title: Prioritized multi-task compliance control of redundant manipulators

abstract: We propose a new approach for dynamic control of redundant manipulators to deal with multiple prioritized tasks at the same time by utilizing null space projection techniques. The compliance control law is based on a new representation of the dynamics wherein specific null space velocity coordinates are introduced. These allow to efficiently exploit the kinematic redundancy according to the task hierarchy and lead to a dynamics formulation with block-diagonal inertia matrix. The compensation of velocity-dependent coupling terms between the tasks by an additional passive feedback action facilitates a stability analysis for the complete hierarchy based on semi-definite Lyapunov functions. No external forces have to be measured. Finally, the performance of the control approach is evaluated in experiments on a torque-controlled robot.


title: Distributed Energy Management for Vehicle-to-Grid Networks

abstract: Making full use of V2G services, EVs with batteries may assist the smart grid in alleviating peaks of energy consumption. Aiming to develop a systematic understanding of the interplay between smart grid and EVs, an architecture for the V2G networks with the EV aggregator is designed to maintain the balance between energy suppliers (the grid side) and consumers (the EV side). We propose a combined control and communication approach considering distributed features and vehicle preferences in order to ensure efficient energy transfer. In our model, the integrated communication and control unit can achieve realtime and intelligent management with the logic controller and collected data. On the consumers' side, we theoretically analyze how to satisfy the charging constraints that we incorporate in the form of willingness to pay, and propose a distributed framework to coordinate the energy delivery behaviors for satisfying service demands. Moreover, illustrative results indicate that the proposed approach can yield higher revenue than the conventional pricing mechanism in V2G networks.


title: ARSENAL: Automatic Requirements Specification Extraction from Natural Language

abstract: Requirements are informal and semi-formal descriptions of the expected behavior of a complex system from the viewpoints of its stakeholders customers, users, operators, designers, and engineers. However, for the purpose of design, testing, and verification for critical systems, we can transform requirements into formal models that can be analyzed automatically. ARSENAL is a framework and methodology for systematically transforming natural language NL requirements into analyzable formal models and logic specifications. These models can be analyzed for consistency and implementability. The ARSENAL methodology is specialized to individual domains, but the approach is general enough to be adapted to new domains.


title: Balancing accuracy, complexity and interpretability in consumer credit decision making: A C-TOPSIS classification approach

abstract: Accuracy, complexity and interpretability are very important in credit classification. However, most approaches cannot perform well in all the three aspects simultaneously. The objective of this study is to put forward a classification approach named C-TOPSIS that can balance the three aspects well. C-TOPSIS is based on the rationale of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). TOPSIS is famous for reliable evaluation results and quick computing process and it is easy to understand and use. However, it is a ranking approach and three challenges have to be faced for modifying TOPSIS into a classification approach. C-TOPSIS works out three strategies to overcome the challenges and retains the advantages of TOPSIS. So C-TOPSIS is deduced to have reliable classification results, high computational efficiency and ease of use and understanding. Our findings in the experiment verify the advantages of C-TOPSIS. In comparison with 7 popular approaches on 2 widely used UCI credit datasets, C-TOPSIS ranks 2nd in accuracy, 1st in complexity and is in 1st rank in interpretability. Only C-TOPSIS ranks among the top 3 in all the three aspects, which verifies that C-TOPSIS can balance accuracy, complexity and interpretability well.


title: 100G to 1T based Coherent PON Technology

abstract: We review the recent progress of the latest 100G to 1T class coherent PON technology using a simplified DSP suitable for forthcoming 5G era optical access systems. The highlight is the presentation of the first demonstration of 100 Gb/s/λ × 8 (800 Gb/s) based PON.


title: Low complexity approximate maximum throughput scheduling for LTE

abstract: In this paper we address the challenge of multiuser scheduling in the downlink of 3GPP UMTS/LTE. Long Term Evolution (LTE) imposes the constraint of using the same code rate, modulation order and transmit power for all resources a User Equipment (UE) is scheduled onto. This, in addition to the lack of channel knowledge, prohibits theoretical concepts such as capacity maximization to be applied for resource allocation. Based on the Channel Quality Indicator (CQI) feedback we derive a linearized model for multiuser scheduling. In contrast to other proposals we use Mutual Information Effective SNR Mapping (MIESM) to calculate an average CQI value for all UE resources. This enables a rate increase while still guaranteeing an imposed Block Error Ratio (BLER) constraint. The proposed framework can also be applied to implement other scheduling strategies. This is demonstrated by comparing different standard schedulers in terms of achieved throughput and fairness.


title: Evaluating topic models for digital libraries

abstract: Topic models could have a huge impact on improving the ways users find and discover content in digital libraries and search interfaces through their ability to automatically learn and apply subject tags to each and every item in a collection, and their ability to dynamically create virtual collections on the fly. However, much remains to be done to tap this potential, and empirically evaluate the true value of a given topic model to humans. In this work, we sketch out some sub-tasks that we suggest pave the way towards this goal, and present methods for assessing the coherence and interpretability of topics learned by topic models. Our large-scale user study includes over 70 human subjects evaluating and scoring almost 500 topics learned from collections from a wide range of genres and domains. We show how scoring model -- based on pointwise mutual information of word-pair using Wikipedia, Google and MEDLINE as external data sources - performs well at predicting human scores. This automated scoring of topics is an important first step to integrating topic modeling into digital libraries


title: A novel gate assisted circuit to reduce switching loss and eliminate shoot-through in SiC half bridge configuration

abstract: The trend towards SiC-based high power density converter requires to drive SiC MOSFET with high-speed switching. However, it tends to aggravate dv/dt effect due to the impact of parasitic parameters, resulting in shoot-through and high device stress in the half bridge configuration. In this work, a novel gate assisted circuit is proposed to eliminate shoot-through issue without compromise on switching speed. The mathematical model for the gate assisted circuit is developed. Then the switching test experiment is set up to validate its performance with commercial SiC half bridge module, which shows a high dv/dt up to 20 and 31 V/ns at turn-on and turn-off respectively and a 34% reduction of switching loss without any shoot-through issue.


title: Trials of cybernetic human HRP-4C toward humanoid business

abstract: We have developed a humanoid robot (a cybernetic human called “HRP-4C”) which has the appearance and shape of a human being, can walk and move like one, and interacts with humans using speech recognition. Standing 158 cm tall and weighing 43 kg (including the battery), with the joints and dimensions set to average values for young Japanese females, HRP-4C looks very human-like. In this paper, we present ongoing challenges to create a new bussiness in the contents industry with HRP-4C.


title: NOMA-Based D2D Communications: Towards 5G

abstract: In this paper, a novel non-orthogonal multiple access (NOMA)-based device-to-device (D2D) communications framework is proposed. A major novelty of the proposed framework is that it introduces the new concept of ``D2D group" which utilizes NOMA transmission, enabling one D2D transmitter to communicate with multiple D2D receivers simultaneously. Based on the considered framework, a resource allocation optimization problem is formulated, where multiple D2D groups are allowed to reuse the same subchannel. The objective of this work is to maximize the system sum rate by satisfying the signal-to-interference- plus-noise (SINR) constraints of both D2D and traditional cellular users. Note that the formulated problem is non-deterministic polynomial-time (NP) hard in nature, thus a novel resource allocation algorithm based on the many- to-one two-sided matching theory is proposed for obtaining a suboptimal solution. It is proved that the proposed algorithm converges to a stable state within limited number of iterations. Numerical results illustrate that: i) the proposed algorithm is an effective approach for obtaining near- optimal performance with acceptable complexity; and ii) the proposed NOMA-based D2D framework is capable of achieving promising gains over traditional orthogonal multiple access (OMA)-based D2D framework.


title: Feedback Control For Cassie With Deep Reinforcement Learning

abstract: Bipedal locomotion skills are challenging to develop. Control strategies often use local linearization of the dynamics in conjunction with reduced-order abstractions to yield tractable solutions. In these model-based control strategies, the controller is often not fully aware of many details, including torque limits, joint limits, and other non-linearities that are necessarily excluded from the control computations for simplicity. Deep reinforcement learning (DRL) offers a promising model-free approach for controlling bipedal locomotion which can more fully exploit the dynamics. However, current results in the machine learning literature are often based on ad-hoc simulation models that are not based on corresponding hardware. Thus it remains unclear how well DRL will succeed on realizable bipedal robots. In this paper, we demonstrate the effectiveness of DRL using a realistic model of Cassie, a bipedal robot. By formulating a feedback control problem as finding the optimal policy for a Markov Decision Process, we are able to learn robust walking controllers that imitate a reference motion with DRL. Controllers for different walking speeds are learned by imitating simple time-scaled versions of the original reference motion. Controller robustness is demonstrated through several challenging tests, including sensory delay, walking blindly on irregular terrain and unexpected pushes at the pelvis. We also show we can interpolate between individual policies and that robustness can be improved with an interpolated policy.


title: Ensemble of PANORAMA-based convolutional neural networks for 3D model classification and retrieval

abstract: Abstract A novel method for the classification and retrieval of 3D models is proposed; it exploits the 2D panoramic view representation of 3D models as input to an ensemble of convolutional neural networks which automatically compute the features. The first step of the proposed pipeline, pose normalization is performed using the SYMPAN method, which is also computed on the panoramic view representation. In the training phase, three panoramic views corresponding to the major axes, are used for the training of an ensemble of convolutional neural networks. the panoramic views consist of 3-channel images, containing the Spatial Distribution Map, the Normals’ Deviation Map and the magnitude of the Normals’ Devation Map Gradient Image. The proposed method aims at capturing feature continuity of 3D models, while simultaneously minimizing data preprocessing via the construction of an augmented image representation. It is extensively tested in terms of classification and retrieval accuracy on two standard large scale datasets: ModelNet and ShapeNet.


title: Rey-Osterrieth complex figure: normative values in an Italian population sample

abstract: The Rey-Osterrieth complex figure test (ROCF) is a neuropsychological test extensively used in clinical practice to investigate visuospatial constructional functions, visuographic memory and some aspects of planning and executive function. The aim of the present study was to collect normative values in an Italian normal population sample (n=280) for the direct copying and delayed (10 min) reproduction of the ROCF. Multiple regression analysis revealed significant effects of age and education on performance of both copying tasks, whereas sex appeared to affect only performance on the delayed copying task. Inferential cut-offs have been determined and equivalent scores computed. The availability of equivalent scores for the ROCF will prove useful in clinical assessment since it allows the comparison of a subject's performance on the ROCF with that on other neuropsychological tests for which normative values collected with similar methods are already available for the Italian population.


title: Traffic Sign Recognition Using Kernel Extreme Learning Machines With Deep Perceptual Features

abstract: Traffic sign recognition plays an important role in autonomous vehicles as well as advanced driver assistance systems. Although various methods have been developed, it is still difficult for the state-of-the-art algorithms to obtain high recognition precision with low computational costs. In this paper, based on the investigation on the influence that color spaces have on the representation learning of convolutional neural network, a novel traffic sign recognition approach called DP-KELM is proposed by using a kernel-based extreme learning machine (KELM) classifier with deep perceptual features. Unlike the previous approaches, the representation learning process in DP-KELM is implemented in the perceptual Lab color space. Based on the learned deep perceptual feature, a kernel-based ELM classifier is trained with high computational efficiency and generalization performance. Through the experiments on the German traffic sign recognition benchmark, the proposed method is demonstrated to have higher precision than most of the state-of-the-art approaches. In particular, when compared with the hinge loss stochastic gradient descent method which has the highest precision, the proposed method can achieve a comparable recognition rate with significantly fewer computational costs.


title: On the Impact of Various Types of Noise on Neural Machine Translation

abstract: We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than statistical models. For one especially egregious type of noise they learn to just copy the input sentence.


title: User-level Device Drivers: Achieved Performance

abstract: Running device drivers as unprivileged user-level code, encapsulated into their own process, has often been proposed as a technique for increasing system robustness. However, in the past, systems based on user-level drivers have generally exhibited poor I/O performance. Consequently, user-level device drivers have never caught on to any significant degree. In this paper we demonstrate that it is possible to build systems which employ user-level device drivers, without significant performance degradation, even for high-bandwidth devices such as Gigabit Ethernet.


A Heuristic Logical Effort Approach for Gate Sizing for CNFET-Based Circuits


title: The Function of Fiction is the Abstraction and Simulation of

abstract: Fiction literature has largely been ignored by psychology researchers because its only function seems to be entertainment, with no connection to empirical validity. We argue that literary narratives have a more important purpose. They offer models or simulations of the social world via abstraction, simplification, and compression. Narrative fiction also creates a deep and immersive sim- ulative experience of social interactions for readers. This simulation facilitates the communication and under- standing of social information and makes it more com-


title: Validation of an evaluation model for learning management systems

abstract: This study aims to validate a model for evaluating learning management systems (LMS) used in e-learning fields. A survey of 163 e-learning experts, regarding 81 validation items developed through literature review, was used to ascertain the importance of the criteria. A concise list of explanatory constructs, including two principle factors, was extracted from the responses of 133 respondents by exploratory factor analysis. Factor I was labeled as ‘instruction management, screen design, and technology’ and factor II, ‘interaction and evaluation’. Factors I and II consisted of four and three sub-factors, respectively. Confirmatory factor analysis, in turn, was carried out to assess the degree to which the plausibility of factor models was empirically confirmed. Overall fit measures (RMSEA, CFI, NNFI indexes) showed the model to be quite suitable for LMS evaluation. Reliability estimates using Cronbach's alpha indicated that the evaluation tool had good internal consistency. The overall results of the analyses suggest that this evaluation tool is highly instrumental for LMS.


title: Adaptability and Adaptivity in Learning Systems

abstract: A good learning system may need to provide a protected learning environment (by restrictions or by warnings) to facilitate efficient learning to the students. From the human-computer interaction point of view a careful examination is necessary of how to adapt the learning environment to the learner’s goal and capability in such protected situations. This paper discusses the applicability of adaptability and adaptivity features to fulfil this purpose. The paper also discusses the adaptation needs of learning systems, with particular attention on Intelligent Learning Systems (ILS) by their comparative study with office application systems, which have been an important area of research in the field of adaptation facilitation.


title: Proteasome Inhibitors Activate Autophagy Involving Inhibition of PI3K-Akt-mTOR Pathway as an Anti-Oxidation Defense in Human RPE Cells

abstract: The two major intracellular protein degradation systems, the ubiquitin-proteasome system (UPS) and autophagy, work collaboratively in many biological processes including development, apoptosis, aging, and countering oxidative injuries. We report here that, in human retinal pigment epithelial cells (RPE), ARPE-19 cells, proteasome inhibitors, clasto-lactacystinβ-lactone (LA) or epoxomicin (Epo), at non-lethal doses, increased the protein levels of autophagy-specific genes Atg5 and Atg7 and enhanced the conversion of microtubule-associated protein light chain (LC3) from LC3-I to its lipidative form, LC3-II, which was enhanced by co-addition of the saturated concentration of Bafilomycin A1 (Baf). Detection of co-localization for LC3 staining and labeled-lysosome further confirmed autophagic flux induced by LA or Epo. LA or Epo reduced the phosphorylation of the protein kinase B (Akt), a downstream target of phosphatidylinositol-3-kinases (PI3K), and mammalian target of rapamycin (mTOR) in ARPE-19 cells; by contrast, the induced changes of autophagy substrate, p62, showed biphasic pattern. The autophagy inhibitor, Baf, attenuated the reduction in oxidative injury conferred by treatment with low doses of LA and Epo in ARPE-19 cells exposed to menadione (VK3) or 4-hydroxynonenal (4-HNE). Knockdown of Atg7 with siRNA in ARPE-19 cells reduced the protective effects of LA or Epo against VK3. Overall, our results suggest that treatment with low levels of proteasome inhibitors confers resistance to oxidative injury by a pathway involving inhibition of the PI3K-Akt-mTOR pathway and activation of autophagy.


title: A 0.3–15 GHz SiGe LNA With >1 THz Gain-Bandwidth Product

abstract: A wideband LNA with a frequency range of 0.3–15 GHz is introduced for space-based applications. The LNA achieves less than 2.2 dB noise figure (NF) at room temperature, with minimum measured NF of 1.8 dB and peak gain of 37.3 dB. To the authors’ best knowledge, this is the lowest NF at this bandwidth, with the highest gain-bandwidth product (1.1 THz) reported to date. Moreover, the performance of the LNA was studied across temperature, down to 78 K. The NF improves considerably and achieves a record average value of 1 dB over the whole bandwidth at 78 K, with a minimum NF of 0.74 dB at 1 GHz. Additionally, the bandwidth and gain were enhanced at 78 K without degrading input or output matching. To compensate for small gain variations across temperature, a gain control technique is proposed, which yields more than 15 dB of gain control without breaking the feedback loop and without sacrificing power matching.


title: Automatic Labelling of Topics with Neural Embeddings

abstract: Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a succinct phrase that summarises its theme or idea. Using Wikipedia document titles as label candidates, we compute neural embeddings for documents and words to select the most relevant labels for topics. Compared to a state-of-the-art topic labelling system, our methodology is simpler, more efficient, and finds better topic labels.


Dimensions and Relations of the Dentogingival Junction in Humans


title: Axial-flux PM synchronous machines with air-gap profiling and very high ratio of spoke rotor poles to stator concentrated coils

abstract: Permanent magnet machines including a magnetic gearing effect are attractive for low-speed high-torque applications. This paper proposes a dual-stator axial-flux machine topology with a high ratio for the number of rotor poles to concentrated stator coils. The stator consists of a relatively small number of teeth, with each tooth including multiple smaller/auxiliary teeth. The rotor employs spoke-type permanent magnets, which result in high flux concentration, thereby further improving the torque density. The paper discusses the principle of operation of the proposed machine topology. Finite element results on example designs with 6 stator teeth, each having 2 and 3 auxiliary teeth, and with 20, 32, and 34 rotor poles are presented, respectively. Other feasible slot-pole combinations are also identified.


title: Circuitry and Dynamics of Human Transcription Factor Regulatory Networks

abstract: SUMMARY The combinatorial cross-regulation of hundreds of sequence-specific transcription factors (TFs) defines a regulatory network that underlies cellular identity and function. Here we use genome-wide maps of in vivo DNaseI footprints to assemble an extensive core human regulatory network comprising connections among 475 sequence-specific TFs and to analyze the dynamics of these connections across 41 diverse cell and tissue types. We find that human TF networks are highly cell selective and are driven by cohorts of factors that include regulators with previously unrecognized roles in control of cellular identity. Moreover, we identify many widely expressed factors that impact transcriptional regulatory networks in a cell-selective manner. Strikingly, in spite of their inherent diversity, all cell-type regulatory networks independently converge on a common architecture that closely resembles the topology of living neuronal networks. Together, our results provide an extensive description of the circuitry, dynamics, and organizing principles of the human TF regulatory network.


title: Combinatorial Attacks on Binarized Neural Networks

abstract: Binarized Neural Networks (BNNs) have recently attracted significant interest due to their computational efficiency. Concurrently, it has been shown that neural networks may be overly sensitive to"attacks"- tiny adversarial changes in the input - which may be detrimental to their use in safety-critical domains. Designing attack algorithms that effectively fool trained models is a key step towards learning robust neural networks. The discrete, non-differentiable nature of BNNs, which distinguishes them from their full-precision counterparts, poses a challenge to gradient-based attacks. In this work, we study the problem of attacking a BNN through the lens of combinatorial and integer optimization. We propose a Mixed Integer Linear Programming (MILP) formulation of the problem. While exact and flexible, the MILP quickly becomes intractable as the network and perturbation space grow. To address this issue, we propose IProp, a decomposition-based algorithm that solves a sequence of much smaller MILP problems. Experimentally, we evaluate both proposed methods against the standard gradient-based attack (FGSM) on MNIST and Fashion-MNIST, and show that IProp performs favorably compared to FGSM, while scaling beyond the limits of the MILP.


title: CAML: Fast Context Adaptation via Meta-Learning

abstract: We propose CAML, a meta-learning method for fast adaptation that 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, the context parameters are updated with one or several gradient steps on a task-specific loss that is backpropagated through the shared part of the network. Compared to approaches that adjust all parameters on a new task (e.g., MAML), our method can be scaled up to larger networks without overfitting on a single task, is easier to implement, and saves memory writes during training and network communication at test time for distributed machine learning systems. We show empirically that this approach outperforms MAML, is less sensitive to the task-specific learning rate, can capture meaningful task embeddings with the context parameters, and outperforms alternative partitionings of the parameter vectors.


title: Classifying breast cancer by using decision tree algorithms

abstract: Nowadays, datasets are growing daily to obtain knowledge from big dataset. Extraction operation of useful information from the dataset is called data mining that is one of the major techniques to get the diagnostic results especially in medical care fields as breast cancer. Breast cancer is one of the widespread cancers among women if matched with all other tumors all over the world. Classification is widely used in most important and necessary tasks in the real life applications in all fields. This technique has ability of detecting very similarities/differences that a human analyst may be not notice and therefore create and introduction more accurate/useful categories. This study presents comparison and analyses breast cancer dataset by using classification decision tree algorithms. Decision tree algorithms are applied to these algorithms which are J48, Function Tree, Random Forest Tree, AD Alternating Decision Tree, Decision stump and Best First. A computationally efficient classifies of these decision tree algorithms by employing Waikato Environment for Knowledge Analysis (WEKA) that is development program which includes a set of machine learning algorithms. These masses included 569 with 357 benign and 212 malignant cases with 32 attributes to test and proof the differences among the classification methods or algorithms. These results found by manner which involves reserving a particular sample of a medical dataset on which do not train the model. The decision tree classification forms forecast breast tumor with lower error average and higher precision of correctly classified cases 97.7%. The predicted accuracy correctly classified instances for decision stump algorithm 88.0% model is the lowest of all.


title: Parser for Abstract Meaning Representation using Learning to Search

abstract: We develop a novel technique to parse English sentences into Abstract Meaning Representation (AMR) using SEARN, a Learning to Search approach, by modeling the concept and the relation learning in a unified framework. We evaluate our parser on multiple datasets from varied domains and show an absolute improvement of 2% to 6% over the state-of-the-art. Additionally we show that using the most frequent concept gives us a baseline that is stronger than the state-of-the-art for concept prediction. We plan to release our parser for public use.


title: The small world of the American corporate elite, 1982–2001

abstract: This paper examines the degree of stability in the structure of the corporate elite network in the US during the 1980s and 1990s. Several studies have documented that board-to-board ties serve as a mechanism for the diffusion of corporate practices, strategies, and structures; thus, the overall structure of the network can shape the nature and rate of aggregate corporate change. But upheavals in the nature of corporate governance and nearly complete turnover in the firms and directors at the core of the network since 1980 prompt a reassessment of the network's topography. We find that the aggregate connec tivity of the network is remarkably stable and appears to be an intrinsic property of the interlock network, resilient to major changes in corporate governance. After a brief review of elite studies in the US, we take advantage of the recent advances in the theoretical and methodological tools for analyzing network structures to examine the network properties of the directors and companies in 1982, 1990,...


title: The Multidimensional Knapsack Problem: Structure and Algorithms

abstract: We study the multidimensional knapsack problem, present some theoretical and empirical results about its structure, and evaluate different integer linear programming (ILP)-based, metaheuristic, and collaborative approaches for it. We start by considering the distances between optimal solutions to the LP relaxation and the original problem and then introduce a new core concept for the multidimensional knapsack problem (MKP), which we study extensively. The empirical analysis is then used to develop new concepts for solving the MKP using ILP-based and memetic algorithms. Different collaborative combinations of the presented methods are discussed and evaluated. Further computational experiments with longer run times are also performed to compare the solutions of our approaches to the best-known solutions of another so-far leading approach for common MKP benchmark instances. The extensive computational experiments show the effectiveness of the proposed methods, which yield highly competitive results in significantly shorter run times than do previously described approaches.


title: Extraction and Adaptation of Fuzzy Rules for Friction Modeling and Control Compensation

abstract: Modeling of friction forces has been a challenging task in mechanical engineering. Parameterized approaches for modeling friction find it difficult to achieve satisfactory performance due to the presence of nonlinearity and uncertainties in dynamical systems. This paper aims to develop adaptive fuzzy friction models by the use of data-mining techniques and system theory. Our main technical contributions are twofold: extraction of fuzzy rules and formulation of a static fuzzy friction model and adaptation of the fuzzy friction model by the use of the Lyapunov stability theory, which is associated with a control compensation of a typical motion dynamics. The proposed framework in this paper shows a successful application of adaptive data-mining techniques in engineering. A single-degree-of-freedom mechanical system is employed as an experimental model in simulation studies. Results demonstrate that our proposed fuzzy friction model has promise in the design of uncertain mechanical control systems.


title: Student Models for Prior Knowledge Estimation

abstract: Intelligent behavior of adaptive educational systems is based ::: on student models. Most research in student modeling focuses on ::: student learning (acquisition of skills). We ocus on prior ::: knowledge, which gets much less attention in modeling and yet ::: can be highly varied and have important consequences for the ::: use of educational systems. We describe several models for ::: prior knowledge estimation – the Elo rating system, its ::: Bayesian extension, a hierarchical model, and a networked model ::: (multivariate Elo). We evaluate their performance on data from ::: application for learning geography, which is a typical case ::: with highly varied prior knowledge. The result show that the ::: basic Elo rating system provides good prediction accuracy. More ::: complex models do improve predictions, but only slightly and ::: their main purpose is in additional information about students ::: and a domain.


title: Algorithm for energy efficient inter-UAV collision avoidance

abstract: Unmanned Aerial Vehicles (UAV) are fast gaining popularity. Due to their many desired features, in the near future, UAVs will be an inevitable part of many fields. This increased use of UAVs, has given rise to the idea of multi-UAV systems, commonly known as UAV swarms, where a number of UAVs ranging from two to hundreds have to collaborate with each other and perform a common shared task or tasks. When sharing a common air space and flying in close proximity of each other, inter-UAV collision avoidance becomes an important factor in UAV swarms. Thus, for effective use of UAV swarms, it is essential to have an efficient inter-UAV collision avoidance mechanism. Although there are a number of suggested techniques, avoiding inter-UAV collisions while minimising the energy consumption of the UAV system is a challenge. In this paper, a Potential Field Method based algorithm to avoid collisions of a UAV system, considering the minimising of system energy usage, is suggested. We consider the energy consumption of a UAV system to depend on the distance travelled and the turns — angular changes, in trajectories. Our method reduces the angular changes in trajectories by an average of 36% and total travel distance by 6–8%. The total time taken to achieve targets is reduced by 3–14% depending on the scenario.


title: ILP-based joint routing and scheduling for time-triggered networks

abstract: Networks in the automotive and aerospace area as well as in production facilities have to support time-critical (i.e., hard real-time) communication. For such applications, time-triggered Ethernet-based networking solutions provide the required timeliness, i.e., reliable packet delivery with deterministic latencies and low jitter. However, the routing and scheduling of the time-triggered traffic is an NP-hard problem. Hence, existing solutions to this problem make certain abstractions to reduce complexity if necessary. Nonetheless, such abstractions exclude feasible routing and scheduling options from the design space. Specifically, it is a typical approach to model routing and scheduling as separate problems, which are solved successively or with heuristic coupling. Therefore, we present a novel ILP formulation that can be used to jointly solve the routing and scheduling problem for time-triggered Ethernet networks. Using this formulation, it is possible to solve various scheduling problems that are infeasible when using a fixed shortest path routing with separate scheduling. Compared to a fixed load balanced routing with separate scheduling, schedules computed with our formulation offer lower communication latencies.


FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE


title: The Pragmatics of Word Meaning

abstract: In this paper, we explore the interaction between lexical semantics and pragmatics. Linguistic processing is nformationally encapsulated and utilises relatively simple 'taxonomic' lexical semantic knowledge. On this basis, defeasible lexical generalisations deliver defeasible parts of logical form. In contrast, pragmatics is open-ended and involves arbitrary knowledge. Two axioms specify when pragmatic defaults override lexical ones. We demonstrate that modelling this interaction allows us to achieve a more refined interpretation of words in a discourse context than either the lexicon or pragmatics could do on their own.


title: Video anomaly detection based on local statistical aggregates

abstract: Anomalies in many video surveillance applications have local spatio-temporal signatures, namely, they occur over a small time window or a small spatial region. The distinguishing feature of these scenarios is that outside this spatio-temporal anomalous region, activities appear normal. We develop a probabilistic framework to account for such local spatio-temporal anomalies. We show that our framework admits elegant characterization of optimal decision rules. A key insight of the paper is that if anomalies are local optimal decision rules are local even when the nominal behavior exhibits global spatial and temporal statistical dependencies. This insight helps collapse the large ambient data dimension for detecting local anomalies. Consequently, consistent data-driven local empirical rules with provable performance can be derived with limited training data. Our empirical rules are based on scores functions derived from local nearest neighbor distances. These rules aggregate statistics across spatio-temporal locations & scales, and produce a single composite score for video segments. We demonstrate the efficacy of our scheme on several video surveillance datasets and compare with existing work.


title: Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps

abstract: We have recently demonstrated using functional magnetic resonance imaging the presence of synchronous low-frequency fluctuations of signal intensities from the resting human brain that have a high degree of temporal correlation (p < 0.0001) both within and across the sensorimotor cortex. A statistically significant overlap between the resting-state functional connectivity map and the task-activation map due to bilateral finger tapping was obtained. Similar results have been obtained in the auditory and visual cortex. Because the pulse sequence used for collecting data was sensitive to blood flow and blood oxygenation, these low-frequency fluctuations of signal intensity may have arisen from variations of both. The objective of this study was simultaneously to determine the contribution of the blood oxygenation level signal and the flow signal to physiological fluctuations in the resting brain using the flow-sensitive alternating inversion recovery pulse sequence. In all subjects, the functional connectivity maps obtained from BOLD had a greater coincidence with task-activation maps than the corresponding functional connectivity maps obtained from blood-flow signals at the same level of statistical significance. Results of this study suggest that while variations in blood flow might contribute to functional connectivity maps, BOLD signals play a dominant role in the mechanism that gives rise to functional connectivity in the resting human brain.


Determinants of stunting and severe stunting among under-fives in Tanzania: evidence from the 2010 cross-sectional household survey


title: Activation of Pseudomonas ochraceae 4-hydroxy-4-methyl-2-oxoglutarate aldolase by inorganic phosphate.

abstract: Pseudomonas ochraceae 4-hydroxy-4-methyl-2-oxoglutarate aldolase [4-hydroxy-4-methyl-2-oxoglutarate pyruvate-lyase: EC 4.1.3.17], one of the metal ion-requiring aldolases, is markedly activated by Pi. The activation is reversible and can be observed in every step of enzyme purification. The extent of activation is almost independent of the metal ion used, but varies with each substrate. The cleavage of l-4-carboxy-4-hydroxy-2-oxoadipate, a physiological substrate of the enzyme, is most strongly activated: Pi gives a hyperbolic activation curve with an activation constant of 0.36 mM and a maximum activation of about 65-fold. Arsenate, phosphorous acid, bicarbonate, acetyl phosphate, thiamine diphosphate, ADP, PPi, and ATP are also effective to various extents. These anions appear to be effective in the free form but not in the metal ion-complex. Many organic and inorganic anions are ineffective. Pi causes parallel increases in Vmax and in Km for substrate or metal ion with a concomitant shift of the optimum pH toward the alkaline side, and the enhancement of activity is closely correlated with the shift of optimum pH. Pi induces no gross change of molecular form of the enzyme protein as evaluated from gel filtration, PAGE, UV, fluorescence, and CD spectral data. Based on these findings, the mechanism and the physiological meaning of the observed activation are discussed.


title: Parameterisation of a stochastic model for human face identification

abstract: Recent work on face identification using continuous density Hidden Markov Models (HMMs) has shown that stochastic modelling can be used successfully to encode feature information. When frontal images of faces are sampled using top-bottom scanning, there is a natural order in which the features appear and this can be conveniently modelled using a top-bottom HMM. However, a top-bottom HMM is characterised by different parameters, the choice of which has so far been based on subjective intuition. This paper presents a set of experimental results in which various HMM parameterisations are analysed. >


title: Studying the dialogue between users and developers of free apps in the google play store

abstract: The popularity of mobile apps continues to grow over the past few years. Mobile app stores, such as the Google Play Store and Apple's App Store provide a unique user feedback mechanism to app developers through app reviews. In the Google Play Store (and most recently in the Apple App Store), developers are able to respond to such user feedback. Over the past years, mobile app reviews have been studied excessively by researchers. However, much of prior work (including our own prior work) incorrectly assumes that reviews are static in nature and that users never update their reviews. In a recent study, we started analyzing the dynamic nature of the review-response mechanism. Our previous study showed that responding to a review often has a positive effect on the rating that is given by the user to an app. In this paper [1], we revisit our prior finding in more depth by studying 4.5 million reviews with 126,686 responses of 2,328 top free-to-download apps in the Google Play Store. One of the major findings of our paper is that the assumption that reviews are static is incorrect. In particular, we find that developers and users in some cases use this response mechanism as a rudimentary user support tool, where dialogues emerge between users and developers through updated reviews and responses. Even though the messages are often simple, we find instances of as many as ten user-developer back-and-forth messages that occur via the response mechanism. Using a mixed-effect model, we identify that the likelihood of a developer responding to a review increases as the review rating gets lower or as the review content gets longer. In addition, we identify four patterns of developers: 1) developers who primarily respond to only negative reviews, 2) developers who primarily respond to negative reviews or to reviews based on their content, 3) developers who primarily respond to reviews which are posted shortly after the latest release of their app, and 4) developers who primarily respond to reviews which are posted long after the latest release of their app. We perform a qualitative analysis of developer responses to understand what drives developers to respond to a review. We manually analyzed a statistically representative random sample of 347 reviews with responses of the top ten apps with the highest number of developer responses. We identify seven drivers that make a developer respond to a review, of which the most important ones are to thank the users for using the app and to ask the user for more details about the reported issue. Our findings show that it can be worthwhile for app owners to respond to reviews, as responding may lead to an increase in the given rating. In addition, our findings show that studying the dialogue between users and developers provides valuable insights that can lead to improvements in the app store and the user support process. The main contributions of this paper are as follows: (1) Our paper is the first work to demonstrate the dynamic nature of reviews. (2) Furthermore, we are the first to demonstrate a peculiar use of the app-review platforms as a user support medium. (3) In addition, our work is the first work to deeply explore developer responses in a systematic manner. (4) Finally, our classification of developer-responses highlights the value of providing canned or even automated responses in next generation app-review platforms.


Solving geometric problems with the rotating calipers


title: Electrical and thermal processes of HEV induction machines taking into account stator winding form

abstract: Internal combustion engine start processes in Hybrid Electric Vehicles (HEVs) are accompanied by large currents in the windings of electrical machines. It can lead to insulation overheating and fault operation mode. Induction squirrel-cage machine is one of the best AC machines for HEVs. The high efficiency of HEV induction machine with compact stator winding form is proposed. Its stator electrical and thermal processes are considered. Irregular cross-section of stator winding leads to irregular current density in particular winding parts. To decrease temperature gradients within induction machine and improve heat transfer, air gap between overhang parts and stator should be increased. Numerical and computer models for researching are based on finite element method.


title: Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images

abstract: Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elements-oriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an $L0$ -norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR land-use data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper.


title: Tag-aware recommender systems by fusion of collaborative filtering algorithms

abstract: Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are "global" descriptions of items, tags are "local" descriptions of items given by the users. To the best of our knowledge, there hasn't been any prior study on tag-aware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three two-dimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.


title: Automatic Loop Summarization via Path Dependency Analysis

abstract: Analyzing loops is very important for various software engineering tasks such as bug detection, test case generation and program optimization. However, loops are very challenging structures for program analysis, especially when (nested) loops contain multiple paths that have complex interleaving relationships. In this paper, we propose the path dependency automaton (PDA) to capture the dependencies among the multiple paths in a loop. Based on the PDA, we first propose a loop classification to understand the complexity of loop summarization. Then, we propose a loop analysis framework, named Proteus, which takes a loop program and a set of variables of interest as inputs and summarizes path-sensitive loop effects (i.e., disjunctive loop summary) on the variables of interest. An algorithm is proposed to traverse the PDA to summarize the effect for all possible executions in the loop. We have evaluated Proteus using loops from five open-source projects and two well-known benchmarks and applying the disjunctive loop summary to three applications: loop bound analysis, program verification and test case generation. The evaluation results have demonstrated that Proteus can compute a more precise bound than the existing loop bound analysis techniques; Proteus can significantly outperform the state-of-the-art tools for loop program verification; and Proteus can help generate test cases for deep loops within one second, while symbolic execution tools KLEE and Pex either need much more time or fail.


title: Critical factors for successful implementation of enterprise systems

abstract: Enterprise resource planning (ERP) systems have emerged as the core of successful information management and the enterprise backbone of organizations. The difficulties of ERP implementations have been widely cited in the literature but research on the critical factors for initial and ongoing ERP implementation success is rare and fragmented. Through a comprehensive review of the literature, 11 factors were found to be critical to ERP implementation success – ERP teamwork and composition; change management program and culture; top management support; business plan and vision; business process reengineering with minimum customization; project management; monitoring and evaluation of performance; effective communication; software development, testing and troubleshooting; project champion; appropriate business and IT legacy systems. The classification of these factors into the respective phases (chartering, project, shakedown, onward and upward) in Markus and Tanis’ ERP life cycle model is presented and the importance of each factor is discussed.


title: Networked Grounded Theory

abstract: The present paper demonstrates a research method combining Grounded Theory and Network Analysis for the inductive generation of a theory solely based on the empirical data being analyzed. This method was developed during my PhD research on the utilization of a virtual community (case study in Wikipedia) in formal Education institutions (tertiary or secondary education). The focus of this paper is on demonstrating the research method developed during this study. Networked Grounded Theory constitutes a remodeling of Grounded Theory and the rationale for the inclusion of Network Analysis techniques into the process of theory generation is explained. The software used and the steps followed are presented in detail in order to help other researchers to utilize the method or adapt it to meet their own research pursuits.


title: TESLA: a formally defined event specification language

abstract: The need for timely processing large amounts of information, flowing from the peripheral to the center of a system, is common to different application domains, and it has justified the development of several languages to describe how such information has to be processed. In this paper, we analyze such languages showing how most approaches lack the expressiveness required for the applications we target, or do not provide the precise semantics required to clearly state how the system should behave. Moving from these premises, we present TESLA, a complex event specification language. Each TESLA rule considers incoming data items as notifications of events and defines how certain patterns of events cause the occurrence of others, said to be "complex". TESLA has a simple syntax and a formal semantics, given in terms of a first order, metric temporal logic. It provides high expressiveness and flexibility in a rigorous framework, by offering content and temporal filters, negations, timers, aggregates, and fully customizable policies for event selection and consumption. The paper ends by showing how TESLA rules can be interpreted by a processing system, introducing an efficient event detection algorithm based on automata.


APT: Arabic Part-of-speech Tagger


Azuma A Survey of Augmented Reality


title: Intelligent search of correlated alarms from database containing noise data

abstract: Alarm correlation plays an important role in improving the service and reliability in modern telecommunication networks. Most previous research of alarm correlation did not consider the effects of noise data in the database. This paper focuses on the method of discovering alarm correlation rules from a database containing noise data. We firstly define two parameters, win-freq and win-add, as the measures of noise data and then present a robust search algorithm to solve the problem. At different size of win-freq and win-add, the experiments on the alarm database containing noise data show that the robust search algorithm can discover more rules with the bigger size of win-add. We also compare, by experiments, two different interestingness measures of confidence and correlation.


title: STRT-seq-2i: dual-index 5ʹ single cell and nucleus RNA-seq on an addressable microwell array

abstract: Single-cell RNA-seq has become routine for discovering cell types and revealing cellular diversity, but archived human brain samples still pose a challenge to current high-throughput platforms. We present STRT-seq-2i, an addressable 9600-microwell array platform, combining sampling by limiting dilution or FACS, with imaging and high throughput at competitive cost. We applied the platform to fresh single mouse cortical cells and to frozen post-mortem human cortical nuclei, matching the performance of a previous lower-throughput platform while retaining a high degree of flexibility, potentially also for other high-throughput applications.


title: Mutation testing implements grammar-based testing

abstract: This paper presents an abstract view of mutation analysis. Mutation was originally thought of as making changes to program source, but similar kinds of changes have been applied to other artifacts, including program specifications, XML, and input languages. This paper argues that mutation analysis is actually a way to modify any software artifact based on its syntactic description, and is in the same family of test generation methods that create inputs from syntactic descriptions. The essential characteristic of mutation is that a syntactic description such as a grammar is used to create tests. We call this abstract view grammar-based testing, and view it as an interface, which mutation analysis implements. This shift in view allows mutation to be defined in a general way, yielding three benefits. First, it provides a simpler way to understand mutation. Second, it makes it easier to develop future applications of mutation analysis, such as finite state machines and use case collaboration diagrams. The third benefit, which due to space limitations is not explored in this paper, is ensuring that existing techniques are complete according to the criteria defined here.


title: AutoODC: Automated generation of orthogonal defect classifications

abstract: Orthogonal defect classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human-intensive and requires experts’ knowledge of both ODC and system domains. This paper presents AutoODC, an approach for automating ODC classification by casting it as a supervised text classification problem. Rather than merely applying the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts’ ODC experience and domain knowledge into the learning process via proposing a novel relevance annotation framework. We have trained AutoODC using two state-of-the-art machine learning algorithms for text classification, Naive Bayes (NB) and support vector machine (SVM), and evaluated it on both an industrial defect report from the social network domain and a larger defect list extracted from a publicly accessible defect tracker of the open source system FileZilla. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves overall accuracies of 82.9 % (NB) and 80.7 % (SVM) on the industrial defect report, and accuracies of 77.5 % (NB) and 75.2 % (SVM) on the larger, more diversified open source defect list.


Detection and defense of DDoS attack–based on deep learning in OpenFlow‐based SDN


Fabrication of Tissue Engineering Scaffolds Using Rapid Prototyping Techniques


title: ADAPTIVE SYNCHRONIZATION FOR A CLASS OF HIGH-DIMENSIONAL AUTONOMOUS UNCERTAIN CHAOTIC SYSTEMS

abstract: This paper addresses the adaptive synchronization and parameters identification problem of a class of high-dimensional autonomous uncertain chaotic systems. It is proved that the controller and update rule can make the states of the drive system and the response system with unknown system parameters asymptotically synchronized, and identify the response system's unknown parameters. Chen system, coupled dynamos system and Rossler hyperchaotic system are used as examples for detailed description. The results of numerical simulations show the effectiveness of the adaptive controller.


title: A power-efficient multi-channel PPG ASIC with 112dB receiver DR for pulse oximetry and NIRS

abstract: We report a photo-plethysmography ASIC realised in a 130nm CMOS technology, which supports multi-channel probe configurations with up to 32 LEDs and 4 photodiodes. The receiver can cover a wide range of (full-scale) input signal strengths between 13nA and 63μA. In a typical pulse oximetry application (0.1–20Hz), overall loop dynamic range is 96dB, with noise contributions by the driver, LED, photodiode and receiver all taken into account. Total power consumption is 5.9mW, including two LEDs pulsed at 600Hz and 50mA. Compared to the state-of-the-art a saving of 68% has been achieved for equal signal quality.


title: Enhanced recognition memory following vagus nerve stimulation in human subjects

abstract: Neuromodulators associated with arousal modulate learning and memory, but most of these substances do not freely enter the brain from the periphery. In rodents, these neuromodulators act in part by initiating neural messages that travel via the vagus nerve to the brain, and electrical stimulation of the vagus enhances memory. We now extend that finding to human verbal learning. We examined word-recognition memory in patients enrolled in a clinical study evaluating the capacity of vagus nerve stimulation to control epilepsy. Stimulation administered after learning significantly enhanced retention. These findings confirm in humans the hypothesis that vagus nerve activation modulates memory formation similarly to arousal.


title: Quantifying the experience of immersion in games.

abstract: Games are undoubtedly the most successful computer application yet it is not easy to attribute their success to any one particular feature. Nonetheless, when talking about games, gamers and reviewers frequently refer to the immersive experience of the game as an important aspect to be attained. However, it is not clear what immersion is and even if it is a comparable experience be-tween diﬀerent players and diﬀerent games. This paper aims to develop more quantiﬁable and therefore objective measures of immersion. We describe a study into switching from an immersive gaming experience to an-other task, not in the game world. Though the degree of immersion does seem to have an impact on the ability to perform the task, the experimental approach is complex and possibly quite fragile. We therefore set out hypotheses for a similar experiment with the aim of exploring whether eye tracking and body motion provide better indicators of the degree of immersion.


title: Helpful or Unhelpful: A Linear Approach for Ranking Product Reviews

abstract: ABSTRACT Most E-commerce web sites and online communities provide interfaces and platforms for consumers to express their opinions about a specific product by writing personal reviews. The fast development of E-commerce has caused such a huge amount of online product reviews to become available to consumers that it is impossible for potential consumers to read through all the reviews and to make a quick purchasing decision. Review readers are asked to vote if a review is "Helpful" or "Unhelpful" and the most positively voted reviews are placed on the top of product review list. However, the accumulation of votes takes time for a review to be fully voted and newly published reviews are always listed at the bottom of the review list. This paper proposes a linear model to predict the helpfulness of online product reviews. Reviews can be quickly ranked and classified by our model and reviews that may help consumers better than others will be retrieved. We compare our model with several machine learning classification algorithms and our experimental results show that our approach effectively classifies online reviews. Also, we provide an evaluation measurement to judge the performance of the helpfulness modeling algorithm and the results show that the helpfulness scores predicted by our approach consistently follow the changing trend of the true helpfulness values. Keywords: recommender system, online product review, helpfulness, evaluation (ProQuest: ... denotes formulae omitted.) 1. Introduction Due to the fast development of Internet and E-commerce, more and more online reviews aggregation web sites, such as Epinions.com etc., have provided consumers with platforms to exchange their opinions about products, services, and merchants. "Online product reviews provided by consumers who previously purchased products have become a major information source for consumers and marketers regarding product quality" [Hu & Zhang 2008]. Park et al. [2007] confirmed that the quality of reviews has a positive effect on product sales and consumers purchase intentions increase with the quantity of product reviews. On the E-commerce web sites, such as Amazon.com and Ebay.com, consumers are asked to write reviews and rate products or services by a number of stars after they finished a transaction. Most of existing recommendation approaches [Goldberg et al. 1992; Resnick et al. 1994; Sarwar et al. 2001] are merely based on the rating of products. With a star rating scale, users can not get `real semantics' of review statements. Since product reviews represent reviewers' feelings, experiences and opinions on a specific product, they are more useful than product ratings and therefore can better help potential consumers make purchase decisions. Search engines are good tools to assist in looking for information; however, there are too many search results returned from a search engine and not all of them are reviews. For instance, if we input `Cyber-shot Digital Camera Review' in Google, 278,000 web pages will be returned. This is absolutely a too huge result set for consumers to go through. Moreover, in an online community, such as Epinion.com or Amazon.com, more than 1000 reviews for a specific product are submitted by different consumers. Therefore, it is important to rank and classify product reviews so that they can be accessed easily and used effectively by consumers. Review aggregation web sites provide a function for consumers to vote whether a review is "Helpful" or "Unhelpful". But this progress takes time far before a really helpful review is discovered and the most recent published reviews will always be the least voted ones. Our goal is to develop a model that filters out reviews which are most likely helpful to consumers and that provide more valuable information for consumer's decision making. Such a model can save a great deal of consumers' effort in surfing for reliable and helpful reviews. …


title: Low-Rank Modeling of Local $k$-Space Neighborhoods (LORAKS) for Constrained MRI

abstract: Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-rank matrices when the image has limited spatial support or slowly varying phase. Based on this, we develop a novel and flexible framework for constrained image reconstruction that uses low-rank matrix modeling of local k-space neighborhoods (LORAKS). A new regularization penalty and corresponding algorithm for promoting low-rank are also introduced. The potential of LORAKS is demonstrated with simulated and experimental data for a range of denoising and sparse-sampling applications. LORAKS is also compared against state-of-the-art methods like homodyne reconstruction, l1-norm minimization, and total variation minimization, and is demonstrated to have distinct features and advantages. In addition, while calibration-based support and phase constraints are commonly used in existing methods, the LORAKS framework enables calibrationless use of these constraints.


Brain MRI Findings of Nitrogen Gas Inhalation for Suicide Attempt: a Case Report


title: Stages for Developing Control Systems using EMG and EEG Signals: A survey

abstract: Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG ::: (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of ::: disabled and elderly people. This technical report aims to review the current deployment of these state of the art ::: control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based ::: control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and ::: controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized. ::: 1


title: Data-Driven Methods for Solving Algebra Word Problems

abstract: We explore contemporary, data-driven techniques for solving math word problems over recent large-scale datasets. We show that well-tuned neural equation classifiers can outperform more sophisticated models such as sequence to sequence and self-attention across these datasets. Our error analysis indicates that, while fully data driven models show some promise, semantic and world knowledge is necessary for further advances.


title: Regulatory focus in the life story: Prevention and promotion as expressed in three layers of personality

abstract: Regulatory Focus Theory (Higgins, 1997) outlines the ways in which people attempt to achieve the presence of positive outcomes (promotion) or preserve the absence of negative outcomes (prevention), suggesting that individuals may differ in chronic orientations. The present work examines regulatory outlooks within a multilayered model of personality composed of dispositional traits, characteristic goals, and life stories (narrative identity). Foregrounding the concept of narrative identity, the current study investigates how narrative themes of prevention and promotion relate to regulatory focus as expressed in dispositional traits and characteristic goals and explores the relative contribution of each layer of personality to psychological well-being and physical health. The findings suggest that dispositional traits, personal goals, and life narratives cohere loosely around the central themes of prevention and promotion. Moreover, promotion focus across layers of personality was related to higher levels of self-reported quality of life, compared to prevention focus. Illustrating the incremental validity of different layers of personality, promotion focus in life stories independently predicted psychological health above and beyond promotion focus in dispositional traits.


title: Internet retail store design: How the user interface influences traffic and sales

abstract: Given the resources needed to launch a retail store on the Internet or change an existing online storefront design, it is important to allocate product development resources to interface features that actually improve store traffic and sales. We identified features that impact store traffic and sales using regression models of 1996 store traffic and dollar sales as dependent variables and interface design features such as number of links into the store, hours of promotional ads, number of products, and store navigation features as the independent variables. Product list navigation features that reduce the time to purchase products online account for 61% of the variance in monthly sales. Other factors explaining the variance in monthly sales include: number of hyperlinks into the store (10%), hours of promotion (4%) and customer service feedback (1%). These findings demonstrate that the user interface is an essential link between the customer and the retail store in Web-based shopping environments.


title: Transmit antenna selection for massive MIMO: A knapsack problem formulation

abstract: Massive multiple input multiple output communication is now possible using millimeter wave frequency band. In this paper a transmit antenna selection algorithm is developed which satisfies a quality of service (QoS) for a given user. In order to achieve a particular level of QoS, the number of transmit antennas required is determined by remodeling it as a Knapsack Problem (KP). The smallest subset of antenna elements is found at the transmitter side to achieve the desired level of QoS using KP. Furthermore, we have compared our algorithm with the sorted and unsorted sequential selection algorithm (SSA). Our algorithm achieves similar performance as compared to SSA but with lower computational complexity than the sorted SSA. Moreover, the energy efficiency of our algorithm is similar to that of the sorted SSA but superior to unsorted SSA, as it is not sensitive to the arrangement of the antenna gains.


title: Recurrent Neural Networks for Semantic Instance Segmentation

abstract: We present a recurrent model for semantic instance segmentation that sequentially generates pairs of masks and their associated class probabilities for every object in an image. Our proposed system is trainable end-to-end, does not require post-processing steps on its output and is conceptually simpler than current methods relying on object proposals. We observe that our model learns to follow a consistent pattern to generate object sequences, which correlates with the activations learned in the encoder part of our network. We achieve competitive results on three different instance segmentation benchmarks (Pascal VOC 2012, Cityscapes and CVPPP Plant Leaf Segmentation). Code is available at this https URL .


Implementation of Rabin Karp String Matching Algorithm Using MPI


title: ProCrawl: mining test models from multi-user web applications

abstract: Today's web applications demand very high release cycles--and consequently, frequent tests. Automating these tests typically requires a behavior model: A description of the states the application can be in, the transitions between these states, and the expected results. Furthermore one needs scripts to make the abstract actions (transitions) in the model executable. However, specifying such behavior models and writing the necessary scripts manually is a hard task. We present ProCrawl (Process Crawler), a tool that automatically mines (extended) finite-state machines from (multi-user) web applications and generates executable test scripts. ProCrawl explores the behavior of the application by systematically generating program runs and observing changes on the application's user interface. The resulting models can be directly used for effective model-based testing, in particular regression testing.


title: Data profiling revisited

abstract: Data profiling comprises a broad range of methods to efficiently analyze a given data set. In a typical scenario, which mirrors the capabilities of commercial data profiling tools, tables of a relational database are scanned to derive metadata, such as data types and value patterns, completeness and uniqueness of columns, keys and foreign keys, and occasionally functional dependencies and association rules. Individual research projects have proposed several additional profiling tasks, such as the discovery of inclusion dependencies or conditional functional dependencies. Data profiling deserves a fresh look for two reasons: First, the area itself is neither established nor defined in any principled way, despite significant research activity on individual parts in the past. Second, more and more data beyond the traditional relational databases are being created and beg to be profiled. The article proposes new research directions and challenges, including interactive and incremental profiling and profiling heterogeneous and non-relational data.


Cross-domain Authorship Attribution: Author Identification using Char Sequences, Word Uni-grams, and POS-tags Features: Notebook for PAN at CLEF 2018


title: Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation

abstract: Weakly supervised semantic segmentation receives much research attention since it alleviates the need to obtain a large amount of dense pixel-wise ground-truth annotations for the training images. Compared with other forms of weak supervision, image labels are quite efficient to obtain. In this paper, we focus on the weakly supervised semantic segmentation with image label annotations. Recent progress for this task has been largely dependent on the quality of generated pseudo-annotations. In this paper, inspired by spatial neural-attention for image captioning, we propose a decoupled spatial neural attention network for generating pseudo-annotations. Our decoupled attention structure could simultaneously identify the object regions and localize the discriminative parts, which generates high-quality pseudo-annotations in one forward path. The generated pseudo-annotations lead to the segmentation results that achieve the state of the art in weakly supervised semantic segmentation.


title: PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online Courses

abstract: Massive open online courses (MOOCs) aim to facilitate open-access and massive-participation education. These courses have attracted millions of learners recently. At present, most MOOC platforms record the web log data of learner interactions with course videos. Such large amounts of multivariate data pose a new challenge in terms of analyzing online learning behaviors. Previous studies have mainly focused on the aggregate behaviors of learners from a summative view; however, few attempts have been made to conduct a detailed analysis of such behaviors. To determine complex learning patterns in MOOC video interactions, this paper introduces a comprehensive visualization system called PeakVizor. This system enables course instructors and education experts to analyze the "peaks" or the video segments that generate numerous clickstreams. The system features three views at different levels: the overview with glyphs to display valuable statistics regarding the peaks detected; the flow view to present spatio-temporal information regarding the peaks; and the correlation view to show the correlation between different learner groups and the peaks. Case studies and interviews conducted with domain experts have demonstrated the usefulness and effectiveness of PeakVizor, and new findings about learning behaviors in MOOC platforms have been reported.


title: A 90nm-CMOS, 500Mbps, fully-integrated IR-UWB transceiver using pulse injection-locking for receiver phase synchronization

abstract: A fully-integrated, 3.1–5GHz Impulse-Radio UWB transceiver with on-chip flash ADC is designed in 90nm-CMOS. A new scheme for receiver phase acquisition is proposed that uses pulse injection-locking to synchronize the receive clock with the transmitted data, eliminating the need for clock/data recovery (CDR). Occupying 2mm2 die area, the transceiver achieves a maximum data rate of 500 Mbps, energy efficiency of 0.18nJ/b at 500Mbps, and a RX-BER of 10−3 across a distance of 10cm at 125Mbps.


title: A Spreadsheet Algebra for a Direct Data Manipulation Query Interface

abstract: A spreadsheet-like "direct manipulation" interface is more intuitive for many non-technical database users compared to traditional alternatives, such as visual query builders. The construction of such a direct manipulation interfacemay appear straightforward, but there are some significant challenges. First, individual direct manipulation operations cannot be too complex, so expressive power has to be achieved through composing (long) sequences of small operations. Second, all intermediate results are visible to the user, so grouping and ordering are material after every small step. Third, users often find the need to modify previously specified queries. Since manipulations are specified one step at a time, there is no actual queryexpression to modify. Suitable means must be provided to address this need. Fourth, the order in which manipulations are performed by the user should not affect the results obtained, to avoid user confusion. We address the aforementioned challenges by designing a new spreadsheet algebra that: i) operates on recursively grouped multi-sets, ii) contains a selectively designed set of operators capable of expressing at least all single-block SQL queries and can be intuitively implemented in a spreadsheet, iii) enables query modification by the notion of modifiable query state, and iv) requires no ordering in unary data manipulation operators since they are all designed to commute. We built a prototype implementation of the spreadsheet algebra and show, through user studies with non-technical subjects, that the resultant query interface is easier to use than a standard commercial visual query builder.


title: BYOD security challenges: control and protect your most sensitive data

abstract: Several new trends in information access are impacting organisations' ability to control and secure sensitive corporate data. The increase in web applications, cloud computing and Software as a Service (SaaS) offerings, and the Bring Your Own Device (BYOD) phenomenon, means that employees, business partners and customers are increasingly accessing information using a web browser on a device not owned or managed by the organisation. The increase in web applications, cloud computing and Software as a Service (SaaS) offerings, and the Bring Your Own Device (BYOD) phenomenon are driving employees, business partners and customers to increasingly access information on devices are not managed by IT departments. This has resulted in security implications for data leakage, data theft and regulatory compliance. To protect valuable information, organisations must stop making a distinction between devices in the corporate network and devices outside of it, argues Bill Morrow of Quarri Technologies.


Reasoning about Conditional Beliefs for the Winograd Schema Challenge


title: Hybrid Camera Based Real-Time Human Body Segmentation for Virtual Reality E-learning System

abstract: This paper proposes hybrid camera based video segmentation approach of human body for virtual reality e-learning system. Color and IR(Infra-red rays) image are used for human body segmentation at the same time. Human foreground pixels are separated from background in color and IR image respectively. And two foreground pixels are integrated as final foreground result for better performance of human body segmentation. Good foreground human segmentation is useful for virtual reality e-learning system.


title: Characterizing videos, audience and advertising in Youtube channels for kids

abstract: Online video services, messaging systems, games and social media services are tremendously popular among young people and children in many countries. Most of the digital services offered on the internet are advertising funded, which makes advertising ubiquitous in children's everyday life. To understand the impact of advertising-based digital services on children, we study the collective behavior of users of YouTube for kids channels and present the demographics of a large number of users. We collected data from 12,848 videos from 17 channels in US and UK and 24 channels in Brazil. The channels in English have been viewed more than 37 billion times. We also collected more than 14 million comments made by users. Based on a combination of text-analysis and face recognition tools, we show the presence of racial and gender biases in our large sample of users. We also identify children actively using YouTube, although the minimum age for using the service is 13 years in most countries. We provide comparisons of user behavior among the three countries, which represent large user populations in the global North and the global South.


title: A Model for Reasoning with Uncertain Rules in Event Composition Systems

abstract: In recent years, there has been an increased need for the use of active systems - systems required to act automatically based on events, or changes in the environment. Such systems span many areas, from active databases to applications that drive the core business processes of today's enterprises. However, in many cases, the events to which the system must respond are not generated by monitoring tools, but must be inferred from other events based on complex temporal predicates. In addition, in many applications, such inference is inherently uncertain. In this paper, we introduce a formal framework for knowledge representation and reasoning enabling such event inference. Based on probability theory, we define the representation of the associated uncertainty. In addition, we formally define the probability space, and show how the relevant probabilities can be calculated by dynamically constructing a Bayesian network. To the best of our knowledge, this is the first work that enables taking such uncertainty into account in the context of active systems. herefore, our contribution is twofold: We formally define the representation and semantics of event composition for probabilistic settings, and show how to apply these extensions to the quantification of the occurrence probability of events. These results enable any active system to handle such uncertainty.


title: A Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys

abstract: Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to paralyzed patients. So far, BMIs have enabled only one arm to be moved at a time. Control of bimanual arm movements remains a major challenge. We have developed and tested a bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374 to 497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a fifth-order unscented Kalman filter (UKF). The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals' performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. These findings should help in the design of more sophisticated BMIs capable of enabling bimanual motor control in human patients.


title: A Snapback Suppressed Reverse-Conducting IGBT With a Floating p-Region in Trench Collector

abstract: A novel reverse-conducting insulated-gate bipolar transistor (RC-IGBT) featuring an oxide trench placed between the n-collector and the p-collector and a floating p-region (p-float) sandwiched between the n-drift and n-collector is proposed. First, the new structure introduces a high-resistance collector short resistor at low current density, which leads to the suppression of the snapback effect. Second, the collector short resistance can be adjusted by varying the p-float length without increasing the collector cell length. Third, the p-float layer also acts as the base of the n-collector/p-float/n-drift transistor which can be activated and offers a low-resistance current path at high current densities, which contributes to the low on-state voltage of the integrated freewheeling diode and the fast turnoff. As simulations show, the proposed RC-IGBT shows snapback-free output characteristics and faster turnoff compared with the conventional RC-IGBT.


title: scikit-image: Image processing in Python

abstract: scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org.


title: Learning to rank recommendations with the k-order statistic loss

abstract: Making recommendations by learning to rank is becoming an increasingly studied area. Approaches that use stochastic gradient descent scale well to large collaborative filtering datasets, and it has been shown how to approximately optimize the mean rank, or more recently the top of the ranked list. In this work we present a family of loss functions, the k-order statistic loss, that includes these previous approaches as special cases, and also derives new ones that we show to be useful. In particular, we present (i) a new variant that more accurately optimizes precision at k, and (ii) a novel procedure of optimizing the mean maximum rank, which we hypothesize is useful to more accurately cover all of the user's tastes. The general approach works by sampling N positive items, ordering them by the score assigned by the model, and then weighting the example as a function of this ordered set. Our approach is studied in two real-world systems, Google Music and YouTube video recommendations, where we obtain improvements for computable metrics, and in the YouTube case, increased user click through and watch duration when deployed live on www.youtube.com.


title: Leaf shape identification based plant biometrics

abstract: This paper presents a simple and computationally efficient method for plant species recognition using leaf image. This method works only for the plants with broad flat leaves which are more or less two dimensional in nature. The method consists of five major parts. First, images of leaf are acquired with digital camera or scanners. Then the user selects the base point of the leaf and a few reference points on the leaf blades. Based on these points the leaf shape is extracted from the background and a binary image is produced. After that the leaf is aligned horizontally with its base point on the left of the image. Then several morphological features, such as eccentricity, area, perimeter, major axis, minor axis, equivalent diameter, convex area and extent, are extracted. A unique set of features are extracted from the leaves by slicing across the major axis and parallel to the minor axis. Then the feature pointes are normalized by taking the ratio of the slice lengths and leaf lengths (major axis). These features are used as inputs to the probabilistic neural network. The network was trained with 1200 simple leaves from 30 different plant species. The proposed method has been tested using ten-fold cross-validation technique and the system shows 91.41% average recognition accuracy.


title: A tutorial on variational Bayesian inference

abstract: This tutorial describes the mean-field variational Bayesian approximation to inference in graphical models, using modern machine learning terminology rather than statistical physics concepts. It begins by seeking to find an approximate mean-field distribution close to the target joint in the KL-divergence sense. It then derives local node updates and reviews the recent Variational Message Passing framework.


title: Invisible loading: access-driven data transfer from raw files into database systems

abstract: Commercial analytical database systems suffer from a high "time-to-first-analysis": before data can be processed, it must be modeled and schematized (a human effort), transferred into the database's storage layer, and optionally clustered and indexed (a computational effort). For many types of structured data, this upfront effort is unjustifiable, so the data are processed directly over the file system using the Hadoop framework, despite the cumulative performance benefits of processing this data in an analytical database system. In this paper we describe a system that achieves the immediate gratification of running MapReduce jobs directly over a file system, while still making progress towards the long-term performance benefits of database systems. The basic idea is to piggyback on MapReduce jobs, leverage their parsing and tuple extraction operations to incrementally load and organize tuples into a database system, while simultaneously processing the file system data. We call this scheme Invisible Loading, as we load fractions of data at a time at almost no marginal cost in query latency, but still allow future queries to run much faster.


title: More than touch: understanding how people use skin as an input surface for mobile computing

abstract: This paper contributes results from an empirical study of on-skin input, an emerging technique for controlling mobile devices. Skin is fundamentally different from off-body touch surfaces, opening up a new and largely unexplored interaction space. We investigate characteristics of the various skin-specific input modalities, analyze what kinds of gestures are performed on skin, and study what are preferred input locations. Our main findings show that (1) users intuitively leverage the properties of skin for a wide range of more expressive commands than on conventional touch surfaces; (2) established multi-touch gestures can be transferred to on-skin input; (3) physically uncomfortable modalities are deliberately used for irreversible commands and expressing negative emotions; and (4) the forearm and the hand are the most preferred locations on the upper limb for on-skin input. We detail on users' mental models and contribute a first consolidated set of on-skin gestures. Our findings provide guidance for developers of future sensors as well as for designers of future applications of on-skin input.


title: Mirror mirror on the wall… An intelligent multisensory mirror for well-being self-assessment

abstract: The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory platform, in the form of a smart mirror, looking for signs related to cardio-metabolic risk. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. Building the multisensory mirror requires addressing significant scientific and technological challenges, from touch-less data acquisition, to real-time processing and integration of multimodal data.


title: Automatic registration of oblique aerial images with cadastral maps

abstract: In recent years, oblique aerial images of urban regions have become increasingly popular for 3D city modeling, texturing, and various cadastral applications. In contrast to images taken vertically to the ground, they provide information on building heights, appearance of facades, and terrain elevation. Despite their widespread availability for many cities, the processing pipeline for oblique images is not fully automatic yet. Especially the process of precisely registering oblique images with map vector data can be a tedious manual process. We address this problem with a registration approach for oblique aerial images that is fully automatic and robust against discrepancies between map and image data. As input, it merely requires a cadastral map and an arbitrary number of oblique images. Besides rough initial registrations usually available from GPS/INS measurements, no further information is required, in particular no information about the terrain elevation.


A Control method of Interleaved Two-stage Fly back AC – DC Converter for Industrial Lighting and DC Motor


title: Identifying self-admitted technical debt in open source projects using text mining

abstract: Technical debt is a metaphor to describe the situation in which long-term code quality is traded for short-term goals in software projects. Recently, the concept of self-admitted technical debt (SATD) was proposed, which considers debt that is intentionally introduced, e.g., in the form of quick or temporary fixes. Prior work on SATD has shown that source code comments can be used to successfully detect SATD, however, most current state-of-the-art classification approaches of SATD rely on manual inspection of the source code comments. In this paper, we proposed an automated approach to detect SATD in source code comments using text mining. In our approach, we utilize feature selection to select useful features for classifier training, and we combine multiple classifiers from different source projects to build a composite classifier that identifies SATD comments in a target project. We investigate the performance of our approach on 8 open source projects that contain 212,413 comments. Our experimental results show that, on every target project, our approach outperforms the state-of-the-art and the baselines approaches in terms of F1-score. The F1-score achieved by our approach ranges between 0.518 - 0.841, with an average of 0.737, which improves over the state-of-the-art approach proposed by Potdar and Shihab by 499.19%. When compared with the text mining-based baseline approaches, our approach significantly improves the average F1-score by at least 58.49%. When compared with a natural language processing-based baseline, our approach also significantly improves its F1-score by 27.95%. Our proposed approach can be used by project personnel to effectively identify SATD with minimal manual effort.


title: Exploring the Use of Autoencoders for Botnets Traffic Representation

abstract: Botnets are a significant threat to cyber security. Compromised, a.k.a. malicious hosts in a network have, of late, been detected by machine learning from hand-crafted features directly sourced from different types of network logs. Our interest is in automating feature engineering while examining flow data from hosts labeled to be malicious or not. To automatically express full temporal character and dependencies of flow data requires time windowing and a very high dimensional set of features, in our case 30,000. To reduce dimensionality, we generate a lower dimensional embedding (64 dimensions) via autoencoding. This improves detection. We next increase the volume in the flows originating from hosts in our dataset known to be malicious or not by injecting noise we mix in from background traffic. The resulting lower metaphorical signal to noise ratio makes the presence of a bot even more challenging to detect so we resort to a filter encoder or an off-the-shelf denoising autoencoder. Both the filter encoding and denoising autoencoder improve upon detection compared to when hand-crafted features are used and are comparable in performance to the autoencoder.