Reviewerly/exHarmony huggingface dataset:
Low-exposure image frame generation algorithms for feature extraction and classification

Neuromorphic architectures enable machine learning on a faster timescale compared to conventional processors and require encoding of spike trains from images for computer vision applications. We report low-exposure image representation algorithms that can generate multiple short-exposure frames from a given long exposure image. The frame deconvolution is non-linear in the sense that the difference between adjacent short-exposure frames change with exposure time, however the frames have a structural representation of the original image such that the image reconstructed from these frames has a Peak Signal-to-Noise Ratio (PSNR) of over 300 and a Structural Similarity Index Metric (SSIM) close to unity. We show that the low-exposure frames generated by our algorithms enable feature extraction for machine learning or deep learning, e. g., classification using convolutional neural networks. The validation accuracy for classification depends on the range of the random subtraction parameter, a used in our algorithms to simulate low-exposure frames. When the maximum of a, equals to the largest allowed change in the pixel intensity per time step, the validation accuracy for classification of digits in the Digits dataset is 90&plusmn;3% based on the 1st 1 ms frame. The accuracy increases to 97% with only 40% of the 1ms frames generated for a given exposure time. These results show that machine learning can be extended to low exposure images.
Guest Editors Introduction: Special Issue on User Feedback and Software Quality in the Mobile Domain

Background: Software engineering research has a growing interest in grey literature (GL). Aim: To improve the identification of relevant and rigorous GL. Method: We develop and demonstrate heuristics to find more relevant and rigorous GL. The heuristics generate stratified samples of search and post–search datasets using a formally structured set of search keywords. Conclusion: The heuristics require further evaluation. We are developing a tool to implement the heuristics.
Multi-sensor synthetic data generation for performance characterization

This paper introduces an innovative framework for the development of multi-sensor datasets for target recognition. This framework goes beyond the paradigm of generating synthetic data to augment algorithm training; it employs carefully generated training and test data to characterize algorithm performance over any desired operating conditions, culminating in the ability to generate algorithm performance models for use in fusion, sensor resource management, and mission simulation. The current system instantiates the full path, from operating conditions to synthetic data to results, for synthetic aperture radar. Fully integrated electro-optic and laser radar paths, to be completed in 2019, will comprise a complete multi-sensor testbed for performance prediction. Future work will add sensor modes as well as automated decision and feature fusion for target identification.
An Adaptable Self-Monitoring Framework for Opaque Machines

Diagnostic systems for complex machines are highly specialized and cannot be applied in other domains without significant effort. Our goal is to improve the robustness of diagnostics with an adaptable monitoring framework for identifying and explaining anomalous behavior that can be easily modified for different domains or systems. We define a vocabulary for reasonable data---to precisely identify contradictions between expected and anomalous behavior and a language---to express rules, policies, and constraints/preferences of the user. We combine this framework with explanation mechanisms to describe the core reasons and support for a reasonableness judgment made by running the reasoner over the reasonable data, rules and the state of the related components.
Research on the Multimedia Assisted English Teaching Mode Based on the Computer Platform

The English teaching activities are the complex processes of the language output and acquisition. Teachers need to combine the “language input opportunities” and the “cognitive development opportunities” to the “flexible teaching means”, to make the students improve their language absorption efficiency. The multimedia computer aided English teaching, as a new and special kind of the teaching method in the college English teaching, should first conform to the rules of the language teaching and follow the general principles of the college English teaching. These principles include the system principle, the communicative principle, the cognitive principle, the cultural principle and the emotional principle.
Design of Ergonomic Bipang Molding Tool to Increase Production of Bipang

Humans are a central point in a work system because basically humans are not only acting as planners and designers of work systems, but also as executors and controllers. Human limitations become a guideline in designing an ergonomic work system, where the focus of ergonomics involves three components, namely humans, machines / equipment and the environment that interact with each other which results in an inseparable work system. This study aims to determine the risk of skeletal muscle injury that is potentially experienced by bipang production operators, especially in bipang grading and filling activities. The method used to analyze the operator's work posture is the quick exposure check (QEC) and rapid entire body assessment (REBA) methods, this research is carried out by direct observation of the research object, filling out the operator and observer questionnaires and conducting literature studies to collect data related to research. Based on the QEC method, the work posture of Bipang IKM employees on the flatten section has an exposure level of 66.67% while slicing bipang has an exposure level of 60.49% which means that further research and changes are needed. While using the REBA method, the bipang flatten activity obtained by the final score is 10, which means it is at a high level and immediate handling is needed, while the bipang slicing production process is at level 2 which means that immediate handling is needed, the position can cause muscle injury so it requires work improvement. By proposing improvements to work facilities so that the bipang flatten and slicing activities are carried out simultaneously, with a more efficient processing time of 10 minutes to 2 minutes and more optimal work productivity. Based on the REBA method, there is an action level 1 which means that the work posture can be accepted and there is no need for improvement. So that the increase in production when using bipang molding tool is 9,572 piece/day.
Characterizing the Minimal Essential Graphs of Maximal Ancestral Graphs

Learning ancestor graph is a typical NP-hard problem. We consider the problem to represent a Markov equivalence class of ancestral graphs with a compact representation. Firstly, the minimal essential graph is defined to represent the equivalent class of maximal ancestral graphs with the minimum number of invariant arrowheads. Then, an algorithm is proposed to learn the minimal essential graph of ancestral graphs based on the detection of minimal collider paths. It is the first algorithm to use necessary and sufficient conditions for Markov equivalence as a base to seek essential graphs. Finally, a set of orientation rules is presented to orient edge marks of a minimal essential graph. Theory analysis shows our algorithm is sound, and complete in the sense of recognizing all minimal collider paths in a given ancestral graph. And the experiment results show we can discover all invariant marks by these orientation rules.
Sri Aurobindo’s integral yoga: A research perspective

The paper is about Sri Aurobindo's Integral Yoga. Sri Aurobindo uses the word “integral” as a prefix. Sri Aurobindo’s philosophy is an integral philosophy. Yoga is an integral yoga. The purpose of integral yoga is perfection. The purpose, aims, principles and the major concepts of Sri Aurobindo’s integral yoga are explained in detail in this work.
Minimizing the Misclassification Rate of the Nearest Neighbor Rule Using a Two-stage Method

The kNN classification performance entirely depends on the selected neighbors. In the past, many nearest neighbor (NN)-based methods mainly focus on learning distance measure metrics so that a neighborhood of an approximately constant posteriori probability can be produced, whereas limited works are performed to study the influences of the distribution characteristics of each neighbor. In this paper, we point out why the best distance measurement (BDM) is sensitive to malicious samples, and then a robust best distance measurement (RBDM) is suggested to solve this problem. Moreover, we also investigated the influences of the distribution characteristics of each neighbor for the classification performance, so that a two-stage method, called weighted robust best distance measurement kNN method (WRBDMkNN), is proposed aiming to minimize the misclassification rate of the nearest neighbor rule. Extensive experiments on diversity datasets indicate that the proposed method can achieve more encouraging results compared with some state-of-the-art NN-based methods.
Institution as Social Media Collector: Lessons Learned from the Library of Congress

Social media has become a valuable tool that has helped connect, facilitate, discover, and encourage the use of the Library of Congress’s collections and content. The Library has cultivated trust and approachability as both a collector and user of social media. The Library of Congress has been using social media both as a collector and a user to execute its mission. Authors describe the use of social media in both of these roles beginning with the Library as a collector. The vision, as stated in the Library’s strategic plan, is that all Americans are connected to the Library. Different channels of social media have allowed the Library to extend that connection into discovery and use of the Library’s vast online collections and engage audiences in ways that are more approachable and immediate. As a result, the institution is more accessible than ever before.
Label Comparison Analysis Using Hybrid Similarity for Labeling Systems of Indonesian University Websites (English Menu Version)

Labeling systems are an important component in designing a university website. In order to avoid misinformation, website developers need to design a website labeling system that can represent information that a website owner wants to deliver to the users of her website. One way of designing a labeling system is by comparing and studying the labeling system used on competitor's websites. Syntactic similarity is usually used for comparing the websites' labels. However, it has a limitation that can only calculate similarities based on strings only, so that the possibility of two compared labels having the same meaning will be considered as different labels. To provide the meaning of the label, the comparison using semantic similarity is used. However, semantic similarity has limitations such as not being able to process two words or more and possibly having no path exists between two word senses. Therefore, this research proposes a hybrid similarity where the highest result of syntactic and semantic similarity comparison between two labels is combined and able to cover the limitation of two methods. The hybrid similarity shows the results of possibility of higher score between combined syntactic and semantic similarity score with suggestion on the list label (based on semantic result). The expert suggest output of hybrid list label to help developer create and reviewing existing label, and then participant who answers the questionnaires 82.16% agree with label that show the acceptance of the label. The list label based on hybrid score can help and assisting the web developer to create new labeling systems for a new website. It is also help developer to improving an existing website's labeling systems. Keywords: university website, labeling systems, syntactic similarity, semantic similarity, hybrid similarity, Levensthein Distance, Wu and Palmer.
Quantum Dual Adversary for Hidden Subgroups and Beyond

An explicit quantum dual adversary for the S-isomorphism problem is constructed. As a consequence, this gives an alternative proof that the query complexity of the dihedral hidden subgroup problem is polynomial.
FORMATION OF PSYCHOLOGICAL AND PEDAGOGICAL COMPETENCE OF STUDENTS-MATHEMATICIANS

The analysis of approaches to the solution of the problem of formation of psychological and pedagogical component of a professional competence of students- mathematicians studying in a higher educational institution is presented in the article. The relevance of a research in connection with the increasing requirements to the level and quality of training of future specialists is noted. It is noted that the specified problem requires a high level of ability of students to realize their intellectual opportunities, applying at the same time all the creative resources of continuous increase of the level of professional knowledge. Therefore, contents and technique of teaching subject matters should reflect requirements of employers imposed to students, future graduates of higher school in conditions of social and economic conversions in the country, their high competitiveness, mobility, readiness for self-education and self-realization. It is noted that this contributes to the teaching of the integrated course Psychology and Pedagogics.The analysis of the researches devoted to studying of an entity of the concept psychological and pedagogical competence of students, its criteria is presented in the article. The specificity of its formation in specially created pedagogical conditions is noted. The organizational model of forms, methods and means of formation of psychological and pedagogical competence of students-mathematicians applied in the integrated course Psychology and Pedagogics taking into account both classroom, and out-of-class activity is offered to consider.The article presents the results of a study of the level of formation of the criteria of psychological and pedagogical competence of students-mathematicians: readiness to solve professional problems, readiness for self-education and self-realization, emotional-volitional readiness, readiness for creative self-realization, they revealed mainly the presence of low and medium levels formation. The article notes the effectiveness of the study.
Lightweight Cipher Using GRP Bit Permutation and Tweak

Internet of Things (IoT) is a network of sensors which are able to connect and exchange data. IoT uses RPL (Routing Protocol for Low power and lossy network) as a routing protocol. This protocol uses Rank mechanism where each node is given a rank based on their proximity from the root node. But malicious nodes can advertise fake rank information to be a part of the graph and get sensitive information. RPL uses AES/CCMP as a security mechanism to send messages securely but as AES/CCMP is designed for non-constrained environment and IoT is a network of lightweight constrained devices there is a need for lightweight cryptographic algorithms. PRESENT is the best lightweight encryption protocol. In this paper we have modified the P-Box of PRESENT using GRP bit permutation operations and also incorporated tweak structure to ensure security. Our algorithm shows better results in terms of execution time when compared with existing PRESENT protocol.
A New Password Authentication Scheme Resistant against Shoulder Surfing Attack

Authentication is a common approach to protect user information in the online information systems such as ATMs. One of the easiest ways for user authentication uses Personal Identification Number (PIN). PINs are vulnerable to malicious attacks. The tendency of users to select easy passwords or short password makes the passwords vulnerable to many attacks like camera recording attack and adversary shoulder attacks. In this paper, the proposed textual password authentication scheme is introduced as an alternative to graphical password schemes. In this technique, no need to use the traditional keyboard or even pressing the keys that represent the password characters. This technique gives the user a more secure session to enter the password and solves most of the defects exist in the authentication systems that depend on the use of the textual or graphical passwords.
Human–agent transfer from observations

Abstract Learning from human demonstration (LfD), among many speedup techniques for reinforcement learning (RL), has seen many successful applications. We consider one LfD technique called human–agent transfer (HAT), where a model of the human demonstrator’s decision function is induced via supervised learning and used as an initial bias for RL. Some recent work in LfD has investigated learning from observations only, that is, when only the demonstrator’s states (and not its actions) are available to the learner. Since the demonstrator’s actions are treated as labels for HAT, supervised learning becomes untenable in their absence. We adapt the idea of learning an inverse dynamics model from the data acquired by the learner’s interactions with the environment and deploy it to fill in the missing actions of the demonstrator. The resulting version of HAT—called state-only HAT (SoHAT) —is experimentally shown to preserve some advantages of HAT in benchmark domains with both discrete and continuous actions. This paper also establishes principled modifications of an existing baseline algorithm—called A3C—to create its HAT and SoHAT variants that are used in our experiments.
Study of Learning Ability in Profit Sharing Using Convolutional Neural Network

Profit Sharing using Convolutional Neural Network (PS-CNN) has been proposed as a method of deep reinforcement learning. In the previous work, experiments have been conducted using Atari 2600’s Asterix in the Profit Sharing using Convolutional Neural Networks, and it is known that a better score can be obtained than Deep Q-Network. However, experiments have not been conducted on games other than Asterix, and sufficient consideration has not been made. In this paper, we report on the results of studying learning ability for some Atari 2600’ games in Profit Sharing using Convolution Neural Network. By comparing the results with the results in Deep Q-Network, we confirmed that this method can acquire higher score than the Deep Q-Network in some games. The common feature of these games is that the number of actions and the number of states are relatively large.
Deep network based 3D hand keypoints prediction from single RGB images

3D hand keypoints prediction is an important and fundamental task in Human-Computer Interaction. In this paper, we present an approach to predict 3D hand keypoints from single RGB images. Single RGB images are very common in daily life. However, it is challenging to predict 3D hand keypoints using single RGB images, because of depth ambiguities and occlusions. To deal with these challenges, we exploit deep neural networks to predict 3D hand keypoints. So far, there are several methods which predict 3D hand keypoints from single RGB images. Most of them separate the task into three stages. i.e., hand detection, 2D hand keypoints estimation and 3D hand keypoints prediction. We follow the idea and focus on the 2D hand keypoints estimation and 3D hand keypoints prediction. We improve an existing deep-network-based technique and get better results. Specifically, we combine the convolution and deconvolution network to get the pixel-wise estimation of 2D hand keypoints, and propose a new loss function to predict 3D hand keypoints from 2D keypoints. We evaluate our network on several public datasets and get better results than several other methods. Besides, ablation studies demonstrate that our network is valid.
Comparison of Rating Tendencies between Native and Non-native Speakers in Korean Speaking Test

The purpose of this study was to compare the rating tendencies of Korean and Chinese raters in a Korean Speaking Test.For this purpose, graduate students majoring in Korean education were trained, and an individual rating process was performed.The results of the rating were then analyzed using the multifaceted Rasch model, focusing on rating consistency, severity, and bias.The results of the analysis indicate that rating severity differed among raters even in the same group, and rating consistency was either an overfit or a misfit in the Chinese rating (CR) group.The results also showed that the CR group tended to be more tolerant of assessment evaluation items with less difficulty than the Korean rating (KR) group, and to score assessment evaluation items with higher difficulty more strictly.In the analysis of criteria, the KR group scored more strictly than the CR group on the three criteria except vocabulary and grammar, and organization.In contrast, there was a statistically significant tendency toward the opposite for "organization".The two groups differed in judging the proficiency of test takers, with differences between the test takers in cases where proficiency is relatively high and when specific languages are reflected in the test taker's pronunciation or intonation, some of which were analyzed as significant bias.Finally, the use of the evaluation scale showed that the CR group had a wider distribution, but the reliability of using the zero scale was low, and the difference between the mean scores of each scale was not uniform.This group also tended toward the middle point rather than toward the peak.
Utilizing Model Driven Architectures in Designing Real-Time Embedded Control Systems

Developers of Embedded systems use the Unified Modeling Language (UML) to model their system behavior. The UML and the extensions added by the Systems Modeling Language (SysML) provide rich expressive power for embedded systems' developers to closely model these systems. By making use of the UML extension mechanism, these additions provide more direct way for developers to define domain-specific languages based on UML. This paper proposes a model-driven approach for generating embedded real-time process control systems. The proposed approach defines a UML profile containing extensions for external devices and persistent information as well as formal transformation that generates meta-information that creates the code targeting specific devices. A proof of concept system is designed, and a profile is applied in a real-world example.
Concatenation of Finite Sequences

Summary The coexistence of “classical” finite sequences [1] and their zero-based equivalents finite 0-sequences [6] in Mizar has been regarded as a disadvantage. However the suggested replacement of the former type with the latter [5] has not yet been implemented, despite of several advantages of this form, such as the identity of length and domain operators [4]. On the other hand the number of theorems formalized using finite sequence notation is much larger then of those based on finite 0-sequences , so such translation would require quite an effort. The paper addresses this problem with another solution, using the Mizar system [3], [2]. Instead of removing one notation it is possible to introduce operators which would concatenate sequences of various types, and in this way allow utilization of the whole range of formalized theorems. While the operation could replace existing FS2XFS, XFS2FS commands (by using empty sequences as initial elements) its universal notation (independent on sequences that are concatenated to the initial object) allows to “forget” about the type of sequences that are concatenated on further positions, and thus simplify the proofs.