Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Record Grouping and Consent Management Based on a Public-Private Key Signature Scheme: Theoretical Analysis and Feasibility Study

Background: Clinical and social trials create evidence that enables medical progress. However, the gathering of personal and patient data requires high security and privacy standards. Direct linking of personal information and medical data is commonly hidden through pseudonymization. While this makes unauthorized access to personal medical data more difficult, a centralized pseudonymization list can still pose a security risk. In addition, medical data linked via pseudonyms can still be used for data-driven reidentification.
Anomaly Detection in Surveillance Video Using Pose Estimation

In this paper, we focus on key points detection of a person. We judge the abnormal behavior of the person by detecting the motion of key points of that person. In the starting frame, we select a bounding box. If some numbers of key points are moved out of the bounding box, then the system gives an alert message. So here we will take you through a new approach for detecting an anomaly in the surveillance video. The surveillance in these days has only capable of saving the videos on a hard disk. These are not yet the best solution. The crime record shows that the crimes are recorded. How ever, the criminals escape from the punishment due to mishandling of the recorded videos. The project aims to store only the anomalous behavior, by storing the picture with date and time. This kind of alternative not only decreases the disk space but also provides intelligence to the system. The techniques used to achieve this project are comparable to state of art techniques in the computer vision domain.
THE USE OF LISTENING LOGS THROUGH WHATSAPP IN IMPROVING LISTENING COMPREHENSION OF EFL STUDENTS

The objective of this study is to find out the effect of using listening logs through WhatsApp on listening comprehension of EFL students in the Department of English Education of Palangka Raya University. The listening comprehension of students is categorized low which hampers their proficiency in English. Then an experiment is done to improve students’ comprehension in listening skill by using listening logs through WhatsApp application on smartphones. The experiment treated in this study was to make a learning group of WhatsApp by inviting the students joining the group. The experiment was held for three weeks which practiced four aspects of listening comprehension: (1) identifying main ideas or main gists, (2) identifying supporting details, (3) identifying moods of message, and (4) developing new vocabularies. There were 40 students (24 females and 16 males) fulfilling the sample of the study taken from the second year EFL students of English study program at Palangka Raya University. The method used in this study was pre-experimental design by choosing the type of one group pretest-posttest design. The pretest and posttest evaluation results were analyzed using non-parametric test of Wilcoxon Signed-ranks by two-related samples statistical analysis to find out the significance of the experiment given. The study concludes that the practice of listening through listening logs in WhatsApp gives significant improvement in listening comprehension to EFL learners.Keywords: listening logs, WhatsApp, EFL students
Landmark Based Reward Shaping in Reinforcement Learning with Hidden States

While most of the work on reward shaping focuses on fully observable problems, there are very few studies that couple reward shaping with partial observability. Moreover, for problems with hidden states, where there is no prior information about the underlying states, reward shaping opportunities are unexplored. In this paper, we show that landmarks can be used to shape the rewards in reinforcement learning with hidden states. Proposed approach is empirically shown to improve the learning performance in terms of speed and quality.
IMPROVING EFL LEARNERS’ SPEAKING PROFICIENCY THROUGH INSTAGRAM VLOG

This research investigated whether Instagram Vlog contributed to the improvement of EFL’s speaking ability. It also examined learners’ perceptions on the integration of Instagram Vlog in a speaking class. There were 28 participants involved in this research. To determine the impact of the Instagram Vlog on EFL learners’ speaking ability, pre- and post-tests were administered to measure the improvement. The results indicated that the implementation of Instagram Vlog into a basic-level speaking class improved the learners’ speaking proficiency in terms of pronunciation, fluency, vocabulary, syntax, and general use of the target language. The analysis of the questionnaire demonstrated that the utilization of Instagram vlog contributed to EFL learners’ speaking skill on enhancing fluency, acquiring vocabulary items, and boosting confidence and motivation. DOI: doi.org/10.24071/llt.2019.220111
Learning Visual Instance Retrieval from Failure: Efficient Online Local Metric Adaptation from Negative Samples

Existing visual instance retrieval (VIR) approaches attempt to learn a faithful global matching metric or discriminative feature embedding offline to cover enormous visual appearance variations, so as to directly use it online on various unseen probes for retrieval. However, their requirement for a huge set of positive training pairs is very demanding in practice and the performance is largely constrained for the unseen testing samples due to the severe data shifting issue. In contrast, this paper advocates a different paradigm: part of the learning can be performed online but with nominal costs, so as to achieve online metric adaptation for different query probes. By exploiting easily-available negative samples, we propose a novel solution to achieve the optimal local metric adaptation effectively and efficiently. The insight of our method is the local hard negative samples can actually provide tight constraints to fine tune the metric locally. Our local metric adaptation method is generally applicable to be used on top of any offline-learned baselines. In addition, this paper gives in-depth theoretical analyses of the proposed method to guarantee the reduction of the classification error both asymptotically and practically. Extensive experiments on various VIR tasks have confirmed our effectiveness and superiority.
Multiparametric topological analysis of reconstructed rat brain arterial system

All metabolic processes in living tissues are provided by the vascular system, whose functionality largely depends on its structure and topology. The paper proposes an algorithm for constructing the circulatory system based on a combination of stochastic and deterministic approaches. Analyses of the topological characteristics of arterial tree models with different values of the bifurcation exponent ([Formula: see text]) and length coefficient ([Formula: see text]) show that the maximum agreement with experimental data can be achieved only with the optimal values of both parameters (3.0 and 0.90, respectively). Application of the multiparametric optimization in conjunction with topological analysis makes it possible to quantify the biological division of the distributing and delivering vessels of a tree with a high degree of branching. The proposed approach allows both the correct spatial localization of the main arteries and the complex topology of the complete arterial system down to the capillaries to be reproduced.
A NORMALIZED SURF FOR MULTISPECTRAL IMAGE MATCHING AND BAND CO-REGISTRATION

Abstract. Due to the raw images of multi-lens multispectral (MS) camera has significant misregistration errors, performing image registration for band co-registration is necessary. Image matching is an essential step for image registration, which obtains conjugate features on the overlapped areas, and use them to estimate the coefficients of a transformation model for correcting the geometrical errors. However, due to the none-linear intensity of spectral response, performing feature-based image matching (such as SURF) can only obtain only a few conjugate features on cross-band MS images. Different to SURF that extracts local extremum in a multi-scale space and utilizes a threshold to determine a feature, we proposed a normalized SURF (N-SURF) that extracts features on single scale, calculates the cumulative distribution function (CDF) of features, and obtains consistent features from the CDF. In this study, two datasets acquired from Tetracam MiniMCA-12 and Micasense RedEdge Altum are used for evaluating the matching performance of N-SURF. Results show that N-SURF can extract approximately 2–3 times number of features, match more points, and have more efficient than original SURF. On the other hand, with the successful of MS image matching, we can therefor use the conjugates to compute the coefficients of a geometric transformation model. In this study, three transformation models are used to compare the difference on MS band co-registration, i.e. affine, projective, and extended projective. Results show that extended projective model is better than the others as it can compensate the difference of lens distortion and viewpoint, and has co-registration accuracy of 0.3–0.6 pixels.
The Impact of YouTube on Improving Secondary School Students’ Speaking Skills: English Language Teachers’ Perspectives

The study aimed to identify English language teachers’ perceptions on using YouTube in developing speaking skills and their attitudes towards YouTube usage as well as the best ways to use YouTube. In order to achieve these goals, the descriptive analytical approach was employed and the researcher designed a questionnaire as the data collection tool. The sample of the study comprised (40) Saudi EFL male English language teachers in Riyadh city. The results showed that English language teachers have positive perceptions of using YouTube to develop speaking skills. They expressed that YouTube videos help students guess the meaning of unfamiliar words and improve their speaking skills. Also, it was shown that YouTube videos lower the students’ anxiety, help students learn faster, and retain the students’ attention during the class. Furthermore, the results showed that there are different ways to use YouTube in the class such as using YouTube to ask students to mention some points or details they see in the vides and to give chances to the students to give information, comment, discuss, and understand the material. The researcher recommended that language teachers should use YouTube videos while teaching speaking.
Multiscale Gaussian Process Level Set Estimation

In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is considered. A new algorithm for this problem in the Bayesian framework with a Gaussian Process (GP) prior is proposed. The proposed algorithm employs a hierarchical sequence of partitions to explore different regions of the search space at varying levels of detail depending upon their proximity to the level set boundary. It is shown that this approach results in the algorithm having a low complexity implementation whose computational cost is significantly smaller than the existing algorithms for higher dimensional search space $\X$. Furthermore, high probability bounds on a measure of discrepancy between the estimated level set and the true level set for the the proposed algorithm are obtained, which are shown to be strictly better than the existing guarantees for a large class of GPs. In the process, a tighter characterization of the information gain of the proposed algorithm is obtained which takes into account the structured nature of the evaluation points. This approach improves upon the existing technique of bounding the information gain with maximum information gain.
THU-HCSI at SemEval-2019 Task 3: Hierarchical Ensemble Classification of Contextual Emotion in Conversation

In this paper, we describe our hierarchical ensemble system designed for the SemEval-2019 task3, EmoContext.In our system, three sets of classifiers are trained for different subtargets and the predicted labels of these base classifiers are combined through three steps of voting to make the final prediction.Effective details for developing base classifiers are highlighted.Experiment results show that the ensemble approach manages to obtain better predictive performance in comparison with the base classifiers and our system has achieved the performance within the top 10 ranks in the final evaluation of EmoContext.
Modelling and Exploiting the Temporal Information Associated with Complex Narrative Documents

This paper supplies some details on the solutions adopted in a narrative knowledge representation language (NKRL) context for representing/managing the temporal information included within 'narrative' multi-media documents. They are based on the association of two notions, the 'category' and the 'perspective' of dating. The first allows us to describe the logical implications of the distribution on the time axis of the timestamps associated with an event. The second takes into account the different types of fuzziness with which these timestamps can be captured. From an operational point of view, the two notions have also an important inferential function since they represent the basic building blocks of an advanced indexing system for NKRL knowledge bases. This allows us to reduce to sets of simple comparison operations the computational procedures needed for selecting all the data characterised by temporal information that are congruent with the temporal criteria specified in a given query.
A COMPACT SIFT-BASED STRATEGY FOR VISUAL INFORMATION RETRIEVAL IN LARGE IMAGE DATABASES

This paper applies the Standard Scale Invariant Feature Transform (S-SIFT) algorithm to accomplish the image descriptors of an eye region for a set of human eyes images from the UBIRIS database despite photometric transformations. The core assumption is that textured regions are locally planar and stationary. A descriptor with this type of invariance is sufficient to discern and describe a textured area regardless of the viewpoint and lighting in a perspective image, and it permits the identification of similar types of texture in a figure, such as an iris texture on an eye. It also enables to establish the correspondence between texture regions from distinct images acquired from different viewpoints (as, for example, two views of the front of a house), scales and/or subjected to linear transformations such as translation. Experiments have confirmed that the S-SIFT algorithm is a potent tool for a variety of problems in image identification.
Unsupervised feature selection with adaptive residual preserving

Many feature selection approaches are proposed in recent years. Most approaches utilize graph-based methods in studying the structure and relationship among data. However, many data relationships may loss during the graph construction, such as the residual relationships. To better preserve the relationships between data, in this paper, we propose a novel unified learning framework - unsupervised feature selection with adaptive residual preserving (UFSARP). The framework unifies feature selection, data reconstruction, and local residual preserving into one unified process, in which these tasks are completed simultaneously. We use the distance of projected data to learn the similarity matrix and simultaneously impose it on the data representation term to enforce that similar samples have similar reconstruction residuals. The use of such learning way has three-fold advantages: (1) The reconstruction residuals aim to maintain the residual relationships between data samples, namely, similar samples have similar residuals, and this helps to reconstruct the original data better; (2) Imposing the similarity matrix on the data representation term encourages similar samples not only have similar reconstruction residuals but also have similar reconstruction coefficients; (3) The similarity matrix and the reconstruction coefficient can be promoted by each other during the learning process. The experimental results show that the proposed algorithm is superior to other similar researches.
INVESTIGATING STUDENTS ACCEPTANCE OF ELEARNING USING TECHNOLOGY ACCEPTANCE MODEL AMONG DIPLOMA IN OFFICE MANAGEMENT AND TECHNOLOGY STUDENTS AT UITM MELAKA

The Industrial Revolution 4.0 (IR 4.0) has given a new impetus to educational transformation. In recent years, education experts recognize the profound impact that a myriad of technological innovations in ICT is having on education. They agree that education 4.0 will be shaped by innovations and will indeed have to train students to produce innovations. E-learning has become an increasingly popular learning approach in higher education institutions (HEIs) due to the rapid growth of Internet technologies in Malaysia. The technology acceptance model (TAM) proposes that perceived ease of use and perceived usefulness predict applications usage. It has been integrated into many university programs and is one of the new learning trends. The study aims to investigate students’ acceptance towards e-learning system using the Technology Acceptance Model (TAM). The study was conducted to the students that undergone the Diploma in Office Management and Technology program from the Faculty of Business and Management, UiTM Melaka. The purpose of the study conducted was to investigate the level of students’ acceptance in terms of ease of use and perceived usefulness of e-learning. A set of questionnaires was used for data collection. Descriptive statistics were used to describe the demographic characteristics of the study respondents. All analysis was performed by using SPSS. A total of 50 students completed the survey questionnaire with a satisfactory response rate. It was found that the perceived ease of use and perceived usefulness has a significant effect on attitude toward e-learning. The respondents agreed that e-learning is very useful and makes it easier to study the course content. Students are found to be frequently engaged in using e-learning and believed it enhancing their academic performances. The outcomes will enrich the understanding of students’ acceptance of e-learning and will assist in its continuing implementation at UiTM Melaka.
Natural Analysts in Adaptive Data Analysis.

Adaptive data analysis is frequently criticized for its pessimistic generalization guarantees. The source of these pessimistic bounds is a model that permits arbitrary, possibly adversarial analysts that optimally use information to bias results. While being a central issue in the field, still lacking are notions of natural analysts that allow for more optimistic bounds faithful to the reality that typical analysts aren't adversarial. In this work, we propose notions of natural analysts that smoothly interpolate between the optimal non-adaptive bounds and the best-known adaptive generalization bounds. To accomplish this, we model the analyst's knowledge as evolving according to the rules of an unknown dynamical system that takes in revealed information and outputs new statistical queries to the data. This allows us to restrict the analyst through different natural control-theoretic notions. One such notion corresponds to a recency bias, formalizing an inability to arbitrarily use distant information. Another complementary notion formalizes an anchoring bias, a tendency to weight initial information more strongly. Both notions come with quantitative parameters that smoothly interpolate between the non-adaptive case and the fully adaptive case, allowing for a rich spectrum of intermediate analysts that are neither non-adaptive nor adversarial. Natural not only from a cognitive perspective, we show that our notions also capture standard optimization methods, like gradient descent in various settings. This gives a new interpretation to the fact that gradient descent tends to overfit much less than its adaptive nature might suggest.
Relaxed Locally Correctable Codes in Computationally Bounded Channels*

Error-correcting codes that admit local decoding and correcting algorithms have been the focus of much recent research due to their numerous applications. An important goal is to obtain the best possible tradeoffs between the number of symbols of the codeword that the local decoding algorithm must examine (the locality of the task), and the amount of redundancy in the encoding (the information rate).In Hamming’s classical adversarial channel model, the current tradeoffs are dramatic, allowing either small locality, but superpolynomial blocklength, or small blocklength, but high locality. However, in the computationally bounded, adversarial channel model, proposed by Lipton (STACS 1994), constructions of locally decodable codes suddenly exhibit small locality and small blocklength, but these constructions require strong trusted setup assumptions e.g., Ostrovsky, Pandey and Sahai (ICALP 2007) construct private locally decodable codes in the setting where the sender and receiver already share a symmetric key.We study variants of locally decodable and locally correctable codes in computationally bounded, adversarial channels, in a setting with no public-key or private-key cryptographic setup. The only setup assumption we require is the selection of the public parameters (seed) for a collision-resistant hash function. Specifically, we provide constructions of relaxed locally correctable and relaxed locally decodable codes over the binary alphabet, with constant information rate, and poly-logarithmic locality.Our constructions, which compare favorably with their classical analogs, crucially employ collision-resistant hash functions and local expander graphs, extending ideas from recent cryptographic constructions of memory-hard functions.
Learning Robust Distance Metric with Side Information via Ratio Minimization of Orthogonally Constrained L21-Norm Distances

Metric Learning, which aims at learning a distance metric for a given data set, plays an important role in measuring the distance or similarity between data objects. Due to its broad usefulness, it has attracted a lot of interest in machine learning and related areas in the past few decades. This paper proposes to learn the distance metric from the side information in the forms of must-links and cannot-links. Given the pairwise constraints, our goal is to learn a Mahalanobis distance that minimizes the ratio of the distances of the data pairs in the must-links to those in the cannot-links. Different from many existing papers that use the traditional squared L2-norm distance, we develop a robust model that is less sensitive to data noise or outliers by using the not-squared L2-norm distance. In our objective, the orthonormal constraint is enforced to avoid degenerate solutions. To solve our objective, we have derived an efficient iterative solution algorithm. We have conducted extensive experiments, which demonstrated the superiority of our method over state-of-the-art.
Active Learning within Constrained Environments through Imitation of an Expert Questioner

Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives. However, this may be insufficient in more realistic human domains. This work uses imitation learning to enable an agent in a constrained environment to concurrently reason about both its internal learning goals and environmental constraints externally imposed, all within its objective function. Experiments are conducted on a concept learning task to test generalization of the proposed algorithm to different environmental conditions and analyze how time and resource constraints impact efficacy of solving the learning problem. Our findings show the environmentally-aware learning agent is able to statistically outperform all other active learners explored under most of the constrained conditions. A key implication is adaptation for active learning agents to more realistic human environments, where constraints are often externally imposed on the learner.