title: A LoRa enabled building automation architecture based on MQTT

abstract: The current and future trend in building and home automation sees an increasing pervasiveness of Internet of Things technologies, that allow to implement networks of low power sensors and actuators, and, at the same time, to enable advanced services and applications. In this paper we propose the use of a common protocol in the IoT domain, the MQTT, over a LoRa physical communication link, to jointly exploit the lightweight pub-sub paradigm offered by MQTT and the long range, low power wireless capabilities offered by LoRa, to deliver building automation services. Experiments show that the proposed architecture ensures commands transmission and actuation in less than 200 ms.


title: Calculator problem and the design recipe

abstract: This paper presents an alternative --- and what we believe a superior --- approach to designing a solution to the calculator problem due to Alphonce [1]. While Alphonce presents a fictitious one-act play between professors, this paper consists of a narrative explanation of the Java code written by this paper's coauthor, - a student who just completed the freshman year.The purpose of this paper is to illustrate the use of the DESIGN RECIPE pedagogy and the idea of focusing on the design of the structure of data, the classes, and class hierarchies, rather than the design of program actions (the algorithmics) as the core idea for program design. By contrasting the pedagogical approach and the final outcome with the "traditional" objects-first approach, we illustrate the advantages of our curriculum that teaches the students how to think about program design in a truly object-oriented style.


title: Asperger's syndrome: a clinical account

abstract: The clinical features, course, aetiology, epidemiology, differential diagnosis and management of Asperger's syndrome are described. Classification is discussed and reasons are given for including the syndrome, together with early childhood autism, in a wider group of conditions which have, in common, impairment of development of social interaction, communication and imagination.


title: HELIOS IX tracked vehicle for urban search and rescue operations: Mechanical design and first tests

abstract: Fire brigades and special rescue agencies are often demanded to operate for search and aid of human lives in extremely dangerous scenarios. The use of robotic tools can make those operations safer for the operators and more efficient in terms of time and allocation of resources. This contribution describes the development of the newest tracked vehicle HELIOS IX. This mobile platform equipped with a manipulator represents the first stage of a Japanese national founded project. It aims to the development of a robotic system to be utilized in extreme environments such as rescue operations in heavily stricken urban areas. With respect to the previous prototypes the mechanical design and the system architecture is described. A new gripper is also introduced and tested together with few mobility experiments of the platform.


title: Kernelized structural SVM learning for supervised object segmentation

abstract: Object segmentation needs to be driven by top-down knowledge to produce semantically meaningful results. In this paper, we propose a supervised segmentation approach that tightly integrates object-level top down information with low-level image cues. The information from the two levels is fused under a kernelized structural SVM learning framework. We defined a novel nonlinear kernel for comparing two image-segmentation masks. This kernel combines four different kernels: the object similarity kernel, the object shape kernel, the per-image color distribution kernel, and the global color distribution kernel. Our experiments show that the structured SVM algorithm finds bad segmentations of the training examples given the current scoring function and punishes these bad segmentations to lower scores than the example (good) segmentations. The result is a segmentation algorithm that not only knows what good segmentations are, but also learns potential segmentation mistakes and tries to avoid them. Our proposed approach can obtain comparable performance to other state-of-the-art top-down driven segmentation approaches yet is flexible enough to be applied to widely different domains.


title: DSMDiff : A Differentiation Tool for Domain-Specific Models

abstract: Model differentiation techniques, which provide the capability to identify mappings and differences between models, are essential to many model development and management practices. There has been initial research toward model differentiation applied to Unified Modeling Language (UML) diagrams, but differentiation of domain-specific models has not been explored deeply in the modeling community. Traditional modeling practice using the UML relies on a single fixed general-purpose language (i.e., all UML diagrams conform to a single metamodel). In contrast, Domain-Specific Modeling (DSM) is an emerging model-driven paradigm in which multiple metamodels are used to define various modeling languages that represent the key concepts and abstractions for particular domains. Therefore, domain-specific models may conform to various metamodels, which requires model differentiation algorithms be metamodel-independent and able to apply to multiple domain-specific modeling languages. This paper presents metamodel-independent algorithms and associated tools for detecting mappings and differences between domain-specific models, with facilities for graphical visualization of the detected differences.


title: Two-processor scheduling with start-times and deadlines

abstract: Given a set $\mathcal{T} = \{ T_1 ,T_2 , \cdots ,T_n \} $ of tasks, each $T_i$ having execution time 1, an integer start-time $s_i \geqq 0$ and a deadline $d_i > 0$, along with precedence constraints among the tasks, we examine the problem of determining whether there exists a schedule on two identical processors that executes each task in the time interval between its start-time and deadline. We present an $O(n^3)$ algorithm that constructs such a schedule whenever one exists. The algorithm may also be used in a binary search mode to find the shortest such schedule or to find a schedule that minimizes maximum “tardiness”. A number of natural extensions of this problem are seen to be $NP$ complete and hence probably intractable.


title: Segmentation of Urban Areas Using Road Networks ∗

abstract: Region-based analysis is fundamental and crucial in many geospatialrelated applications and research themes, such as trajectory analysis, human mobility study and urban planning. In this paper, we report on an image-processing-based approach to segment urban areas into regions by road networks. Here, each segmented region is bounded by the high-level road segments, covering some neighborhoods and low-level streets. Typically, road segments are classified into different levels (e.g., highways and expressways are usually high-level roads), providing us with a more natural and semantic segmentation of urban spaces than the grid-based partition method. We show that through simple morphological operators, an urban road network can be efficiently segmented into regions. In addition, we present a case study in trajectory mining to demonstrate the usability of the proposed segmentation method.


title: Dysbiosis of the gut microbiota in disease

abstract: There is growing evidence that dysbiosis of the gut microbiota is associated with the pathogenesis of both intestinal and extra-intestinal disorders. Intestinal disorders include inflammatory bowel disease, irritable bowel syndrome (IBS), and coeliac disease, while extra-intestinal disorders include allergy, asthma, metabolic syndrome, cardiovascular disease, and obesity.


title: A block diagonal jacket matrices for MIMO broadcast channels

abstract: Block diagonalization (BD) is a linear precoding technique for multiuser multi-input multi-output (MIMO) broadcast channels such that it completely eliminates the multiuser interference. We investigate the problem of multi-cell multi-user co-channel transmission, where in both inter-cell interference and inter-user interference should be well managed. Interference alignment (IA) is theoretically proved to be a promising technology in managing co-channel interference. In this paper, we present an efficient multi-cell coordinated linear beamforming method where IA is combined with MU-MIMO to achieve high performance multi-cell cooperative transmission. We extend to a multiple-cells scenario and jointly design transmit and receiver beamforming vectors. We also apply the block diagonal Jacket matrices decomposition and extend the block diagonal channel decomposition with which the MIMO broadcast channel capacity can be achieved.


title: A model-checking approach to analysing organisational controls in a loan origination process

abstract: Demonstrating the safety of a system (ie. avoiding the undesired propagation of access rights or indirect access through some other granted resource) is one of the goals of access control research, e.g. [1-4]. However, the flexibility required from enterprise resource management (ERP) systems may require the implementation of seemingly contradictory requirements (e.g. tight access control but at the same time support for discretionary delegation of workflow tasks and rights).To aid in the analysis of safety problems in workflow-based ERP system, this paper presents a model-checking based approach for automated analysis of delegation and revocation functionalities. This is done in the context of a real-world banking workflow requiring static and dynamic separation of duty properties.We derived information about the workflow from BPEL specifications and ERP business object repositories. This was captured in a SMV specification together with a definition of possible delegation and revocation scenarios. The required separation properties were translated into a set of LTL-based constraints. In particular, we analyse the interaction between delegation and revocation activities in the context of dynamic separation of duty policies.


title: A SLAM-based approach for underwater mapping using AUVs with poor inertial information

abstract: This paper presents a SLAM-based approach for creating maps of underwater terrain using AUVs with poor inertial information. The initial motivating application for this work was mapping in the non-inertial frame of a free-drifting Antarctic iceberg, but poor inertial information can also occur if low-cost, high drift inertial instrumentation is used in standard mapping tasks, or if DVL bottom lock is lost during the mission. This paper presents a SLAM-based approach in which features are extracted from concatenated multibeam data and descriptors are created, allowing these features to be compared against past terrain as the vehicle traverses the area. There have been a number of previous research efforts that used feature-based SLAM techniques for underwater mapping, but they have generally made assumptions or relied on sensors that are inconsistent with this paper's motivating application, such as a flat bottom, the availability of visual imagery, or manmade fiducial markers. The method presented here uses natural terrain, is robust to water turbidity, and can be used in areas with vertical terrain like the walls of canyons and icebergs. Results are presented on data collected from Monterey Canyon using a vehicle with a high-grade IMU but that lost DVL bottom lock during the mapping run.


title: Cryptanalyzing a RGB image encryption algorithm based on DNA encoding and chaos map

abstract: Abstract Recently, a RGB image encryption algorithm based on DNA encoding and chaos map has been proposed. It was reported that the encryption algorithm can be broken with four pairs of chosen plain-images and the corresponding cipher-images. This paper re-evaluates the security of the encryption algorithm, and finds that the encryption algorithm can be broken efficiently with only one known plain-image. The effectiveness of the proposed known-plaintext attack is supported by both rigorous theoretical analysis and experimental results. In addition, two other security defects are also reported.


title: A Perspective on the Challenges and Opportunities for Privacy-Aware Big Transportation Data

abstract: In recent years, and especially since the development of the smartphone, enormous amounts of data relevant for transportation have become available. These data hold out the potential to redefine how transportation system (i.e., design, planning and operations) is done. While researchers in both academia and industry are making advances in using this data to transportation system ends (e.g., information inference from collected data), little attention has been paid to four larger scale challenges that will need to be overcome if the potential for Big Transportation Data is to be harnessed for transportation decision-making purposes. This paper aims to provide awareness of these large-scale challenges and provides insight into how we believe these challenges are likely to be met.


title: Machine Learning: Deepest Learning as Statistical Data Assimilation Problems

abstract: We formulate a strong equivalence between machine learning, artificial intelligence methods and the formulation of statistical data assimilation as used widely in physical and biological sciences. The correspondence is that layer number in the artificial network setting is the analog of time in the data assimilation setting. Within the discussion of this equivalence we show that adding more layers (making the network deeper) is analogous to adding temporal resolution in a data assimilation framework. ::: How one can find a candidate for the global minimum of the cost functions in the machine learning context using a method from data assimilation is discussed. Calculations on simple models from each side of the equivalence are reported. ::: Also discussed is a framework in which the time or layer label is taken to be continuous, providing a differential equation, the Euler-Lagrange equation, which shows that the problem being solved is a two point boundary value problem familiar in the discussion of variational methods. The use of continuous layers is denoted "deepest learning". These problems respect a symplectic symmetry in continuous time/layer phase space. Both Lagrangian versions and Hamiltonian versions of these problems are presented. Their well-studied implementation in a discrete time/layer, while respected the symplectic structure, is addressed. The Hamiltonian version provides a direct rationale for back propagation as a solution method for the canonical momentum.


title: Speech segmentation without speech recognition

abstract: In this paper, we presented a semantic speech segmentation approach, in particular sentence segmentation, without speech recognition. In order to get phoneme level information without word recognition information, a novel vowel/consonant/pause (V/C/P) classification is proposed. An adaptive pause detection method is also presented to adapt to various backgrounds and environments. Three feature sets, which include pause, rate of speech and prosody, are used to discriminate the sentence boundary. Experiments on broadcasting news indicate that the performance of the proposed algorithm is satisfying.


title: Filter for the measurement of supraharmonics in public low voltage networks

abstract: The increase of voltage and current distortion as well as interferences in the frequency range of 2 kHz to 150 kHz due to technologies like power line communication or self-commutating converters have led to the urgent need for electromagnetic compatibility (EMC) standards in this frequency range. Measuring compatibility levels or test compliance with limits for emission and immunity requires accurate, reliable and reproducible measurement methods. A major prerequisite for accurate measurements in this frequency range are suitable high-pass filters to reduce the fundamental component and its harmonics below 2 kHz in the measurement signal.


title: Privacy and Security in Mobile Cloud Computing: Review

abstract: Mobile cloud computing is computing of Mobile application through cloud. As we know market of mobile phones is growing rapidly. According to IDC, the premier global market intelligence firm, the worldwide Smartphone market grew 42.5% year over year in the first quarter of 2012.With the growing demand of Smartphone the demand for fast computation is also growing. Inspite of comparatively more processing power and storage capability of Smartphone’s, they still lag behind Personal Computers in meeting processing and storage demands of high end applications like speech recognition, security software, gaming, health services etc. Mobile cloud computing is an answer to intensive processing and storage demand of real-time and high end applications. Being in nascent stage, Mobile Cloud Computing has privacy and security issues which deter the users from adopting this technology. This review paper throws light on privacy and security issues of Mobile Cloud Computing. General Terms Privacy and Security.


title: Fast Context Adaptation via Meta-Learning

abstract: We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, only the context parameters are updated, leading to a low-dimensional task representation. We show empirically that CAVIA outperforms MAML for regression, classification, and reinforcement learning. Our experiments also highlight weaknesses in current benchmarks, in that the amount of adaptation needed in some cases is small.


title: An NFV Orchestration Framework for Interference-Free Policy Enforcement

abstract: Network functions virtualization is a new paradigm to offer flexibility of software network function processing on demand. Policy enforcement satisfies network function policies that requires flows to traverse through given sequences of network functions. We summarize three desired properties of virtual network function placement, namely policy enforcement, interference freedom, and resource isolation. However, none of existing solutions can satisfy all of them. In this paper, we present a novel SDN-based NFV orchestration framework, called APPLE, to enforce network function policies while providing the above properties. We present detailed design considerations and prototype implementation. We conduct experiments using representative network topologies, traffic matrices, and policy chains. The results from both prototype experiments and simulationsshow that APPLE is resource efficient and can quickly react to traffic changes.


title: Serpentine locomotion of a snake-like robot controlled by cyclic inhibitory CPG model

abstract: Based on the structure of both biological snakes and snake-like robots and their rhythm locomotion, the theory of the cyclic inhibitory CPG is adopted as a control method to construct a neuron network model of the snake-like robot. The relation between the CPG parameters and the serpentine locomotion of the snake-like robot is defined in this paper. The validity of the serpentine locomotion controlled by the CPG model is verified through a snake-like robot model. The modulating methods of the CPG parameters are brought forward and simulated to realize the required turn motion and the reconfiguration. Moreover, we present that real snake-like robot can successfully exhibit serpentine locomotion by using controller output of the proposed architecture. Finally, the aspects of future researches are discussed.


title: ECHO: An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge

abstract: The Internet of Things (IoT) is offering unprecedented observational data that are used for managing Smart City utilities. Edge and Fog gateway devices are an integral part of IoT deployments to acquire real-time data and enact controls. Recently, Edge-computing is emerging as first-class paradigm to complement Cloud-centric analytics. But a key limitation is the lack of a platform-as-a-service for applications spanning Edge and Cloud. Here, we propose \(\mathbb {ECHO}\), an orchestration platform for dataflows across distributed resources. \(\mathbb {ECHO}\) ’s hybrid dataflow composition can operate on diverse data models – streams, micro-batches and files, and interface with native runtime engines like TensorFlow and Storm to execute them. It manages the application’s lifecycle, including container-based deployment and a registry for state management. \(\mathbb {ECHO}\) can schedule the dataflow on different Edge, Fog and Cloud resources, and also perform dynamic task migration between resources. We validate the \(\mathbb {ECHO}\) platform for executing video analytics and sensor streams for Smart Traffic and Smart Utility applications on Raspberry Pi, NVidia TX1, ARM64 and Azure Cloud VM resources, and present our results.


title: Torque control based sensorless hand guiding for direct robot teaching

abstract: In recent years, much research has been done to develop various direct teaching schemes, in which an operator directly teaches a robot by hand guiding instead of using a teach pendant. However, conventional direct teaching methods are usually sensor based, thus leading to an expensive solution. To deal with this problem, this study proposes a sensorless hand guiding method based on torque control. The dynamic model of a robot along with the motor current and friction model is used to determine the user's intention to move the end-effector of a robot instead of directly sensing the external force by the user. A friction torque observer is employed for the friction model identification. The proposed method was experimentally verified using a 6 DOF robot. The experimental results show that the proposed sensorless hand guiding scheme is quite useful for practical direct teaching although the sensitivity is slightly lower than the sensor-based approach.


title: Information Bottleneck Learning Using Privileged Information for Visual Recognition

abstract: We explore the visual recognition problem from a main data view when an auxiliary data view is available during training. This is important because it allows improving the training of visual classifiers when paired additional data is cheaply available, and it improves the recognition from multi-view data when there is a missing view at testing time. The problem is challenging because of the intrinsic asymmetry caused by the missing auxiliary view during testing. We account for such view during training by extending the information bottleneck method, and by combining it with risk minimization. In this way, we establish an information theoretic principle for leaning any type of visual classifier under this particular setting. We use this principle to design a large-margin classifier with an efficient optimization in the primal space. We extensively compare our method with the state-of-the-art on different visual recognition datasets, and with different types of auxiliary data, and show that the proposed framework has a very promising potential.


title: A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction

abstract: Face multi-attribute prediction benefits substantially from multi-task learning (MTL), which learns multiple face attributes simultaneously to achieve shared or mutually related representations of different attributes. The most widely used MTL convolutional neural network is heuristically or empirically designed by sharing all of the convolutional layers and splitting at the fully connected layers for task-specific losses. However, it is improper to view all low and mid-level features for different attributes as being the same, especially when these attributes are only loosely related. In this paper, we propose a novel multi-attribute tensor correlation neural network (MTCN) for face attribute prediction. The structure shares the information in low-level features (e.g., the first two convolutional layers) but splits that in high-level features (e.g., from the third convolutional layer to the fully connected layer). At the same time, during high-level feature extraction, each subnetwork (e.g., Age-Net, Gender-Net, ..., and Smile-Net) excavates closely related features from other networks to enhance its features. Then, we project the features of the C9 layers of the fine-tuned subnetworks into a highly correlated space by using a novel tensor correlation analysis algorithm (NTCCA). The final face attribute prediction is made based on the correlation matrix. Experimental results on benchmarks with multiple face attributes (CelebA and LFWA) show that the proposed approach has superior performance compared to state-of-the-art methods.


title: Detecting Multi-Oriented Text with Corner-based Region Proposals

abstract: Abstract Previous approaches for scene text detection usually rely on manually defined sliding windows. This work presents an intuitive two-stage region-based method to detect multi-oriented text without any prior knowledge regarding the textual shape. In the first stage, we estimate the possible locations of text instances by detecting and linking corners instead of shifting a set of default anchors. The quadrilateral proposals are geometry adaptive, which allows our method to cope with various text aspect ratios and orientations. In the second stage, we design a new pooling layer named Dual-RoI Pooling which embeds data augmentation inside the region-wise subnetwork for more robust classification and regression over these proposals. Experimental results on public benchmarks confirm that the proposed method is capable of achieving comparable performance with state-of-the-art methods. The code is publicly available at https://github.com/xhzdeng/crpn .


title: Improving Gait Cryptosystem Security Using Gray Code Quantization and Linear Discriminant Analysis

abstract: Gait has been considered as an efficient biometric trait for user authentication. Although there are some studies that address the task of securing gait templates/models in gait-based authentication systems, they do not take into account the low discriminability and high variation of gait data which significantly affects the security and practicality of the proposed systems. In this paper, we focus on addressing the aforementioned deficiencies in inertial-sensor based gait cryptosystem. Specifically, we leverage Linear Discrimination Analysis to enhance the discrimination of gait templates, and Gray code quantization to extract high discriminative and stable binary template. The experimental results on 38 different users showed that our proposed method significantly improve the performance and security of the gait cryptosystem. In particular, we achieved the False Acceptant Rate of \(6\times 10^{-5} \%\) (i.e., 1 fail in 16983 trials) and False Rejection Rate of \(9.2 \%\) with 148-bit security.


title: Pseudocapacitive Sodium Storage in Mesoporous Single-Crystal-like TiO2–Graphene Nanocomposite Enables High-Performance Sodium-Ion Capacitors

abstract: Sodium-ion capacitors can potentially combine the virtues of high power capability of conventional electrochemical capacitors and high energy density of batteries. However, the lack of high-performance electrode materials has been the major challenge of sodium-based energy storage devices. In this work, we report a microwave-assisted synthesis of single-crystal-like anatase TiO2 mesocages anchored on graphene as a sodium storage material. The architecture of the nanocomposite results in pseudocapacitive charge storage behavior with fast kinetics, high reversibility, and negligible degradation to the micro/nanostructure. The nanocomposite delivers a high capacity of 268 mAh g–1 at 0.2 C, which remains 126 mAh g–1 at 10 C for over 18 000 cycles. Coupling with a carbon-based cathode, a full cell of sodium-ion capacitor successfully demonstrates a high energy density of 64.2 Wh kg–1 at 56.3 W kg–1 and 25.8 Wh kg–1 at 1357 W kg–1, as well as an ultralong lifespan of 10 000 cycles with over 90% of capacity rete...


title: Autism and attachment difficulties: overlap of symptoms, implications and innovative solutions

abstract: This article explores overlap of symptoms between autism and attachment difficulties and suggests innovative solutions based on formulation. Currently, clinicians express difficulties in differentiating between these conditions contributing to misdiagnosis. Research into the prevalence of attachment difficulties among children with autism often fails to reflect detailed knowledge of attachment theory. Consequently, studies in this area employ questionable modifications to attachment measures and methods of analysis. The findings of such studies are confusing and inconsistent. Children with autism and their parents are, however, known to be at high risk of developing insecure attachment patterns. Clinical assessments based on formulation may be helpful in these cases, as they include consideration of developmental and relational factors contributing to symptom presentation. Research suggests that where parents of children with autism establish secure relationships with their children outcomes are improved....


title: Attack-Resilient State Estimation for Noisy Dynamical Systems

abstract: Several recent incidents have clearly illustrated the susceptibility of cyberphysical systems (CPS) to attacks, raising attention to security challenges in these systems. The tight interaction between information technology and the physical world has introduced new vulnerabilities that cannot be addressed with the use of standard cryptographic security techniques. Accordingly, the problem of state estimation in the presence of sensor and actuator attacks has attracted significant attention in the past. Unlike the existing work, in this paper, we consider the problem of attack-resilient state estimation in the presence of bounded-size noise. We focus on the most general model for sensor attacks where any signal can be injected via compromised sensors. Specifically, we present an $l_0$ -based state estimator that can be formulated as a mixed-integer linear program and its convex relaxation based on the $l_1$ norm. For both attack-resilient state estimators, we derive rigorous analytic bounds on the state-estimation errors caused by the presence of noise. Our analysis shows that the worst-case error is linear with the size of the noise and, thus, the attacker cannot exploit the noise to introduce unbounded state-estimation errors. Finally, we show how the $l_0$ and $l_1$ -based attack-resilient state estimators can be used for sound attack detection and identification; we provide conditions on the size of attack vectors that ensure correct identification of compromised sensors.


title: Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing

abstract: Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject’s trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%.


title: Integrity for autonomous driving: A survey

abstract: Autonomous driving has become a focus for many universities and automotive companies alike, aiming for public introduction within the next few years. However, this requires assurance that the inherent risk in critical systems such as localization is sufficiently low. In aviation, the concept of integrity was established for this purpose. It relies upon a localization system's ability to provide timely and correct alerts notifying when the system must not be used. For autonomous vehicles, alert generation is still a topic of research because the environment is more complex. This work contributes by identifying two alert generation approaches applicable to autonomous driving and comparing their conceptual merits and drawbacks. The focus lies on methods for fault detection and isolation as well as those based on a bounded-error assumption. Both methods addressed here are guaranteed to either detect out-of-tolerance errors or determine a valid solution. However, they differ in how risk is dealt with.


title: Kinds of being : a study of individuation, identity and the logic of sortal terms

abstract: Sortal Terms and Criteria of Identity Individuals, Sorts and Instantiation The Absoluteness of Identity - A Defence Appendix - Some Formal Principles and Arguments Identity and Constitution Parts and Wholes Persons and their Bodies - Matter and Organisms, Organisms and Persons, Is There a Criterion of Personal Identity? Sortal Terms and Natural Laws Laws, Dispositions and Sortal Logic Appendix - An Axiomatic System of Sortal Logic Complex Sortal Terms and the Differentiation of Sorts.


title: Analysis of robot work characteristics

abstract: The abilities and limitations of today's industrial robots are reviewed and compared to similar human parameters. The robot abilities were determined from a survey of 282 current robot models. This discussion is held within the general framework of a skills analysis approach to robot task performance.


title: Automatic Vandalism Detection in Wikipedia

abstract: We present results of a new approach to detect destructive article revisions, so-called vandalism, inWikipedia. Vandalism detection is a one-class classification problem, where vandalism edits are the target to be identified among all revisions. Interestingly, vandalism detection has not been addressed in the Information Retrieval literature by now. In this paper we discuss the characteristics of vandalism as humans recognize it and develop features to render vandalism detection as a machine learning task. We compiled a large number of vandalism edits in a corpus, which allows for the comparison of existing and new detection approaches. Using logistic regression we achieve 83% precision at 77% recall with our model. Compared to the rule-based methods that are currently applied in Wikipedia, our approach increases the F-Measure performance by 49% while being faster at the same time.


title: Fully DNN-based Multi-label regression for audio tagging

abstract: Acoustic event detection for content analysis in most cases relies on lots of labeled data. However, manually annotating data is a time-consuming task, which thus makes few annotated resources available so far. Unlike audio event detection, automatic audio tagging, a multi-label acoustic event classification task, only relies on weakly labeled data. This is highly desirable to some practical applications using audio analysis. In this paper we propose to use a fully deep neural network (DNN) framework to handle the multi-label classification task in a regression way. Considering that only chunk-level rather than frame-level labels are available, the whole or almost whole frames of the chunk were fed into the DNN to perform a multi-label regression for the expected tags. The fully DNN, which is regarded as an encoding function, can well map the audio features sequence to a multi-tag vector. A deep pyramid structure was also designed to extract more robust high-level features related to the target tags. Further improved methods were adopted, such as the Dropout and background noise aware training, to enhance its generalization capability for new audio recordings in mismatched environments. Compared with the conventional Gaussian Mixture Model (GMM) and support vector machine (SVM) methods, the proposed fully DNN-based method could well utilize the long-term temporal information with the whole chunk as the input. The results show that our approach obtained a 15% relative improvement compared with the official GMM-based method of DCASE 2016 challenge.


title: DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow

abstract: Modern deep neural networks have a large number of parameters, making them very powerful machine learning systems. A critical issue for training such large networks on large-scale data-sets is to prevent overfitting while at the same time providing enough model capacity. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks. In the first D step, we train a dense network to learn which connections are important. In the S step, we regularize the network by pruning the unimportant connections and retrain the network given the sparsity constraint. In the final D step, we increase the model capacity by freeing the sparsity constraint, re-initializing the pruned parameters, and retraining the whole dense network. Experiments show that DSD training can improve the performance of a wide range of CNN, RNN and LSTMs on the tasks of image classification, caption generation and speech recognition. On the Imagenet dataset, DSD improved the absolute accuracy of AlexNet, GoogleNet, VGG-16, ResNet50, ResNet-152 and SqueezeNet by a geo-mean of 2.1 points (Top-1) and 1.4 points (Top-5). On the WSJ’92 and WSJ’93 dataset, DSD improved DeepSpeech2 WER by 0.53 and 1.08 points. On the Flickr-8K dataset, DSD improved the NeuralTalk BLEU score by 2.0 points. DSD training flow produces the same model architecture and doesn’t incur any inference overhead.


title: Metaphors of human thinking for usability inspection and design

abstract: Usability inspection techniques are widely used, but few focus on users' thinking and many are appropriate only for particular devices and use contexts. We present a new technique (MOT) that guides inspection by metaphors of human thinking. The metaphors concern habit, the stream of thought, awareness and associations, the relation between utterances and thought, and knowing. The main novelty of MOT is its psychological basis combined with its use of metaphors to stimulate inspection. The first of three experiments shows that usability problems uncovered with MOT are more serious and more complex to repair than problems found with heuristic evaluation. Problems found with MOT are also judged more likely to persist for expert users. The second experiment shows that MOT finds more problems than cognitive walkthrough, and has a wider coverage of a reference collection of usability problems. Participants prefer using MOT over cognitive walkthrough; an important reason being the wider scope of MOT. The third experiment compares MOT, cognitive walkthrough, and think aloud testing, in the context of nontraditional user interfaces. Participants prefer using think aloud testing, but identify few problems with that technique that are not found also with MOT or cognitive walkthrough. MOT identifies more problems than the other techniques. Across experiments and measures of usability problems' utility in systems design, MOT performs better than existing inspection techniques and is comparable to think aloud testing.


title: MSVM-kNN: Combining SVM and k-NN for Multi-class Text Classification

abstract: Text categorization is the process of assigning documents to a set of previously fixed categories. It is widely used in many data-oriented management applications. Many popular algorithms for text categorization have been proposed, such as Naive Bayes, k-Nearest Neighbor (k-NN), Support Vector Machine (SVM). However, those classification approaches do not perform well in every case, for example, SVM can not identify categories of documents correctly when the texts are in cross zones of multi-categories, k-NN cannot effectively solve the problem of overlapped categories borders. In this paper, we propose an approach named as Multi-class SVM-kNN (MSVM-kNN) which is the combination of SVM and k-NN. In the approach, SVM is first used to identify category borders, then k-NN classifies documents among borders. MSVM-kNN can overcome the shortcomings of SVM and k-NN and improve the performance of multi-class text classification. The experimental results show MSVM-kNN performs better than SVM or kNN.


title: TORCS, The Open Racing Car Simulator

abstract: The open racing car simulator (TORCS [14]), is a modern, modular, highlyportable multi-player, multi-agent car simulator. Its high degree of modularity and portability render it ideal for artificial intelligence research. Indeed, a number of research-oriented competitions and papers have already appeared that make use of the TORCS engine. The purpose of this document is to introduce the structure of TORCS to the general artificial intelligence and machine learning community and explain how it is possible to tests agents on the platform. TORCS can be used to develop artificially intelligent (AI) agents for a variety of problems. At the car level, new simulation modules can be developed, which include intelligent control systems for various car components. At the driver level, a low-level API gives detailed (but only partial) access to the simulation state. This could be used to develop anything from mid-level control systems to complex driving agents that find optimal racing lines, react successfully in unexpected situations and make good tactical race decisions. Finally, for researchers that like a challenge and are also interested in visual processing, a 3d projection interface is available.


Initial Coin Offering and Platform Building


title: New approaches to storing and manipulating multi-dimensional sparse arrays

abstract: In this paper, we introduce some storage schemes for multi-dimensional sparse arrays (MDSAs) that handle the sparsity of the array with two primary goals; reducing the storage overhead and maintaining efficient data element access. Four schemes are proposed. These are: i.) The PATRICIA trie compressed storage method (PTCS) which uses PATRICIA trie to store the valid non-zero array elements; ii.)The extended compressed row storage (xCRS) which extends CRS method for sparse matrix storage to sparse arrays of higher dimensions and achieves the best data element access efficiency of all the methods; iii.) The bit encoded xCRS (BxCRS) which optimizes the storage utilization of xCRS by applying data compression methods with run length encoding, while maintaining its data access efficiency; and iv.) a hybrid approach that provides a desired balance between the storage utilization and data manipulation efficiency by combining xCRS and the Bit Encoded Sparse Storage (BESS). These storage schemes were evaluated and compared on three basic array operations; constructing the storage scheme, accessing a random element and retrieving a sub-array, using a set of synthetic sparse multi-dimensional arrays.


title: Knowledge expansion over probabilistic knowledge bases

abstract: Information extraction and human collaboration techniques are widely applied in the construction of web-scale knowledge bases. However, these knowledge bases are often incomplete or uncertain. In this paper, we present ProbKB, a probabilistic knowledge base designed to infer missing facts in a scalable, probabilistic, and principled manner using a relational DBMS. The novel contributions we make to achieve scalability and high quality are: 1) We present a formal definition and a novel relational model for probabilistic knowledge bases. This model allows an efficient SQL-based inference algorithm for knowledge expansion that applies inference rules in batches; 2) We implement ProbKB on massive parallel processing databases to achieve further scalability; and 3) We combine several quality control methods that identify erroneous rules, facts, and ambiguous entities to improve the precision of inferred facts. Our experiments show that ProbKB system outperforms the state-of-the-art inference engine in terms of both performance and quality.


title: Conformal Asymmetric Meandered Flare (AMF) Antenna for Body-Worn Applications

abstract: A conformal body-worn antenna is presented for communications at 300 MHz. The antenna consists of a thin broadband flared-dipole element printed on a thin (0.1 mm) FR4 substrate without metallic backing or other shielding. The letter presents the design approach for tuning, matching, and mounting the antenna on the human body. Measurements are given for a human body phantom, and these are compared to simulations. Different antenna positions for improved coverage are also presented.


title: Bodily ownership and self-location: Components of bodily self-consciousness

abstract: Recent research on bodily self-consciousness has assumed that it consists of three distinct components: the experience of owning a body (body ownership); the experience of being a body with a given location within the environment (self-location); and the experience of taking a first-person, body-centered, perspective on that environment (perspective). Here we review recent neuroimaging studies suggesting that at least two of these components—body ownership and self-location—are implemented in rather distinct neural substrates, located, respectively, in the premotor cortex and in the temporo-parietal junction. We examine these results and consider them in relation to clinical evidence from patients with altered body perception and work on a variety of multisensory, body-related illusions, such as the rubber hand illusion, the full body illusion, the body swap illusion and the enfacement illusion. We conclude by providing a preliminary synthesis of the data on bodily self-consciousness and its neural correlates.


title: Classifying Spam Emails Using Text and Readability Features

abstract: Supervised machine learning methods for classifying spam emails are long-established. Most of these methods use either header-based or content-based features. Spammers, however, can bypass these methods easily-especially the ones that deal with header features. In this paper, we report a novel spam classification method that uses features based on email content-language and readability combined with the previously used content-based task features. The features are extracted from four benchmark datasets viz. CSDMC2010, Spam Assassin, Ling Spam, and Enron-Spam. We use five well-known algorithms to induce our spam classifiers: Random Forest (RF), BAGGING, ADABOOSTM1, Support Vector Machine (SVM), and Naive Bayes (NB). We evaluate the classifier performances and find that BAGGING performs the best. Moreover, its performance surpasses that of a number of state-of-the-art methods proposed in previous studies. Although applied only to English language emails, the results indicate that our method may be an excellent means to classify spam emails in other languages, as well.


title: System Design for Geosynchronous Synthetic Aperture Radar Missions

abstract: Geosynchronous synthetic aperture radar (GEO SAR) has been studied for several decades but has not yet been implemented. This paper provides an overview of mission design, describing significant constraints (atmosphere, orbit, temporal stability of the surface and atmosphere, measurement physics, and radar performance) and then uses these to propose an approach to initial system design. The methodology encompasses all GEO SAR mission concepts proposed to date. Important classifications of missions are: 1) those that require atmospheric phase compensation to achieve their design spatial resolution; and 2) those that achieve full spatial resolution without phase compensation. Means of estimating the atmospheric phase screen are noted, including a novel measurement of the mean rate of change of the atmospheric phase delay, which GEO SAR enables. Candidate mission concepts are described. It seems likely that GEO SAR will be feasible in a wide range of situations, although extreme weather and unstable surfaces (e.g., water, tall vegetation) prevent 100% coverage. GEO SAR offers an exciting imaging capability that powerfully complements existing systems.


title: Research on the electro-optical assistant landing system based on the dual camera photogrammetry algorithm

abstract: Based on the location technique of beacon photogrammetry, Dual Camera Photogrammetry (DCP) algorithm was used to assist helicopters landing on the ship. In this paper, ZEMAX was used to simulate the two Charge Coupled Device (CCD) cameras imaging four beacons on both sides of the helicopter and output the image to MATLAB. Target coordinate systems, image pixel coordinate systems, world coordinate systems and camera coordinate systems were established respectively. According to the ideal pin-hole imaging model, the rotation matrix and translation vector of the target coordinate systems and the camera coordinate systems could be obtained by using MATLAB to process the image information and calculate the linear equations. On the basis mentioned above, ambient temperature and the positions of the beacons and cameras were changed in ZEMAX to test the accuracy of the DCP algorithm in complex sea status. The numerical simulation shows that in complex sea status, the position measurement accuracy can meet the requirements of the project.


title: On homogeneous least-squares problems and the inconsistency introduced by mis-constraining

abstract: Abstract The term “homogeneous least-squares” refers to models of the form Ya ≈ 0 , where Y is some data matrix, and a is an unknown parameter vector to be estimated. Such problems are encountered, e.g., when modeling auto-regressive (AR) processes. Naturally, in order to apply a least-squares (LS) solution to such models, the parameter vector a has to be somehow constrained in order to avoid the trivial solution a = 0 . Usually, the problem at hand leads to a “natural” constraint on a . However, it will be shown that the use of some commonly applied constraints, such as a quadratic constraint, can lead to inconsistent estimates of a . An explanation to this apparent discrepancy is provided, and the remedy is shown to lie with a necessary modification of the LS criterion, which is specified for the case of Gaussian model-errors. As a result, the modified LS minimization becomes a highly non-linear problem. For the case of quadratic constraints in the context of AR modeling, the resulting minimization involves the solution of an equation reminiscent of a “secular equation”. Numerically appealing solutions to this equation are discussed.


title: Population Bias in Geotagged Tweets

abstract: Geotagged tweets are an exciting and increasingly popular data source, but like all social media data, they potentially have biases in who are represented. Motivated by this, we investigate the question, ‘are users of geotagged tweets randomly distributed over the US population’? We link approximately 144 million geotagged tweets within the US, representing 2.6m unique users, to high-resolution Census population data and carry out a statistical test by which we answer this question strongly in the negative. We utilize spatial models and integrate further Census data to investigate the factors associated with this nonrandom distribution. We find that, controlling for other factors, population has no effect on the number of geotag users, and instead it is predicted by a number of factors including higher median income, being in an urban area, being further east or on a coast, having more young people, and having high Asian, Black or Hispanic/Latino populations.


title: Moodbile: A Framework to Integrate m-Learning Applications with the LMS

abstract: Learning Management Systems (LMS) have become widespread among most centres for education and training. Being a mature technology, LMS have left the vanguard of innovation. Educational usages of the Web 2.0, Personal Learning Environments, Game-based Learning and particularly the introduction of mobile phones and tablets in education are happening outside the boundaries of the LMS. This paper proposes a way to integrate mobile devices and educational applications with the LMS through webservices; introducing the Moodbile project that provides an extension of Moodle 2.0 webservices for mobile integration and two mobile clients ready to use on real courses.


title: A highly scalable 8-layer Vertical Gate 3D NAND with split-page bit line layout and efficient binary-sum MiLC (Minimal Incremental Layer Cost) staircase contacts

abstract: We demonstrate an 8-layer 3D Vertical Gate NAND Flash with WL half pitch =37.5nm, BL half pitch=75nm, 64-WL NAND string with 63% array core efficiency. This is the first time that a 3D NAND Flash can be successfully scaled to below 3Xnm half pitch in one lateral dimension, thus an 8-layer stack device already provides a very cost effective technology with lower cost than the conventional sub-20nm 2D NAND. Our new VG architecture has two key features: (1) To improve the manufacturability a new layout that twists the even/odd BL's (and pages) in the opposite direction (split-page BL) is adopted. This allows the island-gate SSL devices [1] and metal interconnections be laid out in double pitch, creating much larger process window for BL pitch scaling; (2) A novel staircase BL contact formation method using binary sum of only M lithography and etching steps to achieve 2M contacts. This not only allows precise landing of the tight-pitch staircase contacts, but also minimizes the process steps and cost. We have successfully fabricated an 8-layer array using TFT BE-SONOS charge-trapping device. The array characteristics including reading, programming, inhibit, and block erase are demonstrated.


title: Study of a T-Shaped Slot With a Capacitor for High Isolation Between MIMO Antennas

abstract: In this letter, an isolation improvement method between two closely packed planar inverted-F antennas (PIFAs) is proposed via a miniaturized ground slot with a chip capacitor. The proposed T-shaped ground slot acts as a notch filter, and the capacitor is utilized to reduce the slot length. The equivalent circuit model of the proposed slot with the capacitor is derived. The measured isolation between two PIFAs is down to below -20 dB at the whole WLAN band of 2.4 GHz.


title: PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environments

abstract: Our goal is to learn control policies for robots that provably generalize well to novel environments given a dataset of example environments. The key technical idea behind our approach is to leverage tools from generalization theory in machine learning by exploiting a precise analogy (which we present in the form of a reduction) between generalization of control policies to novel environments and generalization of hypotheses in the supervised learning setting. In particular, we utilize the Probably Approximately Correct (PAC)-Bayes framework, which allows us to obtain upper bounds that hold with high probability on the expected cost of (stochastic) control policies across novel environments. We propose policy learning algorithms that explicitly seek to minimize this upper bound. The corresponding optimization problem can be solved using convex optimization (Relative Entropy Programming in particular) in the setting where we are optimizing over a finite policy space. In the more general setting of continuously parameterized policies (e.g., neural network policies), we minimize this upper bound using stochastic gradient descent. We present simulated results of our approach applied to learning (1) reactive obstacle avoidance policies and (2) neural network-based grasping policies. We also present hardware results for the Parrot Swing drone navigating through different obstacle environments. Our examples demonstrate the potential of our approach to provide strong generalization guarantees for robotic systems with continuous state and action spaces, complicated (e.g., nonlinear) dynamics, rich sensory inputs (e.g., depth images), and neural network-based policies.


title: Topic Memory Networks for Short Text Classification

abstract: Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.


Rapid Style Adaptation Using Residual Error Embedding for Expressive Speech Synthesis


title: A Real-Time Multi-Task Single Shot Face Detector

abstract: Face, fiducial detection, and 3D head pose estimation are important face preprocessing modules for face recognition which are usually performed separately and loosely coupled. In this paper, we propose a unifying framework to simultaneously detect face, fiducial points, and head pose in real-time. In addition, since no single dataset contains all the required and best annotations, we develop a progressive training strategy to overcome the annotation discrepancy across different datasets. Extensive experiments on face detection, fiducial detection, and pose estimation benchmarks demonstrate the proposed approach can achieve comparable performance to a state-of-the-art system [1] but runs 60 times faster. (i.e., 20 frames per second)


Hierarchical Recurrent Neural Networks for Acoustic Modeling


title: Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System

abstract: The detection of emotion is becoming an increasingly important field for human-computer interaction as the advantages emotion recognition offer become more apparent and realisable. Emotion recognition can be achieved by a number of methods, one of which is through the use of bio-sensors. Bio-sensors possess a number of advantages against other emotion recognition methods as they can be made both inobtrusive and robust against a number of environmental conditions which other forms of emotion recognition have difficulty to overcome. In this paper, we describe a procedure to train computers to recognise emotions using multiple signals from many different bio-sensors. In particular, we describe the procedure we adopted to elicit emotions and to train our system to recognise them. We also present a set of preliminary results which indicate that our neural net classifier is able to obtain accuracy rates of 96.6% and 89.9% for recognition of emotion arousal and valence respectively.


title: Customer segmentation and analysis of a mobile telecommunication company of Pakistan using two phase clustering algorithm

abstract: Pakistan hosts a competitive and fluid telecommunication market and for a company to sustain, create customer value and increase economic efficiency, it needs to better understand its customers. The purpose of clustering or customer segmentation is to deliver actionable results for marketing, product development and business planning. In this paper, we focus on customer segmentation using clustering algorithms on real data of a telecommunication company in Pakistan. After choosing appropriate attributes for clustering, we used the two-step clustering algorithm in order to create different customer segments. Moreover, the insights obtained from each segment were analyzed before suggesting marketing strategies for up-selling and better targeted campaigns.


title: A Wide-Range Model for Metal-Oxide Surge Arrester

abstract: This paper presents an electric model of a metal-oxide surge arrester (MOSA). The proposed electric model accurately represents the MOSA in a wide range of frequencies and amplitudes. The model was developed and validated based on MOSA electrical behavior in each one of the three operating regions of the zinc-oxide (ZnO) surge arresters, and in a database composed of voltage and current waveforms measured from tests performed in 12 ZnO varistors having different physical dimensions and electrical characteristics—from five different manufacturers. These varistors were subjected to different voltage levels in the low current region, and multilevel amplitude of switching current impulses (30/60 $\mu$ s), lightning current impulses (8/20 $\mu$ s), high current impulses (4/10 $\mu$ s), and fast-front current impulses (1.5/26 $\mu$ s and 3/6 $\mu$ s) encompass the three regions of operation and a wide range of frequencies and amplitudes. The results provided by the MOSA wide-range (MWR) model were compared with those obtained in the laboratory. The MWR model has shown good agreement in terms of waveform, peak value, and absorbed energy for the evaluated cases.


title: A 5.6 nV/ $\surd $ Hz Chopper Operational Amplifier Achieving a 0.5 $\mu$ V Maximum Offset Over Rail-to-Rail Input Range With Adaptive Clock Boosting Technique

abstract: This paper presents a standalone 5.6 nV/√Hz chopper op-amp that operates from a 2.1-5.5 V supply. Frequency compensation is achieved in a power-and area-efficient manner by using a current attenuator and a dummy differential output. As a result, the overall op-amp only consumes 1.4 mA supply current and 1.26 mm2 die area. Up-modulated chopper ripple is suppressed by a local feedback technique, called auto correction feedback (ACFB). The charge injection of the input chopping switches can cause residual offset voltages, especially with the wider switches needed to reduce thermal noise. By employing an adaptive clock boosting technique with NMOS input switches, the amount of charge injection is minimized and kept constant as the input common-mode voltage changes. This results in a 0.5 μV maximum offset and 0.015 μV/°C maximum drift over the amplifier's entire rail-to-rail input common-mode range and from -40 °C to 125 °C. The design is implemented in a 0.35 μm CMOS process augmented by 5 V CMOS transistors.


title: Bayesian generic priors for causal learning

abstract: The article presents a Bayesian model of causal learning that incorporates generic priors—systematic assumptions about abstract properties of a system of cause–effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes—causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P. W. Cheng, 1997). The authors tested this and other Bayesian models, as well as leading nonnormative models, by fitting multiple data sets in which several parameters were varied parametrically across multiple types of judgments. The SS power model accounted for data concerning judgments of both causal strength and causal structure (whether a causal link exists). The model explains why human judgments of causal structure (relative to a Bayesian model lacking these generic priors) are influenced more by causal power and the base rate of the effect and less by sample size. Broader implications of the Bayesian framework for human learning are discussed.


title: Leveraging Document Structure for Better Classification of Complex Legal Documents

abstract: Document classification is a machine learning application that has been as impactful as it has been successful in a myriad of domains and applications. However, when the documents being classified are large and highly-complex, and when the set of potential classes is large as well, these models could be improved by incorporating more information about the documents’ overall structure. Most approaches use bag-of-words type models that discard local structure and focus on types of words or n-grams used. In this paper, we examine several models and attempt to leverage both local (e.g. n-gram) and global (e.g. structure and organization) document features. We apply these approaches to a new dataset of legal documents.


title: MVE-An image-based reconstruction environment

abstract: We present an image-based reconstruction system, the Multi-View Environment. MVE is an end-to-end multi-view geometry reconstruction software which takes photos of a scene as input and produces a textured surface mesh as result. The system covers a structure-from-motion algorithm, multi-view stereo reconstruction, generation of extremely dense point clouds, reconstruction of surfaces from point clouds, and surface texturing. In contrast to most image-based geometry reconstruction approaches, our system is focused on reconstruction of multi-scale scenes, an important aspect in many areas such as cultural heritage. It allows to reconstruct large datasets containing some detailed regions with much higher resolution than the rest of the scene. Our system provides a graphical user interface for visual inspection of the individual steps of the pipeline, i.e., the structure-from-motion result, multi-view stereo depth maps, and rendering of scenes and meshes. Display Omitted HighlightsEnd-to-end multi-view geometry reconstruction and texturing pipeline.Multi-scale reconstruction approach.


Cardiac Dysrhythmias in Pediatric Patients During Anesthesia and the Role of Oximetry and Capnography


title: The Accuracy-Privacy Tradeoff of Mobile Crowdsensing

abstract: Mobile crowdsensing has emerged as an efficient sensing paradigm which combines the crowd intelligence and the sensing power of mobile devices, e.g.,~mobile phones and Internet of Things (IoT) gadgets. This article addresses the contradicting incentives of privacy preservation by crowdsensing users and accuracy maximization and collection of true data by service providers. We firstly define the individual contributions of crowdsensing users based on the accuracy in data analytics achieved by the service provider from buying their data. We then propose a truthful mechanism for achieving high service accuracy while protecting the privacy based on the user preferences. The users are incentivized to provide true data by being paid based on their individual contribution to the overall service accuracy. Moreover, we propose a coalition strategy which allows users to cooperate in providing their data under one identity, increasing their anonymity privacy protection, and sharing the resulting payoff. Finally, we outline important open research directions in mobile and people-centric crowdsensing.


title: Recognizing in the depth: Selective 3D Spatial Pyramid Matching Kernel for object and scene categorization

abstract: This paper proposes a novel approach to recognize object and scene categories in depth images. We introduce a Bag of Words (BoW) representation in 3D, the Selective 3D Spatial Pyramid Matching Kernel (3DSPMK). It starts quantizing 3D local descriptors, computed from point clouds, to build a vocabulary of 3D visual words. This codebook is used to build the 3DSPMK, which starts partitioning a working volume into fine sub-volumes, and computing a hierarchical weighted sum of histogram intersections of visual words at each level of the 3D pyramid structure. With the aim of increasing both the classification accuracy and the computational efficiency of the kernel, we propose two selective hierarchical volume decomposition strategies, based on representative and discriminative sub-volume selection processes, which drastically reduce the pyramid to consider. Results on different RGBD datasets show that our approaches obtain state-of-the-art results for both object recognition and scene categorization. Display Omitted We introduce the 3DSPMK for object and scene recognition in depth images.Our model repeatedly subdivides a cube inscribed in the point cloud.Then, a weighted sum of histogram of visual word occurrences is computed.Results on publicly available benchmarks have been reported.


title: Language Assessment and Development in Toddlers with Autism Spectrum Disorders

abstract: One of the primary diagnostic criteria for the diagnosis of autism spectrum disorders (ASD) is the presence of a language delay or impairment. Children with ASD are now being identified at significantly younger ages, and prior research has consistently found that early language skills in this population are heterogeneous and an important predictor for later outcome. The goal of this study was to systematically investigate language in toddlers with ASD and to identify early correlates of receptive and expressive language in this population. The study included 164 toddlers with ASD between the ages of 18 and 33 months who were evaluated on several cognitive, language and behavioral measures. Results suggested good agreement among different measures of early language, including direct assessment and parent report measures. Significant concurrent predictors of receptive language included gestures, non-verbal cognitive ability and response to joint attention. For expressive language, the most significant predictors were non-verbal cognitive ability, gestures and imitation. These findings have important implications for intervention programs targeting this population.


title: The Progressive Smart Grid System from Both Power and Communications Aspects

abstract: The present electric power system structure has lasted for decades; it is still partially proprietary, energy-inefficient, physically and virtually (or cyber) insecure, as well as prone to power transmission congestion and consequent failures. Recent efforts in building a smart grid system have focused on addressing the problems of global warming effects, rising energy-hungry demands, and risks of peak loads. One of the major goals of the new system is to effectively regulate energy usage by utilizing the backbone of the prospectively deployed Automatic Meter Reading (AMR), Advanced Meter Infrastructure (AMI), and Demand Response (DR) programs via the advanced distribution automation and dynamic pricing models. The function of the power grid is no longer a system that only supplies energy to end users, but also allows consumers to contribute their clean energy back to the grid in the future. In the meantime, communications networks in the electric power infrastructure enact critical roles. Intelligent automation proposed in smart grid projects include the Supervisory Control And Data Acquisition/Energy Management Systems (SCADA/EMS) and Phasor Management Units (PMU) in transmission networks, as well as the AMR/AMI associated with field/neighborhood area networks (FAN/NAN) and home area networks (HAN) at the distribution and end-use levels. This article provides an overview of the essentials of the progressive smart grid paradigm and integration of different communications technologies for the legacy power system. Additionally, foreseeable issues and challenges in designing communications networks for the smart grid system are also rigorously deliberated in this paper.


title: Swarms and Swarm Intelligence

abstract: Nature-inspired intelligent swarm technologies deals with complex problems that might be impossible to solve using traditional technologies and approaches. Swarm intelligence techniques (note the difference from intelligent swarms) are population-based stochastic methods used in combinatorial optimization problems in which the collective behavior of relatively simple individuals arises from their local interactions with their environment to produce functional global patterns. Swarm intelligence represents a meta heuristic approach to solving a variety of problems


title: A 140 dB-CMRR Current-Feedback Instrumentation Amplifier Employing Ping-Pong Auto-Zeroing and Chopping

abstract: This paper presents a precision general-purpose current-feedback instrumentation amplifier (CFIA) that employs a combination of ping-pong auto-zeroing and chopping to cancel its offset and 1/f noise. A comparison of offset-cancellation techniques shows that neither chopping nor auto-zeroing is an ideal solution for general-purpose CFIAs, since chopping results in output ripple, and auto-zeroing is associated with increased low-frequency noise. The presented CFIA mitigates these unintended side effects through a combination of these techniques. A ping-pong auto-zeroed input stage with slow-settling offset-nulling loops is applied to limit the bandwidth of the increased noise to less than half of the auto-zeroing frequency. This noise is then modulated away from DC by chopping the input stage at half the auto-zeroing frequency, reducing the low-frequency noise to the 27 nV/ white-noise level, without introducing extra output ripple. The auto-zeroing is augmented with settling phases to further reduce output transients. The CFIA was realized in a 0.5 μm analog CMOS process and achieves a typical offset of 2.8 μV and a CMRR of 140 dB in a common-mode voltage range that includes the negative supply.


AN EMPIRICAL STUDY OF FACTORS AFFECTING CUSTOMER LOYALTY OF TELECOMMUNICATION INDUSTRY IN THE KINGDOM OF SAUDI ARABIA


title: A Discriminative Hierarchical Model for Fast Coreference at Large Scale

abstract: Methods that measure compatibility between mention pairs are currently the dominant approach to coreference. However, they suffer from a number of drawbacks including difficulties scaling to large numbers of mentions and limited representational power. As these drawbacks become increasingly restrictive, the need to replace the pairwise approaches with a more expressive, highly scalable alternative is becoming urgent. In this paper we propose a novel discriminative hierarchical model that recursively partitions entities into trees of latent sub-entities. These trees succinctly summarize the mentions providing a highly compact, information-rich structure for reasoning about entities and coreference uncertainty at massive scales. We demonstrate that the hierarchical model is several orders of magnitude faster than pairwise, allowing us to perform coreference on six million author mentions in under four hours on a single CPU.


title: SMTR® module - Evolution towards airborne applications

abstract: Modern active electronically steered antennas (AESA) operate in different platforms and systems. Inside AIRBUS Defence & Space, the focus on X-band antennas today is on airborne and fighter nose radars, in satellite based SAR antennas (synthetic aperture radar) for earth observation, and ground surveillance and security radars.


title: Dual-fisheye lens stitching and error correction

abstract: 360-Degree panoramic cameras have been widely used in the field of computer vision and virtual reality recently. The use of fisheye lens to actualize a panoramic camera has become the industry trend. Fisheye lens has large distortion, and fisheye images have to be unwarped and blended to get 360-degree panoramic images, which has become two difficulties in fisheye lens practice. In this paper, a set of automatic 360-degree panoramic image generation algorithm which can be easily realized is proposed to solve these difficulties. The result shows that this software method can achieve high quality and low cost.


title: High-efficiency high-power-density 380V/12V DC/DC converter with a novel matrix transformer

abstract: Isolated high output current DC/DC converters are critical for future data center power architecture. LLC converters with matrix transformer are suitable for these applications due to its high efficiency and high power density. Different matrix transformer structures are investigated. Flux cancellation can be utilized to reduce core size and loss. Synchronous rectifiers and output capacitors can be integrated into the secondary windings to minimize leakage and termination loss. To further improve the current design practice, several efforts have been made and a detailed design procedure is provided in this paper. A novel matrix transformer structure is proposed to integrate four elemental transformers into one magnetic core with simple four-layer PCB windings, fully utilize the available PCB copper and space, and further reduce core loss. The proposed matrix transformer is superior to the state-of-art, due to the much reduced core loss, integrated magnetics and simple four-layer PCB winding. With the proposed matrix transformer, LLC converter can achieve both high efficiency and high power density by operating at MHz. A 1MHz 380V/12V 800W LLC converter with GaN devices is demonstrated. The prototype can fit into the quarter-brick footprint and achieves a peak efficiency of 97.6% and a power density of 900W/inch3.


title: An on-chip glitchy-clock generator for testing fault injection attacks

abstract: This paper presents a glitchy-clock generator integrated in FPGA for evaluating fault injection attacks and their countermeasures on cryptographic modules. The proposed generator exploits clock management capabilities, which are common in modern FPGAs, to generate clock signal with temporal voltage spike. The shape and timing of the glitchy-clock cycle are configurable at run time. The proposed generator can be embedded in a single FPGA without any external instrument (e.g., a pulse generator and a variable power supply). Such integration enables reliable and reproducible fault injection experiments. In this paper, we examine the characteristics of the proposed generator through experiments on Side-channel Attack Standard Evaluation Board (SASEBO). The result shows that the timing of the glitches can be controlled at the step of about 0.17 ns. We also demonstrate its application to the safe-error attack against an RSA processor.


Unsupervised LTE D 2 D-Case Study for Safety-Critical V 2 X Communications


title: Algebraic feature extraction of image for recognition

abstract: Abstract The extraction of image features is one of the fundamental tasks in image recognition. Up until now, there have been several kinds of features to be used for the purpose of image recognition as follows: (1) visual features; (2) statistical features of pixel; (3) transform coefficient features. In addition, there is another kind of feature which the author believes is very useful, i.e. (4) algebraic features which represent intrinsic attributions of an image. Singular Values (SV) of image are this kind of feature. In this paper, we prove that SV feature vector has some important properties of algebraic and geometric invariance, and insensitiveness to noise. These properties are very useful for the description and recognition of images. As an example, SV feature vector is used for the problem of recognizing human facial images. In this paper, using SV feature vector samples of facial images, a normal pattern Bayes classification model based on Sammon's optimal descriminant plane is constructed. The experimental result shows that SV feature vector has good performance of class separation.


The Relationship between Impulsivity and Internet Addiction in Chinese College Students: A Moderated Mediation Analysis of Meaning in Life and Self-Esteem


title: A fuzzy approach for supplier evaluation and selection in supply chain management

abstract: Abstract This paper is aimed to present a fuzzy decision-making approach to deal with the supplier selection problem in supply chain system. During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions usually is complex and unstructured. In general, many quantitative and qualitative factors such as quality, price, and flexibility and delivery performance must be considered to determine suitable suppliers. In this paper, linguistic values are used to assess the ratings and weights for these factors. These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Then, a hierarchy multiple criteria decision-making (MCDM) model based on fuzzy-sets theory is proposed to deal with the supplier selection problems in the supply chain system. According to the concept of the TOPSIS, a closeness coefficient is defined to determine the ranking order of all suppliers by calculating the distances to the both fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) simultaneously. Finally, an example is shown to highlight the procedure of the proposed method at the end of this paper. This paper shows that the proposed model is very well suited as a decision-making tool for supplier selection decisions.


title: A Survey on Versatility for Publish/Subscribe Infrastructures

abstract: Current publish/subscribe middleware infrastructures fall short of mechanisms that allow their customization and configuration to comply with the requirements of different appli- cation domains. This shortcoming is a consequence of their original design which does not account for mechanisms or approaches that allow the evolution of this kind of ser- vices. This survey introduces the concept of versatility in publish/subscribe infrastructures and examines the current approaches to versatility in publish-subscribe middleware as well as approaches to versatility that have been applied in other kinds of middleware and may possibly succeed in the context of publish/subscribe infrastructures. In this context, versatility is defined as a set of properties (such as variability, reuse, dy- namism and usability) that allows the customization, extension and compression of mid- dleware. This paper surveys existing and advanced software engineering approaches to address those requirements. A comparative framework on software versatility, as a set of properties, is presented to help researches and practitioners to evaluate and compare the strengths and limitations of such approaches that have been or might be applied to this problem. Our goal is not to compare the approaches with one another, but to show how those approaches can be used to provide some of the versatility properties we identify. An agenda for future research in this topic is also presented. This survey addresses the following questions: What is versatility? How is versatility de- fined in the context of publish/subscribe middleware? Which software engineering tech- niques have been used to provide versatility to middleware in general, and specifically to publish/subscribe middleware? What other techniques may be used to approach this problem? What are their limitations and strengths? What are some of the important open research questions in this area?


Control of vehicles with second-order nonholonomic constraints: Underactuated vehicles


title: Plagiarism Detection through Internet using Hybrid Artificial Neural Network and Support Vectors Machine

abstract: Currently, most of the plagiarism detections are using similarity measurement techniques. Basically, a pair of similar sentences describes the same idea. However, not all like that, there are also sentences that are similar but have opposite meanings. This is one problem that is not easily solved by use of the technique similarity. Determination of dubious value similarity threshold on similarity method is another problem. The plagiarism threshold was adjustable, but it means uncertainty. Another problem, although the rules of plagiarism can be understood together but in practice, some people have a different opinion in determining a document, whether or not classified as plagiarism. Of the three problems, a statistical approach could possibly be the most appropriate solution. Machine learning methods like k-nearest neighbors (KNN), support vector machine (SVM), artificial neural networks (ANN) is a technique that is commonly used in solving the problem based on statistical data. This method of learning process based on statistical data to be smart resembling intelligence experts. In this case, plagiarism is data that has been validated by experts. This paper offers a hybrid approach of SVM method for detecting plagiarism. The data collection method in this work using an Internet search to ensure that a document is in the detection is up-to-date. The measurement results based on accuracy, precision and recall show that the hybrid machine learning does not always result in better performance. There is no better and vice versa. Overall testing of the four hybrid combinations concluded that the hybrid ANN-SVM method is the best performance in the case of plagiarism.


title: Graphene: Status and Prospects

abstract: Graphene is a wonder material with many superlatives to its name. It is the thinnest known material in the universe and the strongest ever measured. Its charge carriers exhibit giant intrinsic mobility, have zero effective mass, and can travel for micrometers without scattering at room temperature. Graphene can sustain current densities six orders of magnitude higher than that of copper, shows record thermal conductivity and stiffness, is impermeable to gases, and reconciles such conflicting qualities as brittleness and ductility. Electron transport in graphene is described by a Dirac-like equation, which allows the investigation of relativistic quantum phenomena in a benchtop experiment. This review analyzes recent trends in graphene research and applications, and attempts to identify future directions in which the field is likely to develop.


title: The use of the reamer-irrigator-aspirator to harvest mesenchymal stem cells

abstract: The scarcity of mesenchymal stem cells (MSCs) in iliac crest bone marrow aspirate (ICBMA), and the expense and time in culturing cells, has led to the search for alternative harvest sites. The reamer-irrigation-aspirator (RIA) provides continuous irrigation and suction during reaming of long bones. The aspirated contents pass via a filter, trapping bony fragments, before moving into a ‘waste’ bag from which MSCs have been previously isolated. We examined the liquid and solid phases, performed a novel digestion of the solid phase, and made a comparative assessment in terms of number, phenotype and differentiation capacity with matched ICBMA. ::: ::: The solid fraction from the filtrate was digested for 60 minutes at 37°C with collagenase. Enumeration was performed via the colony-forming unit fibroblast (CFU-F) assay. Passage (P2) cells were differentiated towards osteogenic, adipogenic and chondrogenic lineages, and their phenotypes assessed using flow cytometry (CD33, CD34, CD45, CD73, CD90, and CD105). ::: ::: MSCs from the RIA phases were able to differentiate at least as well as those from ICBMA, and all fractions had phenotypes consistent with other established sources. The median number of colonies for the three groups was: ICBMA = 8.5 (2 to 86), RIA-liquid = 19.5 (4 to 90), RIA-solid = 109 (67 to 200) per 200 μl. The mean total yield of cells for the three groups was: ICBMA = 920 (0 to 4275), RIA-liquid = 114 983 (16 500 to 477 750), RIA-solid = 12 785 (7210 to 28 475). ::: ::: The RIA filtrate contains large numbers of MSCs that could potentially be extracted without enzymatic digestion and used for bone repair without prior cell expansion.


title: Integrating Social and Auxiliary Semantics for Multifaceted Topic Modeling in Twitter

abstract: Microblogging platforms, such as Twitter, have already played an important role in recent cultural, social and political events. Discovering latent topics from social streams is therefore important for many downstream applications, such as clustering, classification or recommendation. However, traditional topic models that rely on the bag-of-words assumption are insufficient to uncover the rich semantics and temporal aspects of topics in Twitter. In particular, microblog content is often influenced by external information sources, such as Web documents linked from Twitter posts, and often focuses on specific entities, such as people or organizations. These external sources provide useful semantics to understand microblogs and we generally refer to these semantics as auxiliary semantics. In this article, we address the mentioned issues and propose a unified framework for Multifaceted Topic Modeling from Twitter streams. We first extract social semantics from Twitter by modeling the social chatter associated with hashtags. We further extract terms and named entities from linked Web documents to serve as auxiliary semantics during topic modeling. The Multifaceted Topic Model (MfTM) is then proposed to jointly model latent semantics among the social terms from Twitter, auxiliary terms from the linked Web documents and named entities. Moreover, we capture the temporal characteristics of each topic. An efficient online inference method for MfTM is developed, which enables our model to be applied to large-scale and streaming data. Our experimental evaluation shows the effectiveness and efficiency of our model compared with state-of-the-art baselines. We evaluate each aspect of our framework and show its utility in the context of tweet clustering.


title: Metaheuristic Design Pattern: Surrogate Fitness Functions

abstract: Certain problems have characteristics that present difficulties for metaheuristics: their objective function may be either prohibitively expensive, or they may only give a partial ordering over the solutions, lacking a suitable gradient to guide the search. In such cases, it may be more efficient to use a surrogate fitness function to replace or supplement the objective function. This paper provides a broad perspective on surrogate fitness functions, described in the form of a metaheuristic design pattern.


title: FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

abstract: This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.


Evaluation of Eleven Thermal Unit Methods for Predicting Soybean Development1


title: Discrete Connection and Covariant Derivative for Vector Field Analysis and Design

abstract: In this article, we introduce a discrete definition of connection on simplicial manifolds, involving closed-form continuous expressions within simplices and finite rotations across simplices. The finite-dimensional parameters of this connection are optimally computed by minimizing a quadratic measure of the deviation to the (discontinuous) Levi-Civita connection induced by the embedding of the input triangle mesh, or to any metric connection with arbitrary cone singularities at vertices. From this discrete connection, a covariant derivative is constructed through exact differentiation, leading to explicit expressions for local integrals of first-order derivatives (such as divergence, curl, and the Cauchy-Riemann operator) and for L2-based energies (such as the Dirichlet energy). We finally demonstrate the utility, flexibility, and accuracy of our discrete formulations for the design and analysis of vector, n-vector, and n-direction fields.


title: Solving the Shortest Vector Problem in 2n Time Using Discrete Gaussian Sampling: Extended Abstract

abstract: We give a randomized 2n+o(n)-time and space algorithm for solving the Shortest Vector Problem (SVP) on n-dimensional Euclidean lattices. This improves on the previous fastest algorithm: the deterministic ~O(4n)-time and ~O(2n)-space algorithm of Micciancio and Voulgaris (STOC 2010, SIAM J. Comp. 2013). In fact, we give a conceptually simple algorithm that solves the (in our opinion, even more interesting) problem of discrete Gaussian sampling (DGS). More specifically, we show how to sample 2n/2 vectors from the discrete Gaussian distribution at any parameter in 2n+o(n) time and space. (Prior work only solved DGS for very large parameters.) Our SVP result then follows from a natural reduction from SVP to DGS. In addition, we give a more refined algorithm for DGS above the so-called smoothing parameter of the lattice, which can generate 2n/2 discrete Gaussian samples in just 2n/2+o(n) time and space. Among other things, this implies a 2n/2+o(n)-time and space algorithm for 1.93-approximate decision SVP.


title: User Participation in an Academic Social Networking Service: A Survey of Open Group Users on Mendeley

abstract: Although there are a number of social networking services that specifically target scholars, little has been published about the actual practices and the usage of these so-called academic social networking services ASNSs. To fill this gap, we explore the populations of academics who engage in social activities using an ASNS; as an indicator of further engagement, we also determine their various motivations for joining a group in ASNSs. Using groups and their members in Mendeley as the platform for our case study, we obtained 146 participant responses from our online survey about users' common activities, usage habits, and motivations for joining groups. Our results show that a participants did not engage with social-based features as frequently and actively as they engaged with research-based features, and b users who joined more groups seemed to have a stronger motivation to increase their professional visibility and to contribute the research articles that they had read to the group reading list. Our results generate interesting insights into Mendeley's user populations, their activities, and their motivations relative to the social features of Mendeley. We also argue that further design of ASNSs is needed to take greater account of disciplinary differences in scholarly communication and to establish incentive mechanisms for encouraging user participation.


Target-side Word Segmentation Strategies for Neural Machine Translation


title: Compiling Knowledge into Decomposable Negation Normal Form

abstract: We propose a method for compiling propositional theories into a new tractable form that we refer to as decomposable negation normal form (DNNF). We show a number of results about our compilation approach. First, we show that every propositional theory can be compiled into DNNF and present an algorithm to this effect. Second, we show that if a clausal form has a bounded treewidth, then its DNNF compilation has a linear size and can be computed in linear time - treewidth is a graphtheoretic parameter which measures the connectivity of the clausal form. Third, we show that once a propositional theory is compiled into DNNF, a number of reasoning tasks, such as satisfiability and forgetting, can be performed in linear time. Finally, we propose two techniques for approximating the DNNF compilation of a theory when the size of such compilation is too large to be practical. One of the techniques generates a sound but incomplete compilation, while the other generates a complete but unsound compilation. Together, these approximations bound the exact compilation from below and above in terms for their ability to answer queries.


title: Power struggles: Revisiting the RISC vs. CISC debate on contemporary ARM and x86 architectures

abstract: RISC vs. CISC wars raged in the 1980s when chip area and processor design complexity were the primary constraints and desktops and servers exclusively dominated the computing landscape. Today, energy and power are the primary design constraints and the computing landscape is significantly different: growth in tablets and smartphones running ARM (a RISC ISA) is surpassing that of desktops and laptops running x86 (a CISC ISA). Further, the traditionally low-power ARM ISA is entering the high-performance server market, while the traditionally high-performance x86 ISA is entering the mobile low-power device market. Thus, the question of whether ISA plays an intrinsic role in performance or energy efficiency is becoming important, and we seek to answer this question through a detailed measurement based study on real hardware running real applications. We analyze measurements on the ARM Cortex-A8 and Cortex-A9 and Intel Atom and Sandybridge i7 microprocessors over workloads spanning mobile, desktop, and server computing. Our methodical investigation demonstrates the role of ISA in modern microprocessors' performance and energy efficiency. We find that ARM and x86 processors are simply engineering design points optimized for different levels of performance, and there is nothing fundamentally more energy efficient in one ISA class or the other. The ISA being RISC or CISC seems irrelevant.


title: Count Time Series Models

abstract: Abstract We review regression models for count time series. We discuss the approach that is based on generalized linear models and the class of integer autoregressive processes. The generalized linear models' framework provides convenient tools for implementing model fitting and prediction using standard software. Furthermore, this approach provides a natural extension to the traditional ARMA methodology. Several models have been developed along these lines, but conditions for stationarity and valid asymptotic inference were given in the literature only recently. We review several of these facts. In addition, we consider integer autoregressive models for count time series and discuss estimation and possible extensions based on real data applications.