title: A local LDA based method for Latent Aspect Rating Analysis on reviews

abstract: The expanding volume of online reviews has made it an important and challenging task to mine detailed information of opinions in those reviews. In many cases, along with the comment, a user also gives an overall rating on the target entity, which in fact could not reflect the detailed opinions on each aspect of the entity. Therefore, Latent Aspect Rating Analysis (LARA) came into being. The goal of LARA is to infer a latent rating and weight for each aspect based on the overall rating and the review content. Although some methods have been applied to solve this problem, they rely too much on the predefinition of aspects with keywords, which needs supervision and may hence introduce some biases. In this paper, we propose a Local LDA based method for LARA, which includes two stages. In the first stage, we employ Local LDA to discover aspects automatically. In the second stage, we use LRR model to infer the latent rating and weight for each of the discovered aspects. The experimental results on the review dataset demonstrate the advantages of the proposed method over the state-of-the-art methods.


title: Privacy, Social Network Sites, and Social Relations

abstract: With the growth of the Internet comes a growth in a ubiquitous networked society. Common Web 2.0 applications include a rapidly growing trend for social network sites. Social network sites typically converged different relationship types into one group of “friends.” However, with such vast interconnectivity, convergence of relationships, and information sharing by individual users comes an increased risk of privacy violations. We asked a small sample of participants to discuss what friendship and privacy meant to them and to give examples of a privacy violation they had experienced. A thematic analysis was conducted on the interviews to determine the issues discussed by the participants. Many participants experienced privacy issues using the social network site Facebook. The results are presented here and discussed in relation to online privacy concerns, notably social network site privacy concerns and managing such information.


title: How Progressive Visualizations Affect Exploratory Analysis

abstract: The stated goal for visual data exploration is to operate at a rate that matches the pace of human data analysts, but the ever increasing amount of data has led to a fundamental problem: datasets are often too large to process within interactive time frames. Progressive analytics and visualizations have been proposed as potential solutions to this issue. By processing data incrementally in small chunks, progressive systems provide approximate query answers at interactive speeds that are then refined over time with increasing precision. We study how progressive visualizations affect users in exploratory settings in an experiment where we capture user behavior and knowledge discovery through interaction logs and think-aloud protocols. Our experiment includes three visualization conditions and different simulated dataset sizes. The visualization conditions are: (1) blocking , where results are displayed only after the entire dataset has been processed; (2) instantaneous , a hypothetical condition where results are shown almost immediately; and (3) progressive , where approximate results are displayed quickly and then refined over time. We analyze the data collected in our experiment and observe that users perform equally well with either instantaneous or progressive visualizations in key metrics, such as insight discovery rates and dataset coverage, while blocking visualizations have detrimental effects.


title: Near field magnetic probe applied to switching power supply

abstract: Near field measurements are a useful tool in the design process of a PCB board. It is a fast method to find which part of the circuit generates electromagnetic emissions and check if the solutions proposed actually works. This paper shows the construction of a Near Field Magnetic Probe, its characterization, and the explanation that how the correction factor can be obtained and measured as a function of the frequency. The Near field magnetic probe is basically a short loop antenna with a magnetic core to increase the sensibility, at the frequencies of switching power supply of a the Personal Computer. The construction of this probe is the first step for the construction of an automatized measurement system.


title: Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning

abstract: Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses. We assessed three unique radiomics-based predictive models, each of which employed different fundamental design principles ranging from a pattern recognition method via deep-learning convolution neural network (DL-CNN), to a more deterministic radiomics feature-based approach and then a bridging method between the two, utilizing a system which extracts radiomics features from the image patterns. Our study indicates that the computerized assessment using radiomics information from the pre- and post-treatment CT of bladder cancer patients has the potential to assist in assessment of treatment response.


title: Lab kits using the Arduino prototyping platform

abstract: We present a lab kit platform based on an Arduino microcontroller board and open hardware that enables students to use low-cost, course specific hardware to complete lab exercises at home. The platform is designed to be accessible to a wide range of students and easily adapted for other applications. Careful hardware selection allows existing laboratory exercises to be modified for use with the platform, and the platform enables new exercises that would not be possible in a traditional lab. We describe the adoption of these kits in a course designed for on-campus and remote students that teaches the science and technology of modern display systems. We find that the platform delivers a consistent, high quality laboratory experience for both on-campus and remote students.


Implicit and Explicit Knowledge about Language


title: Human amnesia and the medial temporal region: Enduring memory impairment following a bilateral lesion limited to field CA1 of the hippocampus

abstract: During the past 100 years clinical studies of amnesia have linked memory impairment to damage of the hippocampus. Yet the damage in these cases has not usually been confined to the hippocampus, and the status of memory functions has often been based on incomplete neuropsychological information. Thus, the human cases have until now left some uncertainty as to whether lesions limited to the hippocampus are sufficient to cause amnesia. Here we report a case of amnesia in a patient (R.B.) who developed memory impairment following an ischemic episode. During the 5 years until his death, R.B. exhibited marked anterograde amnesia, little if any retrograde amnesia, and showed no signs of cognitive impairment other than memory. Thorough histological examination revealed a circumscribed bilateral lesion involving the entire CA1 field of the hippocampus. Minor pathology was found elsewhere in the brain (e.g., left globus pallidus, right postcentral gyrus, left internal capsule), but the only damage that could be reasonably associated with the memory defect was the lesion in the hippocampus. To our knowledge, this is the first reported case of amnesia following a lesion limited to the hippocampus in which extensive neuropsychological and neuropathological analyses have been carried out.


title: Memory traces in dynamical systems

abstract: Abstract ::: To perform nontrivial, real-time computations on a sensory input stream, biological systems must retain a short-term memory trace of their recent inputs. It has been proposed that generic high-dimensional dynamical systems could retain a memory trace for past inputs in their current state. This raises important questions about the fundamental limits of such memory traces and the properties required of dynamical systems to achieve these limits. We address these issues by applying Fisher information theory to dynamical systems driven by time-dependent signals corrupted by noise. We introduce the Fisher Memory Curve (FMC) as a measure of the signal-to-noise ratio (SNR) embedded in the dynamical state relative to the input SNR. The integrated FMC indicates the total memory capacity. We apply this theory to linear neuronal networks and show that the capacity of networks with normal connectivity matrices is exactly 1 and that of any network of N neurons is, at most, N. A nonnormal network achieving this bound is subject to stringent design constraints: It must have a hidden feedforward architecture that superlinearly amplifies its input for a time of order N, and the input connectivity must optimally match this architecture. The memory capacity of networks subject to saturating nonlinearities is further limited, and cannot exceed . This limit can be realized by feedforward structures with divergent fan out that distributes the signal across neurons, thereby avoiding saturation. We illustrate the generality of the theory by showing that memory in fluid systems can be sustained by transient nonnormal amplification due to convective instability or the onset of turbulence. ::: ::: Fisher information ::: fluid mechanics ::: network dynamics


title: Expert identification of visual primitives used by CNNs during mammogram classification

abstract: This work interprets the internal representations of deep neural networks trained for classification of diseased tissue in 2D mammograms. We propose an expert-in-the-loop inter- pretation method to label the behavior of internal units in convolutional neural networks (CNNs). Expert radiologists identify that the visual patterns detected by the units are correlated with meaningful medical phenomena such as mass tissue and calcificated vessels. We demonstrate that several trained CNN models are able to produce explanatory descriptions to support the final classification decisions. We view this as an important first step toward interpreting the internal representations of medical classification CNNs and explaining their predictions.


title: A Lyapunov-based Approach to Safe Reinforcement Learning

abstract: In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints. In particular, besides optimizing performance it is crucial to guarantee the safety of an agent during training as well as deployment (e.g. a robot should avoid taking actions - exploratory or not - which irrevocably harm its hardware). To incorporate safety in RL, we derive algorithms under the framework of constrained Markov decision problems (CMDPs), an extension of the standard Markov decision problems (MDPs) augmented with constraints on expected cumulative costs. Our approach hinges on a novel \emph{Lyapunov} method. We define and present a method for constructing Lyapunov functions, which provide an effective way to guarantee the global safety of a behavior policy during training via a set of local, linear constraints. Leveraging these theoretical underpinnings, we show how to use the Lyapunov approach to systematically transform dynamic programming (DP) and RL algorithms into their safe counterparts. To illustrate their effectiveness, we evaluate these algorithms in several CMDP planning and decision-making tasks on a safety benchmark domain. Our results show that our proposed method significantly outperforms existing baselines in balancing constraint satisfaction and performance.


' s personal copy Maturity levels for interoperability in digital government


title: AP3: cooperative, decentralized anonymous communication

abstract: This paper describes a cooperative overlay network that provides anonymous communication services for participating users. The Anonymizing Peer-to-Peer Proxy (AP3) system provides clients with three primitives: (i) anonymous message delivery, (ii) anonymous channels, and (iii) secure pseudonyms. AP3 is designed to be lightweight, low-cost and provides "probable innocence" anonymity to participating users, even under a large-scale coordinated attack by a limited fraction of malicious overlay nodes. Additionally, we use AP3's primitives to build novel anonymous group communication facilities (multicast and anycast), which shield the identity of both publishers and subscribers.


Design and Development of GPS-GSM Based Tracking System with Google Map Based Monitoring


title: Tailoring Group Package Recommendations to Large Heterogeneous Groups Based on Multi-criteria Optimization

abstract: A group package recommender framework is proposed to provide recommendations on dynamically defined packages of products and services to large heterogeneous groups based on multi-criteria optimization. The framework is based on: (1) sampling the entire large group, (2) eliciting the utility function for each member, (3) clustering the sample heterogeneous group into a number of relatively small homogeneous subgroups, (4) extracting the representative utility function for each subgroup, (5) estimating the utility function of the entire group, and use it to find an optimal recommendation alternative, (6) diversify recommendations across those subgroups, (7) applying a group decision-making method, from social choice theories, to refine the recommendations. A preliminary experimental study is conducted, which shows that the proposed framework is able to produce a small set of ranked recommendations that retains close to optimal precision and recall, as compared to the baseline method applied directly to original large groups.


title: The Degradation of Polysorbates 20 and 80 and its Potential Impact on the Stability of Biotherapeutics

abstract: PURPOSE ::: To study the potential impact of the degradation of Polysorbates (PS) 20 and 80 on the stability of therapeutic proteins in parenteral formulations. ::: ::: ::: METHOD ::: First, degradation products of PS20 and 80 were identified. Subsequently, the effect of degraded polysorbate on physical characteristics and long-term stability of protein formulations was assessed. Further, the impact of polysorbate degradation on protein stability was evaluated via shaking stress studies on formulations spiked with artificially degraded polysorbate or degradants like fatty acids. Additionally, aged formulations with reduced polysorbate content were shaken. ::: ::: ::: RESULTS ::: The degradation of polysorbate leads to a buildup of various molecules, some of which are poorly soluble, including fatty acids and polyoxyethylene (POE) esters of fatty acids. Spiking studies showed that the insoluble degradants could potentially impact protein stability and that the presence of sufficient intact polysorbate was crucial to prevent this. End-of-shelf-life shaking of protein formulations showed that the stability of various monoclonal antibodies was, however, not affected. ::: ::: ::: CONCLUSIONS ::: Although some degradants can potentially influence the stability of the protein (as discerned from spiking studies), degradation of polysorbates did not impact the stability of the different proteins tested in pharmaceutically relevant temperature and storage conditions.


title: Visual servoing simulator by using ROS and Gazebo

abstract: In this paper, a simulator for five degree of freedom (DOF) visual servoing robot is presented with eye-in-hand configuration. This simulator has been developed in Robot Operating System (ROS) and Gazebo environment. The designed simulator eases the process of testing and debugging visual servoing schemes, and robot controllers. Among different methods, one of the existing Image based visual servoing schemes, image moments, has been implemented to verify the functionality and performance of designed simulator.


Characterizing Population EEG Dynamics throughout Adulthood


title: End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level

abstract: This paper demonstrates end-to-end neural network architectures for Vietnamese named entity recognition. Our best model is a combination of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), Conditional Random Field (CRF), using pre-trained word embeddings as input, which achieves an \(F_{1}\) score of 88.59% on a standard test set. Our system is able to achieve a comparable performance to the first-rank system of the VLSP campaign without using any syntactic or hand-crafted features. We also give an extensive empirical study on using common deep learning models for Vietnamese NER, at both word and character level.


title: A Survey on Open-source Cloud Computing Solutions

abstract: Cloud computing is an attractive computing model since it allows for resources to be provisioned according on a demand basis, i.e., cloud users can rent resources as they become necessary. This model motivated several academic and non-academic institutions to develop open-source cloud solutions. This paper presents and discusses the state-of-the of open-source solutions for cloud computing. The authors hope that the observation and classification of such solutions can leverage the cloud computing research area providing a good starting point to cope with some of the problems present in cloud computing environments.


title: Design Optimization of Short-Stroke Single-Phase Tubular Permanent-Magnet Motor for Refrigeration Applications

abstract: This paper describes a design methodology to achieve optimal performance for a short-stroke single-phase tubular permanent-magnet motor which drives a reciprocating vapor compressor. The steady-state characteristic of the direct-drive linear-motor compressor system is analyzed, an analytical formula for predicting iron loss is presented, and a motor-design procedure which takes into account the effect of compressor loads under nominal operating condition is formulated. It is shown that the motor efficiency can be optimized with respect to two leading dimensional ratios. Experimental results validate the proposed design methodology.


title: Clean Answers over Dirty Databases: A Probabilistic Approach

abstract: The detection of duplicate tuples, corresponding to the same real-world entity, is an important task in data integration and cleaning. While many techniques exist to identify such tuples, the merging or elimination of duplicates can be a difficult task that relies on ad-hoc and often manual solutions. We propose a complementary approach that permits declarative query answering over duplicated data, where each duplicate is associated with a probability of being in the clean database. We rewrite queries over a database containing duplicates to return each answer with the probability that the answer is in the clean database. Our rewritten queries are sensitive to the semantics of duplication and help a user understand which query answers are most likely to be present in the clean database. The semantics that we adopt is independent of the way the probabilities are produced, but is able to effectively exploit them during query answering. In the absence of external knowledge that associates each database tuple with a probability, we offer a technique, based on tuple summaries, that automates this task. We experimentally study the performance of our rewritten queries. Our studies show that the rewriting does not introduce a significant overhead in query execution time. This work is done in the context of the ConQuer project at the University of Toronto, which focuses on the efficient management of inconsistent and dirty databases.


title: Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation

abstract: This article proposes Adversarially-Trained Normalized Noisy-Feature Auto-Encoder (ATNNFAE) for byte-level text generation. An ATNNFAE consists of an auto-encoder where the internal code is normalized on the unit sphere and corrupted by additive noise. Simultaneously, a replica of the decoder (sharing the same parameters as the AE decoder) is used as the generator and fed with random latent vectors. An adversarial discriminator is trained to distinguish training samples reconstructed from the AE from samples produced through the random-input generator, making the entire generator-discriminator path differentiable for discrete data like text. The combined effect of noise injection in the code and shared weights between the decoder and the generator can prevent the mode collapsing phenomenon commonly observed in GANs. Since perplexity cannot be applied to non-sequential text generation, we propose a new evaluation method using the total variance distance between frequencies of hash-coded byte-level n-grams (NGTVD). NGTVD is a single benchmark that can characterize both the quality and the diversity of the generated texts. Experiments are offered in 6 large-scale datasets in Arabic, Chinese and English, with comparisons against n-gram baselines and recurrent neural networks (RNNs). Ablation study on both the noise level and the discriminator is performed. We find that RNNs have trouble competing with the n-gram baselines, and the ATNNFAE results are generally competitive.


Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings


title: Four challenges for a theory of informational privacy

abstract: In this article, I summarise the ontological theory of informational privacy (an approach based on information ethics) and then discuss four types of interesting challenges confronting any theory of informational privacy: (1) parochial ontologies and non-Western approaches to informational privacy; (2) individualism and the anthropology of informational privacy; (3) the scope and limits of informational privacy; and (4) public, passive and active informational privacy. I argue that the ontological theory of informational privacy can cope with such challenges fairly successfully. In the conclusion, I discuss some of the work that lies ahead.


title: Application of a PID Controller using MRAC Techniques for Control of the DC Electromotor Drive

abstract: In DC motor drives, device aging and environmental factors can degrade the performance of the control system and make it difficult to redesign controller parameters in real time. In this paper, an algorithm that combines PID control scheme and model reference adaptive control (MRAC) to autotune the controller parameters in real time when system performances change is proposed and applied to control of the DC electromotor drive. The algorithm modifies the traditional PID control scheme by replacing the error between the reference and the system output with the direct system output in the derivative part. Compared to the traditional MRAC approach, the algorithm is normalized so that a reference model can be easily generated given bandwidth requirement. To be practical for implementation, the algorithm also simplifies the transfer function of the speed open-loop as an integrator by adding a current inner loop. The results of simulation on a two-loop servo control system show that the adjusted controller parameters meet the system design very well. The algorithm was implemented on a test platform and the performance of the system was evaluated with a dynamic signal analyzer. The effectiveness of the proposed method is shown through the experimental results.


Calculation of Reproductive Number Using Data from H 1 N 1 Patients in Tamilnadu


title: A Fast Unified Model for Parsing and Sentence Understanding

abstract: Tree-structured neural networks exploit valuable syntactic parse information as they interpret the meanings of sentences. However, they suer from two key technical problems that make them slow and unwieldyforlarge-scaleNLPtasks: theyusually operate on parsed sentences and they do not directly support batched computation. We address these issues by introducingtheStack-augmentedParser-Interpreter NeuralNetwork(SPINN),whichcombines parsing and interpretation within a single tree-sequence hybrid model by integrating tree-structured sentence interpretation into the linear sequential structure of a shiftreduceparser. Ourmodelsupportsbatched computation for a speedup of up to 25◊ over other tree-structured models, and its integrated parser can operate on unparsed data with little loss in accuracy. We evaluate it on the Stanford NLI entailment task and show that it significantly outperforms other sentence-encoding models.


Evaluation Patterns for Design Science Research Artefacts


title: Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

abstract: We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve forward, inverse, and mixed stochastic problems in a unified manner based on a limited number of scat...


title: A tool for generating synthetic data

abstract: It is popular to use real-world data to evaluate data mining techniques. However, there are some disadvantages to use real-world data for such purposes. Firstly, real-world data in most domains is difficult to obtain for several reasons, such as budget, technical or ethical. Secondly, the use of many of the real-world data is restricted, those data sets do either not contain specific patterns that are easy to mine or the data needs special preparation and the algorithm needs very specific settings in order to find patterns in it. The solution to this could be the generation of synthetic, "meaningful data" (data with intrinsic patterns). This paper presents a novel approach for generating synthetic data by developing a tool, including novel algorithms for specific data mining patterns, and a user-friendly interface, which is able to create large data sets with predefined classification rules, multilinear regression patterns. A preliminary run of the prototype proves that the generation of large amounts of such "meaningful data" is possible. Also the proposed approach could be extended to a further development for generating synthetic data with other intrinsic patterns.


title: FigureQA: An Annotated Figure Dataset for Visual Reasoning

abstract: We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection. To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure. To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives. In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements. We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as a strong baseline. Preliminary results indicate that the task poses a significant machine learning challenge. We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.


title: Instance-Level Explanations for Fraud Detection: A Case Study

abstract: Fraud detection is a difficult problem that can benefit from predictive modeling. However, the verification of a prediction is challenging; for a single insurance policy, the model only provides a prediction score. We present a case study where we reflect on different instance-level model explanation techniques to aid a fraud detection team in their work. To this end, we designed two novel dashboards combining various state-of-the-art explanation techniques. These enable the domain expert to analyze and understand predictions, dramatically speeding up the process of filtering potential fraud cases. Finally, we discuss the lessons learned and outline open research issues.


title: Blockchain Access Privacy: Challenges and Directions

abstract: Privacy, facilitated by a confluence of cryptography and decentralization, is one of the primary motivations for the adoption of cryptocurrencies like Bitcoin. Alas, Bitcoins privacy promise has proven illusory, and despite growing interest in privacy-centric blockchains, most blockchain users remain susceptible to privacy attacks that exploit network-layer information and access patterns that leak as users interact with blockchains. Understanding if and how blockchain-based applications can provide strong privacy guarantees is a matter of increasing urgency. Many researchers advocate using anonymous communications networks, such as Tor, to ensure access privacy. We challenge this approach, showing the need for mechanisms through which non-anonymous users can (i) publish transactions that cannot be linked to their network addresses or to their other transactions, and (ii) fetch details of specific transactions without revealing which transactions they seek. We hope this article inspires blockchain researchers to think beyond Tor and tackle these important access privacy problems head-on.


title: Deception in cyberspace: A comparison of text-only vs. avatar-supported medium

abstract: The use of anthropomorphic avatars provides Internet users the opportunity and freedom to manipulate their identity. As cyberspace becomes a haven for deceptive behavior, human-computer interaction research will need to be carried out to study and understand these deceptive behaviors. The objective of this research is to investigate the behavior of deceivers and non-deceivers (or truth-tellers) in the cyberspace environment. We examine if the intention to deceive others influences one's choice of avatars in the online chat environment. We also investigate if communication medium (text-only vs. avatar-supported chat) influences one's perception of trustworthiness of the communication partner. A lab experiment was conducted in an online chat environment with dyads. The results indicate that in the text-only chat environment, subjects who were deceiving their partner experienced higher anxiety levels than those who were truthful to their partner; however, the same phenomenon was not observed in the avatar-supported chat environment. This suggests that ''wearing a mask'' in cyberspace may reduce anxiety in deceiving others. Additionally, deceivers are more likely to choose avatars that are different from their real selves. The results also show that the use of avatars in a computer-mediated chat environment does not have an impact on one's perceived trustworthiness.


title: Weighted-Tuple: Fast and Accurate Synchronization for Parallel Architecture Simulators

abstract: Computer architecture research relies on software simulation to evaluate processor performance. Single-threaded simulators have unacceptable simulation times when modeling complex architectures with hundreds of cores. While parallelizing a simulator can improve performance, parallel simulators face the issue of synchronizing threads, which forces them to trade performance for accuracy. We study relaxed synchronization policies for parallel architecture simulators and introduce the weighted-tuple synchronization policy. Weighted-tuple is a distributed synchronization scheme which improves upon existing policies. We evaluate weighted-tuple for two parallel simulator settings: multicore simulation and network-on-chip simulation. For the multicore setting using weighted-tuple synchronization, average simulation time is reduced by $8$ percent over barrier synchronization; error is also reduced by $28$ percent. For network-on-chip simulation, weighted-tuple synchronization improves simulation speed by $42$ percent with an $0.3$ percent error increase compared to the barrier baseline.


Meaning Representation – a survey –


title: A Machine Independent LISP Compiler And Its Implications For Ideal Hardware

abstract: A LISP compiler is constructed without any a priori assumptions about the target machine. In parallel with the compiler a LISP oriented instruction set is developed. The instruction set can be seen as either an intermediarylanguage for a traditional computer oras the instruction set for a special purpose LISP machine. The code produced by the compiler is evaluated with regard to its static and dynamic properties. Finally some architectural aspects on LISP oriented hardware are discussed. The notion of segments with different word lengths, under program control, is developed and a proposed implementation of this is described.


title: Ten challenges in 3D printing

abstract: Three dimensional printing has gained considerable interest lately due to the proliferation of inexpensive devices as well as open source software that drive those devices. Public interest is often followed by media coverage that tends to sensationalize technology. Based on popular articles, the public may create the impression that 3D printing is the Holy Grail; we are going to print everything as one piece, traditional manufacturing is at the brink of collapse, and exotic applications, such as cloning a human body by 3D bio-printing, are just around the corner. The purpose of this paper is to paint a more realistic picture by identifying ten challenges that clearly illustrate the limitations of this technology, which makes it just as vulnerable as anything else that had been touted before as the next game changer.


title: Social isolation and inmate behavior: A conceptual framework for theorizing prison visitation and guiding and assessing research

abstract: Scholarship suggests that prison visitation is important because it allows inmates access to social ties that, in turn, can offset social isolation and help inmates cope with the transition back into society upon release. Only a small number of empirical assessments of visitation exist, however, and existing studies have typically overlooked how the heterogeneity inherent in visitation may influence whether visitation is beneficial, harmful, or has no effect. The goal of this paper is to develop a conceptual framework for theorizing this heterogeneity and its impacts, evaluating visitation research, and guiding future research aimed at estimating visitation effects.


title: Ultra-Wideband Dielectric-Loaded Horn Antenna with Dual-Linear Polarization Capability

abstract: In order to improve measurement e-ciency in electromag- netic test facilities (compact range or near-fleld range), testing probes are required to have ultra-wideband (UWB) radiation characteristics, dual-polarization capability, and interchangeable E- and H-plane far- fleld patterns. In this context, we propose a new type of UWB antenna that satisfles these requirements in the frequency range of 4 to 18GHz. For the proposed antenna, a low-loss dielectric material is loaded over pairs of balanced feeds to overcome radiation performance degrada- tion addressed in purely metallic antennas. Also, radiation patterns are further improved by employing a lens aperture and shielding struc- tures. The measured results of the flnal antenna design demonstrate broad and stable far-fleld patterns (half-power beamwidth > 45 - ), low voltage standing-wave ratio ( 23dB).


title: A method of driver distraction evaluation using fuzzy logic: Phone usage as a driver's secondary activity: Case study

abstract: A novel method for evaluating driver distraction and situation awareness while performing a secondary task using a fuzzy set theory is proposed in this paper. A fuzzy inference engine realization process based on simple matrix operations is described in detail. The drivers' performance is evaluated referring to the vehicle behavior, in particular, the abilities to keep the vehicle in the center of the lane and to observe the speed limit. The evaluation technique was tested on a vehicle mock-up driving simulator. Text messaging on a cell phone is studied as a secondary distractive activity. Driver-in-the-loop experimental results as well as the conclusions regarding different age, gender, and driving experience groups are discussed.


title: CompactNet: High Accuracy Deep Neural Network Optimized for On-Chip Implementation

abstract: Recent research has focused on Deep Neural Networks (DNNs) implemented directly in hardware. However, larger DNNs require significant energy and area, thereby limiting their wide adoption. We propose a novel DNN quantization technique and a corresponding hardware solution, CompactNet that optimizes the use of hardware resources even further, through dynamic allocation of memory for each parameter. Experimental results for the MNIST and CIFAR-10 datasets, show that CompactNet reduces the memory requirement by over 80%, the energy requirement by 12-fold, and the area requirement by 7-fold, when compared to the conventional DNN. This is achieved with minimal degradation to the classification accuracy. We demonstrate that, CompactNet provides pareto-optimal designs to make trade-offs between accuracy and resource requirement. The applications of CompactNet can be extended to datasets like ImageNet, and into models like MobileNet.


title: Continuous Location Statistics Sharing Algorithm with Local Differential Privacy

abstract: Continuous sharing of location statistics produces valuable knowledge to understand important phenomena, such as popular places or pattern behaviors. Most importantly, data should be shared without jeopardizing users’ privacy. Differential privacy becomes de-facto technique for private statistical data release. Much work focuses on the centralized setting where users send their original data to a trusted server. Then the server adds controlled noises to generate differentially private statistics. This centralized approach is vulnerable to attacks where an adversary may access the true data by attacking the trusted server. Local differential privacy neutralizes this type of attacks by allowing each user to obfuscate their data before it reaches the server for statistical analysis. In this paper, we propose an algorithm to share location statistics that leverages local differential privacy combined with w-event privacy. Our solution guarantees the user’s privacy when continuously releasing statistics over infinite streams. Experimental evaluation on real-life data shows our solution with strong privacy guarantee.


title: Purple VLC: Accelerating Visible Light Communication in Room-Area through PRU Offloading

abstract: Embedded VLC (Visible Light Communication) has attracted significant research attention in recent years. A reliable and robust VLC system can become one of the IoT communication technologies for indoor environment. VLC could become a wireless technology complementary to existing RF-based technology but with no RF interference. However, existing low cost indicator-type LED based VLC platforms have limited throughput and reliability. In this work, we introduce a new embedded VLC platform that can achieve 100 kbps aggregate throughput at a distance of 6 meters, which is 6-7x improvement over state-of-the-art. Our design combines I/O offloading in computation, concurrent communication with polarized light, and full-duplexing to offer more than 99% link reliability at a distance of 6 meters.


title: Fashion Conversation Data on Instagram

abstract: The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of {24,752} labeled images on fashion conversations, containing visual and textual cues, available for the research community.


title: A review on cloud robotics based frameworks to solve simultaneous localization and mapping (slam) problem

abstract: Cloud Robotics is one of the emerging area of robotics. It has created a lot of attention due to its direct practical implications on Robotics. In Cloud Robotics, the concept of cloud computing is used to offload computational extensive jobs of the robots to the cloud. Apart from this, additional functionalities can also be offered on run to the robots on demand. Simultaneous Localization and Mapping (SLAM) is one of the computational intensive algorithm in robotics used by robots for navigation and map building in an unknown environment. Several Cloud based frameworks are proposed specifically to address the problem of SLAM, DAvinCi, Rapyuta and C2TAM are some of those framework. In this paper, we presented a detailed review of all these framework implementation for SLAM problem.


title: Cross-dataset learning and person-specific normalisation for automatic Action Unit detection

abstract: Automatic detection of Facial Action Units (AUs) is crucial for facial analysis systems. Due to the large individual differences, performance of AU classifiers depends largely on training data and the ability to estimate facial expressions of a neutral face. In this paper, we present a real-time Facial Action Unit intensity estimation and occurrence detection system based on appearance (Histograms of Oriented Gradients) and geometry features (shape parameters and landmark locations). Our experiments show the benefits of using additional labelled data from different datasets, which demonstrates the generalisability of our approach. This holds both when training for a specific dataset or when a generic model is needed. We also demonstrate the benefits of using a simple and efficient median based feature normalisation technique that accounts for person-specific neutral expressions. Finally, we show that our results outperform the FERA 2015 baselines in all three challenge tasks - AU occurrence detection, fully automatic AU intensity and pre-segmented AU intensity estimation.


title: A tool for analyzing and fine tuning the real-time properties of an embedded system

abstract: This paper describes a computer-aided software engineering (CASE) tool that helps designers analyze and fine-tune the timing properties of their embedded real-time software. Existing CASE tools focus on the software specification and design of embedded systems. However, they provide little, if any, support after the software has been implemented. Even if the developer used a CASE tool to design the system, their system most likely does not meet the specifications on the first try. This paper includes guidelines for implementing analyzable code, profiling a real-time system, filtering and extracting measured data, analyzing the data, and interactively predicting the effect of changes to the real-time system. The tool is a necessary first step towards automating the debugging and fine tuning of an embedded system's temporal properties.


A Comparative Study of Regression and Classification Algorithms for Modelling Students' Academic Performance


title: A two-dimensional topic-aspect model for discovering multi-faceted topics

abstract: This paper presents the Topic-Aspect Model (TAM), a Bayesian mixture model which jointly discovers topics and aspects. We broadly define an aspect of a document as a characteristic that spans the document, such as an underlying theme or perspective. Unlike previous models which cluster words by topic or aspect, our model can generate token assignments in both of these dimensions, rather than assuming words come from only one of two orthogonal models. We present two applications of the model. First, we model a corpus of computational linguistics abstracts, and find that the scientific topics identified in the data tend to include both a computational aspect and a linguistic aspect. For example, the computational aspect of GRAMMAR emphasizes parsing, whereas the linguistic aspect focuses on formal languages. Secondly, we show that the model can capture different viewpoints on a variety of topics in a corpus of editorials about the Israeli-Palestinian conflict. We show both qualitative and quantitative improvements in TAM over two other state-of-the-art topic models.


title: Model-Driven Engineering : A Survey Supported by a Unified Conceptual Model

abstract: During the last decade a new trend of approaches has emerged, which considers models not just documentation artefacts, but also central artefacts in the software engineering field, allowing the creation or automatic execution of software systems starting from those models. These proposals have been classified generically as Model-Driven Engineering (MDE) and share common concepts and terms that need to be abstracted, discussed and understood. This paper presents a survey on MDE based on a unified conceptual model that clearly identifies and relates these essential concepts, namely the concepts of system, model, metamodel, modeling language, transformations, software platform, and software product. In addition, this paper discusses the terminologies relating MDE, MDD, MDA and others. This survey is based on earlier work, however, contrary to those, it intends to give a simple, broader and integrated view of the essential concepts and respective terminology commonly involved in the MDE, answering to key questions such as: What is a model? What is the relation between a model and a metamodel? What are the key facets of a modeling language? How can I use models in the context of a software development process? What are the relations between models and source code artefacts and software platforms? and What are the relations between MDE, MDD, MDA and other MD approaches?


title: Kali: Scalable encryption fingerprinting in dynamic malware traces

abstract: Binary analysis of malware to determine uses of encryption is an important primitive with many critical applications, such as reverse-engineering of malware network communications and decryption of files encrypted by ransomware. The state of the art for encryption fingerprinting in dynamic execution traces, the ALIGOT algorithm — while effective in identifying a range of known ciphers — suffers from significant scalability limitations: in certain cases, even analyzing traces of a few thousands of machine instructions may require prohibitive time/space. In this work, we propose KALI, an enhanced algorithm based on ALIGOT which significantly reduces time/space complexity and increases scalability. Moreover, we propose a technique to focalize the analysis on encryption used for specific purposes, further improving efficiency. Results show that KALI achieves orders of magnitude reduction in execution time and memory utilization compared to ALIGOT, and processes real-world program traces in minutes to hours.


title: Factors of people-centric security climate: conceptual model and exploratory study in Vietnam

abstract: There is an increasing focus on the persuasive approach to develop a people-centric security climate where employees are aware of the priority of security and perform conscious security behaviour proactively. Employees can evaluate the priority of security as they observe and interact with the security features that constitute the security climate of the workplace. We examined the fundamental challenge that not every employee could recognise those features. In this multi-stage research, we adopted the theoretical lens of symbolic interactionism to advance a conceptual model which explains the relationship between organisation's social networks and the formation of information security climate. A descriptive case study in Vietnam was then conducted to refine the proposed model. The findings validated and extended the dimensions of information security climate, as well as identified the relevant organisation's social networks (i.e. information, affect, and power) that lead to its formation.


title: Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes

abstract: We extend neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing a trainable memory addressing scheme. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing strategies including both linear and nonlinear ones. We implement the D-NTM with both continuous, differentiable and discrete, non-differentiable read/write mechanisms. We investigate the mechanisms and effects of learning to read and write into a memory through experiments on Facebook bAbI tasks using both a feedforward and GRUcontroller. The D-NTM is evaluated on a set of Facebook bAbI tasks and shown to outperform NTM and LSTM baselines. We have done extensive analysis of our model and different variations of NTM on bAbI task. We also provide further experimental results on sequential pMNIST, Stanford Natural Language Inference, associative recall and copy tasks.


Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing


Formal analysis of Facebook Connect Single Sign-On authentication protocol ?


title: Recursive Sampling for the Nystrom Method

abstract: We give the first algorithm for kernel Nystrom approximation that runs in linear time in the number of training points and is provably accurate for all kernel matrices, without dependence on regularity or incoherence conditions. The algorithm projects the kernel onto a set of s landmark points sampled by their ridge leverage scores, requiring just O(ns) kernel evaluations and O(ns^2) additional runtime. While leverage score sampling has long been known to give strong theoretical guarantees for Nystrom approximation, by employing a fast recursive sampling scheme, our algorithm is the first to make the approach scalable. Empirically we show that it finds more accurate kernel approximations in less time than popular techniques such as classic Nystrom approximation and the random Fourier features method.


title: Using User Created Game Reviews for Sentiment Analysis: A Method for Researching User Attitudes

abstract: This paper presents a method for gathering and evaluating user attitudes towards previously released video games. All user reviews from two video game franchise were collected. The most frequently ...


title: Classification in Pattern Recognition: A Review

abstract: Pattern Recognition is one of the very important and actively searched trait or branch of artificial intelligence. It is the science which tries to make machines as intelligent as human to recognize patterns and classify them into desired categories in a simple and reliable way. This review paper introduces the basic concepts of pattern recognition, the underlying system architecture and provides the understanding of various research models and related algorithms for classification and clustering.


title: Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures

abstract: This paper introduces a novel approach for generating videos called Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also perform text-to-video generation which, to the best of our knowledge, makes it the first approach of its kind. It combines a Variational Autoencoder(VAE) with a Recurrent Attention Mechanism in a novel manner to create a temporally dependent sequence of frames that are gradually formed over time. The recurrent attention mechanism in Sync-DRAW attends to each individual frame of the video in sychronization, while the VAE learns a latent distribution for the entire video at the global level. Our experiments with Bouncing MNIST, KTH and UCF-101 suggest that Sync-DRAW is efficient in learning the spatial and temporal information of the videos and generates frames with high structural integrity, and can generate videos from simple captions on these datasets.


title: Inferring Touch from Motion in Real World Data

abstract: Most modern smartphones are equipped with motion sensors to measure the movement and orientation of the device. On Android and iOS, accessing the motion sensors does not require any special permissions. On the other hand, touch input is only available to the application currently in the foreground because it may reveal sensitive information such as passwords. In this paper, we present a side channel attack on touch input by analyzing motion sensor readings. Our data set contains more than a million gestures from 1’493 users with 615 distinct device models. To infer touch from motion inputs, we use a classifier based on the Dynamic Time Warping algorithm. The evaluation shows that our method performs significantly better than random guessing in real world usage scenarios.


title: A multicenter study of ranitidine treatment of duodenal ulcers in the United States

abstract: Treatment of duodenal ulcer with the histamine H2-receptor antagonist, ranitidine, was assessed in a double-blind, randomized, multicenter trial in which patients were treated for two consecutive 4-week periods with ranitidine 150 mg b.i.d. or a placebo. All patients were allowed to take antacids as necessary for symptoms. Three hundred eighty-two patients were entered and 355 completed the first 4-week trial period. Ranitidine significantly improved healing at 2 weeks (37 versus 19%, p


title: AIPS 2000 Planning Competition: The Fifth International Conference on Artificial Intelligence Planning and Scheduling Systems

abstract: The planning competition has become a regular part of the biennial Artificial Intelligence Planning and Scheduling (AIPS) conferences. AIPS'98 featured the very first competition, and for AIPS'00, we built on this foundation to run the second competition. The 2000 competition featured a much larger group of participants and a wide variety of different approaches to planning. Some of these approaches were refinements of known techniques, and others were quite different from anything that had been tried before. Besides the dramatic increase in participation, the 2000 competition demonstrated that planning technology has taken a giant leap forward in performance since 1998. The 2000 competition featured planning systems that were orders of magnitude faster than the planners of just two years prior. This article presents an overview of the competition and reviews the main results.


title: Intent detection using semantically enriched word embeddings

abstract: State-of-the-art targeted language understanding systems rely on deep learning methods using 1-hot word vectors or off-the-shelf word embeddings. While word embeddings can be enriched with information from semantic lexicons (such as WordNet and PPDB) to improve their semantic representation, most previous research on word-embedding enriching has focused on improving intrinsic word-level tasks such as word analogy and antonym detection. In this work, we enrich word embeddings to force semantically similar or dissimilar words to be closer or farther away in the embedding space to improve the performance of an extrinsic task, namely, intent detection for spoken language understanding. We utilize several semantic lexicons, such as WordNet, PPDB, and Macmillan Dictionary to enrich the word embeddings and later use them as initial representation of words for intent detection. Thus, we enrich embeddings outside the neural network as opposed to learning the embeddings within the network, and, on top of the embeddings, build bidirectional LSTM for intent detection. Our experiments on ATIS and a real log dataset from Microsoft Cortana show that word embeddings enriched with semantic lexicons can improve intent detection.


title: A Case for Efficient Hardware / Software Cooperative Management of Storage and Memory

abstract: Most applications manipulate persistent data, yet traditional systems decouple data manipulation from persistence in a two-level storage model. Programming languages and system software manipulate data in one set of formats in volatile main memory (DRAM) using a load/store interface, while storage systems maintain persistence in another set of formats in non-volatile memories, such as Flash and hard disk drives in traditional systems, using a file system interface. Unfortunately, such an approach suffers from the system performance and energy overheads of locating data, moving data, and translating data between the different formats of these two levels of storage that are accessed via two vastly different interfaces. Yet today, new non-volatile memory (NVM) technologies show the promise of storage capacity and endurance similar to or better than Flash at latencies comparable to DRAM, making them prime candidates for providing applications a persistent single-level store with a single load/store interface to access all system data. Our key insight is that in future systems equipped with NVM, the energy consumed executing operating system and file system code to access persistent data in traditional systems becomes an increasingly large contributor to total energy. The goal of this work is to explore the design of a Persistent Memory Manager that coordinates the management of memory and storage under a single hardware unit in a single address space. Our initial simulation-based exploration shows that such a system with a persistent memory can improve energy efficiency and performance by eliminating the instructions and data movement traditionally used to perform I/O operations.


title: Surface Reconstruction of Noisy and Defective Data Sets

abstract: We present a novel surface reconstruction algorithm that can recover high-quality surfaces from noisy and defective data sets without any normal or orientation information. A set of new techniques are introduced to afford extra noise tolerability, robust orientation alignment, reliable outlier removal, and satisfactory feature recovery. In our algorithm, sample points are first organized by an octree. The points are then clustered into a set of monolithically singly-oriented groups. The inside/outside orientation of each group is determined through a robust voting algorithm. We locally fit an implicit quadric surface in each octree cell. The locally fitted implicit surfaces are then blended to produce a signed distance field using the modified Shepardýs method. We develop sophisticated iterative fitting algorithms to afford improved noise tolerance both in topology recognition and geometry accuracy. Furthermore, this iterative fitting algorithm, coupled with a local model selection scheme, provides a reliable sharp feature recovery mechanism even in the presence of bad input.


title: CrossFAB: Bridging the Gap between Personal Fabrication Research in HCI, Computer Graphics, Robotics, Art, Architecture, and Material Science.

abstract: Since 2012, personal fabrication has emerged as a new topic in the HCI community with an increasing number of publications every year. While within HCI, fabrication researchers have already built a strong community; almost no collaboration with other related disciplines, such as computer graphics, robotics, art, architecture, and material science, exists. We believe that especially in the context of HCI, collaboration with other fields can provide valuable input and create new perspectives on HCI challenges. This one-day workshop aims at bridging the gap between related disciplines in the area of personal fabrication by bringing together researchers from different fields. The focus of this workshop is to form connections across disciplines that can give rise to ongoing collaborations and to inspire HCI researchers to reach out into other topic areas for future research.


title: Variety in a Meal Enhances Food Intake in Man 1

abstract: We find that in man satiety can be partly specific to foods eaten [12]. The possibility that this specificity of satiety leads to overeating if a wide variety of foods is readily available is tested here. The intakes of subjects offered a variety of foods in succession during a meal were compared to intakes when the same food was offered throughout. Subjects (n=36) ate a third more when offered sandwiches with four different fillings than when just one filling was offered (p<0.001). In another study subjects (n=24) ate significantly more when three flavors of yogurt (hazelnut, blackcurrant, orange) which were distinctive in taste, texture and color were offered than when offered just one of the flavors (p<0.01), even if the flavor was the favorite (p<0.01). However, when subjects (n=24) were offered three flavors of yogurt (strawberry, raspberry, cherry) which differed only in taste there was no enhancement of intake when the variety was offered. Having a variety of foods presented in succession during a meal enhances intake, and the more different the foods are the greater the enhancement is likely to be.


MEI: Mutual Enhanced Infinite Community–Topic Model for Analyzing Text-Augmented Social Networks


title: Beyond block I/O: Rethinking traditional storage primitives

abstract: Over the last twenty years the interfaces for accessing persistent storage within a computer system have remained essentially unchanged. Simply put, seek, read and write have defined the fundamental operations that can be performed against storage devices. These three interfaces have endured because the devices within storage subsystems have not fundamentally changed since the invention of magnetic disks. Non-volatile (flash) memory (NVM) has recently become a viable enterprise grade storage medium. Initial implementations of NVM storage devices have chosen to export these same disk-based seek/read/write interfaces because they provide compatibility for legacy applications. We propose there is a new class of higher order storage primitives beyond simple block I/O that high performance solid state storage should support. One such primitive, atomic-write, batches multiple I/O operations into a single logical group that will be persisted as a whole or rolled back upon failure. By moving write-atomicity down the stack into the storage device, it is possible to significantly reduce the amount of work required at the application, filesystem, or operating system layers to guarantee the consistency and integrity of data. In this work we provide a proof of concept implementation of atomic-write on a modern solid state device that leverages the underlying log-based flash translation layer (FTL). We present an example of how database management systems can benefit from atomic-write by modifying the MySQL InnoDB transactional storage engine. Using this new atomic-write primitive we are able to increase system throughput by 33%, improve the 90th percentile transaction response time by 20%, and reduce the volume of data written from MySQL to the storage subsystem by as much as 43% on industry standard benchmarks, while maintaining ACID transaction semantics.


title: Attenuate Influence of Parasitic Elements in 13.56-MHz Inverter for Wireless Power Transfer Systems

abstract: At 13.56 MHz, the circuit parasitic elements strongly affect the stability, performance, and efficiency of the inverter. In this paper, the effect of parasitic elements in the 13.56-MHz half-bridge inverter is analyzed in detail based on the point of view of the high-frequency ringing in the circuit. A proposed inverter design that aims to attenuate the influence of the parasitic elements is presented, including optimized printed circuit board (PCB) design and ringing damping design. The simulation and experiment results show that the proposed PCB design not only reduces the parasitic inductance in the ringing loop but also improves the stability of the inverter by avoiding the subresonant at low frequency and reducing the radiation electromagnetic interference noise. This paper also points out that the ringing current exists in the circuit even with the proposed optimized PCB design lead to the instability of the inverter at high voltage operating condition. The proposed damping circuit significantly suppresses the ringing in the circuit and improves the stability as well as the efficiency of the inverter. The experiment results confirm that by using the proposed design, the half-bridge inverter module using Silicon mosfet is stable at 13.56-MHz switching frequency and its efficiency obtains ${\text{93.1}}\%$ at 1.2-kW output power.


PATTERNS OF NEIGHBORHOOD RACIAL AND ETHNIC CHANGE IN MULTIETHNIC METROS, 1970-2010 *


title: An Application of UTAUT2 on Social Recommender Systems: Incorporating Social Information for Performance Expectancy

abstract: The recently proposed extended Unified Theory of Acceptance and Use of Technology (UTAUT2) offers new opportunities for exploring the acceptance of consumer technologies. This study utilizes UTAUT2 to explore the user acceptance of social recommender systems that have become more attractive owing to improved content personalization and adaptation to user preferences. Scholars have shown that these systems could improve a recommendation's accuracy. However, the UTAUT2's applicability and the explanation of performance expectancy for social recommender systems are still unclear. We developed a UTAUT2-based framework and tested it in a quantitative study with 266 participants. The structural equation model results show that UTAUT2 is applicable in the context of social recommender systems. Furthermore, the user's social network information, profile information, and reading behavior positively influence performance expectancy and the intention to adopt a social recommender system. Therefore, incorporating social information might overcome the shortcomings of other classic recommender systems.


title: The evolution, challenges, and future of knowledge representation in product design systems

abstract: Product design is a highly involved, often ill-defined, complex and iterative process, and the needs and specifications of the required artifact get more refined only as the design process moves toward its goal. An effective computer support tool that helps the designer make better-informed decisions requires efficient knowledge representation schemes. In today's world, there is a virtual explosion in the amount of raw data available to the designer, and knowledge representation is critical in order to sift through this data and make sense of it. In addition, the need to stay competitive has shrunk product development time through the use of simultaneous and collaborative design processes, which depend on effective transfer of knowledge between teams. Finally, the awareness that decisions made early in the design process have a higher impact in terms of energy, cost, and sustainability, has resulted in the need to project knowledge typically required in the later stages of design to the earlier stages. Research in design rationale systems, product families, systems engineering, and ontology engineering has sought to capture knowledge from earlier product design decisions, from the breakdown of product functions and associated physical features, and from customer requirements and feedback reports. VR (Virtual reality) systems and multidisciplinary modeling have enabled the simulation of scenarios in the manufacture, assembly, and use of the product. This has helped capture vital knowledge from these stages of the product life and use it in design validation and testing. While there have been considerable and significant developments in knowledge capture and representation in product design, it is useful to sometimes review our position in the area, study the evolution of research in product design, and from past and current trends, try and foresee future developments. The goal of this paper is thus to review both our understanding of the field and the support tools that exist for the purpose, and identify the trends and possible directions research can evolve in the future.


title: A Combinatorial Optimization for Energy-Efficient Mobile Cloud Offloading over Cellular Networks

abstract: Recently, mobile cloud offloading is a promising technique to deal with the increasingly complex applications on mobile devices, meeting the ever- increasing energy requirements. However, cloud offloading with multiple mobile devices may cause considerable mutual interference, which may result in intolerable time delay and more energy consumption. In this paper, a novel offloading decision method is investigated to minimize the total energy consumption of mobile devices over cellular networks. Generally, mobile devices can execute a sequence of tasks in parallel with different characteristics, i.e., communication- intensive and computation-intensive. And recent advances show that only computation-intensive tasks are applicable to be offloaded for energy saving. The offloading decision issue is formulated as a NP- hard combinatorial optimization problem with the time deadline and communication quality constraints. Combining the problem linearization method and decision variables mapping from integer to the real domain, a rapid and efficient iterative approximation method is proposed, helping the cloud controller to select the best tasks for offloading aiming at minimizing the total energy consumption. Numerical simulation demonstrates that considerable energy can be saved with the proposed task offloading method in mobile cloud scenarios.


title: The SPEC cloud group's research vision on FaaS and serverless architectures

abstract: Cloud computing enables an entire ecosystem of developing, composing, and providing IT services. An emerging class of cloud-based software architectures, serverless, focuses on providing software architects the ability to execute arbitrary functions with small overhead in server management, as Function-as-a-service (FaaS). However useful, serverless and FaaS suffer from a community problem that faces every emerging technology, which has indeed also hampered cloud computing a decade ago: lack of clear terminology, and scattered vision about the field. In this work, we address this community problem. We clarify the term serverless, by reducing it to cloud functions as programming units, and a model of executing simple and complex (e.g., workflows of) functions with operations managed primarily by the cloud provider. We propose a research vision, where 4 key directions (perspectives) present 17 technical opportunities and challenges.


American Heart Association/american College of Cardiology Foundation/heart Rhythm Society scientific statement on noninvasive risk stratification techniques for identifying patients at risk for sudden cardiac death: a scientific statement from the American Heart Association Council on Clinical Cardi


title: Image and Video-Based Artistic Stylisation

abstract: Non-photorealistic rendering (NPR) is a combination of computer graphics and computer vision that produces renderings in various artistic, expressive or stylized ways such as painting and drawing. This book focuses on image and video based NPR, where the input is a 2D photograph or a video rather than a 3D model. 2D NPR techniques have application in areas as diverse as consumer and professional digital photography and visual effects for TV and film production. The book covers the full range of the state of the art of NPR with every chapter authored by internationally renowned experts in the field, covering both classical and contemporary techniques. It will enable both graduate students in computer graphics, computer vision or image processing and professional developers alike to quickly become familiar with contemporary techniques, enabling them to apply 2D NPR algorithms in their own projects.


title: A Role for NBR1 in Autophagosomal Degradation of Ubiquitinated Substrates

abstract: Autophagy is a catabolic process where cytosolic cellular components are delivered to the lysosome for degradation. Recent studies have indicated the existence of specific receptors, such as p62, which link ubiquitinated targets to autophagosomal degradation pathways. Here we show that NBR1 (neighbor of BRCA1 gene 1) is an autophagy receptor containing LC3- and ubiquitin (Ub)-binding domains. NBR1 is recruited to Ub-positive protein aggregates and degraded by autophagy depending on an LC3-interacting region (LIR) and LC3 family modifiers. Although NBR1 and p62 interact and form oligomers, they can function independently, as shown by autophagosomal clearance of NBR1 in p62-deficient cells. NBR1 was localized to Ub-positive inclusions in patients with liver dysfunction, and depletion of NBR1 abolished the formation of Ub-positive p62 bodies upon puromycin treatment of cells. We propose that NBR1 and p62 act as receptors for selective autophagosomal degradation of ubiquitinated targets.


title: Determining Optical Flow

abstract: Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. A method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. An iterative implementation is shown which successfully computes the optical flow for a number of synthetic image sequences. The algorithm is robust in that it can handle image sequences that are quantified rather coarsely in space and time. It is also insensitive to quantization of brightness levels and additive noise. Examples are included where the assumption of smoothness is violated at singular points or along lines in the image.


title: Towards A Computational Model of Poetry Generation

abstract: Proceedings of the AISB'00 Symposium on Creative; Cultural Aspects and Applications of AI; Cognitive Science


title: Multi-label learning: a review of the state of the art and ongoing research

abstract: Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. This study presents an up-to-date overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed, pitfalls and guidelines, and ongoing research are presented. WIREs Data Mining Knowl Discov 2014, 4:411-444. doi: 10.1002/widm.1139


title: Big Data in Medical Applications and Health Care

abstract: Big Data can unify all patient related data to get a 360-degree view of the patient to analyze and predict outcomes. It can improve clinical practices, new drug development and health care financing process. It offers a lot of benefits such as early disease detection, fraud detection and better healthcare quality and efficiency. This paper introduces the Big Data concept and characteristics, health care data and some major issues of Big Data. These issues include Big Data benefits, its applications and opportunities in medical areas and health care. Methods and technology progress about Big Data are presented in this study. Big Data challenges in medical applications and health care are also discussed.


title: The Complexity of Quantum States and Transformations: From Quantum Money to Black Holes

abstract: These are lecture notes from a weeklong course in quantum complexity theory taught at the Bellairs Research Institute in Barbados, February 21-25, 2016. The focus is quantum circuit complexity---i.e., the minimum number of gates needed to prepare a given quantum state or apply a given unitary transformation---as a unifying theme tying together several topics of recent interest in the field. Those topics include the power of quantum proofs and advice states; how to construct quantum money schemes secure against counterfeiting; and the role of complexity in the black-hole information paradox and the AdS/CFT correspondence (through connections made by Harlow-Hayden, Susskind, and others). The course was taught to a mixed audience of theoretical computer scientists and quantum gravity / string theorists, and starts out with a crash course on quantum information and computation in general.


title: Towards Image Understanding from Deep Compression without Decoding

abstract: Motivated by recent work on deep neural network (DNN)-based image compression methods showing potential improvements in image quality, savings in storage, and bandwidth reduction, we propose to perform image understanding tasks such as classification and segmentation directly on the compressed representations produced by these compression methods. Since the encoders and decoders in DNN-based compression methods are neural networks with feature-maps as internal representations of the images, we directly integrate these with architectures for image understanding. This bypasses decoding of the compressed representation into RGB space and reduces computational cost. Our study shows that accuracies comparable to networks that operate on compressed RGB images can be achieved while reducing the computational complexity up to $2\times$. Furthermore, we show that synergies are obtained by jointly training compression networks with classification networks on the compressed representations, improving image quality, classification accuracy, and segmentation performance. We find that inference from compressed representations is particularly advantageous compared to inference from compressed RGB images for aggressive compression rates.


title: Measurements and Simulations of the Convective Heat Transfer Coefficients on the End Windings of an Electrical Machine

abstract: The prediction of the thermal behavior of an electrical machine in the design process basically depends on the used boundary conditions such as the convective heat transfer coefficient. Due to the complicated shape of the end windings, the heat transfer coefficient has to be identified empirically by using thermopiles as heat flux sensors placed at certain positions on the end windings, because an analytical derivation is not feasible. This paper presents the results of the measured values of the heat transfer on the end windings of an electrical machine. The gained coefficients are compared to the results obtained by numerical simulations using commercial software for computational fluid dynamics. The simulation results are discussed according to the validity of the gained heat transfer coefficient. The configuration of the simulation has been tested on a cylindrical shape in a cross-flow.


title: An Effective Immersive Cyber Security Awareness Learning Platform for Businesses in the Hospitality Sector

abstract: The rapid digitalisation of the hospitality industry over recent years has brought forth many new points of attack for consideration. The hasty implementation of these systems has created a reality in which businesses are using the technical solutions, but employees have very little awareness when it comes to the threats and implications that they might present. This gap in awareness is further compounded by the existence of preestablished, often rigid, cultures that drive how hospitality businesses operate. Potential attackers are recognising this and the last two years have seen a huge increase in cyber-attacks within the sector.Attempts at addressing the increasing threats have taken the form of technical solutions such as encryption, access control, CCTV, etc. However, a high majority of security breaches can be directly attributed to human error. It is therefore necessary that measures for addressing the rising trend of cyber-attacks go beyond just providing technical solutions and make provision for educating employees about how to address the human elements of security. Inculcating security awareness amongst hospitality employees will provide a foundation upon which a culture of security can be created to promote the seamless and secured interaction of hotel users and technology.One way that the hospitality industry has tried to solve the awareness issue is through their current paper-based training. This is unengaging, expensive and presents limited ways to deploy, monitor and evaluate the impact and effectiveness of the content. This leads to cycles of constant training, making it very hard to initiate awareness, particularly within those on minimum waged, short-term job roles.This paper presents a structured approach for eliciting industry requirement for developing and implementing an immersive Cyber Security Awareness learning platform. It used a series of over 40 interviews and threat analysis of the hospitality industry to identify the requirements for designing and implementing cyber security program which encourage engagement through a cycle of reward and recognition. In particular, the need for the use of gamification elements to provide an engaging but gentle way of educating those with little or no desire to learn was identified and implemented. Also presented is a method for guiding and monitoring the impact of their employee’s progress through the learning management system whilst monitoring the levels of engagement and positive impact the training is having on the business.


title: Neural correlations, population coding and computation

abstract: How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.


title: PGQ: Combining policy gradient and Q-learning

abstract: Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advantage of off-policy data. In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experience from a replay buffer. This is motivated by making a connection between the fixed points of the regularized policy gradient algorithm and the Q-values. This connection allows us to estimate the Q-values from the action preferences of the policy, to which we apply Q-learning updates. We refer to the new technique as 'PGQL', for policy gradient and Q-learning. We also establish an equivalency between action-value fitting techniques and actor-critic algorithms, showing that regularized policy gradient techniques can be interpreted as advantage function learning algorithms. We conclude with some numerical examples that demonstrate improved data efficiency and stability of PGQL. In particular, we tested PGQL on the full suite of Atari games and achieved performance exceeding that of both asynchronous advantage actor-critic (A3C) and Q-learning.


title: Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

abstract: Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakly supervised learning. In this case, the supervised information is restricted to binary labels that indicate the absence/presence of object instances in the image, without their locations. We follow a multiple-instance learning approach that iteratively trains the detector and infers the object locations in the positive training images. Our main contribution is a multi-fold multiple instance learning procedure, which prevents training from prematurely locking onto erroneous object locations. This procedure is particularly important when using high-dimensional representations, such as Fisher vectors and convolutional neural network features. We also propose a window refinement method, which improves the localization accuracy by incorporating an objectness prior. We present a detailed experimental evaluation using the PASCAL VOC 2007 dataset, which verifies the effectiveness of our approach.


Using Hardware Counters to Predict Vectorization


title: Diurnal and seasonal fluctuations in wood ant (Formica polyctena) nest temperature in two geographically distant populations along a south – north gradient

abstract: Temperature changes were recorded continuously in eleven nests of Formica polyctena located in Southern Finland and the Czech Republic. Nest temperatures were recorded in the Czech site from 2003 to 2005 and in Finland during 2004 – 2005. Seasonal changes in nest temperature were very similar in both locations. During summer, the ants maintained a relatively high and constant temperature in their nest in both locations. Summer nest temperatures in Finland were higher than those in Czech during 2004 –2005 but did not differ from those in Czech during 2003– 2004. This indicated that the geographical differences were less important than difference between seasons. Nest temperature peaked in June. During summer, daily temperature fluctuations were significantly lower in Finland than in Czechia probably due to shorter nights in Finland. Apparent differences in daily temperature patterns were found in nests with different moisture in both locations. In dry nests, temperature peaked in the evening and decreased overnight, whereas the apparent increase was delayed to the night in wet nests. The spring increase in temperature in wet nests was much steeper that that in the dry ones when whole spring period is considered. During one day, temperatures fluctuated much more in dry nests than in wet ones. These differences seem to be caused by differences in thermal capacity between dry and wet nests.


title: Method validation and application of protein biomarkers: basic similarities and differences from biotherapeutics.

abstract: Protein drug development and biomarkers share common bioanalytical technologies that are applied for different purposes. A fit-for-purpose approach should be used for biomarker assays at various stages of novel biomarker development and their application to drug development. Biomarker quantifications can be absolute or relative, depending upon the characteristics of the standard curve, which include the reference standard, substituted matrix and parallelism. Appropriate method-validation experiments should be carried out on sample collection, relative accuracy and precision, range finding, parallelism, selectivity, specificity and stability in order to meet the need for exploratory or advanced application that is specified for a study. The interaction of a biotherapeutic with the target ligand or inter-related biomarkers should be taken into consideration for method platform choice and validation. Direct adoption of commercial diagnostic kits can produce confounding data. Therefore, kit comparison, modifi...


title: Platform-as-a-Service Architecture for Real-Time Quality of Service Management in Clouds

abstract: Cloud computing offers the potential to dramatically reduce the cost of software services through the commoditization of information technology assets and on-demand usage patterns. However, the complexity of determining resource provision policies for applications in such complex environments introduces significant inefficiencies and has driven the emergence of a new class of infrastructure called Platform-as-a-Service (PaaS). In this paper, we present a novel PaaS architecture being developed in the EU IST IRMOS project targeting real-time Quality of Service (QoS) guarantees for online interactive multimedia applications. The architecture considers the full service lifecycle including service engineering, service level agreement design, provisioning and monitoring. QoS parameters at both application and infrastructure levels are given specific attention as the basis for provisioning policies in the context of temporal constraints. The generic applicability of the architecture is being verified and validated through implemented scenarios from three important application sectors (film post-production, virtual augmented reality for engineering design, collaborative e-Learning in virtual worlds).


title: A clock-less ultra-low power bit-serial LVDS link for Address-Event multi-chip systems

abstract: We present a power efficient clock-less fully asynchronous bit-serial Low Voltage Differential Signaling (LVDS) link with event-driven instant wake-up and self-sleep features, optimized for high speed inter-chip communication of asynchronous address-events between neuromorphic chips. The proposed LVDS link makes use of the Level-Encoded Dual-Rail (LEDR) representation and a token-ring architecture to encode and transmit data, avoiding the use of conventional large ClockData Recovery (CDR) modules with power-hungry DLL or PLL circuits. We implemented the LVDS circuits in a device fabricated with a standard 0.18 um CMOS process. The total silicon area used for such block is of 0.14 mm^2. We present experimental measurement results to demonstrate that, with a bit rate of 1.5 Gbps and an event width of 32-bit, the proposed LVDS link can achieve transmission event rates of 35.7 M Events/second with current consumption of 19.3 mA and 3.57 mA for receiver and transmitter blocks, respectively. Given the clock-less and instant on/off design choices made, the power consumption of the whole link depends linearly on the data transmission rate. We show that the current consumption can go down to sub-uA for low event rates (e.g., <1k Events/second), with a floor of 80 nA for transmitter and 42 nA for receiver, determined mainly by static off-leakage currents.


Bengali character recognition using Bidirectional Associative Memories ( BAM ) neural network


title: A Wavelet-Based Anytime Algorithm for K-Means Clustering of Time Series

abstract: The emergence of the field of data mining in the last decade has sparked an increasing interest in clustering of time series. Although there has been much research on clustering in general, most classic machine learning and data mining algorithms do not work well for time series due to their unique structure. In particular, the high dimensionality, very high feature correlation, and the (typically) large amount of noise that characterize time series data present a difficult challenge. In this work we address these challenges by introducing a novel anytime version of k-Means clustering algorithm for time series. The algorithm works by leveraging off the multi-resolution property of wavelets. In particular, an initial clustering is performed with a very coarse resolution representation of the data. The results obtained from this “quick and dirty” clustering are used to initialize a clustering at a slightly finer level of approximation. This process is repeated until the clustering results stabilize or until the “approximation” is the raw data. In addition to casting k-Means as an anytime algorithm, our approach has two other very unintuitive properties. The quality of the clustering is often better than the batch algorithm, and even if the algorithm is run to completion, the time taken is typically much less than the time taken by the original algorithm. We explain, and empirically demonstrate these surprising and desirable properties with comprehensive experiments on several publicly available real data sets.


title: Fast calculation of inverse square root with the use of magic constant $-$ analytical approach

abstract: We present a mathematical analysis of transformations used in fast calculation of inverse square root for single-precision floating-point numbers. Optimal values of the so called magic constants are derived in a systematic way, minimizing either relative or absolute errors. We show that the value of the magic constant can depend on the number of NewtonRaphson iterations. We present results for one and two iterations.


title: Learning Dynamic Bayesian Networks

abstract: This paper presents a probabilistic framework for learning models of temporal data. We express these models using the Bayesian network formalism, a marriage of probability theory and graph theory in which dependencies between variables are expressed graphically. The graph not only allows the user to understand which variables affect which other ones, but also serves as the backbone for efficiently computing marginal and conditional probabilities that may be required for inference and learning.


title: Cloud Mobile Media: Reflections and Outlook

abstract: This paper surveys the emerging paradigm of cloud mobile media. We start with two alternative perspectives for cloud mobile media networks: an end-to-end view and a layered view. Summaries of existing research in this area are organized according to the layered service framework: i) cloud resource management and control in infrastructure-as-a-service (IaaS), ii) cloud-based media services in platform-as-a-service (PaaS), and iii) novel cloud-based systems and applications in software-as-a-service (SaaS). We further substantiate our proposed design principles for cloud-based mobile media using a concrete case study: a cloud-centric media platform (CCMP) developed at Nanyang Technological University. Finally, this paper concludes with an outlook of open research problems for realizing the vision of cloud-based mobile media.


title: Face spoofing detection with highlight removal effect and distortions

abstract: With rapid development of face recognition and detection techniques, the face has been frequently used as a biometric to find illegitimate access. It relates to a security issues of system directly, and hence, the face spoofing detection is an important issue. However, correctly classifying spoofing or genuine faces is challenging due to diverse environment conditions such as brightness and color of a face skin. Therefore we propose a novel approach to robustly find the spoofing faces using the highlight removal effect, which is based on the reflection information. Because spoofing face image is recaptured by a camera, it has additional light information. It means that spoofing image could have much more highlighted areas and abnormal reflection information. By extracting these differences, we are able to generate features for robust face spoofing detection. In addition, the spoofing face image and genuine face image have distinct textures because of surface material of medium. The skin and spoofing medium are expected to have different texture, and some genuine image characteristics are distorted such as color distribution. We achieve state-of-the-art performance by concatenating these features. It significantly outperforms especially for the error rate.


title: Identity-Based Broadcast Encryption with Constant Size Ciphertexts and Private Keys

abstract: This paper describes the first identity-based broadcast encryption scheme (IBBE) with constant size ciphertexts and private keys. In our scheme, the public key is of size linear in the maximal size m of the set of receivers, which is smaller than the number of possible users (identities) in the system. Compared with a recent broadcast encryption system introduced by Boneh, Gentry and Waters (BGW), our system has comparable properties, but with a better efficiency: the public key is shorter than in BGW. Moreover, the total number of possible users in the system does not have to be fixed in the setup.


title: Wi-friend: Identifying potential real life friends nearby

abstract: Nowadays, with the help of various on-line social networking applications, one can freely interact with any person in their friends list. However, in most conditions, people in your friends list are those you know a priori, either via face-to-face talking or introducing by a common friend. This way of establishing ones friends list can miss many potential friends nearby. These ‘potential real life friends nearby are those who are invisible to you (you do not know before), physically close to you (e.g. taking the same subway for commuting, doing exercises at the same time slot), and share the same interest with you. Correspondingly, we developed Wi-friend. Wi-friend, once installed in your mobile phone, can help to find these ‘potential friends nearby’. Using Wi-friend, one only needs to briefly specify his/her interest. Then the smartphone starts to look for nearby people with the same interest and add each other into ones friends list automatically. One special feature of Wi-friend is that it does not rely on Internet connection. We leverage the Wi-Fi tethering technique, and let ones smartphone to switch between Wi-Fi hot-spot mode and Wi-Fi client mode to exchange information with nearby people. In addition, Wi-friend can dynamically determine how long to stay in each mode to maximize the number of people who can exchange information. Extensive experiments and simulations demonstrated the technical feasibility and the effectiveness of our Wi-friend system.