title: Early Warning of Human Crowds Based on Query Data from Baidu Map: Analysis Based on Shanghai Stampede

abstract: Without sufficient preparation and on-site management, the mass-scale unexpected huge human crowd is a serious threat to public safety. A recent impressive tragedy is the 2014 Shanghai Stampede, where 36 people died and 49 were injured in celebration of the New Year’s Eve on December 31st 2014 in the Shanghai Bund. Due to the innately stochastic and complicated individual movement, it is not easy to predict collective gatherings, which can potentially leads to crowd events. In this chapter, with leveraging the big data generated on Baidu Maps, we propose a novel approach to early warning such potential crowd disasters, which has profound public benefits. An insightful observation is that, with the prevalence and convenience of web map service, users usually search on the Baidu Maps to plan a routine, which reveals the users’ future destinations. Therefore, aggregating users’ query data on Baidu Maps can obtain priori and indication information for estimating future human population in a specific area ahead of time. Our careful analysis and deep investigation on the Baidu Maps’ data on various events also demonstrate a strong correlation pattern between the number of map query and the number of positioning in an area. Based on such observation, we propose a decision method utilizing query data on Baidu Maps to invoke warnings for potential crowd events about 1~3 h in advance. Then we also construct a machine learning model with heterogeneous data (such as query data and positioning data) to quantitatively measure the risk of the potential crowd disasters. We evaluate the effectiveness of our methods on the data of Baidu Maps.


title: Spatial versus Object Working Memory: PET Investigations

abstract: We used positron emission tomography (PET) to answer the following question: Is working memory a unitary storage system, or does it instead include different storage buffers for different kinds of information? In Experiment 1, PET measures were taken while subjects engaged in either a spatial-memory task (retain the position of three dots for 3 sec) or an object-memory task (retain the identity of two objects for 3 sec). The results manifested a striking double dissociation, as the spatial task activated only right-hemisphere regions, whereas the object task activated primarily left-hemisphere regions. The spatial (right-hemisphere) regions included occipital, parietal, and prefrontal areas, while the object (left-hemisphere) regions included inferotemporal and parietal areas. Experiment 2 was similar to Experiment 1 except that the stimuli and trial events were identical for the spatial and object tasks; whether spatial or object memory was required was manipulated by instructions. The PET results once more showed a double dissociation, as the spatial task activated primarily right-hemisphere regions (again including occipital, parietal and prefrontal areas), whereas the object task activated only left-hemisphere regions (again including inferotemporal and parietal areas). Experiment 3 was a strictly behavioral study, which produced another double dissociation. It used the same tasks as Experiment 2, and showed that a variation in spatial similarity affected performance in the spatial but not the object task, whereas a variation in shape similarity affected performance in the object but not the spatial task. Taken together, the results of the three experiments clearly imply that different working-memory buffers are used for storing spatial and object information.


title: Autonomous undulatory serpentine locomotion utilizing body dynamics of a fluidic soft robot

abstract: Soft robotics offers the unique promise of creating inherently safe and adaptive systems. These systems bring man-made machines closer to the natural capabilities of biological systems. An important requirement to enable self-contained soft mobile robots is an on-board power source. In this paper, we present an approach to create a bio-inspired soft robotic snake that can undulate in a similar way to its biological counterpart using pressure for actuation power, without human intervention. With this approach, we develop an autonomous soft snake robot with on-board actuation, power, computation and control capabilities. The robot consists of four bidirectional fluidic elastomer actuators in series to create a traveling curvature wave from head to tail along its body. Passive wheels between segments generate the necessary frictional anisotropy for forward locomotion. It takes 14 h to build the soft robotic snake, which can attain an average locomotion speed of 19 mm s(-1).


title: Topographic organization in the brain: searching for general principles

abstract: The neurons comprising many cortical areas have long been known to be arranged topographically such that nearby neurons have receptive fields at nearby locations in the world. Although this type of organization may be universal in primary sensory and motor cortex, in this review we demonstrate that associative cortical areas may not represent the external world in a complete and continuous fashion. After reviewing evidence for novel principles of topographic organization in macaque lateral intraparietal area (LIP) – one of the most-studied associative areas in the parietal cortex – we explore the implications of these new principles for brain function.


title: Machine learning for neuroimaging with scikit-learn

abstract: Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.


title: From Word Embeddings to Item Recommendation

abstract: Social network platforms can use the data produced by their users to serve them better. One of the services these platforms provide is recommendation service. Recommendation systems can predict the future preferences of users using their past preferences. In the recommendation systems literature there are various techniques, such as neighborhood based methods, machine-learning based methods and matrix-factorization based methods. In this work, a set of well known methods from natural language processing domain, namely Word2Vec, is applied to recommendation systems domain. Unlike previous works that use Word2Vec for recommendation, this work uses non-textual features, the check-ins, and it recommends venues to visit/check-in to the target users. For the experiments, a Foursquare check-in dataset is used. The results show that use of continuous vector space representations of items modeled by techniques of Word2Vec is promising for making recommendations.


title: Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery

abstract: Detection of surgical instruments plays a key role in ensuring patient safety in minimally invasive surgery. In this paper, we present a novel method for 2D vision-based recognition and pose estimation of surgical instruments that generalizes to different surgical applications. At its core, we propose a novel scene model in order to simultaneously recognize multiple instruments as well as their parts. We use a Convolutional Neural Network architecture to embody our model and show that the cross-entropy loss is well suited to optimize its parameters which can be trained in an end-to-end fashion. An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation. This allows our approach to be relatively parameter free at test time and shows good performance for both instrument detection and tracking. We show that our approach surpasses state-of-the-art results on in-vivo retinal microsurgery image data, as well as ex-vivo laparoscopic sequences.


title: Deep Image Retrieval: Learning global representations for image search

abstract: We propose a novel approach for instance-level image retrieval. It produces a global and compact fixed-length representation for each image by aggregating many region-wise descriptors. In contrast to previous works employing pre-trained deep networks as a black box to produce features, our method leverages a deep architecture trained for the specific task of image retrieval. Our contribution is twofold: (i) we leverage a ranking framework to learn convolution and projection weights that are used to build the region features; and (ii) we employ a region proposal network to learn which regions should be pooled to form the final global descriptor. We show that using clean training data is key to the success of our approach. To that aim, we use a large scale but noisy landmark dataset and develop an automatic cleaning approach. The proposed architecture produces a global image representation in a single forward pass. Our approach significantly outperforms previous approaches based on global descriptors on standard datasets. It even surpasses most prior works based on costly local descriptor indexing and spatial verification. Additional material is available at www.xrce.xerox.com/Deep-Image-Retrieval.


title: Simulating Human Game Play for Level Difficulty Estimation with Convolutional Neural Networks

abstract: This thesis presents an approach to predict the difficulty of levels in a game by simulating game play following a policy learned from human game play. Using state-action pairs tracked from players ...


title: Quick training of probabilistic neural nets by importance sampling. AISTATS

abstract: The device of this invention uses electrostatic recording to apply original titles, legends, or other indicia to discrete record receivers. The system includes changeable stencils which, for the purposes of this description, are constructed as counters. The desired information is set up, a voltage is applied between the stencil and an electrode behind a record receiver, ionized pigmented particles are attracted through the stencils to the record receiver, and the record receiver is moved to a fixing station. By rendering the individual items of information changeable, any desired information within the range of the apparatus can be applied to an appropriate record receiver.


title: A statistical model for tensor PCA

abstract: We consider the Principal Component Analysis problem for large tensors of arbitrary order $k$ under a single-spike (or rank-one plus noise) model. On the one hand, we use information theory, and recent results in probability theory, to establish necessary and sufficient conditions under which the principal component can be estimated using unbounded computational resources. It turns out that this is possible as soon as the signal-to-noise ratio $\beta$ becomes larger than $C\sqrt{k\log k}$ (and in particular $\beta$ can remain bounded as the problem dimensions increase). ::: On the other hand, we analyze several polynomial-time estimation algorithms, based on tensor unfolding, power iteration and message passing ideas from graphical models. We show that, unless the signal-to-noise ratio diverges in the system dimensions, none of these approaches succeeds. This is possibly related to a fundamental limitation of computationally tractable estimators for this problem. ::: We discuss various initializations for tensor power iteration, and show that a tractable initialization based on the spectrum of the matricized tensor outperforms significantly baseline methods, statistically and computationally. Finally, we consider the case in which additional side information is available about the unknown signal. We characterize the amount of side information that allows the iterative algorithms to converge to a good estimate.


title: Distant supervision for relation extraction without labeled data

abstract: Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size. Our experiments use Freebase, a large semantic database of several thousand relations, to provide distant supervision. For each pair of entities that appears in some Freebase relation, we find all sentences containing those entities in a large unlabeled corpus and extract textual features to train a relation classifier. Our algorithm combines the advantages of supervised IE (combining 400,000 noisy pattern features in a probabilistic classifier) and unsupervised IE (extracting large numbers of relations from large corpora of any domain). Our model is able to extract 10,000 instances of 102 relations at a precision of 67.6%. We also analyze feature performance, showing that syntactic parse features are particularly helpful for relations that are ambiguous or lexically distant in their expression.


title: JPEG Steganography Detection with Benford's Law

abstract: In this paper we present a novel approach to the problem of steganography detection in JPEG images by applying a statistical attack. The method is based on the empirical Benford's Law and, more specifically, on its generalized form. We prove and extend the validity of the logarithmic rule in colour images and introduce a blind steganographic method which can flag a file as a suspicious stego-carrier. The proposed method achieves very high accuracy and speed and is based on the distributions of the first digits of the quantized Discrete Cosine Transform coefficients present in JPEGs. In order to validate and evaluate our algorithm, we developed steganographic tools which are able to analyse image files and we subsequently applied them on the popular Uncompressed Colour Image Database. Furthermore, we demonstrate that not only can our method detect steganography but, if certain criteria are met, it can also reveal which steganographic algorithm was used to embed data in a JPEG file.


title: Biodiversity ofSaccharomycesyeast strains from grape berries of wine-producing areas using starter commercial yeasts

abstract: The use of commercial wine yeast strains as starters has grown extensively over the past two decades. In this study, a large-scale sampling plan was devised over a period of 3 years in three different vineyards in the south of France, to evaluate autochthonous wine yeast biodiversity in vineyards around wineries where active dry yeasts have been used as fermentation starters for more than 5 years. Seventy-two spontaneous fermentations were completed from a total of 106 grape samples, and 2160 colonies were isolated. Among these, 608 Saccharomyces strains were identified and 104 different chromosomal patterns found. The large majority of these (91) were found as unique patterns, indicating great biodiversity. There were differences in biodiversity according to the vineyard and year, showing that the biodiversity of Saccharomyces strains is influenced by climatic conditions and specific factors associated with the vineyards, such as age and size. Strains that were terroir yeast candidates were not found. The biodiversity of S. cerevisiae strains after harvest was similar to that in the early campaign; moreover, a temporal succession of S. cerevisiae strains is shown. This fact, together with the differences in biodiversity levels verifies that other factors were more important than commercial yeast utilization in the biodiversity of the vineyard.


title: The Situated Inference Model : An Integrative Account of the Effects of Primes on Perception , Behavior , and Motivation

abstract: The downstream consequences of a priming induction range from changes in the perception of objects in the environment to the initiation of prime-related behavior and goal striving. Although each of these outcomes has been accounted for by separate mechanisms, we argue that a single process could produce all three priming effects. In this article, we introduce the situated inference model of priming, discuss its potential to account for these divergent outcomes with one mechanism, and demonstrate its ability to organize the priming literatures surrounding these effects. According to the model, primes often do not cause direct effects, instead altering only the accessibility of prime-related mental content. This information produces downstream effects on judgment, behavior, or motivation when it is mistakenly viewed as originating from one’s own internal thought processes. When this misattribution occurs, the prime-related mental content becomes a possible source of information for solving whatever problems are afforded by the current situation. Because different situations afford very different questions and concerns, the inferred meaning of this prime-related content can vary greatly. The use of this information to answer qualitatively different questions can lead a single prime to produce varied effects on judgment, behavior, and motivation.


title: Mapping the local protein interactome of the NuA3 histone acetyltransferase.

abstract: Protein-protein interactions modulate cellular functions ranging from the activity of enzymes to signal transduction cascades. A technology termed transient isotopic differentiation of interactions as random or targeted (transient I-DIRT) is described for the identification of stable and transient protein-protein interactions in vivo. The procedure combines mild in vivo chemical cross-linking and non-stringent affinity purification to isolate low abundance chromatin-associated protein complexes. Using isotopic labeling and mass spectrometric readout, purified proteins are categorized with respect to the protein 'bait' as stable, transient, or contaminant. Here we characterize the local interactome of the chromatin-associated NuA3 histone lysine-acetyltransferase protein complex. We describe transient associations with the yFACT nucleosome assembly complex, RSC chromatin remodeling complex and a nucleosome assembly protein. These novel, physical associations with yFACT, RSC, and Nap1 provide insight into the mechanism of NuA3-associated transcription and chromatin regulation.


title: Wikification and Beyond: The Challenges of Entity and Concept Grounding

abstract: Contextual disambiguation and grounding of concepts and entities in natural language are essential to progress in many natural language understanding tasks and fundamental to many applications. Wikification aims at automatically identifying concept mentions in text and linking them to referents in a knowledge base (KB) (e.g., Wikipedia). Consider the sentence, "The Times report on Blumenthal (D) has the potential to fundamentally reshape the contest in the Nutmeg State.". A Wikifier should identify the key entities and concepts and map them to an encyclopedic resource (e.g., “D” refers to Democratic Party, and “the Nutmeg State” refers to Connecticut. Wikification benefits end-users and Natural Language Processing (NLP) systems. Readers can better comprehend Wikified documents as information about related topics is readily accessible. For systems, a Wikified document elucidates concepts and entities by grounding them in an encyclopedic resource or an ontology. Wikification output has improved NLP down-stream tasks, including coreference resolution, user interest discovery , recommendation and search. This task has received increased attention in recent years from the NLP and Data Mining communities, partly fostered by the U.S. NIST Text Analysis Conference Knowledge Base Population (KBP) track, and several versions of it has been studied. These include Wikifying all concept mentions in a single text document; Wikifying a cluster of co-referential named entity mentions that appear across documents (Entity Linking), and Wikifying a whole document to a single concept. Other works relate this task to coreference resolution within and across documents and in the context of multiple text genres. 2 Content Overview


title: High-Responsivity and High-Sensitivity Graphene Dots/a-IGZO Thin-Film Phototransistor

abstract: An a-IGZO thin-film phototransistor incorporating graphene absorption layer was proposed to enhance the responsivity and sensitivity simultaneously for photodetection from ultraviolet to visible regime. The spin-coated graphene dots absorb incident light, transferring electrons to the underlying a-IGZO to establish a photochannel. The 5 A/W responsivity and 1000 photo-to-dark current ratio were achieved for graphene phototransistor at 500 nm. As compared with 2700 transistor gain. The highest responsivity and photo-to-dark current ratio is 897 A/W and $10^{6}$ , respectively, under 340-nm light illumination.


title: Efficient ConvNet for real-time semantic segmentation

abstract: Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in an unified way. ConvNets excel at this task, as they can be trained end-to-end to accurately classify multiple object categories in an image at the pixel level. However, current approaches normally involve complex architectures that are expensive in terms of computational resources and are not feasible for ITS applications. In this paper, we propose a deep architecture that is able to run in real-time while providing accurate semantic segmentation. The core of our ConvNet is a novel layer that uses residual connections and factorized convolutions in order to remain highly efficient while still retaining remarkable performance. Our network is able to run at 83 FPS in a single Titan X, and at more than 7 FPS in a Jetson TX1 (embedded GPU). A comprehensive set of experiments demonstrates that our system, trained from scratch on the challenging Cityscapes dataset, achieves a classification performance that is among the state of the art, while being orders of magnitude faster to compute than other architectures that achieve top precision. This makes our model an ideal approach for scene understanding in intelligent vehicles applications.


title: Management of Moisture-Associated Skin Damage: A Scoping Review

abstract: BACKGROUND: Protecting the skin against moisture-associated damage is an important component of comprehensive skin and wound care. Based on a review of literature, the authors propose key interventions to protect and prevent damage in the skin folds, perineum, and areas surrounding a wound or stoma. OBJECTIVE: The aim of this scoping review is to identify and provide a narrative integration of the existing evidence related to the management and prevention of moisture-associated skin damage (MASD). METHODS: Study authors searched several databases for a broad spectrum of published and unpublished studies in English, published between 2000 and July 2015. Selected study information was collated in several different formats; ultimately, key findings were aggregated into a thematic description of the evidence to help generate a set of summative statements or recommendations. RESULTS: Based on inclusion criteria, 37 articles were considered appropriate for this review. Findings included functional definitions and prevalence rates of the 4 types of MASD, assessment scales for each, and 7 evidence-based strategies for the management of MASD. CONCLUSIONS: Based on this scoping review of literature, the authors propose key interventions to protect and prevent MASD including the use of barrier ointments, liquid polymers, and cyanoacrylates to create a protective layer that simultaneously maintains hydration levels while blocking external moisture and irritants.


title: Adaptive constraint handling and Success History Differential Evolution for CEC 2017 Constrained Real-Parameter Optimization

abstract: This paper presents Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including Linear population size reduction (L-SHADE), enhanced with adaptive constraint violation handling, applied to the benchmark for CEC 2017 Competition on Constrained Real-Parameter Optimization. The constraint handling method prioritizes the feasible solutions before infeasible, while disregarding the constraint violation values below an adaptive threshold, i.e. adaptive ϵ-constraint handling. The 28 constrained test functions on 10, 30, 50, and 100 dimensions are assessed on the benchmark and the required resulting final fitnesses, constraints violations, and success rates are reported for 25 independent runs of the proposed algorithm under the budget of fixed maximum number of fitness evaluations for 10, 30, 50, and 100 dimensional test functions.


title: DeepDive: declarative knowledge base construction

abstract: The dark data extraction or knowledge base construction (KBC) problem is to populate a SQL database with information from unstructured data sources including emails, webpages, and pdf reports. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems. The key idea in DeepDive is that statistical inference and machine learning are key tools to attack classical data problems in extraction, cleaning, and integration in a unified and more effective manner. DeepDive programs are declarative in that one cannot write probabilistic inference algorithms; instead, one interacts by defining features or rules about the domain. A key reason for this design choice is to enable domain experts to build their own KBC systems. We present the applications, abstractions, and techniques of DeepDive employed to accelerate construction of KBC systems.


title: Adaptive Information Extraction from Text by Rule Induction and Generalisation

abstract: (LP)2 is a covering algorithm for adaptive Information Extraction from text (IE). It induces symbolic rules that insert SGML tags into texts by learning from examples found in a user-defined tagged corpus. Training is performed in two steps: initially a set of tagging rules is learned; then additional rules are induced to correct mistakes and imprecision in tagging. Induction is performed by bottom-up generalization of examples in the training corpus. Shallow knowledge about Natural Language Processing (NLP) is used in the generalization process. The algorithm has a considerable success story. From a scientific point of view, experiments report excellent results with respect to the current state of the art on two publicly available corpora. From an application point of view, a successful industrial IE tool has been based on (LP)2. Real world applications have been developed and licenses have been released to external companies for building other applications. This paper presents (LP)2, experimental results and applications, and discusses the role of shallow NLP in rule induction.


title: Robust Facial Expression Classification Using Shape and Appearance Features

abstract: Facial expression recognition has many potential applications which has attracted the attention of researchers in the last decade. Feature extraction is one important step in expression analysis which contributes toward fast and accurate expression recognition. This paper represents an approach of combining the shape and appearance features to form a hybrid feature vector. We have extracted Pyramid of Histogram of Gradients (PHOG) as shape descriptors and Local Binary Patterns (LBP) as appearance features. The proposed framework involves a novel approach of extracting hybrid features from active facial patches. The active facial patches are located on the face regions which undergo a major change during different expressions. After detection of facial landmarks, the active patches are localized and hybrid features are calculated from these patches. The use of small parts of face instead of the whole face for extracting features reduces the computational cost and prevents the over-fitting of the features for classification. By using linear discriminant analysis, the dimensionality of the feature is reduced which is further classified by using the support vector machine (SVM). The experimental results on two publicly available databases show promising accuracy in recognizing all expression classes.


title: A Survey on Opportunistic Routing in Wireless Communication Networks

abstract: The great advances made in the wireless technology have enabled the deployment of wireless communication networks in some of the harshest environments such as volcanoes, hurricane-affected regions, and underground mines. In such challenging environments suffering from the lack of infrastructure, traditional routing is not efficient and sometimes not even feasible. Moreover, the exponential growth of the number of wireless connected devices has created the need for a new routing paradigm that could benefit from the potentials offered by these heterogeneous wireless devices. Hence, in order to overcome the traditional routing limitations, and to increase the capacity of current dynamic heterogeneous wireless networks, the opportunistic routing paradigm has been proposed and developed in recent research works. Motivated by the great interest that has been attributed to this new paradigm within the last decade, we provide a comprehensive survey of the existing literature related to opportunistic routing. We first study the main design building blocks of opportunistic routing. Then, we provide a taxonomy for opportunistic routing proposals, based on their routing objectives as well as the optimization tools and approaches used in the routing design. Hence, five opportunistic routing classes are defined and studied in this paper, namely, geographic opportunistic routing, link-state-aware opportunistic routing, probabilistic opportunistic routing, optimization-based opportunistic routing, and cross-layer opportunistic routing. We also review the main protocols proposed in the literature for each class. Finally, we identify and discuss the main future research directions related to the opportunistic routing design, optimization, and deployment.


title: An enhanced pre-processing model for big data processing: A quality framework

abstract: With the ever growing trends and technologies a huge volume of data is being evolved each and every second big data has become a supreme approach in data inception, accession, processing and analyzing the heterogeneous, huge amount of data so as to derive useful insights out of it. With data and without quality there is no point in having the data. Thus, data with quality is required to use or leverage the data in a more appropriate manner. With the evolution of big data many technologies are being developed. The input to it must be processed in such a way that the quality data yields quality effective results. An effective pre-processing model is proposed in this paper for the processing of the big data. Using relief algorithm and fast mRMR together as a hybrid approach can be used for the pre-processing of the data. Analysis shows that this hybrid approach is more effective and can greatly enhance the quality of the data. This approach can yield better performance upon the big data platform using the Spark framework.


title: Security Scenario Generator (SecGen): A Framework for Generating Randomly Vulnerable Rich-scenario VMs for Learning Computer Security and Hosting {CTF} Events

abstract: Computer security students benefit from hands-on experience applying security tools and techniques to attack and defend vulnerable systems. Virtual machines (VMs) provide an effective way of sharing targets for hacking. However, developing these hacking challenges is time consuming, and once created, essentially static. That is, once the challenge has been "solved" there is no remaining challenge for the student, and if the challenge is created for a competition or assessment, the challenge cannot be reused without risking plagiarism, and collusion. Security Scenario Generator (SecGen) can build complex VMs based on randomised scenarios, with a number of diverse use-cases, including: building networks of VMs with randomised services and in-thewild vulnerabilities and with themed content, which can form the basis of penetration testing activities; VMs for educational lab use; and VMs with randomised CTF challenges. SecGen has a modular architecture which can dynamically generate challenges by nesting modules, and a hints generation system, which is designed to provide scaffolding for novice security students to make progress on complex challenges. SecGen has been used for teaching at universities, and hosting a recent UK-wide CTF event.


title: Video coding with H.264/AVC: tools, performance, and complexity

abstract: H.264/AVC, the result of the collaboration between the ISO/IEC Moving Picture Experts Group and the ITU-T Video Coding Experts Group, is the latest standard for video coding. The goals of this standardization effort were enhanced compression efficiency, network friendly video representation for interactive (video telephony) and non-interactive applications (broadcast, streaming, storage, video on demand). H.264/AVC provides gains in compression efficiency of up to 50% over a wide range of bit rates and video resolutions compared to previous standards. Compared to previous standards, the decoder complexity is about four times that of MPEG-2 and two times that of MPEG-4 Visual Simple Profile. This paper provides an overview of the new tools, features and complexity of H.264/AVC.


title: Value of Information and Cost of Privacy in the Internet of Things

abstract: Will the Internet of Things happen? Clearly, the hardware and software components comprising the Internet of Things are technologically feasible, but the sweeping adoption we envision might not take place. The success of technological innovations depends on the creation of a business model that both customers and providers perceive as beneficial. As the recently abandoned Google Glass project shows, privacy concerns can kill an otherwise technologically feasible product. On the other hand, the example of Twitter illustrates that very popular products might fail to make money. Both academic researchers and businesses are becoming increasingly aware that we need to reason about the economic and social implications of provided value and privacy in a rigorous, quantitative way. In this article we call these quantities value of information, which appears to both the service providers and the customers, and cost of privacy, which normally is only relevant to the customers. We describe the importance of assessing these values in the context of the Internet of Things, possible directions of their formalization, their relationships to other problems, and related areas as well as future directions of the field.


title: A branch-price-and-cut algorithm for the workover rig routing problem

abstract: In an onshore oil field, the productivity of oil wells decreases when they require maintenance. To restore full productivity at a well, it must be visited by a specially equipped vehicle, called a workover rig. Given a set of wells needing maintenance and a heterogeneous fleet of workover rigs, the workover rig routing problem (WRRP) consists of finding rig routes that minimize the total production loss of the wells over a finite horizon. The wells have different loss rates, require various services, and may not be serviced within the horizon due to rig availability. The rigs have initial positions and do not have the same equipment. This paper presents the first exact algorithm for the WRRP, namely, a branch-price-and-cut algorithm that relies on some of the most recent techniques introduced for the vehicle routing problem with time windows. Our computational experiments show that this exact algorithm can solve practical-sized instances in reasonable computational times.


title: Active kernel learning

abstract: Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. A number of kernel learning algorithms have been proposed to learn kernel functions or matrices from side information (e.g., either labeled examples or pairwise constraints). However, most previous studies are limited to "passive" kernel learning in which side information is provided beforehand. In this paper we present a framework of Active Kernel Learning (AKL) that actively identifies the most informative pairwise constraints for kernel learning. The key challenge of active kernel learning is how to measure the informativeness of an example pair given its class label is unknown. To this end, we propose a min-max approach for active kernel learning that selects the example pair that results in a large classification margin regardless of its assigned class label. We furthermore approximate the related optimization problem into a convex programming problem. We evaluate the effectiveness of the proposed algorithm by comparing it to two other implementations of active kernel learning. Empirical study with nine datasets on semi-supervised data clustering shows that the proposed algorithm is more effective than its competitors.


title: Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update

abstract: We propose Episodic Backward Update (EBU) - a novel deep reinforcement learning algorithm with a direct value propagation. In contrast to the conventional use of the experience replay with uniform random sampling, our agent samples a whole episode and successively propagates the value of a state to its previous states. Our computationally efficient recursive algorithm allows sparse and delayed rewards to propagate directly through all transitions of the sampled episode. We theoretically prove the convergence of the EBU method and experimentally demonstrate its performance in both deterministic and stochastic environments. Especially in 49 games of Atari 2600 domain, EBU achieves the same mean and median human normalized performance of DQN by using only 5% and 10% of samples, respectively.


title: Oxytocin and social motivation

abstract: Humans are fundamentally social creatures who are ‘motivated’ to be with others. In this review we examine the role of oxytocin (OT) as it relates to social motivation. OT is synthesized in the brain and throughout the body, including in the heart, thymus, gastrointestinal tract, as well as reproductive organs. The distribution of the OT receptor (OTR) system in both the brain and periphery is even more far-reaching and its expression is subject to changes over the course of development. OTR expression is also sensitive to changes in the external environment and the internal somatic world. The OT system functions as an important element within a complex, developmentally sensitive biobehavioral system. Other elements include sensory inputs, the salience, reward, and threat detection pathways, the hypothalamic-pituitary-gonadal axis, and the hypothalamic-pituitary-adrenal stress response axis. Despite an ever expanding scientific literature, key unresolved questions remain concerning the interplay of the central and peripheral components of this complex biobehavioral system that dynamically engages the brain and the body as humans interact with social partners over the course of development.


title: Network Analysis Of Knowledge Construction In Asynchronous Learning Networks

abstract: Asynchronous Learning Networks (ALNs) make the process of collaboration more transparent, because a transcript of conference messages can be used to assess individual roles and contributions and the collaborative process itself. This study considers three aspects of ALNs: the design; the quality of the resulting knowledge construction process; and cohesion, role and power network structures. The design is evaluated according to the Social Interdependence Theory of Cooperative Learning. The quality of the knowledge construction process is evaluated through Content Analysis; and the network structures are analyzed using Social Network Analysis of the response relations among participants during online discussions. In this research we analyze data from two three-monthlong ALN academic university courses: a formal, structured, closed forum and an informal, nonstructured, open forum. We found that in the structured ALN, the knowledge construction process reached a very high phase of critical thinking and developed cohesive cliques. The students took on bridging and triggering roles, while the tutor had relatively little power. In the non-structured ALN, the knowledge construction process reached a low phase of cognitive activity; few cliques were constructed; most of the students took on the passive role of teacher-followers; and the tutor was at the center of activity. These differences are statistically significant. We conclude that a well-designed ALN develops significant, distinct cohesion, and role and power structures lead the knowledge construction process to high phases of critical thinking.


title: Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis

abstract: A central theme in learning from image data is to develop appropriate image representations for the specific task at hand. Traditional methods used handcrafted local features combined with high-level image representations to generate image-level representations. Thus, a practical challenge is to determine what features are appropriate for specific tasks. For example, in the study of gene expression patterns in Drosophila melanogaster, texture features based on wavelets were particularly effective for determining the developmental stages from in situ hybridization (ISH) images. Such image representation is however not suitable for controlled vocabulary (CV) term annotation because each CV term is often associated with only a part of an image. Here, we developed problem-independent feature extraction methods to generate hierarchical representations for ISH images. Our approach is based on the deep convolutional neural networks (CNNs) that can act on image pixels directly. To make the extracted features generic, the models were trained using a natural image set with millions of labeled examples. These models were transferred to the ISH image domain and used directly as feature extractors to compute image representations. Furthermore, we employed multi-task learning method to fine-tune the pre-trained models with labeled ISH images, and also extracted features from the fine-tuned models. Experimental results showed that feature representations computed by deep models based on transfer and multi-task learning significantly outperformed other methods for annotating gene expression patterns at different stage ranges. We also demonstrated that the intermediate layers of deep models produced the best gene expression pattern representations.


title: Product design, semantics and emotional response

abstract: This paper explores theoretical issues in ergonomics related to semantics and the emotional content of design. The aim is to find answers to the following questions: how to design products triggering ‘happiness’ in one's mind; which product attributes help in the communication of positive emotions; and finally, how to evoke such emotions through a product. In other words, this is an investigation of the ‘meaning’ that could be designed into a product in order to ‘communicate’ with the user at an emotional level. A literature survey of recent design trends, based on selected examples of product designs and semantic applications to design, including the results of recent design awards, was carried out in order to determine the common attributes of their design language. A review of Good Design Award winning products that are said to convey and/or evoke emotions in the users has been done in order to define good design criteria. These criteria have been discussed in relation to user emotional responses and a...


title: The promise of a DNA taxonomy

abstract: Not only is the number of described species a very small proportion of the estimated extant number of taxa, but it also appears that all concepts of the extent and boundaries of 'species' fail in many cases. Using conserved molecular sequences it is possible to define and diagnose molecular operational taxonomic units (MOTU) that have a similar extent to traditional 'species'. Use of a MOTU system not only allows the rapid and effective identification of most taxa, including those not encountered before, but also allows investigation of the evolution of patterns of diversity. A MOTU approach is not without problems, particularly in the area of deciding what level of molecular difference defines a biologically relevant taxon, but has many benefits. Molecular data are extremely well suited to re-analysis and meta-analysis, and data from multiple independent studies can be readily collated and investigated by using new parameters and assumptions. Previous molecular taxonomic efforts have focused narrowly. Advances in high-throughput sequencing methodologies, however, place the idea of a universal, multi-locus molecular barcoding system in the realm of the possible.


Object detection and identification by hierarchical segmentation


title: Automated Fact Checking: Task formulations, methods and future directions

abstract: The recently increased focus on misinformation has stimulated research in fact checking, the task of assessing the truthfulness of a claim. Research in automating this task has been conducted in a variety of disciplines including natural language processing, machine learning, knowledge representation, databases, and journalism. While there has been substantial progress, relevant papers and articles have been published in research communities that are often unaware of each other and use inconsistent terminology, thus impeding understanding and further progress. In this paper we survey automated fact checking research stemming from natural language processing and related disciplines, unifying the task formulations and methodologies across papers and authors. Furthermore, we highlight the use of evidence as an important distinguishing factor among them cutting across task formulations and methods. We conclude with proposing avenues for future NLP research on automated fact checking.


title: Bullying and Victimization in Adolescence: Concurrent and Stable Roles and Psychological Health Symptoms

abstract: From an initial sample of 1,278 Italian students, the authors selected 537 on the basis of their responses to a self-report bully and victim questionnaire. Participants' ages ranged from 13 to 20 years (M = 15.12 years, SD = 1.08 years). The authors compared the concurrent psychological symptoms of 4 participant groups (bullies, victims, bully/victims [i.e., bullies who were also victims of bullying], and uninvolved students). Of participants, 157 were in the bullies group, 140 were in the victims group, 81 were in the bully/victims group, and 159 were in the uninvolved students group. The results show that bullies reported a higher level of externalizing problems, victims reported more internalizing symptoms, and bully/victims reported both a higher level of externalizing problems and more internalizing symptoms. The authors divided the sample into 8 groups on the basis of the students' recollection of their earlier school experiences and of their present role. The authors classified the participants as ...


title: Personality Identification through Handwriting Analysis: A Review

abstract: Whenever we listen to or meet a new person we try to predict personality attributes of the person. Our behavior towards the person is hugely influenced by the predictions we make. Personality is made up of the characteristic patterns of thoughts, feelings and behaviors that make a person unique. Your personality affects your success in the role. Recognizing about yourself and reflecting on your personality can help you to understand how you might shape your future. Various approaches like personality prediction through speech, facial expression, video, and text are proposed in literature to recognize personality. Personality predictions can be made out of one's handwriting as well. The objective of this paper is to discuss methodology used to identify personality through handwriting analysis and present current state-of-art related to it.


title: Fast-Slow Recurrent Neural Networks

abstract: Processing sequential data of variable length is a major challenge in a wide range of applications, such as speech recognition, language modeling, generative image modeling and machine translation. Here, we address this challenge by proposing a novel recurrent neural network (RNN) architecture, the Fast-Slow RNN (FS-RNN). The FS-RNN incorporates the strengths of both multiscale RNNs and deep transition RNNs as it processes sequential data on different timescales and learns complex transition functions from one time step to the next. We evaluate the FS-RNN on two character level language modeling data sets, Penn Treebank and Hutter Prize Wikipedia, where we improve state of the art results to $1.19$ and $1.25$ bits-per-character (BPC), respectively. In addition, an ensemble of two FS-RNNs achieves $1.20$ BPC on Hutter Prize Wikipedia outperforming the best known compression algorithm with respect to the BPC measure. We also present an empirical investigation of the learning and network dynamics of the FS-RNN, which explains the improved performance compared to other RNN architectures. Our approach is general as any kind of RNN cell is a possible building block for the FS-RNN architecture, and thus can be flexibly applied to different tasks.


title: Houdini: Fooling Deep Structured Prediction Models

abstract: Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines. So far, most existing methods only work for classification and are not designed to alter the true performance measure of the problem at hand. We introduce a novel flexible approach named Houdini for generating adversarial examples specifically tailored for the final performance measure of the task considered, be it combinatorial and non-decomposable. We successfully apply Houdini to a range of applications such as speech recognition, pose estimation and semantic segmentation. In all cases, the attacks based on Houdini achieve higher success rate than those based on the traditional surrogates used to train the models while using a less perceptible adversarial perturbation.


title: Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders

abstract: We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information. Our model consists of an encoder, which uses monolingual and bilingual context (i.e. a parallel sentence) to choose a sense for a given word, and a decoder which predicts context words based on the chosen sense. The two components are estimated jointly. We observe that the word representations induced from bilingual data outperform the monolingual counterparts across a range of evaluation tasks, even though crosslingual information is not available at test time.


1 Capacity-approaching DNA storage


title: Online vehicle detection using deep neural networks and lidar based preselected image patches

abstract: In this paper we present a vehicle detection system using convolutional neural networks on 2d image data. Since realtime capabilities are crucial for object detection systems running in real-traffic situations, we will show how the calculation time of our algorithm can be significantly reduced by taking advantage of depth information from lidar sensors. One part of this work focusses on useful network topologies and network parameters to increase the classification precision. We will test the presented algorithm on an autonomous car in different real-traffic scenarios with regards to detection accuracy and calculation time and show experimental results.


title: Comparison of standard low pass filter types in two-path half-band IIR filter structures

abstract: Two-path half-band recursive filters are a standard structure used to realize efficient up-sampling or down-sampling filtering tasks [1]. In the most conventional implementation the two path filters form quadrature mirror filters offering simultaneous low-pass and high-pass characteristics from the sum and difference of the two paths. These filters are called self reciprocal (SR) and can be a Butterworth filter or an elliptic filter with coupled pass-band and stop-band ripple levels. The two path structure can also be used to implement Tchebyshev-I, Tchebyshev-II, and arbitrary ripple Elliptic filters. The latter options are not self reciprocal and require more arithmetic operations than do the SR options. In this paper we examine and compare the full set half band recursive filter types.


title: Time scales in motor learning and development

abstract: A theoretical framework based on the concepts and tools of nonlinear dynamical systems is advanced to account for both the persistent and transitory changes traditionally shown for the learning and development of motor skills. The multiple time scales of change in task outcome over time are interpreted as originating from the system's trajectory on an evolving attractor landscape. Different bifurcations between attractor organizations and transient phenomena can lead to exponential, power law, or S-shaped learning curves. This unified dynamical account of the functions and time scales in motor learning and development offers several new hypotheses for future research on the nature of change in learning theory.


title: A unique failure mechanism induced by chip to board interaction on fan-out wafer level package

abstract: Despite many fatigue mechanisms published for Fan-out Wafer Level Package (FOWLP), most merely focused on stand-alone package reliability or solder joint capability. Only a few explored the failures of Chip to Board Interaction (CBI) due to the difficulty for perceiving the induced CBI failures. In this paper, a unique failure mechanism was first observed by an innovative test methodology in interconnects of FOWLP induced by CBI under thermal stress gradient. Circular cracks were detected on passivation and redistribution layer (RDL) owing to mismatched thermal expansion among modules. CBI were studied and improved by appropriate passivation properties, fine-tuned process window, optimized RDL patterns and suitable solder ball material. A novel simulation model was established and an in-house design guideline was generated for reliability robustness.


title: Design of a novel Simulink model for surface electromyographic (SEMG) sensor design for prosthesis control

abstract: This paper presents design, simulation and fabrication of a surface electromyographic (SEMG) sensor for control of prosthetic devices. EMG activity is mainly the generation of a bio-potential signal (electrical signals) due to muscle action. These signals picked from motor points of muscles are contaminated with various intrinsic/extrinsic noises, which must be removed through different filtering techniques in order to develop a sensor that has a high signal-to-noise ratio. Power spectral density (PSD) of any EMG signal plays a vital role to determine the signal strength. A novel Simulink model has been developed which mimics various elements of an active SEMG sensor. By using this model, low/high/notch filters are designed and optimized. The model is also used to simulate the effects of these filters on power spectral density (PSD) of the EMG signal. Simulation of double rectification and smoothing (envelopment) is also carried out. EMG signals recorded from the Tibialis anterior, monopolar needle, and fine wire isometric contraction were used in this simulation. Finally, on the basis of simulation results, instrumentation of surface EMG sensor is designed and fabricated. Performance/results of developed SEMG sensor are in accordance with the simulation results of the developed Simulink model.


title: Eating less from bigger packs: Preventing the pack size effect with diet primes

abstract: An increase in the package size of food has been shown to lead to an increase in energy intake from this food, the so-called pack size effect. Previous research has shown that providing diet-concerned individuals with a reminder, or prime, of their dieting goal can help them control their consumption. Here, we investigated if providing such a prime is also effective for reducing the magnitude of the pack size effect. We conducted two experiments in which the cover of a dieting magazine (Experiment 1) and diet-related commercials (Experiment 2) served as diet goal primes. Both experiments had a 2 (pack size: small vs. large) × 2 (prime: diet vs. control) × 2 (dietary restraint: high vs. low) between participants design. We measured expected consumption of four snack foods in Experiment 1 (N = 477), and actual consumption of M&M's in Experiment 2 (N = 224). Results showed that the diet prime reduced the pack size effect for both restrained and unrestrained eaters in Experiment 1 and for restrained eaters only in Experiment 2. Although effect sizes were small, these findings suggest that a diet prime motivates restrained eaters to limit their consumption, and as a result the pack size has less influence on the amount consumed. We discuss limitations of this research as well as potential avenues for further research and theoretical and practical implications.


title: Navigation Techniques in Augmented and Mixed Reality: Crossing the Virtuality Continuum

abstract: Exploring and surveying the world has been an important goal of humankind for thousands of years. Entering the twenty-first century, the Earth has almost been fully digitally mapped. Widespread deployment of GIS (Geographic Information Systems) technology and a tremendous increase of both satellite and street-level mapping over the last decade enables the public to view large portions of the world using computer applications such as Bing Maps1 or Google Earth2.


title: A 28-GHz fractional-N frequency synthesizer with reference and frequency doublers for 5G cellular

abstract: This paper presents a 27.5-29.6GHz fractional-N frequency synthesizer using reference and frequency doublers to achieve low in-band and out-of-band phase-noise for 5G mobile communications. The push-push amplifier and 28GHz balun help achieving differential signals with low out-of-band phase noise while consuming low power. A charge pump with gated offset as well as reference doubler help reducing noise-folding effect resulting low in-band phase noise while sampling loop filter helps reducing spurs. The proposed synthesizer has been implemented in 65nm CMOS technology achieving an in-band and out-of-band phase noise of -78dBc/Hz and -126dBc/Hz, respectively while consuming only 33mW. The jitter-power figure-of-merit (FoM) is -231dB which is the highest among the state-of-the-art >20GHz fractional-N PLLs. Reference spurs are less than -80 dBc.


title: Stackguard: Simple stack smash protection for GCC

abstract: Since 1998, StackGuard patches to GCC have been used to protect entire distributions from stack smashing buffer overflows. Performance overhead and software compatibility issues have been minimal. In its history, the parts of GCC that StackGuard has operated in have twice changed enough to require complete overhauls of the StackGuard patch. Since StackGuard is a mature technology, even seeing re-implementations in other compilers, we propose that GCC adopt StackGuard as a standard feature. This paper describes our recent work to bring StackGuard fully up to date with current GCC, introduce architecture independence, and extend the protection of stack data structures, while keeping the StackGuard patch as small, simple, and modular as possible.


title: Practical Dynamic Searchable Encryption with Small Leakage

abstract: In this paper we revisit the DSSE problem. We propose the first DSSE scheme that achieves the best of both worlds, i.e., both small leakage and efficiency. In particular, our DSSE scheme leaks significantly less information than any other previous DSSE construction and supports both updates and searches in sublinear time in the worst case, maintaining at the same time a data structure of only linear size. We finally provide an implementation of our construction, showing its practical efficiency.


title: Analysis of the flow cytometer stain Hoechst 33342 on human spermatozoa

abstract: objective of this study was to determine the effect of bisbenzimide on spermatozoa assessed by means of the sperm survival test and to analyse the p-globin gene in sperm DNA after exposure to increasing concentrations of bisbenzimide. Donor (n = 16) sperm specimens were pooled and washed in a discontinuous Percoll gradient 95:47%, divided and incubated in tubes containing bisbenzimide at concentrations 0 (control), 0.9, 9, 90, 900 and 9000 uJvl at 25°C and scanned on a computer-aided sperm motility analyser at 0, 1, 4 and 24 h. Spermatozoa were also incubated in a known mutagen, ethidium bromide, as positive control. After 24 h of incubation, the treated sperm cells were processed through DNA extraction and polymerase chain reaction (PCR) performed with primers targeting the p-globin gene. The amplified DNA products were analysed for evidence of mutation in 5% polyacrylamide gel electrophoresis and 20:80 denaturing gradient gel electrophoresis (DGGE) and further confirmed in 30:40 DGGE. The results showed complete cessation of motility in sperm incubated in the presence of 900 uJVI or higher concentrations of bisbenzimide. The beat cross frequency sperm parameter was significantly different at the 90 uM or higher concentration of bisbenzimide compared with the control. At concentrations <900 uJvl bisbenzimide, there were no differences in the remaining sperm kinematic parameters (percentage rapid progressive, percentage total progressive, sperm velocities, linearity, straightness, amplitude of lateral head displacement and percentage hyperactive motility). PCR and DGGE analyses of spermatozoa treated with bisbenzimide showed no evidence of mutation in the representative region of the p-globin gene at concentrations <900 |iM. The data suggest an inhibitory effect of bisbenzimide on human sperm motility at 900 uJvl or higher concentrations of bisbenzimide. The decrease in sperm motility and rapid progression were not due to changes in pH. Point mutation in the representative region of the p-globin gene in human spermatozoa was detected only at high concentrations (^ 900 u.M) of bisbenzimide. The data suggest that incubating sperm in low concentrations of bisbenzimide (<90 uM) for up to 24 h does not significantly affect all the sperm kinematic parameters including the beat cross frequency parameter when compared with the control.


title: Effective Training of a Neural Network Character Classifier for Word Recognition

abstract: We have combined an artificial neural network (ANN) character classifier with context-driven search over character segmentation, word segmentation, and word recognition hypotheses to provide robust recognition of hand-printed English text in new models of Apple Computer's Newton Message Pad. We present some innovations in the training and use of ANNs as character classifiers for word recognition, including normalized output error, frequency balancing, error emphasis, negative training, and stroke warping. A recurring theme of reducing a priori biases emerges and is discussed.


title: Semi-supervised Clustering for Short Text via Deep Representation Learning

abstract: In this work, we propose a semi-supervised method for short text clustering, where we represent texts as distributed vectors with neural networks, and use a small amount of labeled data to specify our intention for clustering. We design a novel objective to combine the representation learning process and the k-means clustering process together, and optimize the objective with both labeled data and unlabeled data iteratively until convergence through three steps: (1) assign each short text to its nearest centroid based on its representation from the current neural networks; (2) re-estimate the cluster centroids based on cluster assignments from step (1); (3) update neural networks according to the objective by keeping centroids and cluster assignments fixed. Experimental results on four datasets show that our method works significantly better than several other text clustering methods.


title: LSAfun - An R package for computations based on Latent Semantic Analysis

abstract: In this article, the R package LSAfun is presented. This package enables a variety of functions and computations based on Vector Semantic Models such as Latent Semantic Analysis (LSA) Landauer, Foltz and Laham (Discourse Processes 25:259–284, 1998), which are procedures to obtain a high-dimensional vector representation for words (and documents) from a text corpus. Such representations are thought to capture the semantic meaning of a word (or document) and allow for semantic similarity comparisons between words to be calculated as the cosine of the angle between their associated vectors. LSAfun uses pre-created LSA spaces and provides functions for (a) Similarity Computations between words, word lists, and documents; (b) Neighborhood Computations, such as obtaining a word’s or document’s most similar words, (c) plotting such a neighborhood, as well as similarity structures for any word lists, in a two- or three-dimensional approximation using Multidimensional Scaling, (d) Applied Functions, such as computing the coherence of a text, answering multiple choice questions and producing generic text summaries; and (e) Composition Methods for obtaining vector representations for two-word phrases. The purpose of this package is to allow convenient access to computations based on LSA.


title: Optimal design of a manufacturing-remanufacturing-transport system within a reverse logistics chain

abstract: Due to environmental preoccupations and hard economic competition, many production firms are devoted to collect and remanufacture used end-of-life products. Therefore, throughout the past years, design and optimization of manufacturing remanufacturing supply chains have been receiving great attention from academic researchers and firms leaders. In the literature, most works have assumed that new products have the same quality and price as remanufactured ones. Furthermore, rare existing works have considered transport activities in manufacturing remanufacturing supply chains. However, in real-world systems, the remanufactured products are perceived as lower quality compared to new ones, and the transportation time usually has an impact on the inventory management. In this work, we aim to bridge the gap in works on manufacturing/remanufacturing, by investigating an optimal design of manufacturing-remanufacturing-transport system, while taking into account the distinction between new and remanufactured products, and transport activities between the manufacturing and remanufacturing stores and the purchasing warehouses in the cities. This paper considers a manufacturing/remanufacturing system composed of two parallel machines, manufacturing and remanufacturing stocks, purchasing warehouses, transport vehicles, and recovery inventories. The objective is to determine the optimal capacities of the manufacturing and remanufacturing stocks, purchasing warehouses, transport vehicles, as well as the optimal percentage of end-of-life returned products. These decision variables maximize the total profit function. An optimization method inspired from evolutionary algorithm is developed to find the optimal value of decision variables. Eventually, numerical results are presented to show the impact of returned products and transportation time on decision variables.


title: GSR and HRV: its application in clinical diagnosis

abstract: Galvanic skin response is a method of regulating the internal physical process by giving a biofeedback, which is effective in the treatment of phobias, anxiety and to increase the relaxation process of the subject during the hypnosis. Presently the application of GSR by certain Physiologist and Psychologist is substantially restricted by its inability to generate time varying responses. By generating these time varying graphical responses we would be able to monitor only the skin resistance response over a period of time. Moreover the condition of the most vital organ of the body. i.e.: the heart, in condition of stress, anxiety, anger as well as pressure is unavailable. To evaluate the cardiac responses at this point we have combine heart rate variability along with galvanic skin response. Here, we will be monitoring the GSR signal and ECG signal. These signals are then computer interfaced to give the responses of GSR and HRV. With the help of these responses a physician would be able to guide his patient under condition of stress, anxiety and pressure through biofeedback. Thus the physician would be able to give clinical diagnosis and management of different heart disorders, its regulation and stability, which would help the individual to self regulate his/her own health and growth. Regardless, of the method of analysis, GSR and HRV would provide a very robust biofeedback that can be easily used for diagnosis and communicating responses.


title: RWTH-PHOENIX-Weather: A Large Vocabulary Sign Language Recognition and Translation Corpus

abstract: This paper introduces the RWTH-PHOENIX-Weather corpus, a video-based, large vocabulary corpus of German Sign Language suitable for statistical sign language recognition and translation. In contrast to most available sign language data collections, the RWTH-PHOENIX-Weather corpus has not been recorded for linguistic research but for the use in statistical pattern recognition. The corpus contains weather forecasts recorded from German public TV which are manually annotated using glosses distinguishing sign variants, and time boundaries have been marked on the sentence and the gloss level. Further, the spoken German weather forecast has been transcribed in a semi-automatic fashion using a state-of-the-art automatic speech recognition system. Moreover, an additional translation of the glosses into spoken German has been created to capture allowable translation variability. In addition to the corpus, experimental baseline results for hand and head tracking, statistical sign language recognition and translation are presented.


title: DDoS Attack Detection System: Utilizing Classification Algorithms with Apache Spark

abstract: Cloud computing is a model of configurable computing resources such as servers, networks, storages, applications, and services that are available from anywhere at any time. In addition, cloud computing is managed by experts from different computer science fields to provide high reliability, availability, mobility, security, and scalability. Of course, security against all form of attacks, including DDoS attack, must be provided. Numerous DDoS attacks have been launched against different organizations in the last decade and numerous approaches have been proposed and tried to detect and prevent DDoS attacks by utilizing classification algorithms. In this research, we propose a DDoS detection system that benefits from cloud computing resources. Our proposed system consists of three concepts: classification algorithms, parallelism computing, and a fuzzy logic system. Classification algorithms are used in our system to classify and predict DDoS attacks on traffic packets. The parallelism concept is used to efficiently accelerate the execution of the utilized classification algorithms. The fuzzy logic is used to choose which of the classification algorithms is to be used next. We evaluated the classification algorithm and the parallel processing of the DDoS detection by configuring a test-bed that consists of one master and three slaves. We validated the fuzzy logic system by using the MATLAB statistical tool.


title: Attribute Truss Community Search

abstract: Recently, community search over graphs has gained significant interest. In applications such as analysis of protein-protein interaction (PPI) networks, citation graphs, and collaboration networks, nodes tend to have attributes. Unfortunately, most previous community search algorithms ignore attributes and result in communities with poor cohesion w.r.t. their node attributes. In this paper, we study the problem of attribute-driven community search, that is, given an undirected graph G where nodes are associated with attributes, and an input query Q consisting of nodes Vq and attributes Wq, find the communities containing Vq, in which most community members are densely inter-connected and have similar attributes. ::: ::: We formulate this problem as finding attributed truss communities (ATC), i.e., finding connected and close k-truss subgraphs containing Vq, with the largest attribute relevance score. We design a framework of desirable properties that good score function should satisfy. We show that the problem is NP-hard. However, we develop an efficient greedy algorithmic framework to iteratively remove nodes with the least popular attributes, and shrink the graph into an ATC. In addition, we also build an elegant index to maintain k-truss structure and attribute information, and propose efficient query processing algorithms. Extensive experiments on large real-world networks with ground-truth communities show that our algorithms significantly outperform the state of the art and demonstrates their efficiency and effectiveness.


title: Attachment Theory, Psychoanalysis, Personality Development, and Psychopathology

abstract: Blatt and Levy place attachment theory and research in a broad theoretical matrix by considering the relationship of attachment patterns to personality development and to different types of psychopathology in adults. Thus Blatt and Levy construct conceptual bridges between the two configurations of personality development and psychopathology that Blatt and colleagues have developed over the past quarter century (e.g., Behrends and Blatt, 1985; Blatt, 1974, 1995; Blatt and Behrends, 1987; Blatt and Blass, 1990, 1996; Blatt and Shichman, 1983) and attachment theory and research. Blatt and Levy identify a polarity that is central to attachment theory and research, the polarity of attachment and separation, and they note that this polarity has also been central in much of classic psychoanalytic theory (e.g., Freud, 1930; Loewald, 1962). This polarity is expressed in attachment theory and research in the differences between avoidant and anxious-preoccupied insecure attachment patterns as well in the distinctio...


title: Prevalence and impact of migrainous vertigo in mid-life women: A community-based study

abstract: We surveyed 1436 women aged 40–54 years in the community. Of these, 278 (19.4%) were diagnosed with migraine or probable migraine based on the International Classification of Headache Disorder criteria. The diagnoses of migrainous vertigo (MV) were modified from Neuhauser’s criteria. Of the 278 women, 238 (85.6%) underwent an interview and 72 (30.3%) of those with migraine or probable migraine had MV. The 1-year prevalence of MV among mid-life women was 5.0% (95% CI, 4.0–6.3%). The MV subjects had a higher proportion of aura (23.6% vs 9.6%; OR, 2.9), nausea (76.4% vs 55.8%; OR, 2.6) and photophobia (61.1% vs 41.7%; OR, 2.2) compared with the migraine subjects without MV. The migrainous subjects with and without MV had similar Short-Form 36 (SF-36) scores. Our study showed that MV was common in mid-life women. Their health-related quality of life was similar in migrainous subjects with MV and those without MV.


title: Measuring user credibility in social media

abstract: People increasingly use social media to get first-hand news and information. During disasters such as Hurricane Sandy and the tsunami in Japan people used social media to report injuries as well as send out their requests. During social movements such as Occupy Wall Street (OWS) and the Arab Spring, people extensively used social media to organize their events and spread the news. As more people rely on social media for political, social, and business events, it is more susceptible to become a place for evildoers to use it to spread misinformation and rumors. Therefore, users have the challenge to discern which piece of information is credible or not. They also need to find ways to assess the credibility of information. This problem becomes more important when the source of the information is not known to the consumer. ::: ::: In this paper we propose a method to measure user credibility in social media. We study the situations in which we cannot assess the credibility of the content or the credibility of the user (source of the information) based on the user's profile. We propose the CredRank algorithm to measure user credibility in social media. The algorithm analyzes social media users' online behavior to measure their credibility.


title: The serial position effect of free recall.

abstract: The serial position curve is characterized by a steep, possibly exponential, primacy effect extending over the 1st 3 or 4 words in the list, an S-shaped recency effect extending over the last 8 words in the list, and a horizontal asymptote spanning the primacy and recency effect. The shape of the cu


title: Virtual Engineering Factory: Creating Experience Base for Industry 4.0

abstract: In recent times, traditional manufacturing is upgrading and adopting Industry 4.0, which supports computerization of manufacturing by round-the-clock connection and communication of engineering objects. Consequently, Decisional DNA-based knowledge representation of manufacturing objects, processes, and system is achieved by virtual engineering objects VEO, virtual engineering processes VEP, and virtual engineering factories VEF, respectively. In this study, assimilation of VEO-VEP-VEF concept in the Cyber-physical system-based Industry 4.0 is proposed. The planned concept is implemented on a case study. Also, Decisional DNA features such as similarity identification and phenotyping are explored for validation. It is concluded that this approach can support Industry 4.0 and can facilitate in real time critical, creative, and effective decision making.


title: Dynamic trajectory planning for mobile robot intercepting a moving target using Bezier curve

abstract: This paper presents a dynamic trajectory planning approach for a nonholonomic mobile robot intercepting a moving target. The path is formulated using cubic Bezier curves. Location data from an overhead camera is used to compute the initial trajectory. The algorithm alters the trajectory in real time according to change in position and velocity of the target and the mobile robot without exceeding acceleration limits. The wheel velocities are then computed from the compliant center velocity generated from the velocity profile analysis of the calculated path. The approach is verified by simulation. Extensions of the algorithm to parallel applications are also discussed.


title: Extreme View Synthesis

abstract: We present Extreme View Synthesis, a solution for novel view extrapolation that works even when the number of input images is small---as few as two. In this context, occlusions and depth uncertainty are two of the most pressing issues, and worsen as the degree of extrapolation increases. We follow the traditional paradigm of performing depth-based warping and refinement, with a few key improvements. First, we estimate a depth probability volume, rather than just a single depth value for each pixel of the novel view. This allows us to leverage depth uncertainty in challenging regions, such as depth discontinuities. After using it to get an initial estimate of the novel view, we explicitly combine learned image priors and the depth uncertainty to synthesize a refined image with less artifacts. Our method is the first to show visually pleasing results for baseline magnifications of up to 30x.


title: A 1 Volt CMOS Pseudo Differential Amplifier

abstract: This paper presents a 1 V CMOS pseudo differential amplifier using simple rail-to-rail CMFB circuit. The proposed circuit employs the complementary common mode feedback (CMFB) consisting of common mode detector, transimpedance and transconductance amplifiers. The simulation results using HSPICE under a 0.18 mum CMOS technology shows that the rail to rail output swing is achieved with low common mode gain (-36 dB). The differential output swing of the circuit is plusmn0.7 V. The power dissipation of the circuit is 0.23 mW


title: Searching Parsimonious Solutions with GA-PARSIMONY and XGBoost in High-Dimensional Databases

abstract: EXtreme Gradient Boosting (XGBoost) has become one of the most successful techniques in machine learning competitions. It is computationally efficient and scalable, it supports a wide variety of objective functions and it includes different mechanisms to avoid over-fitting and improve accuracy. Having so many tuning parameters, soft computing (SC) is an alternative to search precise and robust models against classical hyper-tuning methods. In this context, we present a preliminary study in which a SC methodology, named GA-PARSIMONY, is used to find accurate and parsimonious XGBoost solutions. The methodology was designed to optimize the search of parsimonious models by feature selection, parameter tuning and model selection. In this work, different experiments are conducted with four complexity metrics in six high dimensional datasets. Although XGBoost performs well with high-dimensional databases, preliminary results indicated that GA-PARSIMONY with feature selection slightly improved the testing error. Therefore, the choice of solutions with fewer inputs, between those with similar cross-validation errors, can help to obtain more robust solutions with better generalization capabilities.


title: Social Networks and Entrepreneurship

abstract: We study network activities of entrepreneurs through three phases of establishing a firm in four countries. Entrepreneurs access people in their networks to discuss aspects of establishing and running a business. We find that entrepreneurs build networks that systematically vary by the phase of entrepreneurship, analyzing number of their discussion partners, and the time spent networking. Entrepreneurs talk with more people during the planning than other phases. Family members are present in their networks in all phases, particularly among those who took over an existing firm. However, women use their kin to a larger extent than men, and even more than men when they take over an existing firm. Experienced entrepreneurs have the same networking patterns as novices. Moreover, these networking patterns are the same in all countries. However, there are country differences in size of discussion networks and time spent networking.


title: Non-contact Water Level Monitoring System Implemented Using LabVIEW and Arduino

abstract: This paper shows non-contact water level monitoring system implemented using LabVIEW and arduino. Water depth of the tank is measured by Ultrasonic sensor. Hence the water level present in the tank is known. Depending on the sensor reading LabVIEW program sends the data to arduino and through arduino board the pump is switched ON if the water level in the upper tank is low and pump is switched OFF if the water level is completely filled in the upper tank. The input to arduino board is from ultrasonic sensor. Arduino read the height and reports the water depth of the tank and the same is displayed in LabVIEW front panel. Advantage of this over other existing automatic system is it provides non-contact water level measurement using ultrasonic sensor, but existing automatic system using ss(stainless steel) sensor which is a contact type water level sensor and also these sensors quickly corroded by some chemicals. The same is present in LabVIEW using the graphical user interface for visualization, LabVIEW communicate with add on devices like arduino, ultrasonic sensor, pump through makerhub.


title: Sentiment analysis at sentence level for heterogeneous datasets

abstract: Sentiment analysis is a process used to automatically extract useful information from a collection of unstructured documents, in the form of customer reviews, blog comments or online social network sites data respectively. This useful information plays a significant role for decision making to know people's sentiments regarding online product reviews, services, events, entities. In this paper, rules based supervised machine learning method is proposed for sentiment analysis at sentence level for heterogeneous datasets. The text document in heterogeneous datasets is divided into two classes i.e., subjective and objective. We determine the semantic orientation and strength of subjective sentences based on the semantic score of its sentiment bearing words through SentiWordnet. Our experiment results show that the proposed method improves the performance of existing work for sentiment analysis in term of accuracy.


title: DefSense: Computational Design of Customized Deformable Input Devices

abstract: We present a novel optimization-based algorithm for the design and fabrication of customized, deformable input devices, capable of continuously sensing their deformation. We propose to embed piezoresistive sensing elements into flexible 3D printed objects. These sensing elements are then utilized to recover rich and natural user interactions at runtime. Designing such objects is a challenging and hard problem if attempted manually for all but the simplest geometries and deformations. Our method simultaneously optimizes the internal routing of the sensing elements and computes a mapping from low-level sensor readings to user-specified outputs in order to minimize reconstruction error. We demonstrate the power and flexibility of the approach by designing and fabricating a set of flexible input devices. Our results indicate that the optimization-based design greatly outperforms manual routings in terms of reconstruction accuracy and thus interaction fidelity.


title: Classification of MRI brain images using k-nearest neighbor and artificial neural network

abstract: Magnetic resonance imaging (MRI) is often the medical imaging method of choice when soft tissue delineation is necessary. This paper presents a new approach for automated diagnosis based on classification of the magnetic resonance images (MRI). The proposed method consists of two stages namely feature extraction and classification. In the first stage, we have obtained the features related to MRI images using discrete wavelet transformation (DWT). Wavelet transform based methods are a well known tool for extracting frequency space information from non-stationary signals. The features extracted using DWT of magnetic resonance images have been reduced, using principal component analysis (PCA), to the more essential features. In the classification stage, two classifiers have been developed. The first classifier is based on feed forward back propagation artificial neural network (FP-ANN) and the second classifier is based on k-nearest neighbor (k-NN). The features hence derived are used to train a neural network based binary classifier, which can automatically infer whether the image is that of a normal brain or a pathological brain, suffering from brain lesion. A classification with a success of 90% and 99% has been obtained by FP-ANN and k-NN, respectively. This result shows that the proposed technique is robust and effective compared with other recent work.


title: Causal impact analysis for app releases in google play

abstract: App developers would like to understand the impact of their own and their competitors’ software releases. To address this we introduce Causal Impact Release Analysis for app stores, and our tool, CIRA, that implements this analysis. We mined 38,858 popular Google Play apps, over a period of 12 months. For these apps, we identified 26,339 releases for which there was adequate prior and posterior time series data to facilitate causal impact analysis. We found that 33% of these releases caused a statistically significant change in user ratings. We use our approach to reveal important characteristics that distinguish causal significance in Google Play. To explore the actionability of causal impact analysis, we elicited the opinions of app developers: 56 companies responded, 78% concurred with the causal assessment, of which 33% claimed that their company would consider changing its app release strategy as a result of our findings.


title: Security metrics maturity model for operational security

abstract: Information Technology (IT) is continuously evolving at faster rate and enterprises are always trying to keep pace with the changes. So do the threats. As the complexity of IT increase, the unprecedented threat environment and security challenges also have increased multi fold over the years. Security Managers are continuously having challenging task not only protecting their company but also convincing the stakeholders on the security investments. The well informed stakeholder demands higher level transparency, Return on Investment (ROI) and security. Security metrics plays a key role in responding to these demands. However the security metrics alone are not enough but must be substantiated. The stakeholders always question and challenge the metrics provided. They are always skeptical on the numbers shown in metrics. As such, more information is needed to substantiate the metrics claims. Therefore, this research will explore the identification of quality security elements to determine the matured security metrics within operational security environment. The research will categorize the metrics maturity into three types: infant, evolving and matured metrics. The classification is performed by analyzing the quality of a metric through a scorecard and by providing a scoring. Towards the end Security Metrics Maturity Index (SM-Mi) is introduced to label a company on how trustable and confidence on can feel when look into the metrics. The entire classification uses operational security taxonomy for better understanding. The end result of this research will be a guide for the Security Managers to produce a convincing and close to accurate report for C Level management of an organization. This research will look into various studies done on existing measurements and security elements for Security Metrics and produce a method that will portray the maturity of security metrics used in an organization.


title: A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration

abstract: Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data. We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.


title: DART: a Dataset of Arguments and their Relations on Twitter

abstract: The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.


title: CASIA Online and Offline Chinese Handwriting Databases

abstract: This paper introduces a pair of online and offline Chinese handwriting databases, containing samples of isolated characters and handwritten texts. The samples were produced by 1,020 writers using Anoto pen on papers for obtaining both online trajectory data and offline images. Both the online samples and offline samples are divided into six datasets, three for isolated characters (DB1.0-C1.2) and three for handwritten texts (DB2.0-C2.2). The (either online or offline) datasets of isolated characters contain about 3.9 million samples of 7,356 classes (7,185 Chinese characters and 171 symbols), and the datasets of handwritten texts contain about 5,090 pages and 1.35 million character samples. Each dataset is segmented and annotated at character level, and is partitioned into standard training and test subsets. The online and offline databases can be used for the research of various handwritten document analysis tasks.


Environmental and Recreational Hazards Associated with Adventure Travel.


title: A Critical Review of Marketing Research on Diffusion of New Products

abstract: We critically examine alternate models of the diffusion of new products and the turning points of the diffusion curve. On each of these topics, we focus on the drivers, specifications, and estimation methods researched in the literature. We discover important generalizations about the shape, parameters, and turning points of the diffusion curve and the characteristics of diffusion across early stages of the product life cycle. We point out directions for future research.


title: Capacitive power transfer for slip ring replacement in wound field synchronous machines

abstract: Permanent magnet synchronous machines are utilized in a wide variety of applications due to their many desirable characteristics, including high torque density capability and high efficiency. In the near future, however, the demand for the permanent magnet rare earth materials is projected to exceed world production. As a result, electric machines which do not rely on rare earth materials, such as wound field synchronous machines (WFSMs), are receiving renewed attention for use in traction and wind energy applications. However, WFSMs require a current delivery mechanism to the rotor such as mechanical slip rings whose components require periodic replacement and generate adverse debris within the machine enclosure. Rotary transformers may replace slip rings but also introduce rotor speed dependencies and magnetic coupling difficulties. This paper proposes a capacitive non-contact power transfer technique to eliminate the need for mechanical slip rings while also avoiding the pitfalls of rotating transformer technologies. The capacitive power transfer system is compared to traditional rotor power coupling techniques and its performance is validated with experimental results.


title: Analysis and design of a dual-rotor axial-flux vernier permanent magnet machine

abstract: This paper proposes a dual-rotor, toroidal-winding, axial-flux vernier permanent magnet (VPM) machine. By the combination of toroidal windings with the rotor-stator-rotor topology, the end winding length of the machine is significantly reduced when compared with the regular VPM machine. Based on the airgap permeance function, the back-EMF and torque expressions are derived, through which the nature of this machine is revealed. The influence of pole ratio (ratio of rotor pole pair number to stator pole pair number) and main geometric parameters such as slot opening, magnet thickness etc., on torque performance is then analytically investigated. Both the quasi-3-dimensional (quasi-3D) finite element analysis (FEA) and 3D FEA are applied to verify the theoretical analysis. With the current density of 4.2 A/mm2, the torque density of the proposed machine can reach 32.6 kNm/m3. A prototype has been designed and is in manufacturing process. Experimental validation will be presented in the future.


title: Fostering consumers' energy market through smart contracts

abstract: Micro-generation promises to greatly contribute to the energy balance of the energy grid; however, so far, its market penetration is going slow due to the few, or not-existing, direct economic benefits end-users would enjoy by deploying an in-house micro-generation system. In this paper, taking advantage of the potentialities of blockchain technologies, we propose a solar energy production and distribution architecture using smart contracts, a particular distributed ledger paradigm, to support automatic energy exchanges and auctions, potentially enabling a new, open and more fruitful, under an end-user perspective, energy micro-generation market. We present the conceptual design of the approach, as well the energy grid prototype and the control layer, running on the Ethereum platform. The proposed architecture has been implemented and validated through an in-house developed test-bed.