Reviewerly/exHarmony huggingface dataset:
Boosting Temporal Binary Coding for Large-Scale Video Search

In recent years, there has been an explosive increase in the amount of existing visual data. Hashing techniques have been successfully applied to deal with the large-scale nearest neighbor search problem among data on this massive scale. However, existing hashing methods usually learn a single hash code for each data point, and only by taking the content correlations among them into account. In practice, however, when handling complex visual data such as video, strong temporal relations exist among the successive frames. Moreover, if the preferred performance for large-scale video search is to be delivered, multiple hash codes are required for each data point in order to build multiple hash table indices. To address these problems, in this paper, we first study the multi-table learning problem for video search and attempt to learn binary codes by capturing the intrinsic video similarities from both the visual and the temporal aspects. By regarding the search over multiple tables as an ensemble prediction, the whole multi-table learning problem can be solved in a boosting learning manner to complementarily cover the nearest neighbors. For each table, a temporal binary coding solution is devised that thinks over the intrinsic relations among the visual content and the temporal consistency among the successive frames simultaneously. More specifically, we approximate the intrinsic visual similarities using a low-rank matrix based on sparse, non-negative feature expression. Furthermore, to essentially preserve the temporal consistency, we introduce a subspace rotation to model the variation among the successive frames. Under the boosting learning framework, the binary codes, hash functions and temporal variation of each table can be efficiently and jointly optimized. Extensive experiments on three large video datasets demonstrate that the proposed approach significantly outperforms a number of state-of-the-art hashing methods.
Construction of Machine-Labeled Data for Improving Named Entity Recognition by Transfer Learning

Deep neural networks (DNNs) require a large amount of manually labeled training data to make significant achievements. However, manual labeling is laborious and costly. In this study, we propose a method for automatically generating training data and effectively using the generated data to reduce the labeling cost. The generated data (called “machine-labeled data”) is generated using a bagging-based bootstrapping approach. However, using the machine-labeled data does not guarantee high performance because of errors in the automatic labeling. In order to reduce the impact of mislabeling, we applied a transfer learning approach. The effect of our proposed method was verified with two versions of DNN-based named entity recognition (NER) models: bidirectional LSTM-CRF and vanilla BERT. We conducted NER tasks in two languages (English and Korean). The proposed method results in average F1 scores of 78.87% (3.9% point improvement) with bidirectional LSTM-CRF and 82.08% (1% point improvement) with BERT on three Korean NER datasets. In English, the performance increased by an average of 0.45% points with the two DNN-based models. The proposed NER systems outperform the baseline systems in both languages without the need for additional manual labeling.
Subgraph Matching With Effective Matching Order and Indexing

Subgraph matching finds all embeddings from a data graph that are identical to a query graph. Recent algorithms work by generating a tree-structured index on the data graph based on the query graph, ordering the vertices path-by-path in the tree, and enumerating the embeddings following the matching order. However, we find such path-based ordering and tree-structured index based enumeration inherently limit the performance due to the lack of consideration on the edges among the vertices across tree paths. To address this problem, we propose an approach that generates the matching order based on a cost model considering both the edges among query vertices and the number of candidates. Furthermore, we create a bigraph index for candidate vertices and their selected neighbors in the data graph, and use this index to perform enumeration along the matching order. Our experiments on both real-world and synthetic datasets show that our method outperforms the state of the art by orders of magnitude.
Feeling numbers

Abstract For means of communication, persuasion is a natural and critical part of conveying a message. Data visualizations, being means of communication themselves, are used as rhetorical instruments, but how they persuade has yet to be fully understood. Based on George Campbell’s rhetorical theory, this paper presents the results of an empirical study testing the effectiveness of appeals to emotion through proximity techniques – the contextual framing of a visualization. The findings indicate that people feel greater interest towards a topic when the visualized data are more relevant to them, and that data representing events closer in time are more affecting.
QEC: A Quantum Entropy Chip and Its Applications

Quantum phenomena cannot be predicted by the uncertainty principle. As a quantum phenomenon, radioactive decay has been used as an entropy source to generate random numbers. In this article, we present the design and development of an innovative quantum entropy chip (QEC) that produces analog random pulses when emitted alpha particles resulted from radioactive isotope (americium-241) decay hit the sensor. The analog pulse generated by a QEC can be digitized into random numbers by an entropy extractor. The QEC provides security foundation for device authentication as well as a quantum random number generator (QRNG), especially suited for the Internet of Things (IoT) devices due to its small size. We have successfully designed and fabricated the QEC as a wafer for supporting a system-on-chip (SoC) Internet Protocol (IP) so that the QEC can be embedded into a microcontroller unit (MCU) or central processing unit (CPU). In addition, we built a stochastic model to estimate the entropy of the quantum source and evaluated statistical randomness and robustness against temperature, voltage variations, aging effects, and physical attacks. Finally, we demonstrate various applications using the QEC such as side-channel-resistant primitives and device authentication.
Random k-Labelsets Method for Human Activity Recognition with Multi-Sensor Data in Smart Home

In today's world, we are surrounded by ambient sensors everywhere that record our data of activities of daily living. Moreover, the solutions to various applications such as health care, surveillance, home monitoring, and so on are possible by inferring this data. Thus, human activity recognition, especially in the smart home environment, has been a very actively researched problem. Multiple residents in a single home environment pose several challenges making multi-resident activity recognition a daunting task. Therefore, in this paper, we model the Random k-Labelsets method of the Multi-Label Classification to tackle this activity recognition problem. The proposed method not only takes label dependencies into account which is essential for multi-resident activity recognition but also overcomes the drawbacks of other problem transformation methods. Experiments are carried on a real smart home dataset and accuracy, precision and hamming loss are selected as metrics for evaluating the results of the proposed method.
Ripple Effect Identification in Software Applications

Changes are made frequently in software to incorporate new requirements. The changes made to one class are not limited to that particular class, but they also affect other entities. Early identification of these change prone entities is very essential for minimizing future faults in the software applications. Thus, it is very important to develop quality models for identifying the ripple effect of changed classes to effectively utilize the limited resources during the software development lifecycle. Association rule mining is a popular approach suggested in literature, but a major limitation of this approach is its inability to generate recommendations in case of new addition of classes. This article suggests the development of prediction model using learning techniques to overcome this limitation. The authors evaluate the performance of thirteen statistical, ML, and search-based techniques using eight open source software applications in this work. The findings of this study are promising and support the application of SBT and ML techniques for ripple effect identification.
Identification of Outliers in Graph Signals

Outlier detection, or the identification of observations that differ significantly from the norm, is an important aspect of data mining. Conventional outlier detection tools have limited applicability to networks, in which there are interdependencies between the variables. In this paper, we consider the problem of identifying unusual spatial distributions of nodal signals on a graph. Leveraging tools from graph signal processing and statistical analysis, we propose a methodology to identify outliers in graph signals in a computationally efficient manner. Specifically, we examine a projection of the graph signal into a lower dimensional representation that enables easier outlier identification. Additionally, we derive analytical expressions for the outlier bounds. We apply our technique by identifying off-nominal days in the context of the US airport network using aviation delay data.
ROC

Due to the end of Moore’s law and Dennard scaling, we are entering a new era of processors. Computing systems are increasingly facing power and performance challenges due to both device- and circuit-related challenges with resistive and capacitive charging. Non-von Neumann architectures are needed to support future computations through innovative post-Moore’s law architectures. To enable these emerging architectures with high-performance and at ultra-low power, both parallel computation and inter-node communication on-the-chip can be supported using photons. To this end, we introduce ROC, a reconfigurable optical computer that can solve partial differential equations (PDEs). PDE solvers form the basis for many traditional simulation problems in science and engineering that are currently performed on supercomputers. Instead of solving problems iteratively, the proposed engine uses a resistive mesh architecture to solve a PDE in a single iteration (one-shot). Instead of using actual electrical circuits, the physical underlying hardware emulates such structures using a silicon-photonics mesh that splits light into separate pathways, allowing it to add or subtract optical power analogous to programmable resistors. The time to obtain the PDE solution then only depends on the time-of-flight of a photon through the programmed mesh, which can be on the order of 10’s of picoseconds given the millimeter-compact integrated photonic circuit. Numerically validated experimental results show that, over multiple configurations, ROC can achieve several orders of magnitude improvement over state-of-the-art GPUs when speed, power, and size are taken into account. Further, it comes within approximately 90% precision of current numerical solvers. As such, ROC can be a viable reconfigurable, approximate computer with the potential for more precise results when replacing silicon-photonics building blocks with nanoscale photonic lumped-elements.
Applying reranking strategies to route recommendation using sequence-aware evaluation

Venue recommendation approaches have become particularly useful nowadays due to the increasing number of users registered in location-based social networks (LBSNs), applications where it is possible to share the venues someone has visited and establish connections with other users in the system. Besides, the venue recommendation problem has certain characteristics that differ from traditional recommendation, and it can also benefit from other contextual aspects to not only recommend independent venues, but complete routes or venue sequences of related locations. Hence, in this paper, we investigate the problem of route recommendation under the perspective of generating a sequence of meaningful locations for the users, by analyzing both their personal interests and the intrinsic relationships between the venues. We divide this problem into three stages, proposing general solutions to each case: First, we state a general methodology to derive user routes from LBSNs datasets that can be applied in as many scenarios as possible; second, we define a reranking framework that generate sequences of items from recommendation lists using different techniques; and third, we propose an evaluation metric that captures both accuracy and sequentiality at the same time. We report our experiments on several LBSNs datasets and by means of different recommendation quality metrics and algorithms. As a result, we have found that classical recommender systems are comparable to specifically tailored algorithms for this task, although exploiting the temporal dimension, in general, helps on improving the performance of these techniques; additionally, the proposed reranking strategies show promising results in terms of finding a trade-off between relevance, sequentiality, and distance, essential dimensions in both venue and route recommendation tasks.
Hyperspherical Deterministic Sampling Based on Riemannian Geometry for Improved Nonlinear Bingham Filtering

We present a novel geometry-driven scheme for generating equally weighted deterministic samples of Bingham distributions in arbitrary dimensions. Unlike existing approaches, our method provides flexibility in the sampling size with samples satisfying requirements of the unscented transform while approximating higher-order moments of the Bingham distribution. This is done by first using Dirac mixture approximation as a sampling scheme on the tangent plane at the mode with respect to the Bingham density via gnomonic projection. Subsequently, the tangent sigma points are retracted backwards to the hypersphere, after which an on-manifold moment correction is performed via Riemannian optimization. The proposed approach is further applied to quaternion Bingham filtering for recursive orientation estimations. Evaluation results show that the geometry-adaptive sampling scheme gives better tracking accuracy and robustness for nonlinear orientation estimations.
SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual Embedding

Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. First, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Second, traditional text mining methods fail to effectively extract concepts through words and phrases. Third, the textual contents are temporally skewed, which can affect the semantic understanding by multiple time facets. Finally, using knowledge-bases can make the results biased to the content of the external database and deviate the meaning from the input short text corpus. To overcome these challenges, we devise a neural network-based temporal-textual framework that generates the subgraphs with highly correlated authors from short-text contents. Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors. Experimental results show that compared to other knowledge-centered competitors, our multi-aspect vector space model can achieve a higher performance in linking short-text authors. In addition, given the author linking task, the more comprehensive the dataset is, the higher the significance of the extracted concepts will be.
A Simple Video-Based Technique for Measuring Latency in Virtual Reality or Teleoperation

Designers of virtual reality (VR) systems are aware of the need to minimize delays between the user's tracked physical actions and the consequent displayed actions in the virtual environment. Such delays, also referred to as end-to-end latency, are known to degrade user performance and even cause simulator sickness. Though a wide variety of hardware and software design strategies have been used to reduce delays, techniques for measuring and minimizing latency continue to be needed since transmission and switching delays are likely to continue to introduce new sources of latency, especially in wireless mobile environments. This article describes a convenient low-cost technique for measuring end-to-end latencies using a human evaluator and an ordinary consumer camera (e.g., cell phone camera). Since the technique does not depend upon the use of specialized hardware and software, it differs from other methods in that it can easily be used to measure latencies of systems in the specific hardware and software configuration and the relevant performance environments. The achievable measurement accuracy was assessed in an experimental trial. Results indicate a measurement uncertainty below 10 ms. Some refinements to the technique are discussed, which may further reduce the measurement uncertainty to approximately 1 ms.
Abusive Language Detection on Indonesian Online News Comments

Abusive language is an expression used by a person with insulting delivery of any person's aspect. In the modern era, the use of harsh words is often found on the internet, one of them is in the comment section of online news articles which contains harassment, insult, or a curse. An abusive language detection system is important to prevent the negative effect of such comments. Detecting abusive language in the online comment section is a challenge since abusive languages can be expressed in various words. Moreover, only a few studies have been conducted in Indonesian language. In this paper, we present an Indonesian abusive language detection system by tackling this problem as a classification task and solving it using the following classifiers: Naive Bayes, SVM, and KNN. We also performed feature selection procedure based on Mutual Information value between words. The experimental results show that SVM is the best classifier for detecting the abusive language in news comment with an accuracy score of 90,19% and the use of Mutual Information able to improve the classification accuracy by 1.63%. Mutual Information can increase the accuracy performance of the classifier.
Securing IoT RF Fingerprinting Systems with Generative Adversarial Networks

Recently, a number of neural network approaches to physical-layer wireless security have been introduced. In particular, these approaches are able to authenticate the identity of different wireless transmitters by the device-specific imperfections present in their transmitted signals. In this paper, we introduce a weakness in the training protocol of these approaches, namely, that a generative adversarial network (GAN) can be trained to produce signals that are realistic enough to force classifier errors. We show that the GAN can learn to introduce signal imperfections without modifying the bandwidth or data contents of the signal, and demonstrate via experiment that classifiers trained only on transmissions from real devices are vulnerable to this sort of attack. Finally, we demonstrate that by augmenting the training dataset of the classifier with adversarial examples from a different GAN, we are able to strengthen the classifier against this vulnerability.
P-LPN: Towards Real Time Pedestrian Location Perception in Complex Driving Scenes

Semantic segmentation is one of the most critical modules in road scene understanding. In this paper, we focus on the challenging task of pedestrian’s relative location perception in the semantic graph of complex driving scenes. Prevalent research on semantic segmentation mainly concentrate on improving the segmentation accuracy with less attention paid to computational efficiency. Furthermore, little effort has been made in pedestrian location perception in complex driving scenes. For example, current semantic segmentation methods classify all pedestrians as a mono category, regardless of whether the pedestrians are penetrating into the vehicular lane or standing still in the safe sidewalk area. We propose a pedestrian location perception network (P-LPN). P-LPN can produce real-time semantic segmentation while simultaneously providing location inference for each pedestrian in semantic maps. This enables autonomous driving system to categorize pedestrians into different safety levels. We comprehensively evaluated P-LPN on CityScapes benchmark through comparative studies. Our proposal achieved competitive performance in both accuracy and efficiency. It yields quality inference with real-time speed at ~22 fps.
Contextual Documentation Referencing on Stack Overflow

Software engineering is knowledge-intensive and requires software developers to continually search for knowledge, often on community question answering platforms such as Stack Overflow. Such information sharing platforms do not exist in isolation, and part of the evidence that they exist in a broader software documentation ecosystem is the common presence of hyperlinks to other documentation resources found in forum posts. With the goal of helping to improve the information diffusion between Stack Overflow and other documentation resources, we conducted a study to answer the question of how and why documentation is referenced in Stack Overflow threads. We sampled and classified 759 links from two different domains, regular expressions and Android development, to qualitatively and quantitatively analyze the links’ context and purpose, including attribution, awareness, and recommendations. We found that links on Stack Overflow serve a wide range of distinct purposes, ranging from citation links attributing content copied into Stack Overflow, over links clarifying concepts using Wikipedia pages, to recommendations of software components and resources for background reading. This purpose spectrum has major corollaries, including our observation that links to documentation resources are a reflection of the information needs typical to a technology domain. We contribute a framework and method to analyze the context and purpose of Stack Overflow links, a public dataset of annotated links, and a description of five major observations about linking practices on Stack Overflow. Those observations include the above-mentioned purpose spectrum, its interplay with documentation resources and applications domains, and the fact that links on Stack Overflow often lack context in form of accompanying quotes or summaries. We further point to potential tool support to enhance the information diffusion between Stack Overflow and other documentation resources.