Reviewerly/exHarmony huggingface dataset:
Implementation of Multi-Entry Onscreen Keyboard Model on Android-Based Mobile Application to Prevent Shoulder Surfing Attack

The increasing number of Android-based smartphone users and the usage of smartphones for electronic payments have led to the threat of shoulder surfing attacks of PIN and password theft. Various methods of preventing shoulder surfing have been created to keep PIN and password confidential, one of which is by customizing the keyboard graphically, textually, and patterns for entering passwords. Some of the existing methods lack complexity, security, and are impractical to use by themselves. Therefore, a shoulder surfing prevention method was created using a multi-entry onscreen keyboard (meosk) model which combines several textual authentication methods with the creation of entry model technique in order to maximize the usability and complexity. In this research, the implementation of multi-entry onscreen keyboard model was carried out on an Android-based mobile application to determine the level of security from shoulder surfing. Testing was done by simulating shoulder surfing attacks on application that has been built. The research results showed that multi-entry onscreen keyboard model can prevent shoulder surfing attacks by combining several methods and the results of chi square test were significant for all test variables.
THE DEVELOPMENT OF VDEO SCRIBE IN IMPROVING SHORT STORY WRITING SKILL AT TENTH GRADE STUDENTS OF SMAN 1 TANGERANG REGENCY

The purpose of this study is to know the development of video scribe in improving short story writing skills of Tenth Grade Students students at SMAN 1 Tangerang Regency. The type of this research is developmental research (R&D). Population in this study are Tenth Grade Students SMAN 1 Tangerang Regency. Samples in this study are Tenth Grade Students IPS 1. Data collection techniques in this study using questionnaires and tests. The results of this study are on the validation of expert video learning media experts, the average score of scribe video learning media expert score is 87.3%. The validation of expert of short story writing material scores averaged 90%. Based on those two results, it can be assumed that video scribe was appropriate and effective to be used in writing short stories. The average short story writing ability of students based on the pretest results is 61.5 increased to 73.5 in the posttest. The lowest value at the pretest was 50, and the posttest value was 53. The highest value at the pretest was 73 and 83 in the posttest. Thus, it can be concluded that short story writing skill at Tenth Grade Students at SMAN 1 Tangerang Regency has improved by using the video scribes.
Singlish Sentiment Analysis Based Rating For Public Transportation

As the primary transportation mode in Sri Lanka, Public transportation is tightly attached with the daily life of the people. The important factor in transportation is providing a door to door mobility. This paper presents a study focused on measuring the service quality of public transportation based on the comments and feedbacks provided by the passengers with the aim of producing a better service for users. Comments were gained from social media and were used to produce a comprehensive rating mechanism to rate the transportation service providers. This tool was developed using natural language processing techniques using the data from the Sri Lankan context. With the popularity of Social Media in Sri Lanka, many people use Singlish (Sinhala written using English scripts) for online communication. The rating engine presented in this paper is capable of performing Sentiment analysis on comments given in Singlish Language in the Transportation domain.
Video Pedestrian Re-identification Based on Human-Machine Collaboration

The purpose of pedestrian re-identification is to find pedestrian targets from many real surveillance videos. It has multiple realistic application scenarios such as criminal search, intelligent security, and cross-camera tracking. However, the current stage of pedestrian re-identification faces many challenges, including blurring of surveillance video, changes in surveillance angles, changes in pedestrian posture, and pedestrian occlusion. Although the existing research methods have a good performance on some datasets, there is still a big gap from the practical application. Taking into account the advanced intelligence, flexibility, and adaptability of human beings in the face of complex problems and environmental changes, this paper attempts to establish a model based on human-machine collaboration, in order to improve the accuracy and enhance the practicability. Along this line, we first construct a new human-machine collaboration video pedestrian re-identification model (HMRM) and design the task allocation strategies and result fusion strategies accordingly. Then, based on the YOLOv3 object detection algorithm and the ResNet-50 network structure, we perform the pedestrian detection part and the pedestrian re-identification part respectively. Moreover, since HMRM allocates human-machine tasks based on overlapping thresholds, we do exploratory experiments on human workload, overlapping thresholds, and model accuracy. Finally, based on pyqt5 and multi-threading technology, a human-machine collaboration video pedestrian re-identification interface is developed. Experiments on the public datasets show that HMRM is superior to other pedestrian re-identification approaches.
Analysis for an intelligent behavior of encrypted network

In view of the low monitoring efficiency of encryption agent technology, the paper studies the key technologies, such as network behavior analysis and feature extraction, secret data detection, encryption agent behavior recognition and accurate host fingerprint recognition, in order to realize the intelligent detection, identification and location of encryption agent behavior, encryption agent system topology and key nodes.
Novel Census Transform Hardware IP

Census Transform is a commonly used pixel transform technique in Computer Vision (CV) algorithms to provide robustness against illumination variance. Implementing this as a low gate-count and power-efficient HW IP is critical to support CV algorithms in SoCs targeting Automotive and Industrial markets. This paper gives an introduction to designing a HW IP for Census Transform as defined in the literature and proposes an efficient implementation by removing redundant computation by up-to 50% compared to the original algorithm. The paper also proposes a novel Census Transform algorithm by introducing a new pre-processing step which eliminates a key equality check during comparisons in the above implementation. The proposed novel algorithm results in a 27% lower gate-count and 13% lower power IP compared to the original implementation. The quality simulation of the proposed novel algorithm in the context of Optical Flow (an example CV algorithm) application shows only 1.48% flow vectors have differences compared to the original algorithm, resulting in a negligible impact to optical flow application. The proposed novel solution is implemented in Texas Instruments (TI) Jacinto7 series of product line.
Saccadic intrusion recognition intelligent algorithms based on different eye movement date classification methods

The saccadic intrusion recognition algorithm proposed by Tokuda doesn’t take into account individual differences and also, since the accuracy is not high enough, three aspects in this regard have been vastly improved. Firstly, the algorithm is tailored to deal with three types of missing data such as low confidence, not on the screen, and time missing, which improves the data fault tolerance of the algorithm. Secondly, the improved algorithm refers to the E&amp;K algorithm, utilizing the ratio of saccade speed to overall speed. Then, the adaptive speed threshold has been determined accurately, which improves the sensitivity of the algorithm. Finally, the algorithm adds the upper limit of the amplitude for identifying regular saccades and filters out rapid retrospectives with large amplitude. A combination of all three minor improvements boosts the overall accuracy of the algorithm. In addition, the N-back task experiment provided data, which has been processed by the improved algorithm, and aided in arriving at a conclusion consistent with the previous ones. The experimental data compares the effect of the improved algorithm and DBSCAN in identifying fixation points. Furthermore, the results demonstrate the improved algorithm being significantly better than the DBSCAN algorithm in regard to sequence and sensitivity of data points.
A Novel Gait Analysis Method Based on The Pseudo-Velocity Model for Depression Detection

As the occurrence of depression in society becomes increasingly more common, it is an urgent task to find more objective and effective tools for real-time depression assessment. Gait analysis offers a new low-cost and contactless method for depression diagnosis. Therefore, interest in gait-based depression detection using depth sensors, such as Kinect, has grown rapidly in recent years. In this paper, a pseudo-velocity model is built to analyze the abnormal gait related to the depression by combining the velocity and angular velocity of the joints. Subsequently, we extract some features in time and frequency domain from our model to establish the classification model for depression detection. Experimental results on depression gait data recordings from 43 scored-depressed and 52 non-depressed individuals show that the proposed method achieves a good classification accuracy of 92.35 % and is superior to other existing methods. The outstanding classification performance suggests that the proposed method has notential clinical value in depression detection.
Design and Implementation of Basic Data Platform for University Library

Abstract Considering the problems existing in the data management and use of university library, this paper puts forward the concept of constructing “basic data platform”, which can provide the guarantee for the production and business data of library and university. The system includes data acquisition system, library data exchange system, data access API interface, cold data transfer system, data visualization and management platform, data analysis and mining modules, forming a complete system of data storage, management and use.
Epithermal Precious Metal Deposits in South Korea—History and Pursuit

Abstract The gold and silver endowment of Korea has historically been well known, with records alluding to production as far back as 1122 BC. The main gold production period was from 1925 to 1943 during the Japanese occupation of Korea, with more than 1 Moz recorded in 1939. Muguk was the most productive gold mining operation, located within the central region of South Korea, with a recorded 590 koz of gold produced from 1934 to 1998 (first mined in AD 912). The majority of the historical mining operations were closed by government order in 1943 during the Second World War and never reopened. A number of small mines operated between 1971 and 1998, with limited production during a period of gold prices generally lower than at present (~25–50% of current inflation adjusted prices, apart from a four-year period 1979–83). It is likely that significant resources remain within these historical mining areas. Gold-silver deposit types historically recognized and exploited in Korea include placers and orogenic and intrusion-related vein systems. Only more recently have epithermal vein and breccia systems been recognized. This is not surprising, given that the geologic and tectonic setting of the Southern Korean peninsula is prospective for epithermal precious metal deposits, spatially associated with basin-scale brittle fault systems in Cretaceous volcanic terranes. South Korea is an underexplored jurisdiction, with limited modern exploration and drilling until the mid-1990s, when Ivanhoe Mines Ltd. discovered the Gasado, Eunsan, and Moisan epithermal gold-silver deposits, all of which became mines. Exploration was limited for another 20 years until Southern Gold Ltd., an Australian Securities Exchange (ASX)-listed company, commenced regional-scale exploration for epithermal deposits, using a strategy similar to that successfully employed by Ivanhoe.
RuREBus: A Case Study of Joint Named Entity Recognition and Relation Extraction from E-Government Domain

We show-case an application of information extraction methods, such as named entity recognition (NER) and relation extraction (RE) to a novel corpus, consisting of documents, issued by a state agency. The main challenges of this corpus are: 1) the annotation scheme differs greatly from the one used for the general domain corpora, and 2) the documents are written in a language other than English. Unlike expectations, the state-of-the-art transformer-based models show modest performance for both tasks, either when approached sequentially, or in an end-to-end fashion. Our experiments have demonstrated that fine-tuning on a large unlabeled corpora does not automatically yield significant improvement and thus we may conclude that more sophisticated strategies of leveraging unlabelled texts are demanded. In this paper, we describe the whole developed pipeline, starting from text annotation, baseline development, and designing a shared task in hopes of improving the baseline. Eventually, we realize that the current NER and RE technologies are far from being mature and do not overcome so far challenges like ours.
Hard Example Mining based Adversarial Autoencoder Recommendation Algorithm

Commonly used datasets in recommendation research suffer from unbalanced data distribution, sparsity, and different user rating preferences. All these problems affect the quality of recommendation. Thus, this paper proposed a recommendation model by combining hard example mining with adversarial autoencoder. Considering the difference in users' preference, Mean Model based triplet loss algorithm was introduced to classify the dataset into positive and negative samples and thus improve the quality of the training data. Using classified samples, the rating prediction model was trained from both reconstruction and adversarial aspects. Adam optimization algorithm was used to calculate different update gradients for different parameters. Experimental results show that the recommendation model improves the recommendation accuracy significantly, and several performance indicators are better than baseline models.
Geographical information system for disaster relief distribution on natural disaster response

Abstract The geographic information system that was designed was a geographic information system as a tool for the distribution process of earthquake and tsunami disasters in Padang City. This design is based on the problems that occur on the process of distributing the disaster relief such as obstruction of the distribution process of disaster relief due to abnormal road conditions, lack of information about the data of disaster relief needs at the evacuation posts so that the disaster relief provided does not match with the disaster relief needed on the evacuation post. Based on these problems, the system designed aims to provide information on optimal disaster relief distribution routes, as well as providing information about the disaster relief needs data so that the process of exchanging information about disaster relief needs data can be done more effectively. The determination of the disaster relief distribution network is using the Dijkstra Algorithm. That the shortest distribution path is obtained using the Dijkstra Algorithm with assuming that the path is traversed only to the main lane and the bridge that can be traversed is only the bridge in the Tsunami green zone. The system design is done using the waterfall method. The results obtained are that the system has four main functions. The first is to provide the optimal distribution routes, providing information about the shelter location data, providing information about disaster relief needs data, and providing information about disaster relief needs stock data. The distribution channel in the system consists of 5 safe routes to pass with the starting point is the Management Operation Center of Regional Disaster Management Authority. This research was conducted to simplify the process of distributing disaster relief because at this time officers who carry out the evacuation process are still doing data recap manually.
Can You Make the Cut? Exploring the Effect of Frequency of Cuts in Virtual Reality Storytelling

Straight cuts are a storytelling tool that is used to direct the viewer’s attention towards the main events of the plot. This research evaluated the effect of frequency of straight cuts that are controlled by the system (rather than by the viewer) on the viewer’s experience of VR (i.e., presence, narrative immersion, sense of agency, simulator sickness, and enjoyment). We used both quantitative and qualitative methods to examine the viewer’s experience and understanding of a one-minute long VR movie in three versions (i.e., 15 cuts/min; 8 cuts/min; no cuts). The study illustrates that sense of agency, enjoyment, and understanding of the story are influenced by the frequency of straight cuts. Also, straight cuts focus viewers on events in the story, which in turn helps them understand the plot better. However, we need a balanced frequency of cuts to preserve the sense of agency of users. Finally, the study highlights that the viewers’ experience is a result of an interplay between narrative immersion, sense of agency, and presence.
Detection of Credit Card Fraudulent Transactions using Boosting Algorithms

In today’s socio-economic scenario, people rely heavily on credit cards. Moreover, credit cards are a requisite financial tool that enables its holders to make assets. It is true that credit cards, as a new method of payment, have become socially amenable to the masses. But nowadays, improvement in technology directs to growth in illegal activities. During credit card transactions many fraudsters can breach security and make fraudulent transactions to withdraw or transfer funds from one’s account or e-wallets. In this paper, three Boosting algorithms such as CatBoost, XGBoost and Stochastic gradient boosting algorithms are applied for the identification of frauds achieved using credit cards. Boosting assists to obtain an accurate result. For CatBoost algorithm, the evaluation of metric parameters namely precision, recall and confusion matrix is the best when compared to XGBoost and SGB boosting algorithms for the classification of fraudulent or non-fraudulent transactions.
Compositionality in Context

Compositionality is a principle used in logic, philosophy, mathematics, linguistics, and computer science for assigning meanings to language expressions in a systematic manner following syntactic construction, thereby allowing for a perspicuous algebraic view of the syntax-semantics interface. Yet the status of the principle remains under debate, with positions ranging from compositionality always being achievable to its having genuine empirical content. This paper attempts to sort out some major issues in all this from a logical perspective. First, we stress the fundamental harmony between Compositionality and its apparent antipode of Contextuality that locates meaning in interaction with other linguistic expressions and in other settings than the actual one. Next, we discuss basic further desiderata in designing and adjudicating a compositional semantics for a given language in harmony with relevant contextual syntactic and semantic cues. In particular, in a series of concrete examples in the realm of logic, we point out the dangers of over-interpreting compositional solutions, the ubiquitous entanglement of assigning meanings and the key task of explaining given target inferences, and the dynamics of new language design, illustrating how even established compositional semantics can be rethought in a fruitful manner. Finally, we discuss some fresh perspectives from the realm of game semantics for natural and formal languages, the general setting for Samson Abramsky’s influential work on programming languages and process logics. We highlight outside-in coalgebraic perspectives on meanings as finite or infinitely unfolding behavior that might challenge and enrich current discussions of compositionality.
A Solver of Fukunaga Koontz Transformation without Matrix Decomposition

Fukunaga Koontz Transformation provides a powerful tool for extracting discriminant subspaces in pattern classification. The discriminant subspaces are generally extracted by a matrix decomposition procedure involving scatter matrices where a nontrivial singularity problem is inevitable when sample number is limited. In this work, instead of matrix decomposition, a novel subspace extraction procedure based on solving a set of least- norm equations is proposed. This subspace extraction procedure does not rely on a large sample number and its computational complexity is only related to the number of samples. Experiments based on benchmark MNIST and PIE face recognition datasets show a promising potential of using the proposed method for certain image based recognition application where the image size is large while the sample number is limited.
Mathematical woes caused by muons

The standard model of particle physics explains a lot, but we are still confused about how to use it to calculate things, writes Chanda Prescod-Weinstein
Probability Guided Maxout

In this paper, we propose an original CNN training strategy that brings together ideas from both dropout-like regularization methods and solutions that learn discriminative features. We propose a dropping criterion that, differently from dropout and its variants, is deterministic rather than random. It grounds on the empirical evidence that feature descriptors with larger L2-norm and highly-active nodes are strongly correlated to confident class predictions. Thus, our criterion guides towards dropping a percentage of the most active nodes of the descriptors, proportionally to the estimated class probability. We simultaneously train a per-sample scaling factor to balance the expected output across training and inference. This further allows us to keep high the descriptor's L2-norm, which we show enforces confident predictions. The combination of these two strategies resulted in our “Probability Guided Maxout” solution that acts as a training regularizer. We prove the above behaviors by reporting extensive image classification results on the CIFAR10, CIFAR100, and Caltech256 datasets. Code is available at https://github.com/clferrari/probability-guided-maxout.
A Study on Website’s Column Optimization by User Preference Analysis: Taking “Donghu Circle” as an Example

Abstract In the era of big data, the development of localized information platform has made a certain breakthrough, which can be operated and optimized with the help of data analysis and around user preferences and column classification. Through the user’s use track and browsing preference on the platform, the reaction degree of users to different column content is explored, and the most concerned and interested sections are found, so as to optimize the column classification and content filling.