Reviewerly/exHarmony huggingface dataset:
Regularized Training of Convolutional Autoencoders using the Rényi-Stratonovich Value of Information

We propose an information-theoretic cost function or the regularized training of convolutional auto encoders that imposes an organization on the bottleneck-layer-projected samples so as to facilitate discrimination. This function is based on a continuous-space, Rényi-mutual-information version of Stratonovich's value of information. It quantifies the maximum benefit that can be obtained for a given bottleneck-layer representation compression amount. The compression amount is controlled by a single hyperparameter that trades off between the autoencoder reconstruction quality and the hidden-layer representation uncertainty.
Collaborative Design Service System Based on Ceramic Cloud Service Platform

Currently enterprise information service system based on the Internet is becoming more and more important for operating activities of enterprises, such as the production and manufacture of enterprises. To improve the efficiency of ceramic products’ design and production, the paper designed and implemented a collaborative design service system based on ceramic cloud service platform. In the proposed system, the principle and technology of workflow are adopted to control and manage the product collaborative design process. In this paper, a collaborative design business process model is presented, and the system structure and engine of collaborative design based on ceramic cloud service platform are also proposed and realized. The application effect of the proposed system shows that it can be used for cross-regional personnel or teams to participate in the design and development of the same products, to improve the efficiency of product design.
Gesture Detection in Digital Image Processing based on the Use of Convolutional Neuronal Networks

The paper presents empirical findings obtained from tests of an image detection system utilizing the YOLO network. Operating principles of the system are discussed, starting from simple convolutional networks, through R-CNN networks, and culminating in the use of the YOLO technology. The second part provides an examination of findings obtained from a system constructed on a custom dataset. Details are provided of the training process employed, together with the examination of accuracies for all pictures as well as for different classes. The main objective of the paper was to present the development of a system offering real-time gesture detection from camera feed images. Potential applications of the postulated gesture recognition system include toy control systems, photography, and broadly defined office assistance.
Towards Generating Anomaly Prediction based on Health Checkup Results

Lifestyle disease is a general term from habits related to personal activities such as stress, overeating, and lack of exercise. Typical examples are hypertension, hyperlipidemia, diabetes, obesity, and cancer. In Japan, lifestyle diseases account for about 30% of annual medical expenses and about 60% of annual deaths. Therefore, it is necessary to reflect lifestyle habits in order to prevent lifestyle diseases accurately. Diabetes mellitus has no symptom in hyperglycemia itself, and symptoms appear only after the vascular disorder that progresses as hyperglycemia persists. In the 2016 National Health and Nutrition Survey conducted by the Ministry of Health, Labour and Welfare, the total number of persons who are strongly suspected of having diabetes and persons who cannot deny the possibility of diabetes is estimated to be 20 million. Since the health condition of individuals is regularly examined at each stage in Japan, this paper employs the results of the regular check-up for predicting future lifestyle diseases by machine learning. From the results of analytics, the characteristics of the potential person were extracted.
An Adaptive Kernel based Correlation Filter Algorithm for Real Time Object Tracking

We proposed an adaptive kernel based correlation filter algorithm (AKCF) for object tracking. The algorithm includes two filters: a translation filter and a scale filter. In our method, we classify objects into three categories: fast speed ones, middle speed ones, and low speed ones. To track an object with different speeds, a “padding” value varies accordingly for adjusting the searching area in the translation filter. After detecting an object, the two filters update their parameters for dealing with the appearance variations and scale changes. In the strategy, the response score in the second frame is remembered as a reference, and an updating range is determined. When the current response score is out of the updating range, the filters stop updating to avoid introducing more interference from background. Finally, the proposed method is evaluated on the OTB dataset. Contrary to other state-of-the-art approaches, our method has provided good performance to track an object with different speed and serious scale changes. Additionally, our method is computational efficient.
Generating of SIBI Animated Gestures from Indonesian Text

Sign System for Bahasa Indonesia (SIBI) is the official sign language authorised by The Ministry of Education and Culture of Indonesia and being used as one of the communication media by School for Children with Special Needs (SLB) for people with hearing impairments in the process of learning. For people who have a lack of knowledge about SIBI gestures certainly will have difficulty to communicate with people with hearing impairments. Thus, a translation system from SIBI gestures to sentences in Bahasa Indonesia or vice versa is needed. This research is the initial stage of a translation system from sentences in Bahasa Indonesia to SIBI Gestures. The focus of this research is to generate sign gestures in the form of 3D Animation from a sentence input in text format and deployed on the smartphone device. The generation process starts from deconstructing the input sentence into its word components using a look-up table that consists of affixes, root words, and a “slang” dictionary. Then, this word components are referred to their corresponding gesture animations. The gesture data are recorded with motion-capture sensor “Perception Neuron v2” and use the official SIBI Dictionary as the reference. The animation is rendered in Unity engine and to create a smooth transition between one gesture animation to another, a LERP (Linear Interpolation) based method is implemented using Animancer API. Based on evaluation results, produced gestures correctly represent smooth SIBI gestures with the most significant accuracy score of 97.56% with a user satisfaction score of Very Satisfied is 84%, Satisfied is 14%, and Fair is 2%.
A Custom based IP Design for object Tracking SoC

This paper involves the implementation of a hardware-software subsystem for object detection and tracking on FPGA. Here we discuss our design of a system to detect and track the object which is in motion. We have integrated a system with existing IPs (Intellectual Property) and designed IP to realize a system for object detection and tracking. From a video-input, the sub-system detects the object of interest and the centroid tracking algorithm is used to track the object. The objective is to reduce resource utilization in a system that uses a simple yet efficient algorithm to analyse moving object's behaviour based on video stream analysis. We have tested the system in an application for human fall detection; to identify human in the image, calculate some parameters of its silhouette shape and analyse them in real-time.
Ultra Short-term Output Forecasting of Distributed Photovoltaic Power Station Based on Feature Extraction

The power output feature of Distributed photovoltaic power stations (DPV) varies dramatically compared to the traditional photovoltaic power station due to various local weather conditions. Thus it is of great importance to analyze the output features to improve the power forecasting performance. In this paper, weather conditions of DPV are studied via statistical approach to obtain the distinguish forecasting models, which are suitable for sunny day, cloudy day, rainy day and foggy day. Then within each model, the clustering algorithm is applied to select most influencing elements among ambient temperature, radiation intensity, local weather, wind speed and direction. Case study shows that the precision of the proposed algorithm is improved by 0.07% (MAPE) and 31MW (RMSE) compared to the traditional neural network forecasting algorithm.
Smaller Fully-Functional Bidirectional BWT Indexes

Burrows-Wheeler indexes that support both extending and contracting any substring of the text T of length n on which they are built, in any direction, provide substantial flexibility in traversing the text and can be used to implement several algorithms. The practical appeal of such indexes is contingent on them being compact, and current designs that are sensitive to the compressibility of the input take either \(O(e+\overline{e})\) words of space, where e and \(\overline{e}\) are the number of right and left extensions of the maximal repeats of T, or \(O(r\log (n/r)+\overline{r}\log (n/\overline{r}))\) words, where r and \(\overline{r}\) are the number of runs in the Burrows-Wheeler transform of T and of its reverse. In this paper we describe a fully-functional bidirectional index that takes \(O(m+r+\overline{r})\) words, where m is the number of maximal repeats of T, as well as a variant that takes \(O(r+\overline{r})\) words.
Improved Stochastic Synapse Reinforcement Learning for Continuous Actions in Sharply Changing Environments

Reinforcement learning in continuous action spaces requires mechanisms that allow for exploration of infinite possible actions. One challenging issue in such systems is the amount of exploration appropriate during learning. This issue is complicated further in sharply changing dynamic environments. Reinforcement learning in artificial neural networks with multiparameter distributions can address all aspects of these issues. However, which equations are most appropriate for updating these parameters remains an open question. Here we consider possible equations derived from two sources: The classic equations proposed for REINFORCE and modern equations introduced for Stochastic Synapse Reinforcement Learning (SSRL), as well as combinations thereof and variations thereon. Using a set of multidimensional robot inverse kinematics problems, we find that novel combinations of these equations outperform either set of equations alone in terms of both learning rate and consistency.
Assessment of Perceptual Depth Intensity on Source Stereoscopic Images

Accurate and efficient objective assessment methods for multidimensional quality of experience (QoE) of stereoscopic 3D content are in urgent demand. Especially, depth perception quantization for source 3D content is necessary to optimize the comprehensive QoE. A few studies evaluate the depth quality degradation due to compression or transmission, while the objective assessment of perceptual depth intensity has not been deeply investigated. In this paper, we propose a depth intensity metric (DIM) to simulate subjective evaluation on uncompressed source 3D images. Considering the multi-cue aggregation mechanism in depth perception as well as visual characteristic of binocular fusion, perceptual features are extracted from binocular and monocular cues to generate the depth intensity perception score. The effectiveness of DIM is verified by conducting subjective experiment on publicly available stereoscopic image databases. Experimental results demonstrate that DIM can achieve higher accuracy compared to the state-of-the-art depth intensity metric.
Discrete Memory Addressing Variational Autoencoder for Visual Concept Learning

A substantial aspect of general intelligence is the ability to summarize basic building blocks from various high-level concepts. Artificial vision systems with such hierarchical property can not only perform accurate reasoning for complex observations, but also learn useful low-level knowledge shared across scenes. To achieve this goal, we propose a discrete memory addressing VAE model (DM-VAE) for explicitly memorizing and reasoning about shared primitives in images. A time-persistence memory module is used to store the learned abstract knowledge and to interact with the generative model. The model decides what to pay attention to at each step, and constructs the primitive library automatically as the learning progresses in a fully unsupervised setting. While performing inference, the model attempts to interpret a new observation as a combination of previously learned elements. We further derive a proper variational lower bound which can be optimized efficiently. We conduct visual comprehension experiments on images and demonstrate that our model is able to search, identify, and memorize semantically meaningful primitive concepts.
Investigation of the Evolutionary Optimization Algorithms for the Neural Network Solution of the Optimal Control Problems

In this paper, we consider the neural network approach to solving optimal control problems with mixed constraints at the stage of optimization of the functional approximations through evolutionary algorithms. Based on the necessary optimality conditions and the Lagrange multiplier method, the initial optimal control problem is reduced to a nonlinear optimization problem and the corresponding approximation model of the neural network for the control function and the trajectory is presented. The convergence of the neural network approach using a genetic algorithm, a population-based gravitational search algorithm, and a basic particle swarm algorithm was studied. Also, the results obtained are compared with the operation of the gradient descent algorithm. Computational experiments have shown that evolutionary algorithms for optimizing functions use the least number of iterations to achieve a given accuracy, but multi-agent methods of gravitational search and particle swarming show the longest execution time per iteration. The genetic optimization algorithm showed the fastest convergence rate relative to the total execution time of the algorithm.
A Web Resource for Exploring the CORD-19 Dataset Using Root- and Rule-Based Phrases

This short paper describes a web resource-the NIST CORD-19 Web Resource-for community explorations of the COVID-19 Open Research Dataset (CORD-19). The tools for exploration in the web resource make use of the NIST-developed Root- and Rule-based method, which exploits underlying linguistic structures to create terms that represent phrases in a corpus. The method allows for auto-suggesting-related terms to discover terms to refine the search of a COVID-19 heterogenous document base. The method also produces taxonomic structures in the target domain as well as providing semantic information about the relationships between terms. This term structure can serve as a basis for creating topic modeling and trend analysis tools. In this paper, we describe use of a novel search engine to demonstrate some of the capabilities above.
Establishing Trust in Binary Analysis in Software Development and Applications

The current exploratory study examined software programmer trust in binary analysis techniques used to evaluate and understand binary code components. Experienced software developers participated in knowledge elicitations to identify factors affecting trust in tools and methods used for understanding binary code behavior and minimizing potential security vulnerabilities. Developer perceptions of trust in those tools to assess implementation risk in binary components were captured across a variety of application contexts. The software developers reported source security and vulnerability reports provided the best insight and awareness of potential issues or shortcomings in binary code. Further, applications where the potential impact to systems and data loss is high require relying on more than one type of analysis to ensure the binary component is sound. The findings suggest binary analysis is viable for identifying issues and potential vulnerabilities as part of a comprehensive solution for understanding binary code behavior and security vulnerabilities, but relying simply on binary analysis tools and binary release metadata appears insufficient to ensure a secure solution.
ESDBSCAN: Enhanced Shuffling Based Density Clustering

Density-based clustering plays a crucial role in various applications and realizes clusters with arbitrary contour. In case of huge amount of data generation, the clustering analysis plays an important role in big data mining. But no universal clustering algorithm is available to solve all problems universally. Usually, the algorithms were designed as per requirement which was based on certain assumptions. This paper focuses on attempts to remove noise parameter from existing algorithm and propose an Enhanced Shuffling DBSCAN (ESDBSCAN) algorithm which outperforms on legacy DBSCAN. ESDBSCAN is better than any other modified version of DBSCAN which gives better performance in terms of run time complexity, when runs in a distributed environment. The paper proposes a proficient and enhanced distributed clustering algorithm using existing data partition on Map-Reduce platform. The ESDBSCAN algorithm has minimum training error and faster than the existing algorithm.
Emitter-Metasurface Interface for Manipulating Emission Characteristics of Quantum Defects

We demonstrate a chip-scale quantum emitter-metamaterial device that emits highly directional photons. Our device opens the door for quantum imaging of yveak sources by adding photon(s) to manipulate the photon statistics for improved signal-to-noise ratio.
A Viewport Prediction Framework for Panoramic Videos

Panoramic video is considered to be an attractive video format, since it provides the viewers with an immersive experience, such as virtual reality (VR) gaming. However, the viewers only focus on part of panoramic video, which is referred to as viewport. Hence, the resources consumed for distributing the remaining part of the panoramic video are wasted. It is intuitive to only deliver the video data within this viewport for reducing the distribution cost. Empirically, viewports within a time interval are highly correlated, hence the historical trajectory may be used for predicting the future viewports. On the other hand, a viewer tends to sustain attention on a specific object in a panoramic video. Motivated by these findings, we propose a deep learning-based viewport Prediction scheme, namely HOP, where the Historical viewport trajectory of viewers and Object tracking are jointly exploited by the long short-term memory (LSTM) networks. Additionally, our solution is capable of predicting multiple future viewports, while a single viewport prediction was supported by the state-of-the-art contributions. Simulation results show that our proposed HOP scheme outperforms the benchmarkers by up to 33.5% in terms of the prediction error.
Following the journey of scores through a complex musical work

We chart the composition and technical development of a non-linear, interactive work for pianist, Disklavier piano and interactive system called Climb!. Collaborating with a composer and performers we focus on the journey of score representations through this complex work, highlighting the processes and challenges faced from composition to performance. We reveal that multiple, distinct score representations of different types and formats were required to define and perform the work. Furthermore, we highlight a number of compromises that were necessitated to create and manage the work's notations, representations and annotations.
Multi-site Evaluation of a Study-Level Classifier for Mammography Using Deep Learning

We present a computer-aided diagnosis algorithm for mammography trained and validated on studies acquired from six clinical sites. We hold out the full dataset from a seventh hospital for testing to assess the algorithm’s ability to generalize to new sites. Our classifiers are convolutional neural networks that take multiple input images from a mammography study and produce classifications for the study. The studies are globally labeled as normal, biopsy benign, high risk or biopsy malignant. We report on experimental results from several network variants, including study-level and breast-level models, single- and multiple-output models, and a novel model architecture that incorporates prior studies. Each model variation includes an image-level classifier that is pre-trained with per-image labels and is used as a feature extractor in our study-level models. Our best study-level model achieves 0.85 area under the ROC curve for normal vs malignant classification on the held-out test site. In comparison with other recent work, we achieve a similar level of classification sensitivity and specificity on a dataset with greater site and vendor variation. Additionally, our test performance is demonstrated on a held-out site to more accurately assess how the model would perform when deployed in the field.
InDubio: A Combinator Library to Disambiguate Ambiguous Grammars

To infer an abstract model from source code is one of the main tasks of most software quality analysis methods. Such abstract model is called Abstract Syntax Tree and the inference task is called parsing. A parser is usually generated from a grammar specification of a (programming) language and it converts source code of that language into said abstract tree representation. Then, several techniques traverse this tree to assess the quality of the code (for example by computing source code metrics), or by building new data structures (e.g, flow graphs) to perform further analysis (such as, code cloning, dead code, etc). Parsing is a well established technique. In recent years, however, modern languages are inherently ambiguous which can only be fully handled by ambiguous grammars.In this setting disambiguation rules, which are usually included as part of the grammar specification of the ambiguous language, need to be defined. This approach has a severe limitation: disambiguation rules are not first class citizens. Parser generators offer a small set of rules that can not be extended or changed. Thus, grammar writers are not able to manipulate nor define a new specific rule that the language he is considering requires.In this paper we present a tool, name InDubio, that consists of an extensible combinator library of disambiguation filters together with a generalized parser generator for ambiguous grammars. InDubio defines a set of basic disambiguation rules as abstract syntax tree filters that can be combined into more powerful rules. Moreover, the filters are independent of the parser generator and parsing technology, and consequently, they can be easily extended and manipulated. This paper presents InDubio in detail and also presents our first experimental results.
Design and development of an objective English accent recognition system based on RankNet's word stress recognition method

In recent years, speech processing technology has been widely used in the field of auxiliary language learning. It provides an auxiliary learning platform for non-native language learners. This paper develops an objective English accent recognition system to help English learners grasp the rhythm of spoken English. On the basis of RankNet's word stress recognition method, local normalization is added to improve the recognition accuracy of RankNet. We further apply RankNet to sentence recognition. According to the RankNet method, the rhythm characteristics of non-specific people and the quality of vowel phonemes are selected to classify them into the stressed and unstressed, and the accuracy is improved according to the result of prosodic recognition of statements.