Experimente mit kunstlichen Wesen, Lit Verlag, 2004). In fact, humans appear to have a strong propensity to anthropomorphize, driven by our inherent desire for predictability that will quickly lead us to discern patterns, cause-and-effect relationships, and yes, emotions, in animated entities, be they natural or artificial. But might there be reasons, that we should intentionally "implement" emotions into artificial entities, such as robots? How would we proceed in creating robot emotions? And what, if any, are the ethical implications of creating "emotional" robots? The following article aims to shed some light on these questions with a multi-disciplinary review of recent empirical investigations into the various facets of emotions in robot psychology.


title: A vision for micro and macro location aware services

abstract: A few decades ago, the Internet was created. Since then, searching for information and services has increased exponentially. With the introduction of GPS-enabled devices, a special type of search appeared offering location-aware services. These services customize search results based on users' location. This includes, but not limited to, (1) service finding, e.g., "find the nearest pizza restaurant", (2) routing, e.g., "obtain the shortest path from a user's home to the airport", (3) transportation, e.g., "what are the bus links to get a user from downtown to the mall", and (4) monitoring, e.g., "alert a parent if their child school-bus deviates from its regular route". Though new hardware and software technologies such as smart watches, voice search, and big-data platforms have been introduced and widely used, each single type of the above services has benefited very little from these technologies. On the local level of each service (the micro level), a full-fledged view is still missing. On the global level of all service types (the macro level), all Location-aware services are still acting as isolated islands and a global optimized service is not available. This paper presents our vision of how to provide an integrated macro location-aware service that acts harmoniously, and how each micro service can be further improved by better incorporation of novel technologies. We also overview the key challenges associated with these suggested improvements. Then, we highlight the potential value-added by the application of our vision.


title: A 2-V 10.7-MHz CMOS limiting amplifier/RSSI

abstract: This paper presents low-voltage low-power CMOS circuit design techniques for an intermediate frequency (IF) limiting amplifier and received signal strength indicator (RSSI). The architecture of the limiting amplifier and RSSI employed is determined by the optimal power consumption for a specified speed, overall gain, and accuracy. Each gain cell of the limiting amplifier employs folded diode load for low-voltage operation. Offset is reduced by a cross-connected source-coupled pair offset subtractor that is along the signal path. Full-wave current rectification and summation are employed in the RSSI circuit to achieve high precision while maintaining low voltage and low power. Using a single 2-V supply voltage, measured results demonstrate the input dynamic range is larger than 75 dB for 10.7-MHz IF application. The prototype occupies an active area of 0.4 mm/sup 2/ using a 0.6-/spl mu/m digital CMOS technology. The power dissipation is 6.2 mW.


title: Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

abstract: Image enhancement plays an important role in image processing and analysis. Among various enhancement algorithms, Retinex-based algorithms can efficiently enhance details and have been widely adopted. Since Retinex-based algorithms regard illumination removal as a default preference and fail to limit the range of reflectance, the naturalness of non-uniform illumination images cannot be effectively preserved. However, naturalness is essential for image enhancement to achieve pleasing perceptual quality. In order to preserve naturalness while enhancing details, we propose an enhancement algorithm for non-uniform illumination images. In general, this paper makes the following three major contributions. First, a lightness-order-error measure is proposed to access naturalness preservation objectively. Second, a bright-pass filter is proposed to decompose an image into reflectance and illumination, which, respectively, determine the details and the naturalness of the image. Third, we propose a bi-log transformation, which is utilized to map the illumination to make a balance between details and naturalness. Experimental results demonstrate that the proposed algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.


title: Geometric and Photometric Consistency in a Mixed Video and Galvanoscopic Scanning Laser Projection Mapping System

abstract: We present a geometric calibration method to accurately register a galvanoscopic scanning laser projection system (GLP) based on 2D vector input data onto an arbitrarily complex 3D-shaped projection surface. This method allows for accurate merging of 3D vertex data displayed on the laser projector with geometrically calibrated standard rasterization-based video projectors that are registered to the same geometry. Because laser projectors send out a laser light beam via galvanoscopic mirrors, a standard pinhole model calibration procedure that is normally used for pixel raster displays projecting structured light patterns, such as Gray codes, cannot be carried out directly with sufficient accuracy as the rays do not converge into a single point. To overcome the complications of accurately registering the GLP while still enabling a treatment equivalent to a standard pinhole device, an adapted version is applied to enable straightforward content generation. Besides the geometrical calibration, we also present a photometric calibration to unify the color appearance of GLPs and standard video projectors maximizing the advantages of the large color gamut of the GLP and optimizing its color appearance to smoothly fade into the significantly smaller gamut of the video projector. The proposed algorithms were evaluated on a prototypical mixed video projector and GLP projection mapping setup.


title: SCMA Codebook Design

abstract: Multicarrier CDMA is a multiple access scheme in which modulated QAM symbols are spread over OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequences allowing us to take advantage of a near optimal message passing algorithm (MPA) receiver with practically feasible complexity. Sparse code multiple access (SCMA) is a multi-dimensional codebook-based non-orthogonal spreading technique. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to multi-dimensional codewords of SCMA codebook sets. Each layer has its dedicated codebook. Shaping gain of a multi-dimensional constellation is one of the main sources of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. Meanwhile, like LDS, SCMA enjoys the low complexity reception techniques due to the sparsity of SCMA codewords. In this paper a systematic approach is proposed to design SCMA codebooks mainly based on the design principles of lattice constellations. Simulation results are presented to show the performance gain of SCMA compared to LDS and OFDMA.


title: Animation and rendering of complex water surfaces

abstract: We present a new method for the animation and rendering of photo-realistic water effects. Our method is designed to produce visually plausible three dimensional effects, for example the pouring of water into a glass (see figure 1) and the breaking of an ocean wave, in a manner which can be used in a computer animation environment. In order to better obtain photorealism in the behavior of the simulated water surface, we introduce a new "thickened" front tracking technique to accurately represent the water surface and a new velocity extrapolation method to move the surface in a smooth, water-like manner. The velocity extrapolation method allows us to provide a degree of control to the surface motion, e.g. to generate a windblown look or to force the water to settle quickly. To ensure that the photorealism of the simulation carries over to the final images, we have integrated our method with an advanced physically based rendering system.


title: Classification and Counting of Composite Objects in Traffic Scenes Using Global and Local Image Analysis

abstract: Object recognition algorithms often focus on determining the class of a detected object in a scene. Two significant phases are usually involved in object recognition. The first phase is the object representation phase, in which the most suitable features that provide the best discriminative power under constraints such as lighting, resolution, scale, and view variations are chosen to describe the objects. The second phase is to use this representation space to develop models for each object class using discriminative classifiers. In this paper, we focus on composite objects, i.e., objects with two or more simpler classes that are interconnected in a complicated manner. One classic example of such a scenario is a bicyclist. A bicyclist consists of a bicycle and a human who rides the bicycle. When we are faced with the task of classifying bicyclists and pedestrians, it is counterintuitive and often hard to come up with a discriminative classifier to distinguish the two classes. We explore global image analysis based on bag of visual words to compare the results with local image analysis, in which we attempt to distinguish the individual parts of the composite object. We also propose a unified naive Bayes framework and a combined histogram feature method for combining the individual classifiers for enhanced performance.


Finding Alternative Translations in a Large Corpus of Movie Subtitle.


title: Factors associated with hamstring injuries. An approach to treatment and preventative measures.

abstract: Following hamstring strain, rehabilitation is often prolonged and frustrating for the athlete and for the sports medicine clinician. Though the initial treatment of rest, ice, compression and elevation is accepted for muscle strains, no consensus exists for rehabilitation of hamstring muscle strains. This lack of agreement concerning rehabilitation of hamstring injury represents our lack of understanding of the mechanism of injury and the factors that contribute to hamstring strain. A hamstring rehabilitation model is proposed that is based on our current understanding of the aetiological factors that contribute to hamstring muscle strain. The influence and interaction of hamstring strength, flexibility, warm-up and fatigue are aetiological factors that should be addressed in the rehabilitation and prevention of hamstring strains. The rehabilitation model is, however, not without limitations and speculations. Further research is needed to clarify the etiological factors of hamstring strain and the efficacy of different rehabilitation protocols.


title: Intermodulation distortion in 1SW-ZVS multi-inverter for induction heating home appliances

abstract: This paper presents an analysis of the intermodulation (IMD) distortion products that appear in a dual one-switch-zero-voltage-switching (1SW-ZVS) resonant inverter. The converter switching frequencies, and therefore output power for each burner, are currently not independent of each other consequently audible noise appears. With this analysis and the increased operation frequency range obtained with silicon carbide (SiC) devices, audible noise can be avoided while maintaining the variable frequency control and reducing the number of EMI filters as one can be shared by two or more inverters. This work enables to establish the simultaneous operation and SW (Square Wave) control for different output power settings. This is important for the future generation of cooking appliances based on multiple mini-inverters.


abstract: gibbons c., dempster m. & moutray m. (2011) Stress, coping and satisfaction in nursing students. Journal of Advanced Nursing 67(3), 621–632. ::: ::: Abstract ::: Aim. This article is a report of a study conducted to explore the relationship between sources of stress and psychological well-being and to consider how different sources of stress and coping resources might function as moderators and mediators on well-being. ::: ::: Background. In most research exploring sources of stress and coping in nursing students, stress has been construed as psychological distress. Sources of stress likely to enhance well-being and, by implication, learning have not been considered. ::: ::: Method. A questionnaire was administered to 171 final year nursing students in 2008. Questions were asked to measure sources of stress when rated as likely to contribute to distress (a hassle) and rated as likely to help one achieve (an uplift). Support, control, self-efficacy and coping style were also measured, along with their potential moderating and mediating effects on well-being, operationalized using the General Health Questionnaire and measures of course and career satisfaction. ::: ::: Findings. Sources of stress likely to lead to distress were more often predictors of well-being than were sources of stress likely to lead to positive, eustress states, with the exception of clinical placement demands. Self-efficacy, dispositional control and support were important predictors, and avoidance coping was the strongest predictor of adverse well-being. Approach coping was not a predictor of well-being. The mere presence of support appeared beneficial as well as the utility of that support to help a student cope. ::: ::: Conclusion. Initiatives to promote support and self-efficacy are likely to have immediate benefits for student well-being. In course reviews, nurse educators need to consider how students’ experiences might contribute not just to potential distress, but to eustress as well.


title: Log-based Abnormal Task Detection and Root Cause Analysis for Spark

abstract: Abstract-Application delays caused by abnormal tasks arecommon problems in big data computing frameworks. Anabnormal task in Spark, which may run slowly withouterror or warning logs, not only reduces its resident node’sperformance, but also affects other nodes’ efficiency.Spark log files report neither root causes of abnormal tasks,nor where and when abnormal scenarios happen. AlthoughSpark provides a “speculation” mechanism to detect stragglertasks, it can only detect tailed stragglers in each stage. Sincethe root causes of abnormal happening are complicated, thereare no effective ways to detect root causes.This paper proposes an approach to detect abnormality andanalyzes root causes using Spark log files. Unlike commononline monitoring or analysis tools, our approach is a pureoff-line method that can analyze abnormality accurately. Ourapproach consists of four steps. First, a parser preprocessesraw log files to generate structured log data. Second, ineach stage of Spark application, we choose features relatedto execution time and data locality of each task, as well asmemory usage and garbage collection of each node. Third,based on the selected features, we detect where and whenabnormalities happen. Finally, we analyze the problems usingweighted factors to decide the probability of root causes. In thispaper, we consider four potential root causes of abnormalities,which include CPU, memory, network, and disk. The proposedmethod has been tested on real-world Spark benchmarks.To simulate various scenario of root causes, we conductedinterference injections related to CPU, memory, network,and Disk. Our experimental results show that the proposedapproach is accurate on detecting abnormal tasks as well asfinding the root causes


title: Artistic Low Poly rendering for images

abstract: This paper presents an automatic approach for generating low poly rendering of images, which is particularly popular in the recent art design community. Distinguishing from the traditional image triangulation methods for the sake of compression or vectorization, we propose some critical principles of such Low Poly rendering problem, and simulate the artists creation procedures straightforwardly. To produce the visual effects with clear boundaries, we constrain the vertices along the feature edges extracted from the input image. By employing the Voronoi diagram iteration guided by a feature flow field, the vertices in the result image well reflect the feature structure of the local shape. Moreover, with the salient region detection, we can achieve different mesh densities between the front object and the background. Some special color processing techniques are employed to make our result more artistic. Our method works well on a wide variety of images, no matter raster photographs or artificial images. Experiments show that our approach is able to generate satisfying results similar to the artwork created by professional artists.


title: Unlock with Your Heart: Heartbeat-based Authentication on Commercial Mobile Phones

abstract: In this paper, we propose to use the vibration of the chest in response to the heartbeat as a biometric feature to authenticate the user on mobile devices. We use the built-in accelerometer to capture the heartbeat signals on commercial mobile phones. The user only needs to press the phone on his/her chest, and the system can identify the user within a few heartbeats. To reliably extract heartbeat features, we design a two-step alignment scheme that can handle the natural variability in human heart rates. We further use an adaptive template selection scheme to authenticate the user under different body postures and body states. Based on heartbeat signals collected on twenty users, the experimental results show that our method can achieve an authentication accuracy of 96.49% and the heartbeat features are stable over a period of three months.


title: Deep networks with probabilistic gates

abstract: We investigate learning to probabilistically bypass computations in a network architecture. Our approach is motivated by AIG, where layers are conditionally executed depending on their inputs, and the network is trained against a target bypass rate using a per-layer loss. We propose a per-batch loss function, and describe strategies for handling probabilistic bypass during inference as well as training. Per-batch loss allows the network additional flexibility. In particular, a form of mode collapse becomes plausible, where some layers are nearly always bypassed and some almost never; such a configuration is strongly discouraged by AIG's per-layer loss. We explore several inference-time strategies, including the natural MAP approach. With data-dependent bypass, we demonstrate improved performance over AIG. With data-independent bypass, as in stochastic depth, we observe mode collapse and effectively prune layers. We demonstrate our techniques on ResNet-50 and ResNet-101 for ImageNet , where our techniques produce improved accuracy (.15--.41% in precision@1) with substantially less computation (bypassing 25--40% of the layers).


title: Closed-Loop Autofocus Scheme for Scanning Electron Microscope

abstract: In this paper, we present a full scale autofocus approach for scanning electron microscope (SEM). The optimal focus (in-focus) position of the microscope is achieved by maximizing the image sharpness using a vision-based closed-loop control scheme. An iterative optimization algorithm has been designed using the sharpness score derived from image gradient information. The proposed method has been implemented and validated using a tungsten gun SEM at various experimental conditions like varying raster scan speed, magnification at real-time. We demonstrate that the proposed autofocus technique is accurate, robust and fast.


title: Interventions to reduce social isolation amongst older people: where is the evidence?

abstract: As the population ages and more people are living alone, social isolation amongst older people is emerging as one of the major issues facing the industrialised world because of the adverse impact it can have on health and wellbeing. This article reviews the empirical literature published over the last 20 years on the effectiveness of interventions that target social isolation amongst older people. The results reveal that although numerous such interventions have been implemented worldwide, there is very little evidence to show that they work. It is concluded that future intervention programmes aimed at reducing social isolation should have evaluation built into them at inception, and that the results of the evaluation studies, whether positive or negative, should be widely disseminated. Where possible, as a cost-effective measure, pilot or demonstration projects should precede these interventions. Some key elements of successful interventions to counter social isolation amongst older people are presented.


title: A Literature Review of Gamification Design Frameworks

abstract: This paper presents a review of the literature on gamification design frameworks. Gamification, understood as the use of game design elements in other contexts for the purpose of engagement, has become a hot topic in the recent years. However, there's also a cautionary tale to be extracted from Gartner's reports on the topic: many gamification-based solutions fail because, mostly, they have been created on a whim, or mixing bits and pieces from game components, without a clear and formal design process. The application of a definite design framework aims to be a path to success. Therefore, before starting the gamification of a process, it is very important to know which frameworks or methods exist and their main characteristics. The present review synthesizes the process of gamification design for a successful engagement experience. This review categorizes existing approaches and provides an assessment of their main features, which may prove invaluable to developers of gamified solutions at different levels and scopes.


title: Computer games as motivation for design patterns

abstract: We present a computer game case study that can be used to motivate the need for design patterns. Design patterns are often misunderstood, but good examples can motivate students to work at understanding patterns. Computer games are used in many books and online resources to teach programming concepts, but these examples are usually simplified or non-object-oriented. We present a simple game that exhibits functionality found in commercial games (animation, collision detection, etc.) and show how it can be used as a case study for design patterns. Specifically, we illustrate how the state, facade, observer, strategy, and visitor patterns can replace procedural approaches for implenting the logic and animation of game written in Java.


title: Simulation and analysis of desulfator for smart battery system

abstract: Lead-acid batteries play a crucial role in our daily life. However, their efficiency deteriorates due to the accumulation of chemical substance, known as sulfate (SO2) on the surface of batteries' plates. Hereby, the methods to overcome this problem is investigated in this work. A workable desulfator found online is adopted in this study. Further, a new and more efficient circuitry is proposed. The advantages and disadvantages of the new circuit is also outlined.


title: DeepLanes: End-To-End Lane Position Estimation Using Deep Neural Networks

abstract: Camera-based lane detection algorithms are one of the key enablers for many semi-autonomous and fullyautonomous systems, ranging from lane keep assist to level-5 automated vehicles. Positioning a vehicle between lane boundaries is the core navigational aspect of a self-driving car. Even though this should be trivial, given the clarity of lane markings on most standard roadway systems, the process is typically mired with tedious pre-processing and computational effort. We present an approach to estimate lane positions directly using a deep neural network that operates on images from laterally-mounted down-facing cameras. To create a diverse training set, we present a method to generate semi-artificial images. Besides the ability to distinguish whether there is a lane-marker present or not, the network is able to estimate the position of a lane marker with sub-centimeter accuracy at an average of 100 frames/s on an embedded automotive platform, requiring no pre-or post-processing. This system can be used not only to estimate lane position for navigation, but also provide an efficient way to validate the robustness of driver-assist features which depend on lane information.


title: Control of a UAV and a UGV cooperating to manipulate an object

abstract: This paper focuses on the control of a system composed of an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) which cooperate to manipulate an object. The two units are subject to actuator saturations and cooperate to move the object to a desired pose, characterized by its position and inclination. The paper proposes a control strategy where the ground vehicle is tasked to deploy the object to a certain position, whereas the aerial vehicle adjusts its inclination. The ground vehicle is governed by a saturated proportional-derivative control law. The aerial vehicle is regulated by means of a cascade control specifically designed for this problem that is able to exploit the mechanical interconnection. The stability of the overall system is proved through Input-to-State Stability and Small Gain theorem arguments. To solve the problem of constraints satisfaction, a nonlinear Reference Governor scheme is implemented. Numerical simulations are provided to demonstrate the effectiveness of the proposed method.


title: Extinction-Based Shading and Illumination in GPU Volume Ray-Casting

abstract: Direct volume rendering has become a popular method for visualizing volumetric datasets. Even though computers are continually getting faster, it remains a challenge to incorporate sophisticated illumination models into direct volume rendering while maintaining interactive frame rates. In this paper, we present a novel approach for advanced illumination in direct volume rendering based on GPU ray-casting. Our approach features directional soft shadows taking scattering into account, ambient occlusion and color bleeding effects while achieving very competitive frame rates. In particular, multiple dynamic lights and interactive transfer function changes are fully supported. Commonly, direct volume rendering is based on a very simplified discrete version of the original volume rendering integral, including the development of the original exponential extinction into a-blending. In contrast to a-blending forming a product when sampling along a ray, the original exponential extinction coefficient is an integral and its discretization a Riemann sum. The fact that it is a sum can cleverly be exploited to implement volume lighting effects, i.e. soft directional shadows, ambient occlusion and color bleeding. We will show how this can be achieved and how it can be implemented on the GPU.


title: Adolescents' attitudes toward sports, exercise, and fitness predict physical activity 5 and 10 years later.

abstract: OBJECTIVE ::: To determine whether adolescent attitudes towards sports, exercise, and fitness predict moderate-to-vigorous physical activity 5 and 10 years later. ::: ::: ::: METHOD ::: A diverse group of 1902 adolescents participating in Project Eating and Activity in Teens, reported weekly moderate-to-vigorous physical activity and attitudes toward sports, exercise, and fitness in Eating and Activity in Teens-I (1998-99), Eating and Activity in Teens-II (2003-04), and Eating and Activity in Teens-III (2008-09). ::: ::: ::: RESULTS ::: Mean moderate-to-vigorous physical activity was 6.4, 5.1, and 4.0 hours/week at baseline, 5-year, and 10-year follow-up, respectively. Attitudes toward sports, exercise, and fitness together predicted moderate-to-vigorous physical activity at 5 and 10 years. Among the predictors of 5- and 10-year moderate-to-vigorous physical activity, attitude's effect size, though modest, was comparable to the effect sizes for sports participation and body mass index. Adolescents with more-favorable attitudes toward sports, exercise, and fitness engaged in approximately 30%-40% more weekly moderate-to-vigorous physical activity at follow-up (2.1 hour/week at 5 years and 1.2 hour/week at 10 years) than those with less-favorable attitudes. ::: ::: ::: CONCLUSION ::: Adolescents' exercise-related attitudes predict subsequent moderate-to-vigorous physical activity independent of baseline behavior suggesting that youth moderate-to-vigorous physical activity promotion efforts may provide long-term benefits by helping youth develop favorable exercise attitudes.


title: Blockchain contract: Securing a blockchain applied to smart contracts

abstract: A new mechanism is proposed for securing a blockchain applied to contracts management such as digital rights management. This mechanism includes a new consensus method using a credibility score and creates a hybrid blockchain by alternately using this new method and proof-of-stake. This makes it possible to prevent an attacker from monopolizing resources and to keep securing blockchains.


title: Understanding Consumers Attitude Toward Advertising

abstract: Literature in advertising and information systems suggests that advertising in both traditional media and the Internet is either easily ignored by the audience or is perceived with little value. However, these studies assumed that the audience was passive and failed to consider the motives of the users. In light of this, the present study measures consumers’ attitudes toward advertisements for different purposes/functions (brand building and directional) and different media (traditional and Internet-based). Literature suggests the following factors that contribute to consumers’ perceptions of ads: entertainment, irritation, informativeness, credibility, and demographic. We believe that interactivity is also a factor that contributes to consumers’ perceptions. By understanding consumers’ attitude towards advertising, designers and marketers can better strategize their advertising designs. A better understanding of interactivity can also help to improve the effectiveness of interactive media such as the Internet. A methodology for studying the factors that contribute to consumers perceptions of ads is proposed and implications for Internet-based advertising and e-commerce is discussed.


title: ROAMing terrain: Real-time Optimally Adapting Meshes

abstract: Terrain visualization is a difficult problem for applications requiring accurate images of large datasets at high frame rates, such as flight simulation and ground-based aircraft testing using synthetic sensor simulation. On current graphics hardware, the problem is to maintain dynamic, view-dependent triangle meshes and texture maps that produce good images at the required frame rate. We present an algorithm for constructing triangle meshes that optimizes flexible view-dependent error metrics, produces guaranteed error bounds, achieves specified triangle counts directly and uses frame-to-frame coherence to operate at high frame rates for thousands of triangles per frame. Our method, dubbed Real-time Optimally Adapting Meshes (ROAM), uses two priority queues to drive split and merge operations that maintain continuous triangulations built from pre-processed bintree triangles. We introduce two additional performance optimizations: incremental triangle stripping and priority-computation deferral lists. ROAM's execution time is proportional to the number of triangle changes per frame, which is typically a few percent of the output mesh size; hence ROAM's performance is insensitive to the resolution and extent of the input terrain. Dynamic terrain and simple vertex morphing are supported.


title: Closed Form Solution for Minimum Conduction Loss Modulation of DAB Converters

abstract: An optimal modulation scheme that enables minimum conduction and copper losses is presented for a bidirectional dual active bridge (DAB) dc-dc converter. The considered converter system is employed for an automotive application and comprises of a high voltage (HV) port with port voltage V1, 240V ≤ V1 ≤ 450 V, and a low voltage (LV) port with port voltage V2, 11 V ≤ V2 ≤ 16 V; the rated output power is 2 kW. The closed-form expressions for the optimal control parameters are derived and implementation details are presented in order to facilitate the direct application to a given DAB converter. The paper further details the properties of the presented modulation scheme with respect to switching losses. Experimental results confirm a considerable increase of the converter efficiency achieved with the proposed optimal modulation scheme, compared to the efficiency obtained with conventional phase shift modulation. The efficiency increase is most distinct at V1=450V and V2 = 11V with an increase from 78.6% to 90.6% at 1 kW output power and from 85.9% to 90.7% at rated output power as compared to conventional phase shift modulation.


title: Cyber Security Awareness Campaigns: Why do they fail to change behaviour?

abstract: The present paper focuses on Cyber Security Awareness Campaigns, and aims to identify key factors regarding security which may lead them to failing to appropriately change people's behaviour. Past and current efforts to improve information-security practices and promote a sustainable society have not had the desired impact. It is important therefore to critically reflect on the challenges involved in improving information-security behaviours for citizens, consumers and employees. In particular, our work considers these challenges from a Psychology perspective, as we believe that understanding how people perceive risks is critical to creating effective awareness campaigns. Changing behaviour requires more than providing information about risks and reactive behaviours - firstly, people must be able to understand and apply the advice, and secondly, they must be motivated and willing to do so - and the latter requires changes to attitudes and intentions. These antecedents of behaviour change are identified in several psychological models of behaviour. We review the suitability of persuasion techniques, including the widely used 'fear appeals'. From this range of literature, we extract essential components for an awareness campaign as well as factors which can lead to a campaign's success or failure. Finally, we present examples of existing awareness campaigns in different cultures (the UK and Africa) and reflect on these.


title: The long tail of recommender systems and how to leverage it

abstract: The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.


title: Force Reproduction Error Depends on Force Level, Whereas the Position Reproduction Error Does Not

abstract: When reproducing a previously perceived force or position humans make systematic errors. This study determined the effect of force level on force and position reproduction, when both target and reproduction force are self-generated with the same hand. Subjects performed force reproduction tasks at different force levels, against a fixed handle, and performed a position reproduction task against a haptic manipulator which applied constant opposing forces. Subjects performed a series of two interchanging trials: matching an onscreen force or position (reference trial), and subsequently reproducing the same force or position without visual feedback (reproduction trial). Subjects generated too high forces for low force levels ( 130 N). No effect of force level on the position reproduction error was found. Previous studies proposed that reafference feedback, i.e., the sensory prediction from self-generated forces, causes the force reproduction error. However, if the force reproduction error is exclusively caused by the reafference feedback, the force reproduction error should disappear when both target and reproduction force are self-generated. The results of this study show an effect of force level on the force reproduction error, indicating that reafference feedback is not the sole factor in force reproduction tasks.


title: Inactivation of vegetative bacterial threat agents on environmental surfaces.

abstract: Following a wide-area biological terror attack, numerous decontamination technologies, techniques, and strategies will be required for rapid remediation. Establishing an understanding of how disinfectants will perform under field conditions is of critical importance. The purpose of this study was to determine the efficacy of several liquid decontaminants, when used to inactivate vegetative biological agents on environmental surfaces. Aluminum, carpet, concrete, glass, and wood coupons were inoculated with 1×10(8) CFU of Burkholderia mallei, Francisella tularensis, Vibrio cholerae, or Yersinia pestis. Using spray-based application methods, decontamination was then attempted with pH-adjusted bleach, 1% citric acid, 70% ethanol, quaternary ammonia, or Pine-Sol®. Results indicated that decontamination efficacy varied significantly by decontaminant and organism. Materials such as wood are difficult to decontaminate, even when using sporicides. The data presented here will help responders develop efficacious remediation strategies following a large-scale contamination incident.


Data Statistics Adviser in Database Management Systems


title: Control of a Three-Phase Boost PFC Converter Using a Single DC-Link Voltage Sensor

abstract: This paper proposes a new methodology to control a three-phase boost power factor correction (PFC) using a single dc output voltage sensor. Typically, a PFC control technique requires measurement from five independent sensors, i.e., two input voltages, one output dc voltage, and two input phase currents. Elimination of four sensors in the control system of a PFC converter is theoretically feasible and implementable without compromising stability and power quality of the converter, as analyzed and presented in this paper. The proposed control technique uses the ripple information of the measured dc-link voltage, converter dynamics, and switching states at a preceding sample in order to estimate the present state of four other unknown state variables and, thus, establishes the control method. A 2.2-kW experimental prototype of a three-phase boost PFC is developed and tested to verify the accuracy and applicability of the proposed control logic at different line and load conditions. According to the experimental measurements, conversion efficiency more than 98%, total harmonic distortion as low as 4.3%, and an output voltage ripple of ±2% are achieved at 2.2-kW output power.


title: A Robust Algorithm for Shadow Removal of Foreground Detection in Video Surveillance

abstract: We proposed an accurate algorithm to prevent moving shadows from being misclassified as part of moving objects in video target segmentation in this paper. Firstly, moving objects were achieved through background subtraction using adaptive Gaussian mixture models. Then, moving shadows were eliminated by a shadow detection algorithm. Finally, we performed a morphological reconstruction algorithm to recover the foreground distorted after shadow removal process. The experimental results proved its validity and accuracy in various fixed outdoor video scenes.


title: The Cognitive Psychology of Lottery Gambling: A Theoretical Review

abstract: Despite the current popularity of the UK National, psychologists have tended to neglect lottery play. This review provides a summary of current research findings and outlines the main cognitive theories of gambling as related to non-pathological lottery play. A discussion of various biases and irrational thinking patterns typically found in lottery gambling will be given. These will include the misunderstanding of lottery odds, a susceptibility to the gambler's fallacy and cognitive entrapment, a belief in hot and cold numbers, unrealistic optimism, a belief in personal luck, superstitious thinking, the illusion of control, the erroneous perception of near misses, a susceptibility to prize size and rollover effects, the framing of gambling outcomes and finally, the influence of social factors on lottery play. It is concluded that the psychology of lottery play needs a more unified theory which whilst largely cognitive in emphasis, should also incorporate social motivations such as those inherent in syndicate based lottery play.


title: RDF Snippets for Semantic Web Search Engines

abstract: Recently, there has been interest in ranking the resources and generating corresponding expressive descriptions from the Semantic Web. This paper proposes an approach for automatically generating snippets from RDF documents and assisting users in better understanding the content of RDF documents returned by Semantic Web search engines. A heuristic method for discovering topics, based on the occurrences of RDF nodes and the URIs of original RDF documents, is presented and experimented in this paper. In order to make the snippets more understandable, two strategies are proposed and used for ranking the topic-related statements and the query-related statements respectively. Finally, the conclusion is drawn based on the discussion about the performances of our topic discovery and the whole snippet generation approaches on a test dataset provided by Sindice.


title: Polarity classification for Spanish tweets using the COST corpus

abstract: It was not until 2010 when businesses, politicians and people in general began to realize the potential of Twitter in Spain. This fact has awoken research interest in the extraction of knowledge from Twitter. This paper aims to fill the gap of the lack of resources for Twitter sentiment analysis in Spanish by performing a study of different features and machine learning algorithms for classifying the polarity of Twitter posts. The result is a new corpus of Spanish tweets called COST, and we have carried out a wide-ranging experiment in which different machine learning algorithms have been used. Furthermore, we have tested the influence of using different weighting schemes for unigrams, the influence of eliminating stop-words and the application of a stemmer process.


Studying User Income through Language, Behaviour and Affect in Social Media


title: A Compact Yagi-Uda Type Pattern Diversity Antenna Driven by CPW-Fed Pseudomonopole

abstract: This paper presents two end-fed compact Yagi-Uda-type pattern diversity antennas, which use a coplanar-waveguide (CPW)-fed printed pseudomonopole antenna (PPMA) as the driver element. The balun-less driver PPMA behaves like a folded strip dipole and adds to its compactness. A common mode/differential mode (CM/DM) decomposition of antenna current on PPMA is carried out to show that the CM current distribution is similar to the current distribution of an asymmetrically fed dipole. The designed three-element Yagi-Uda antenna operates in the 2.4-GHz band with F/B ratio of about 23 dB and gain of 7.3 dBi. The compact pattern diversity antennas are realized by using two Yagi-Uda antennas separated by a common reflector. Two diversity antennas are realized using two different arrangements of Yagi-Uda antennas. The separation between the constituent PPMAs is varied to study its effect on the isolation and element embedded efficiency. The better of the two diversity antennas has overall footprint area of $65\; \times \;58\;\text{mm}^2$ and the separation between two radiating elements is only 34 mm or $0.272\;{\lambda _o}$ . The simulated and measured port isolation ( $\vert{\text S_{12}}\vert$ ) between two diversity branches is about 35 and 27 dB, respectively. The signal envelope correlation is 0.018 and the embedded efficiency is about 93%.


title: Development of recommendations for SEMG sensors and sensor placement procedures.

abstract: The knowledge of surface electromyography (SEMG) and the number of applications have increased considerably during the past ten years. However, most methodological developments have taken place locally, resulting in different methodologies among the different groups of users.A specific objective of the European concerted action SENIAM (surface EMG for a non-invasive assessment of muscles) was, besides creating more collaboration among the various European groups, to develop recommendations on sensors, sensor placement, signal processing and modeling. This paper will present the process and the results of the development of the recommendations for the SEMG sensors and sensor placement procedures. Execution of the SENIAM sensor tasks, in the period 1996-1999, has been handled in a number of partly parallel and partly sequential activities. A literature scan was carried out on the use of sensors and sensor placement procedures in European laboratories. In total, 144 peer-reviewed papers were scanned on the applied SEMG sensor properties and sensor placement procedures. This showed a large variability of methodology as well as a rather insufficient description. A special workshop provided an overview on the scientific and clinical knowledge of the effects of sensor properties and sensor placement procedures on the SEMG characteristics. Based on the inventory, the results of the topical workshop and generally accepted state-of-the-art knowledge, a first proposal for sensors and sensor placement procedures was defined. Besides containing a general procedure and recommendations for sensor placement, this was worked out in detail for 27 different muscles. This proposal was evaluated in several European laboratories with respect to technical and practical aspects and also sent to all members of the SENIAM club (>100 members) together with a questionnaire to obtain their comments. Based on this evaluation the final recommendations of SENIAM were made and published (SENIAM 8: European recommendations for surface electromyography, 1999), both as a booklet and as a CD-ROM. In this way a common body of knowledge has been created on SEMG sensors and sensor placement properties as well as practical guidelines for the proper use of SEMG.


Dermatoscopy of confluent and reticulated papillomatosis (Gougerot-Carteaud syndrome).


title: EvoSpaces: Multi-dimensional Navigation Spaces for Software Evolution

abstract: EvoSpaces is a Swiss-wide research project sponsored by the Hasler foundation. It involves three partners: University of Zurich, University of Lugano and the University of Applied Sciences in Geneva. The overall goal of the project is to explore novel ways to visualize and navigate evolving software systems in a 3D environment. In this paper we briefly describe the particularities of the project and the results obtained so far.


title: An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models

abstract: Today, in addition to traditional mobile services, there are new ones already being used, thanks to the advances in 3G-related technologies. Our work contributed to the emerging body of research by integrating TAM and Diffusion Theory. Based on a sample of 542 Dutch consumers, we found that traditional antecedents of behavioral intention, ease of use and perceived usefulness, can be linked to diffusion-related variables, such as social influence and perceived benefits (flexibility and status).


title: A multi-scene deep learning model for image aesthetic evaluation

abstract: Aesthetic evaluation of images has attracted a lot of research interests recently. Previous work focused on extracting handcrafted image features or generic image descriptors to build statistical model for aesthetic evaluation. However, the effectiveness of these approaches is limited by researchers' understanding on the aesthetic rules. In this paper, we present a multi-scene deep learning model (MSDLM) to enable automatic aesthetic feature learning. This deep learning model achieves better results because it improves performance on some major problems, including limited data amount and categories, scenes dependent evaluation, unbalanced dataset, noise data etc. Major innovations are as follows. (1) We design a scene convolutional layer consist of multi-group descriptors in the network elaborately so that the model has a comprehensive learning capacity for image aesthetic. (2) We design a pre-training procedure to initialize our model. Through pre-training the multi-group descriptors discriminatively, our model can extract specific aesthetic features for various scenes, and reduce the impact of noise data when building the model. Experimental results show that our approach significantly outperforms existing methods on two benchmark datasets. Present a multi-scene deep learning model for image aesthetic evaluation.Design a scene convolutional layer containing multi-group descriptors in the network.Design a pre-training procedure to initialize the model.


title: Dynamic self-clamping at short-circuit turn-off of high-voltage IGBTs

abstract: Measurements show that the IGBT is able to clamp the collector-emitter voltage to a certain value at short-circuit turn-off despite a very low gate turn-off resistor in combination with a high parasitic inductance is applied. The IGBT itself reduces the turn-off diC/dt by avalanche injection. However, device destructions during fast turn-off were observed which cannot be linked with an overvoltage failure mode. Measurements and semiconductor simulations of high-voltage IGBTs explain the self-clamping mechanism in detail. Possible failures which can be connected with filamentation processes are described. Options for improving the IGBT robustness during short-circuit turn-off are discussed.


title: Modern Image Quality Assessment

abstract: This book is about objective image quality assessmentwhere the aim is to provide computational models that can automatically predict perceptual image quality. The early years of the 21st century have witnessed a tremendous growth in the use of digital images as a means for representing and communicating information. A considerable percentage of this literature is devoted to methods for improving the appearance of images, or for maintaining the appearance of images that are processed. Nevertheless, the quality of digital images, processed or otherwise, is rarely perfect. Images are subject to distortions during acquisition, compression, transmission, processing, and reproduction. To maintain, control, and enhance the quality of images, it is important for image acquisition, management, communication, and processing systems to be able to identify and quantify image quality degradations. The goals of this book are as follows; a) to introduce the fundamentals of image quality assessment, and to explain the relevant engineering problems, b) to give a broad treatment of the current state-of-the-art in image quality assessment, by describing leading algorithms that address these engineering problems, and c) to provide new directions for future research, by introducing recent models and paradigms that significantly differ from those used in the past. The book is written to be accessible to university students curious about the state-of-the-art of image quality assessment, expert industrial R&D engineers seeking to implement image/video quality assessment systems for specific applications, and academic theorists interested in developing new algorithms for image quality assessment or using existing algorithms to design or optimize other image processing applications.


title: Two New Species of Madicolous Water Beetle from South Africa (Coleoptera: Hydraenidae)

abstract: Coelometopon glenavoni sp. n. and Oomtelecopon disjunctum sp. n., two madicolous hydraenid beetles are described from the Eastern and Western Cape provinces of South Africa respectively. Diagnostic notes are provided for both species, together with a discussion of the ecology and biogeography of the two genera.


title: Training a Convolutional Neural Network for Appearance-Invariant Place Recognition

abstract: Place recognition is one of the most challenging problems in computer vision, and has become a key part in mobile robotics and autonomous driving applications for performing loop closure in visual SLAM systems. Moreover, the difficulty of recognizing a revisited location increases with appearance changes caused, for instance, by weather or illumination variations, which hinders the long-term application of such algorithms in real environments. In this paper we present a convolutional neural network (CNN), trained for the first time with the purpose of recognizing revisited locations under severe appearance changes, which maps images to a low dimensional space where Euclidean distances represent place dissimilarity. In order for the network to learn the desired invariances, we train it with triplets of images selected from datasets which present a challenging variability in visual appearance. The triplets are selected in such way that two samples are from the same location and the third one is taken from a different place. We validate our system through extensive experimentation, where we demonstrate better performance than state-of-art algorithms in a number of popular datasets.


title: FPGA implementation of polar code based encoder architecture

abstract: Polar codes, introduced by Arikan, achieves the capacity of symmetric channels with “low encoding and decoding complexity” for a large class of underlying channels. Recently, polar code has become the most favourable error correcting code in the viewpoint of information theory due to its property of channel achieving capacity. Although the fully parallel polar code based encoder architecture processes the bits in a fully parallel fashion but suffers with huge hardware complexity with increasing code length. As fully parallel polar code based architecture will cause logic complexity problem, while partial parallel polar code based architecture is limited by memory units of high-throughput applications. In this paper, efficient polar code based encoder architecture is designed and implemented on a FPGA using Vertex 5 for the polar encoding scheme. Here we analyse the encoding process of polar code based encoder architecture and propose a new architecture that is suitable for encoding long polar codes with less hardware complexity.


title: New directions in cryptography: twenty some years later (or cryptograpy and complexity theory: a match made in heaven)

abstract: Diffie and Hellman (1976) published their fundamental paper on new directions in cryptography, in which they announced that "we stand on the brink of a revolution in cryptography". Twenty some years later, we survey some of the progress made in cryptography during this time. We especially focus on the successful interplay between complexity theory and cryptography, witnessed perhaps most vividly by the developments in interactive and probabilistic proof systems and in pseudo random number generation.


title: Considering avoidance and consistency in motion planning for human-robot manipulation in a shared workspace

abstract: This paper presents an approach to formulating the cost function for a motion planner intended for human-robot collaboration on manipulation tasks in a shared workspace. To be effective for human-robot collaboration a robot should plan its motion so that it is both safe and efficient. To achieve this, we propose two factors to consider in the cost function for the robot's motion planner: (1) Avoidance of the workspace previously-occupied by the human, so that the motion is as safe as possible, and (2) Consistency of the robot's motion, so that the motion is as predictable as possible for the human and they can perform their task without focusing undue attention on the robot. Our experiments in simulation and a human-robot workspace sharing study compare a cost function that uses only the first factor and a combined cost that uses both factors vs. a baseline method that is perfectly consistent but does not account for the human's previous motion. We find that using either cost function we outperform the baseline method in terms of task success rate without degrading the task completion time. The best task success rate is achieved with the cost function that includes both the avoidance and consistency terms.


title: A Critical Review of Security Threats in Cloud Computing

abstract: The innovation of Internet has enhanced the use of computers and mobile devices. As a result a large amount of data is being generated on daily basis. Information Technology infrastructure continues to grow with evolving technology and as a result a new business model has emerged known as "Cloud Computing". It is set of resources and services which are obtainable through the Internet. Cloud services are provided from data centers which are located all over the world. It is one of the most electrifying technologies because it has reduced cost related to the computing whereas increased flexibility, scalability, mobility and enhanced storage. The rapid growth in the field of "Cloud Computing" similarly upsurges severe security concerns. Security is actually one of the widely discussed issues in cloud computing, other issues being Availability, Integrity etc. Many research papers have proposed solutions to these problems. This paper provides a critical review of the recent work done in the area of security by doing a thorough review of recent work in this area. Finally, we propose a model to improve security in cloud architecture.


title: Baggy Bounds with Accurate Checking

abstract: Baggy Bounds Checking is a backward-compatible defense against out-of-bounds errors. It is reported as being faster than any previous bounds checking tool. However, it enforces allocation bounds instead of object bounds and thus cannot detect memory errors that are in padding areas. In this paper, we present BBAC: a technique that extends Baggy Bounds Checking to enforce accurate bounds checking. The key insight behind our approach is to store the object size at the end of the padding area, making it efficient to lookup object bounds meta-data at runtime. We show experimentally that BBAC can detect more memory errors than Baggy Bounds Checking. Our experiments also show that BBAC only adds an additional 4.39% performance overhead over the original Baggy Bounds Checking technique for the Olden benchmarks and 2x overhead at most on the real-world applications we tested.


title: Proposal of a New Affordable 2-Pole Resolver and Comparing Its Performance With Conventional Wound-Rotor and VR Resolvers

abstract: In this paper, a new affordable 2-pole resolver is proposed. Since, the commercial 2-pole resolvers are wound rotor (WR) types, at first the performance of a WR resolver is analyzed and shown that despite high accuracy of the sensor, it has a complex structure. The common solution for reducing the complexity of the WR resolvers is using variable reluctance (VR) resolvers. So, the performance of three VR resolvers is discussed. The results show not only the accuracy of the VR resolvers in 2-pole applications is not acceptable but also their accuracy is influenced by imperfection in production and installation. So, a new structure is proposed. The stator of the proposed resolver is exactly the same as WR resolver. While, the complexity of the rotor significantly reduced, even in comparison with VR resolvers. The flux path and the operating principle of the proposed resolver are discussed based on magnetic equivalent circuit. Then, the performance of the proposed resolver is evaluated and compared with traditional resolvers using the 3-D times stepping finite element method. The average of absolute position error and the total harmonic distortion of the voltages’ envelope for the proposed resolver are calculated equal to 0.47° and 1.31%. While those values for the WR resolver are 0.15° and 0.34%, respectively, and 0.48° and 1.31% for the most accurate VR resolver. Finally, the prototype of the proposed resolver is built and tested. Close correlation between the simulation and experimental results validates the success of the design.


title: High-speed videography using a dense camera array

abstract: We demonstrate a system for capturing multi-thousand frame-per-second (fps) video using a dense array of cheap 30fps CMOS image sensors. A benefit of using a camera array to capture high-speed video is that we can scale to higher speeds by simply adding more cameras. Even at extremely high frame rates, our array architecture supports continuous streaming to disk from all of the cameras. This allows us to record unpredictable events, in which nothing occurs before the event of interest that could be used to trigger the beginning of recording. Synthesizing one high-speed video sequence using images from an array of cameras requires methods to calibrate and correct those cameras' varying radiometric and geometric properties. We assume that our scene is either relatively planar or is very far away from the camera and that the images can therefore be aligned using projective transforms. We analyze the errors from this assumption and present methods to make them less visually objectionable. We also present a new method to automatically color match our sensors. Finally, we demonstrate how to compensate for spatial and temporal distortions caused by the electronic rolling shutter, a common feature of low-end CMOS sensors.


title: Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods

abstract: Deep neural networks have achieved remarkable success in a wide range of practical problems. However, due to the inherent large parameter space, deep models are notoriously prone to overfitting and difficult to be deployed in portable devices with limited memory. In this paper, we propose an iterative hard thresholding (IHT) approach to train Skinny Deep Neural Networks (SDNNs). An SDNN has much fewer parameters yet can achieve competitive or even better performance than its full CNN counterpart. More concretely, the IHT approach trains an SDNN through following two alternative phases: (I) perform hard thresholding to drop connections with small activations and fine-tune the other significant filters; (II)~re-activate the frozen connections and train the entire network to improve its overall discriminative capability. We verify the superiority of SDNNs in terms of efficiency and classification performance on four benchmark object recognition datasets, including CIFAR-10, CIFAR-100, MNIST and ImageNet. Experimental results clearly demonstrate that IHT can be applied for training SDNN based on various CNN architectures such as NIN and AlexNet.


title: NUT Midline Carcinoma: A Series of Five Cases, Including One with Unusual Clinical Course

abstract: NUT midline carcinomas (NMCs) are rare, poorly differentiated tumors with aggressive biological behavior and a characteristic molecular signature. Availability of NUT antibody has facilitated diagnosis of NMC without molecular testing. We report a series of head and neck NMCs diagnosed using NUT IHC at our institute, including one case with an unusual course. Immunohistochemistry for NUT was performed in nasal and sinonasal tumors with diagnoses of undifferentiated carcinoma, poorly differentiated squamous cell carcinoma and malignant neoplasm, not otherwise specified, to identify cases of NMC. Clinicopathological features were reviewed. Five cases of NMC were identified, accounting for 9.6% of poorly differentiated/undifferentiated carcinomas of the sinonasal region. These patients had a sex ratio of 2:3, and ranged in age from of 10 to 31 years (mean: 25.2 years). Patient 4 had previously been diagnosed with basal cell carcinoma arising in left nasolacrimal duct, and inverted papilloma of nasal cavity. She presented to us with a left lacrimal fossa mass extending into nasal cavity, which was diagnosed as NMC. NMC is a rare neoplasm, the awareness of which is imperative for pathologists to identify cases in which NUT IHC should be ordered. NUT IHC should be performed in all cases of a poorly differentiated carcinoma, particularly those with foci of squamous differentiation, irrespective of patient age and unusual tumor location, as seen in one of our cases. Although considered a highly aggressive and lethal neoplasm, NMC can have a more prolonged clinical course on occasion.


title: Sentence Directed Video Object Codiscovery

abstract: Video object codiscovery can leverage the weak semantic constraint implied by sentences that describe the video content. Our codiscovery method, like other object codetection techniques, does not employ any pretrained object models or detectors. Unlike most prior work that focuses on codetecting large objects which are usually salient both in size and appearance, our method can discover small or medium sized objects as well as ones that may be occluded for part of the video. More importantly, our method can codiscover multiple object instances of different classes within a single video clip. Although the semantic information employed is usually simple and weak, it can greatly boost performance by constraining the hypothesized object locations. Experiments show promising results on three datasets: an average IoU score of 0.423 on a new dataset with 15 object classes, an average IoU score of 0.373 on a subset of CAD-120 with 5 object classes, and an average IoU score of 0.358 on a subset of MPII-Cooking with 7 object classes. Our result on this subset of MPII-Cooking improves upon those of the previous state-of-the-art methods by significant margins.


title: Incorporating time-of-day usage patterns into non-intrusive load monitoring

abstract: Understanding appliance power consumption can help occupants optimize their power consumption behaviour. One popular class of methods for determining appliance power consumption is known as non-intrusive load monitoring (NILM). This paper shows how to incorporate time-of-day appliance usage patterns into a recent NILM method, resulting in both improved accuracy and reduction in computational complexity.


title: Dependency Tree Kernels For Relation Extraction

abstract: We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. Using this kernel within a Support Vector Machine, we detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles. We examine the utility of different features such as Wordnet hypernyms, parts of speech, and entity types, and find that the dependency tree kernel achieves a 20% F1 improvement over a "bag-of-words" kernel.


title: A Wideband Inductorless LNA With Local Feedback and Noise Cancelling for Low-Power Low-Voltage Applications

abstract: A wideband noise-cancelling low-noise amplifier (LNA) without the use of inductors is designed for low-voltage and low-power applications. Based on the common-gate-common-source (CG-CS) topology, a new approach employing local negative feedback is introduced between the parallel CG and CS stages. The moderate gain at the source of the cascode transistor in the CS stage is utilized to boost the transconductance of the CG transistor. This leads to an LNA with higher gain and lower noise figure (NF) compared with the conventional CG-CS LNA, particularly under low power and voltage constraints. By adjusting the local open-loop gain, the NF can be optimized by distributing the power consumption among transistors and resistors based on their contribution to the NF. The optimal value of the local open-loop gain can be obtained by taking into account the effect of phase shift at high frequency. The linearity is improved by employing two types of distortion-cancelling techniques. Fabricated in a 0.13-μm RF CMOS process, the LNA achieves a voltage gain of 19 dB and an NF of 2.8-3.4 dB over a 3-dB bandwidth of 0.2-3.8 GHz. It consumes 5.7 mA from a 1-V supply and occupies an active area of only 0.025 mm2.


title: Multiple Instance Boosting for Object Detection

abstract: A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set. We can create such an object detector by taking the architecture of the Viola-Jones detector cascade and training it with a new variant of boosting that we call MIL-Boost. MILBoost uses cost functions from the Multiple Instance Learning literature combined with the AnyBoost framework. We adapt the feature selection criterion of MILBoost to optimize the performance of the Viola-Jones cascade. Experiments show that the detection rate is up to 1.6 times better using MILBoost. This increased detection rate shows the advantage of simultaneously learning the locations and scales of the objects in the training set along with the parameters of the classifier.


title: The Best-Laid Plans of "Back Mice" and Men: A Case Report and Literature Review of Episacroiliac Lipoma.

abstract: BACKGROUND ::: Back mice, or episacroiliac lipoma, represent a potentially treatable cause of low back pain that may be under-recognized in clinical practice. Despite being well characterized based on clinical history and physical examination findings, implementation of appropriate treatment may be delayed or missed based on a lack of familiarity with the diagnosis. ::: ::: ::: OBJECTIVES ::: In this case report and literature review, we describe a 47-year-old woman with history of persistent low back pain who presented with a pain exacerbation consistent with a back mouse. The history, epidemiology, clinical characteristics, differential diagnosis, potential mechanisms for pain, and treatment options for back mice were then reviewed. ::: ::: ::: STUDY DESIGN ::: Case report and literature review. ::: ::: ::: SETTING ::: Academic university-based pain management center. ::: ::: ::: RESULTS ::: Studies included one randomized clinical trial, 4 cross-sectional studies, 8 case reports or series, and 16 other publications prior to 1967. ::: ::: ::: LIMITATIONS ::: A single case report. ::: ::: ::: CONCLUSIONS ::: Firm, rubbery, mobile nodules that are located in characteristic regions of the sacroiliac, posterior superior iliac, and the lumbar paraspinal regions may represent fatty tissue that has herniated through fascial layers. When painful, these back mice may be confused with other causes of low back pain. In particular, the presence of point tenderness may mimic myofascial pain, and reports of radicular pain may imitate herniated nucleus pulposus. However, back mice may be distinguished from other entities based on findings from the history and physical examination such as absence of neurological deficit. Treatment consisting of injection of local anesthetic into the nodule with or without corticosteroid followed by repeated, direct needling has been reported to relieve pain in many case reports. The one clinical trial comparing injection of local anesthetic to normal saline, which did not include repeated needling, found only mild and transient benefit in the treatment group.


How Motivated Reasoning and Identity Cues Amplify Opinion Polarization About


title: Network service chaining with optimized network function embedding supporting service decompositions

abstract: The rise of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) introduce opportunities for service providers to reduce CAPEX/OPEX and to offer and quickly deploy novel network services. In particular, SDN and NFV enable the flexible composition of network functions, a generic service concept known as network service chaining (NSC).However, the control of resources, management and configuration of network service chains is challenging. In particular, there typically exist multiple options on how an abstract network service can be decomposed into more refined, inter-connected network functions. Moreover, efficient algorithms have to be devised to allocate the network functions. The underlying algorithmic problem can be seen as a novel generalization of the Virtual Network Embedding Problem (VNEP), where there exist multiple realization options. The joint optimization of decomposition and embedding has not been studied in the literature before.This paper studies the problem of how to optimally decompose and embed network services. In particular, we propose two novel algorithms to map NSCs to the network infrastructure while allowing possible decompositions of network functions. The first algorithm is based on Integer Linear Programming (ILP) which minimizes the cost of the mapping based on the NSCs requirements and infrastructure capabilities. The second one is a heuristic algorithm to solve the scalability issue of the ILP formulation. It targets to minimize the mapping cost by making a reasonable selection of the network function decompositions. The experimental results indicate that considering network function decompositions at the time of the embedding significantly improves the embedding performance in terms of acceptance ratio while decreasing the mapping cost in the long run in both optimal and heuristic solutions.


title: Lifelong Machine Learning

abstract: Abstract NOTE ⁃ A New Edition of This Title is Available: Lifelong Machine Learning, Second Edition Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that le...


title: Automation solutions for the evaluation of plant health in corn fields

abstract: The continuously growing need for increasing the production of food and reducing the degradation of water supplies, has led to the development of several precision agriculture systems over the past decade so as to meet the needs of modern societies. The present study describes a methodology for the detection and characterization of Nitrogen (N) deficiencies in corn fields. Current methods of field surveillance are either completed manually or with the assistance of satellite imaging, which offer infrequent and costly information to the farmers about the state of their fields. The proposed methodology promotes the use of small-scale Unmanned Aerial Vehicles (UAVs) and Computer Vision algorithms that operate with information in the visual (RGB) spectrum. Through this implementation, a lower cost solution for identifying N deficiencies is promoted. We provide extensive results on the use of commercial RGB sensors for delivering the essential information to farmers regarding the condition of their field, targeting the reduction of N fertilizers and the increase of the crop performance. Data is first collected by a UAV that hovers over a stressed area and collects high resolution RGB images at a low altitude. A recommendation algorithm identifies potential segments of the images that are candidates exhibiting N deficiency. Based on the feedback from experts in the area a training set is constructed utilizing the initial suggestions of the recommendation algorithm. Supervised learning methods are then used to characterize crop leaves that exhibit signs of N deficiency. The performance of 84.2% strongly supports the potential of this scheme to identify N-deficient leaves even in the case of images where the unhealthy leaves are heavily occluded by other healthy or stressed leaves.


A 0.5-µm CMOS 4.0-Gbit/s Serial Link Transceiver with Data Recovery Using Oversampling


title: Stair Climbing using a compliant modular robot

abstract: Stair Climbing is a key functionality desired for robots deployed in Urban Search and Rescue (USAR) scenarios. A novel compliant modular robot was proposed earlier to climb steep and big obstacles. This work extends the functionality of this robot to ascend and descend stairs of dimensions that are also typical of an urban setting. Stair Climbing is realized by equipping the robot's link joints with optimally designed passive spring pairs that resist clockwise and counter clockwise moments generated by the ground during the climbing motion. This 3-module robot is only propelled by wheel actuators. Desirable stair climbing configurations are estimated a-priori and used to obtain the optimal stiffness for springs. Extensive numerical simulation results over different stair configurations are shown. The numerical simulations are corroborated by experimentation using the prototype and its performance is tabulated for different types of surfaces.


title: BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing

abstract: Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing apps capable of automatically monitoring multiple aspects of physical and mental health. In this article, we present BeWell+ the next generation of the BeWell smartphone wellbeing app, which monitors user behavior along three health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone's wallpaper. With BeWell+, we introduce new mechanisms to address key limitations of the original BeWell app; specifically, (1) community adaptive wellbeing feedback, which generalizes to diverse user communities (e.g., elderly, children) by promoting better behavior yet remains realistic to the user's lifestyle; and, (2) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions (e.g., sleep) where the user needs additional help. We evaluate BeWell+ with a 27 person, 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer smartphones; but also users understand feedback and respond by taking steps towards leading healthier lifestyles.


Paced and self-paced learning in young and elderly adults.


title: Security Against Hardware Trojan Attacks Using Key-Based Design Obfuscation

abstract: Malicious modification of hardware in untrusted fabrication facilities, referred to as hardware Trojan, has emerged as a major security concern. Comprehensive detection of these Trojans during post-manufacturing test has been shown to be extremely difficult. Hence, it is important to develop design techniques that provide effective countermeasures against hardware Trojans by either preventing Trojan attacks or facilitating detection during test. Obfuscation is a technique that is conventionally employed to prevent piracy of software and hardware intellectual property (IP). In this work, we propose a novel application of key-based circuit structure and functionality obfuscation to achieve protection against hardware Trojans triggered by rare internal circuit conditions. The proposed obfuscation scheme is based on judicious modification of the state transition function, which creates two distinct functional modes: normal and obfuscated. A circuit transitions from the obfuscated to the normal mode only upon application of a specific input sequence, which defines the key. We show that it provides security against Trojan attacks in two ways: (1) it makes some inserted Trojans benign, i.e. they become effective only in the obfuscated mode; and (2) it prevents an adversary from exploiting the true rare events in a circuit to insert hard-to-detect Trojans. The proposed design methodology can thus achieve simultaneous protection from hardware Trojans and hardware IP piracy. Besides protecting ICs against Trojan attacks in foundry, we show that it can also protect against malicious modifications by untrusted computer-aided design (CAD) tools in both SoC and FPGA design flows. Simulation results for a set of benchmark circuits show that the scheme is capable of achieving high levels of security against Trojan attacks at modest area, power and delay overhead.


title: The dissimilarity corner detector

abstract: The authors present a corner detection that works by using dissimilarity along the contour direction to detect curves in the image contour. The operator is fast, robust to noise and almost self-thresholding. The standard deviation of the image noise must be specified, but this value is easily measured and the explicit modeling of image noise contributes to the robustness of the operator to noise. The authors also present a new interpretation of the Kitchen-Rosenfeld corner operator (1982) in which they show that this operator can also be viewed as the second derivative of the image function along the edge direction. >


title: Using Conditional Kernel Density Estimation for Wind Power Density Forecasting

abstract: Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this article, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate vector autoregressive moving average-generalized autoregressive conditional heteroscedastic (VARMA-GARCH) model, with a Student t error distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms.


title: A Novel Approach Toward Fabrication of High Performance Thin Film Composite Polyamide Membranes

abstract: A practical method is reported to enhance water permeability of thin film composite (TFC) polyamide (PA) membranes by decreasing the thickness of the selective PA layer. The composite membranes were prepared by interfacial polymerization (IP) reaction between meta-phenylene diamine (MPD)-aqueous and trimesoyl chloride (TMC)-organic solvents at the surface of polyethersulfone (PES) microporous support. Several PA TFC membranes were prepared at different temperatures of the organic solution ranging from −20 °C to 50 °C. The physico-chemical and morphological properties of the synthesized membranes were carefully characterized using serval analytical techniques. The results confirmed that the TFC membranes, synthesized at sub-zero temperatures of organic solution, had thinner and smoother PA layer with a greater degree of cross-linking and wettability compared to the PA films prepared at 50 °C. We demonstrated that reducing the temperature of organic solution effectively decreased the thickness of the PA active layer and thus enhanced water permeation through the membranes. The most water permeable membrane was prepared at −20 °C and exhibited nine times higher water flux compared to the membrane synthesized at room temperature. The method proposed in this report can be effectively applied for energy- and cost-efficient development of high performance nanofiltration and reverse osmosis membranes.