Reviewerly/exHarmony huggingface dataset:
Development of Human Resources in Android-based Information System for People with Social Welfare Problems

The problem in this study is that the process of data collection and final report of Persons with Social Welfare Problems has not been computerized properly so that the process of procurement of goods and distribution of assistance for has not been effective and well target. With the aim of his research To simplify the data collection process and final report so that the process of procurement of goods and distribution of aid is more effective and on target. The object of this research is the Procurement Information System and Distribution of Assistance for Persons with Social Welfare Problems in the Gorontalo-based Gorontalo City Social Service with the method used in designing this information system is a descriptive method that is research aimed at solving problems systematically and factually about the facts studied. The design phase includes, model design, output design, Input design, database design and technology design, to the system testing stage. Analysis of the data where the data obtained in the field will be analyzed in a qualitative manner. Qualitative analysis is used to describe the efforts made in the form of human resource development in the information service system of assistance for people with problems based on android-based social welfare towards smart city. The results obtained show that the application can be made and used.
Deep Learning based Identification of Primary Speaker in Voice-Controlled Devices

At present, so many low budget recording devices available, recording lectures, meetings, conferences and events have become a very easy option for everyone but also at the same time these devices lead to unclear speeches. An advanced methodology of identifying the primary speaker is introduced to record noisy audio file given in any regional language. The speaker turns and speaker lengths are examples of features which provide greater insight in the detection of primary speakers. This also provides the transcript of the primary speaker audio chunk to the end-user. Speaker diarization is the process of identifying various chunks in given audio belonging to different homogenous speakers where the count of speakers is unknown. This process is a mixture of segmentation and clustering. Speech segmentation detects the speaker change points followed by grouping them based on the speaker. Thus, Speaker Diarization is the most important step in the Identification of the primary speaker.
Study on Comprehensive Evaluation of L3 Automated Vehicles

Automated vehicle testing and evaluation is an important guarantee for vehicle safety and reliability. The current L3 automated vehicle evaluation and evaluation procedures are not yet perfect. For the field test of L3 automated vehicles, we proposed to establish a comprehensive evaluation index system from the five dimensions of safety, intelligence, experience, energy consumption, and efficiency. A scientific method was designed to select and screen indicators in each dimension, and to preprocess behavior indicators based on effect size. The analytical hierarchy process and entropy method were used to determine the index weight, and the BP neural network and grey relation analysis were used to establish two comprehensive evaluation models for automated vehicles. Taking the comprehensive evaluation of the safety of automated vehicles in highway conditions as an example, two comprehensive evaluation models were established to verify the effectiveness of the models.
Black-box Attacks on DNN Classifier Based on Fuzzy Adversarial Examples

The security of deep learning becomes increasing important with the more and more related applications. The adversarial attack is the known method that makes the performance of deep learning network (DNN) decline rapidly. However, adversarial attack needs the gradient knowledge of the target networks to craft the specific adversarial examples, which is the white-box attack and hardly becomes true in reality. In this paper, we implement a black-box attack on DNN classifier via a functionally equivalent network without knowing the internal structure and parameters of the target networks. And we increase the entropy of the noise via deep convolution generative adversarial networks (DCGAN) to make it seems fuzzier, avoiding being probed and eliminated easily by adversarial training. Experiments show that this method can produce a large number of adversarial examples quickly in batch and the target network cannot improve its accuracy via adversarial training simply.
Security Definitions on Time-Lock Puzzles

Time-lock puzzles allow one to encapsulate a message for a pre-determined amount of time. The message is required to be concealed from any algorithm running in parallel time less than the pre-determined amount of time. In the previous works, the security of time-lock puzzles was formalized in an indistinguishability manner. However, it is unclear whether it directly meets the security requirements of time-lock puzzles. In this work, we define semantic security for time-lock puzzles, which captures the security requirements of the time-lock puzzle more directly. We consider three computational restrictions of an adversary and see how the security relationship changes. At first, in the traditional setting, we observe that it is difficult to prove that the semantic security implies the indistinguishability, same as the opposite implication. Secondly, in a slightly relaxed setting, we show that it is possible to prove that the semantic security implies the indistinguishability. By contrast, we observe that it is difficult to prove the opposite implication. Thirdly, in the more relaxed setting, we show that it is possible to prove that semantic security is equivalent to the indistinguishability. This shows that an indistinguishability meets the security requirements of time-lock puzzles in a certain restriction.
Depth Accuracy Determination in 3-D Stereoscopic Image Retargeting using DMA

Selecting the proper aspect ratio and managing the image depth can improve the visual quality of image in terms of lowering or minimizing the depth distortion score. The technology enhanced by observing the people habit to carry the small mobile handheld devices and their interest in watching the contents over it. So, the technology improved by retargeting the images with different sizes and aspect ratio by still preserving the image contents without cropping or scaling the originality. The popular stereo image retargeting method is proposed to visualize the 3D images in better aspect for human stereo visual experience to viewer. We conduct the experimental result that shows the adjustment of aspect ratio in order to achieve the better visualization effect and less depth distortion in the image. During the retargeting processing, the usability of depth similarity is used. The depth similarity can be applied before and after the retargeting. The Disparity Map Acquisition (DMA) along with its modified version will give the better 3D visual effects.
Blockchain-Based Content Sharing and Data Repository System

Conventional centralised data storage methods render users overly dependent on authorities in managing data provided by users. A chain is as strong as the weakest link, issues such as misuse of user data, loss of data ownership, distributed denial-of-service (DDoS) attack, data loss, data tampering and data breach have motivated the use of alternative technologies as a substitute for conventional centralised data storage. This paper demonstrates the application of blockchain technology in data storage mechanism to replace the centralised data storage. Consolidating the Ethereum blockchain and InterPlanetary File System (IPFS) and a decentralised application (DApp) are the deliverables of this research. A partially decentralised and distributed data repository system eliminates the reliance of users on the authorities thus avoiding the undesirable deficiencies of centralised data storage.
Information Visualization and Visual Analytics at IVU Lab

This paper summarizes some works performed in Information Visualization and Visual Analytics in the last two decades at the IVU Lab (Interaction, Visualization and Usability and UX Laboratory) of the University of Bari Aldo Moro. This Lab has a long tradition in HCI. At the IVU Lab, Paolo Buono is the coordinator of the Information Visualization and Visual Analytics activities and he (co)authored the publications that resulted from the research on this topic.
The Classification of Spoiler Comments in Thai Discussion Forums

Classifying comments about movies into spoiler or non-spoiler is a challenging application of text mining. This research proposes using intrinsic and extrinsic attributes for the classification task. Intrinsic attributes consist of words in a bag-of-words model and spoiler cues extracted from the comments, whereas extrinsic ones are genre information gathered from external sources. First, a few methods to select predictive words from the bag of words were compared. Ensemble attribute selector was found to achieve the best results. Then, the classification was done by support vector machine (SVM) and Naïve Bayes. The accuracies on unseen data were around 85--89% when using only bags of selected words, and close to 91% when using all the proposed attributes.
GEPC: Global embeddings with PID control

Global vectors, or global embeddings, are important word representations for many natural language processing tasks. With the popularity of dynamic embeddings (also known as contextual embeddings, such as ELMo and BERT) in recent years, attentions on global vectors have been diverted to a large extent. While, compared to the dynamic embeddings, the global embeddings are faster to train, straightforward to interpret, and eligible to be evaluated by many standard and credible intrinsic benchmarks (e.g., word similarity correlation and analogy accuracy). Thus, they are still widely-used in numerous downstream applications until now. However, the model design of the global embeddings has some limitations, making the learned word representations suboptimal. In this paper, we propose a novel method to deal with these limitations using PID control. To the best of our knowledge, this is one of the first efforts to leverage PID control in the research of word embeddings. Empirical results on standard intrinsic and extrinsic benchmarks show consistent performance boost of the proposed method, suggesting that the method proposed in this paper can be considered as a promising alternative to learn better word representations for the downstream tasks.
A Conceptual Framework for Designing Mobile Augmented Reality Self-Directed Learning Practical Module on Direct-On-Line Motor Control

The pandemic of SARS-COVID-19 has given a major impact on many educational institutions. In this situation, Teaching and Learning (T&L) become an issue, especially for the practical learning session that requires certain teaching aid using the latest technology. Currently, T&L was delivered through an online medium. Direct On-Line (DOL) starter in automation is a method of starting a 3-phase induction motor that is connected directly across its 3-phase supply. Its starter applies the full line voltage to the motor terminals. However, during this pandemic, the lab session for doing the wiring and testing for DOL motor control is limited to a demonstration by the lecturer via video, and students will do the wiring on paper or using the software. Mobile learning can be an alternative learning tool for this group of students. Therefore, this paper aims to propose a conceptual framework for designing a mobile Augmented Reality (AR) Self-Directed Learning (SDL) practical module on DOL motor control for subjects related to industrial automation. The framework consists of an SDL, a motivational Flow Theory (engagement), User Experience Design (UX-D) using an AR environment to learn the practical part of DOL motor control.
Multi-task Feature Learning Based Anomaly Detection of Network Dataflow

Aiming at the problem of network dataflow anomaly detection, a multi-task learning sample set is constructed by using the data in different periods. Low rank subspace and uncorrelated sparse mode are learned from multiple related tasks. The l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm is scaled to l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm, and rank function is scaled to trace-norm. Then the low rank subspace is used to capture the underlying potential similarity of all tasks, and the sparse discriminant features are used to represent the differences between tasks. Feature subsets from k tasks are the input of SVM anomaly detection classifier. The experimental results show that the proposed method can effectively capture the additional special features in addition to the shared features in the network dataflow anomaly detection, and has well achieved detection results.
Study on an improved Lie Group machine learning-based classification algorithm

In order to address the problem that the geodesic distance of Lie Group machine learning method has failed to use the sample class information, this paper proposes an improved Lie Group machine learning algorithm. Firstly, this algorithm maps the vector space consisting of sample dataset into a differential manifold, and each sample corresponds to a point in the manifold structure; then, the differentiation value is used to replace the Euclidean distance for calculation of the geodesic distance between samples, and the class of test sample is determined according to the geodesic distance relation between the test sample and the sample in training dataset. The results of classification experiment prove that compared with the traditional Lie Group machine learning classification (LGC) algorithm, the K-nearest neighbor (KNN) algorithm and support vector machine (SVM) classification\algorithm, the improved classification algorithm has higher classification accuracy.
Centralized and Mapped GIS Web-Based Covid-19 Data Reporting Application with The Waterfall Method

Covid-19 pandemic cases are currently increasing and expanding throughout the world, especially in Indonesia in the province of North Sumatra. Data on the number of cases spread in the province of North Sumatra which is summarized and published, sometimes there are still disputes over the number and lack of organization in the number of records and their distribution, so there are often errors in the data collected by the health department and which will be published to the public. The case study in this research is the design of an information system that regulates the process of recording, moving, accumulating, and mapping GIS data maps to the publication of Positive case data, Patient Under Surveillance, Polymerase Chain Reaction, and Rapid Test results directly through a web-based covid-19 data reporting application. Sourced from each user of every health facility in each district and city from each sub-district in the province of North Sumatra to support the accuracy of data in decision-making built at the North Sumatra Province Information and Communication Office. The method used in developing the application uses the waterfall method, starting from the needs analysis stage, design, implementation to testing until maintenance. The results of the implementation and testing were carried out using the Blackbox and Whitebox methods. Presentation of GIS web data using google maps has not used a threshold value based on a calculated algorithm, but still uses the determination of crisp values so that the results cannot be said to be relevant as a determinant.
ECS Request Prediction with Attention-LSTM

Elastic cloud server (ECS) provides physical servers that can be readily available at any time for its flexibility and scalability. In practice, underestimating or overestimating the provision of computing resources would lead to failure user request, irrational resource under-utilization and high resource cost. Therefore, there is need to predict the future virtual machines (VMs) requests which can be used to allocate cloud resources in advance. In this work, we research on the hot topic of virtual machines (VMs) requests prediction in cloud computing. To address the VMs request prediction problem, we build an Attention-LSTM model (ALSTM), and make comprehensive comparison with baselines to validate the effectiveness of ALSTM prediction, and the experiment shows our method achieves better prediction performance.
2 Detection of Grayscale Image Implementation Using Multilayer Perceptron

Abstract The human eye can instantly recognize two patterns of color images that are almost the same quickly, but the computer cannot directly recognize any pattern in the image. The problem faced is how the computer can recognize the image pattern entered. Pattern recognition is also a technique that aims to classify previously processed images based on similarity or similarity in characteristics. In the Artificial Neural Network there are several methods that can be used to identify image patterns, one of them with the Multilayer Perceptron architecture. Multilayer Perceptron Neural Network is a type of neural network that has the ability to detect or perform analysis for problems that are sufficient or even very complex, such as in language processing problems, recognition of a pattern and processing of an image or image. The results of the study are a system that is able to recognize grayscale image patterns and is able to provide a percentage of pattern recognition in two similar and different images.
Inferring Prerequisite Relationships Among Learning Resources for HPC Education

For online HPC (High-Performance Computing) education, identifying the prerequisite relationships among different HPC learning resources is important for generating learning paths and personalized course recommendations for students. Previously, such prerequisite relationships were often created by domain experts manually. In this paper, we propose a novel framework to infer both concept and learning resource prerequisite relationships and integrate such a framework into an online HPC education platform. We first construct a bi-directional long short-term (BiLSTM) neural network with attention mechanism to automatically mine the latent semantic features from the formal description of concepts. Then, we input two kinds of features into a fully connected neural network for classification and obtain the relationships among concepts. Considering the asymmetry and directivity of the prerequisite relation, we adopt a network embedding model to learn the vector representations of each concept as the prerequisite and subsequent roles. The concept vectors are weighted and summed to generate the feature vectors of learning resources. For a given pair of learning resources, their feature vectors are input into a classifier that outputs the result of relation classification. Finally, we conduct a series of experiments on two large benchmark datasets. The experimental results show that our method outperforms existing methods with significant improvements.
Benchmarking performance of different noise detection techniques on data stream clustering

Numerous internet-based applications produce data streams. A data stream is a succession of available data that may shift over time. Data from the Internet of Things (IoT), social media, traffic lights, financial institutions, phone records, sensor data, banking and healthcare systems are examples of data streams. Obtaining knowledge from data streams presents defiances. The noise reduction process is one of them. Detecting and reducing noise is essential to improve the performance of any machine intelligence technique. In this paper, we create a performance evaluation of four different noise detection algorithms on data stream clustering that are implemented in MOA: Micro-cluster-based Continuous Outlier Detection (MCOD), AbstractC, SimpleCOD and AnyOut. We use each of these techniques to assess the quality of clustering produced by the clustering algorithm known as ClusCTA-MEWMA (Clustering based on Centroid Tracking and Exponentially Weighted Moving Average Chart Detection Method). We reference this algorithm as CM. We set up and monitor experiments using datasets created using a random data generator. The results evidence that CM gets better model quality with with Micro-cluster-based Continuous Outlier Detection algorithm.
View Selection for Graph Pattern Matching

View-based techniques have been investigated on relational data, XML and graphs and proven effective for querying big data. While the pivot of using materialized views for query answering is view selection. Though explored for several years, the view selection problem for graph pattern matching has not been investigated. To this end, we investigate the problem in this paper. We first formalize the problem and show its intractability feature. We next propose an appropriate cost model and develop an effective algorithm to identify a set of view definitions under resource constraints, e.g., space storage and query processing cost, from a query workload. We finally verify the performance of the algorithms, and show that our view selection algorithm can identify a set of views that not only have low cost, e.g., storage space (below specified budget) and query processing cost but also can answer queries efficiently.
Cross-Modal Hashing by <i>l<sub>p</sub> </i>-Norm Multiple Subgraph Combination

With the explosion of multi-modal Web data, effective and efficient techniques are in urgent need for cross-modal data retrieval with relevant semantics. Among all the possible solutions, the hashing techniques provide compact and measurable binary representation, thus gain much attention in related research domain. To better deal with diversified real world data, we propose MSC, a novel cross-modal hashing approach based on the generalized l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm Multiple Subgraph Combination. Specifically, by jointly considering the content similarity, the correspondence and other weak correlation among cross-modal documents, we build the intra-modal similarity with multiple affinity subgraphs, and encode the intermodal correlation with a bipartite subgraph. Then these subgraphs are combined into one multi-modal similarity graph for all the data from heterogeneous modalities, where the weights of multiple intra-modal visual similarity subgraphs are regularized by l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm penalty. The optimal hash codes and the combination coefficients are learned simultaneously by efficient alternating optimization. The hash functions for different modalities are learned separately by utilizing nonlinear classification models, encoding the complicated semantic relations among cross-modal data. Experiments on challenging real world datasets demonstrate the advantage of our method over existing approaches.