Reviewerly/exHarmony huggingface dataset:
An ontology model for content recommendation in personalized learning environment

Personalized Learning Environments (PLEs) are expected to enhance the learning experience by providing tailor-made services based on learner preferences. It is of utmost importance to provide a personalized system which can automatically adapt to learners' learning styles, knowledge level and intelligently recommend resources that would favor and improve the learning. The existing PLEs still exhibit cold-start problems and other issues related with mapping of learning style and learning object. To solve these issues and to improve the dynamicity of the PLEs, an appropriate learner/learning object model is very essential. In this paper, we introduce an ontology model which encapsulates both learner profile and learning object attributes, which can be used for the content recommendation in an e-learning platform. Learner profile is the representation of learner data which includes both static and dynamic characteristics of the learner. The static data is gathered directly from the learner using forms and questionnaires and dynamic data is collected by tracking the behavior of learners, while interacting through a learning management system. The proposed ontology also holds space for learning topics and their corresponding Learning Object (LO) characteristics. The elements which come under the educational category of IEEE LOM standard, is considered for tagging the selected learning objects in the ontology. The JENA API of Java programming language is used for developing the ontology. The data is described using Resource Description Framework (RDF) tools. We have developed an ontology model consisting of an adaptive learner profile and standard LO characteristics which can be used in content recommender systems of e-learning environment.
Tracing with Less Data: Active Learning for Classification-Based Traceability Link Recovery

Previous work has established both the importance and difficulty of establishing and maintaining adequate software traceability. While it has been shown to support essential maintenance and evolution tasks, recovering traceability links between related software artifacts is a time consuming and error prone task. As such, substantial research has been done to reduce this barrier to adoption by at least partially automating traceability link recovery. In particular, recent work has shown that supervised machine learning can be effectively used for automating traceability link recovery, as long as there is sufficient data (i.e., labeled traceability links) to train a classification model. Unfortunately, the amount of data required by these techniques is a serious limitation, given that most software systems rarely have traceability information to begin with. In this paper we address this limitation of previous work and propose an approach based on active learning, which substantially reduces the amount of training data needed by supervised classification approaches for traceability link recovery while maintaining similar performance.
Multilingual aspect clustering for sentiment analysis

In the last few years, there has been growing interest in aspect-based sentiment analysis, which deals with extracting, clustering, and rating the overall opinion about the features of the entity being evaluated. Techniques for aspect extraction can produce an undesirably large number of aspects — with many of those relating to the same product feature. Hence, aspect clustering becomes necessary. Current solutions for aspect clustering are monolingual, but in many practical situations, reviews for a given entity are available in several languages, calling for multilingual integration. In this article, we address the novel task of multilingual aspect clustering, which aims at grouping semantically related aspects extracted from reviews written in several languages. Our method is unsupervised and relies on the contextual information of the aspects, which is represented by word embeddings. This representation allied with a suitable similarity measure allows clustering related aspects. Our experiments on two datasets with five languages each showed that our unsupervised clustering technique achieves results that outperform monolingual baselines adapted to work with multilingual data. We also show the benefits of the multilingual approach compared to using languages in isolation.
Analyzing viewport prediction under different VR interactions

In this paper, we study the problem of predicting a user's viewport movement in a networked VR system (i.e., predicting which direction the viewer will look at shortly). This critical knowledge will guide the VR system through making judicious content fetching decisions, leading to efficient network bandwidth utilization (e.g., up to 35% on LTE networks as demonstrated by our previous work) and improved Quality of Experience (QoE). For this study, we collect viewport trajectory traces from 275 users who have watched popular 360° panoramic videos for a total duration of 156 hours. Leveraging our unique datasets, we compare viewport movement patterns of different interaction modes: wearing a head-mounted device, tilting a smartphone, and dragging the mouse on a PC. We then apply diverse machine learning algorithms - from simple regression to sophisticated deep learning that leverages crowd-sourced data - to analyze the performance of viewport prediction. We find that the deep learning approach is robust for all interaction modes and yields supreme performance, especially when the viewport is more challenging to predict, e.g., for a longer prediction window, or with a more dynamic movement. Overall, our analysis provides key insights on how to intelligently perform viewport prediction in networked VR systems.
Threshold Implementations of $\mathtt{GIFT}$ : A Trade-Off Analysis

Threshold Implementation (TI) is one of the most widely used countermeasure for side channel attacks. Over the years several TI techniques have been proposed for randomizing cipher execution using different variations of secret-sharing and implementation techniques. For instance, sharing without decomposition (4-shares) is the most straightforward implementation of the threshold countermeasure. However, its usage is limited due to its high area requirements. On the other hand, sharing using decomposition (3-shares) countermeasure for cubic non-linear functions significantly reduces area and complexity in comparison to 4-shares. Nowadays, security of ciphers using a side channel countermeasure is of utmost importance. This is due to the wide range of security critical applications from smart cards, battery operated IoT devices, to accelerated crypto-processors. Such applications have different requirements (higher speed, energy efficiency, low latency, small area etc.) and hence need different implementation techniques. Although, many TI strategies and implementation techniques are known for different ciphers, there is no single study comparing these on a single cipher. Such a study would allow a fair comparison of the various methodologies. In this work, we present an in-depth analysis of the various ways in which TI can be implemented for a lightweight cipher. We chose GIFT for our analysis as it is currently one of the most energy-efficient lightweight ciphers. The experimental results show that different implementation techniques have distinct applications. For example, the 4-shares technique is good for applications demanding high throughput whereas 3-shares is suitable for constrained environments with less area and moderate throughput requirements. The techniques presented in the paper are also applicable to other blockciphers. For security evaluation, we performed TVLA (test vector leakage assessment) on all the design strategies. Experiments using up to 50 million traces show that the designs are protected against first-order attacks.
Histogram of Fuzzy Local Spatio-Temporal Descriptors for Video Action Recognition

Feature extraction plays a vital role in visual action recognition. Many existing gradient-based feature extractors, including histogram of oriented gradients, histogram of optical flow, motion boundary histograms, and histogram of motion gradients, build histograms for representing different actions over the spatio-temporal domain in a video. However, these methods require to set the number of bins for information aggregation in advance. Varying numbers of bins usually lead to inherent uncertainty within the process of pixel voting with regard to the bins in the histogram. This article proposes a novel method to handle such uncertainty by fuzzifying these feature extractors. The proposed approach has two advantages: it better represents the ambiguous boundaries between the bins and, thus, the fuzziness of the spatio-temporal visual information entailed in videos; and the contribution of each pixel is flexibly controlled by a fuzziness parameter for various scenarios. The proposed family of fuzzy descriptors and a combination of them are evaluated on two publicly available datasets, demonstrating that the proposed approach outperforms the original counterparts and other state-of-the-art methods.
Parallel Black-Box Complexity With Tail Bounds

We propose a new black-box complexity model for search algorithms evaluating λ search points in parallel.The parallel unary unbiased black-box complexity gives lower bounds on the number of function evaluations every parallel unary unbiased black-box algorithm needs to optimise a given problem.It captures the inertia caused by offspring populations in evolutionary algorithms and the total computational effort in parallel metaheuristics 1 We present complexity results for LeadingOnes and OneMax.Our main result is a general performance limit: we prove that on every function every λ-parallel unary unbiased algorithm needs at least a certain number of evaluations (a function of problem size and λ) to find any desired target set of up to exponential size, with an overwhelming probability.This yields lower bounds for the typical optimisation time on unimodal and multimodal problems, for the time to find any local optimum, and for the time to even get close to any optimum.The power and versatility of this approach is shown for a wide range of illustrative problems from combinatorial optimisation.Our performance limits can guide parameter choice and algorithm design; we demonstrate the latter by presenting an optimal λ-parallel algorithm for OneMax that uses parallelism most effectively.
Highly-Parallel FPGA Accelerator for Simulated Quantum Annealing

Quantum annealing (QA) is a method to find the global optimum of a combinatorial optimization problem by using quantum fluctuations. Quantum annealers such as D-wave are efficient to solve small problems with less than 2048 variables. Simulated quantum annealing on digital computers allows us to solve large real-world problems. However, the processing time increases exponentially with the number of variables. This article proposes a highly-parallel accelerator for simulated quantum annealing exploiting spatial and temporal parallelism. The accelerator is implemented using “open computing language (OpenCL)” on FPGA. For 8,192 spin models, we achieve 145 times speed using 32 Trotters in one FPGA and 290 times speed-up using 64 Trotters in two FPGAs, compared to single-core CPU implementation.
Share, But be Aware: Security Smells in Python Gists

Github Gist is a service provided by Github which is used by developers to share code snippets. While sharing, developers may inadvertently introduce security smells in code snippets as well, such as hard-coded passwords. Security smells are recurrent coding patterns that are indicative of security weaknesses, which could potentially lead to security breaches. The goal of this paper is to help software practitioners avoid insecure coding practices through an empirical study of security smells in publicly-available GitHub Gists. Through static analysis, we found 13 types of security smells with 4,403 occurrences in 5,822 publicly-available Python Gists. 1,817 of those Gists, which is around 31%, have at least one security smell including 689 instances of hard-coded secrets. We also found no significance relation between the presence of these security smells and the reputation of the Gist author. Based on our findings, we advocate for increased awareness and rigorous code review efforts related to software security for Github Gists so that propagation of insecure coding practices are mitigated.
Real-time resource scaling platform for Big Data workloads on serverless environments

The serverless execution paradigm is becoming an increasingly popular option when workloads are to be deployed in an abstracted way, more specifically, without specifying any infrastructure requirements. Currently, such workloads are typically comprised of small programs or even a series of single functions used as event triggers or to process a data stream. Other applications that may also fit on a serverless scenario are stateless services that may need to seamlessly scale in terms of resources, such as a web server. Although several commercial serverless services are available (e.g., Amazon Lambda), their use cases are mostly limited to the execution of functions or scripts that can be adapted to predefined templates or specifications. However, current research efforts point out that it is interesting for the serverless paradigm to evolve from single functions and support more flexible infrastructure units such as operating-system-level virtualization in the form of containers. In this paper we present a novel platform to automatically scale container resources in real time, while they are running, and without any need for reboots. This platform is evaluated using Big Data workloads, both batch and streaming, as representative examples of applications that could be initially regarded as unsuitable for the serverless paradigm considering the currently available services. The results show how our serverless platform can improve the CPU utilization by up to 77% with an execution time overhead of only 6%, while remaining scalable when using a 32-container cluster.
Aiding Code Change Understanding with Semantic Change Impact Analysis

Code reviews are often used as a means for developers to manually examine source code changes to ensure the behavioural effects of a change are well understood. Unfortunately, the behavioural impact of a change can include parts of the system outside of the area syntactically affected by the change. In the context of code reviews this can be problematic, as the impact of a change can extend beyond the diff that is presented to the reviewer. Change impact analysis is a promising technique which could potentially assist developers by helping surface parts of the code not present in the diff but that could be affected by the change. In this work we investigate the utility of change impact analysis as a tool for assisting developers understand the effects of code changes. While we find that traditional techniques may not benefit developers, more precise techniques may reduce time and increase accuracy. Specifically, we propose and study a novel technique which extracts semantic, rather than syntactic, change impact relations from JavaScript commits. We (1) define four novel semantic change impact relations and (2) implement an analysis tool called tool that interprets structural changes over partial JavaScript programs to extract these relations. In a study of 2,000 commits from the version history of three popular NodeJS applications, tool reduced false positives by 9–37% and further reduced the size of change impact sets by 19–91% by splitting up unrelated semantic relations, compared to change impact sets computed with Unix diff and control and data dependencies. Additionally, through a user study in which developers performed code review tasks with tool, we found that reducing false positives and providing stronger semantics had a meaningful impact on their ability to find defects within code change diffs.
Reliability and Scalability of p-Bits Implemented With Low Energy Barrier Nanomagnets

Probabilistic (p-) bits implemented with low energy barrier nanomagnets (LBMs) have recently gained attention because they can be leveraged to perform some computational tasks very efficiently. Although more error-resilient than Boolean computing, p-bit based computing employing LBMs is, however, not completely immune to defects and device-to-device variations. In some tasks (e.g. binary stochastic neurons for machine learning and p-bits for population coding), extended defects, such as variation of the LBM thickness over a significant fraction of the surface, can impair functionality. In this paper, we have examined if unavoidable geometric device-to-device variations can have a significant effect on one of the most critical requirements for probabilistic computing, namely the ability to "program" probability with an external agent, such as a spin-polarized current injected into the LBM. We found that the programming ability is fortunately not lost due to reasonable device-to-device variations. The little variation in the probability versus current characteristic that reasonable device variability causes can be suppressed further by increasing the spin polarization of the current. This shows that probabilistic computing with LBMs is robust against small geometric variations, and hence will be "scalable" to a large number of p-bits.
Integration of Multi-Camera Video Moving Objects and GIS

This work discusses the integration of multi-camera video moving objects (MCVO) and GIS. This integration was motivated by the characteristics of multi-camera videos distributed in the urban environment, namely, large data volume, sparse distribution and complex spatial–temporal correlation of MCVO, thereby resulting in low efficiency of manual browsing and retrieval of videos. To address the aforementioned drawbacks, on the basis of multi-camera video moving object extraction, this paper first analyzed the characteristics of different video-GIS Information fusion methods and investigated the integrated data organization of MCVO by constructing a spatial–temporal pipeline among different cameras. Then, the conceptual integration model of MCVO and GIS was proposed on the basis of spatial mapping, and the GIS-MCVO prototype system was constructed in this study. Finally, this study analyzed the applications and potential benefits of the GIS-MCVO system, including a GIS-based user interface on video moving object expression in the virtual geographic scene, video compression storage, blind zone trajectory deduction, retrieval of MCVO, and video synopsis. Examples have shown that the integration of MCVO and GIS can improve the efficiency of expressing video information, achieve the compression of video data, rapidly assisting the user in browsing video objects from multiple cameras.
Facial Expression Recognition System on SoC FPGA

In this paper, we present a facial expression recognition (FER) system implemented on a SoC FPGA device. Our system can operate independently and automatically classify 7 different types of basic emotions. We designed a processing engine on FPGA that is able to calculate convolutional layers of convolutional neural networks and designed a CNN optimal with a designed hardware for the task of emotions recognition and achieving 66% of accuracy in FER2013 dataset. The whole hardware system is designed on SoC FPGA which can process up to 15 image frames per second at an operating frequency of 130 MHz.
Real-time hand posture recognition using hand geometric features and Fisher Vector

Hand posture recognition (HPR), one of the most effective and intuitive human computer interfaces, has been extensively studied and widely adopted in various multimedia applications. Shape descriptors extracted from a hand contour or silhouette have been proved effective in representing a hand posture. However, it is difficult for these shape-based methods to achieve good balance between accuracy and efficiency. To this end, in this paper, we propose a novel hand shape descriptor based on a set of geometric features (SoGF) and Fisher Vector (FV), for effective and efficient HPR. Three types of geometric features, including distances, angles and curvatures, are extracted from a hand silhouette to form a discriminative local descriptor, and FV is adopted to encode the set of local descriptors for compact hand shape representation. To recognize hand postures, we construct a classifier using a multi-class Support Vector Machine (SVM) with FVs as input. The experimental results on four public HPR datasets show that the proposed method can achieve the mean accuracy of the state-of-the-art methods in real time.
Algorithm and VLSI Architecture Co-Design on Efficient Semi-Global Stereo Matching

Semi-global matching (SGM) is favored for high accuracy real-time stereo matching design as it achieves a good trade-off between disparity image quality and computational complexity. Nevertheless, most of previous SGM designs so far are restricted to the real-time processing of small image resolution and disparity range, or achieve high throughput by simplifying the original algorithm at the penalty of significant disparity image quality degradation. We analyze that the major challenge to efficient SGM design is its memory architecture, including both on-chip memory cost and off-chip memory bandwidth. We address the memory architecture challenge by algorithm and architecture co-design. Based on two observed features of SGM algorithm, i.e. incompleteness and inaccuracy, this paper proposes several efficient techniques to reduce on-chip memory cost and compress off-chip memory bandwidth respectively. Moreover, we also design high throughput and pipelined architecture to implement the proposed techniques. The disparity image quality and hardware efficiency of the proposed SGM design are evaluated on both KITTI2015 and Middlebury V3 stereo datasets. Evaluation results demonstrate that, the throughput of the proposed circuit designs can easily achieve 1080P@30fps at the disparity range of 128, and can reduce the on-chip memory cost and off-chip memory bandwidth by up to 4× and 2× respectively while achieving better or the same disparity image quality, compared with the best reference design techniques.
Realizing Data Features by Deep Nets

This article considers the power of deep neural networks (deep nets) in realizing data features. Based on refined covering number estimates, we find that, to realize data features such as the locality, rotation invariance, and manifold structure, deep nets essentially improve the performances of shallow neural networks (shallow nets) without requiring additional capacity costs. Conversely, to realize some data features, such as the smoothness, we show that deep nets perform similar as shallow nets, provided the depth is not extremely large. Both sides show the advantages and limitations of deep nets in realizing data features and demonstrate that deep nets are not always better than shallow nets.