Reviewerly/exHarmony huggingface dataset:
A Distributed Genetic Algorithm with Adaptive Diversity Maintenance for Ordered Problems

Maintaining population diversity is critical to the performance of a Genetic Algorithm (GA). Applying appropriate strategies for measuring population diversity is important in order to ensure that the mechanisms for controlling population diversity are provided with accurate feedback. Sequence-wise approaches to measuring population diversity have demonstrated their effectiveness in assisting with maintaining population diversity for ordered problems, however these processes increase the computational costs for solving ordered problems. Research in distributed GAs have demonstrated how applying different distribution models can affect an GA's ability to scale and effectively search the solution space. This paper proposes a distributed GA with adaptive parameter controls for solving ordered problems such as the travelling salesman problem(TSP), capacitated vehicle routing problem (CVRP) and the job-shop scheduling problem (JSSP). Extensive experimental results demonstrate the superiority of the proposed approach.
Software Development Framework for Real-Time Face Detection and Recognition in Mobile Devices

With the rapid use of Android OS in mobile devices and related products, face recognition technology is an essential feature, so that mobile devices have a strong personal identity authentication. In this paper, we propose Android based software development framework for real-time face detection and recognition using OpenCV library, which is applicable in several mobile applications. Initially, the Gaussian smoothing and gray-scale transformation algorithm is applied to preprocess the source image. Then, the Haar-like feature matching method is used to describe the characteristics of the operator and obtain the face characteristic value. Finally, the normalization method is used to match the recognition of face database. To achieve the face recognition in the Android platform, JNI (Java Native Interface) is used to call the local Open CV. The proposed system is tested in real-time in two different brands of smart phones, and results average success rate in both devices for face detection and recognition is 95% and 80% respectively.
Neural Networks: How a Multi-Layer Network Learns to Disentangle Exogenous from Self-Generated Signals

Artificial multi-layer networks can learn difficult tasks, such as recognizing faces, but their architecture and learning rules appear to be very different from those of biological neural networks. Experimental and computational studies of a two-layered biological neural network have revealed how the learning rules used in artificial neural networks can be efficiently implemented by neurons with complex dynamics and precisely organized connectivity.
On Quantum Chosen-Ciphertext Attacks and Learning with Errors

Large-scale quantum computing poses a major threat to classical public-key cryptography. Recently, strong “quantum access” security models have shown that numerous symmetric-key cryptosystems are also vulnerable. In this paper, we consider classical encryption in a model that grants the adversary quantum oracle access to encryption and decryption, but where we restrict the latter to non-adaptive (i.e., pre-challenge) queries only. We formalize this model using appropriate notions of ciphertext indistinguishability and semantic security (which are equivalent by standard arguments) and call it QCCA 1 in analogy to the classical CCA 1 security model. We show that the standard pseudorandom function ( PRF )-based encryption schemes are QCCA 1 -secure when instantiated with quantum-secure primitives. Our security proofs use a strong bound on quantum random-access codes with shared randomness. Revisiting plain IND − CPA -secure Learning with Errors ( LWE ) encryption, we show that leaking only a single quantum decryption query (and no other leakage or queries of any kind) allows the adversary to recover the full secret key with constant success probability. Information-theoretically, full recovery of the key in the classical setting requires at least a linear number of decryption queries. Our results thus challenge the notion that LWE is unconditionally “just as secure” quantumly as it is classically. The algorithm at the core of our attack is a new variant of the well-known Bernstein–Vazirani algorithm. Finally, we emphasize that our results should not be interpreted as a weakness of these cryptosystems in their stated security setting (i.e., post-quantum chosen-plaintext secrecy). Rather, our results mean that, if these cryptosystems are exposed to chosen-ciphertext attacks (e.g., as a result of deployment in an inappropriate real-world setting) then quantum attacks are even more devastating than classical ones.
Latest trends and Major Case Studies of Blockchain Technology

As one key infrastructure technology to lead the Fourth Industrial Revolution, blockchain technology is expected to bring about exciting developments across a variety of fields, such as politics, economy, culture and education.This paper is intended as a general overview of the main features of blockchain technology.In addition, the latest trends and development methods of blockchain technology will be briefly reviewed.The terminology of blockchain infrastructure technology has the potential to foster technological innovation across various industrial sectors as well as improve mutual harmony and understanding of complex structures and diverse phenomena in human society, especially antagonism, confrontation, and ideological conflict.With such progress in social innovation, blockchain technology will render human society more transparent and fair.
A comparative analysis of the tracking angles and fixed angle systems during sunny and cloudy days under Iraqi conditions

&lt;p&gt;This paper describes design tow systems tracking angles and fixed angle solar panel to improve the performance of solar energy, during the sunny and cloudy days. Depending on the development of the solar tracking system and amount of solar energy captured by the solar panel per day actually, the project is designed with AT 89552 MC depending upon the light falls on (LDR), data will be reading by AT 89552 MC. and changing the direction of a motor in this direction the solar panel will be moving to capture the maximum of solar energy. The tracking angle solar panel has been efficient than a fixed angle. The results show that the tracking angle solar panel has been more efficient than a fixed angle in sunny and cloudy days in morning and afternoon. The tracking angles system generating higher power than the fixed angle system in a morning. The maximum power occurred during the tracking angles system and fixed angle system in the morning, due to drop efficiency of the panels they get heated during the afternoon.&lt;/p&gt;
Meteorological Radar Fault Diagnosis Based on Deep Learning

The health of meteorological radar is deemed important for observational stations because the faults and failures of radars may incur huge cost. The running status of radars are consecutively recorded in time series to monitor the status of different channels. However, due to the large sum of data, the status data is not rigorously used to diagnose possible radar faults. Although the radar can generate alarm codes, but most of them are false alarms which degrade the validity. Therefore, in this paper, we propose a fault diagnosis approach based on deep learning. Specifically, we formulate the fault diagnosis problem as anomaly detection in large sum of time series. We model the status generated from the radars with an LSTM model and predict the future status based on the historical status data. Based on the assumption that anomaly deviates from normal data, the time periods with large prediction error can be recognized as faults. We also apply a dynamic thresholding method to automatically determine the threshold of the anomaly. Experiments on real-world radar status data is implemented to demonstrate the effectiveness of the method.
Construction of Quantitative Index System of Vocabulary Difficulty in Chinese Grade Reading

Chinese grade reading for children has a broad application prospect. In this paper, Chinese textbooks for grade 1 to 6 of primary schools published by People's Education Press are taken as data sets, and the texts are divided into 12 difficulty levels successively. The effective lexical indexes to measure the readability of texts are discussed, and a regression model to effectively measure the lexical difficulty of Chinese texts is established. The study firstly collected 30 indexes at the text lexical level from the three dimensions of lexical richness, semantic transparency and contextual dependence, selected the 7 indexes with the highest relevance to the text difficulty through Person correlation coefficient, and finally constructed a Regression to predict the text difficulty based on Lasso Regression, ElasticNet, Ridge Regression and other algorithms. The regression results show that the model fits well, and the predicted value could explain 89.3% of the total variation of text difficulty, which proves that the quantitative index of vocabulary difficulty of Chinese text constructed in this paper is effective, and can be applied to Chinese grade reading and computer automatic grading of Chinese text difficulty.
Effective Source Camera Identification based on MSEPLL Denoising Applied to Small Image Patches

Sensor Pattern Noise (SPN) has proven to be an effective fingerprint for source camera identification, while its estimation accuracy heavily relies on denoising algorithm. In this paper, an effective source camera identification scheme based on Multi-Scale Expected Patch Log Likelihood (MSEPLL) denoising algorithm is proposed, firstly. With enhanced prior modeling across multiple scales, MSEPLL can accurately restore the original image. As a consequence, estimated SPN is less influenced by image content. Secondly, the source camera identification problem is formulated by hypothesis testing, where normalized correlation coefficient is adopted for SPN detection. Finally, the effectiveness of the proposed method is verified by abundant experiments in terms of identification accuracy as well as receiver operating characteristic. Performance improvement is more prominent for small image patches, which is more conducive to real forensics applications.
A Single-Column Convolutional Neural Networks for Crowd Counting

Crowd counting is an important task in the surveillance camera system. The methods of crowd counting have changed from counting each object in the scene to estimating the density map and the count will be reached by integrating the entire density map. The previous methods used multi-column convolutional neural networks (CNN) with different sizes of filters to describe objects of different scales. To increase the performance of the multi-column model, some methods suggest adding a classifier to select a single-column CNN that gives the expecting results. However, having multiple columns and filters will complicate the model and trained model with limited crowd data will estimate bad density maps. To solve this problem, we propose the "Single-Column CNN" (SCC) method which uses a single-column CNN with only one size of filters (3x3) and replaces normal convolution with different sizes of filters by the dilated convolution. Our approach makes the density map estimation model become a simple but strong CNN. Experiments show that our method achieves significant improvements compared to the method using multi-column CNN in the ShanghaiTech dataset.
Deep Multi-Modal Metric Learning with Multi-Scale Correlation for Image-Text Retrieval

Multi-modal retrieval is a challenge due to heterogeneous gap and a complex semantic relationship between different modal data. Typical research map different modalities into a common subspace with a one-to-one correspondence or similarity/dissimilarity relationship of inter-modal data, in which the distances of heterogeneous data can be compared directly; thus, inter-modal retrieval can be achieved by the nearest neighboring search. However, most of them ignore intra-modal relations and complicated semantics between multi-modal data. In this paper, we propose a deep multi-modal metric learning method with multi-scale semantic correlation to deal with the retrieval tasks between image and text modalities. A deep model with two branches is designed to nonlinearly map raw heterogeneous data into comparable representations. In contrast to binary similarity, we formulate semantic relationship with multi-scale similarity to learn fine-grained multi-modal distances. Inter-modal and intra-modal correlations constructed on multi-scale semantic similarity are incorporated to train the deep model in an end-to-end way. Experiments validate the effectiveness of our proposed method on multi-modal retrieval tasks, and our method outperforms state-of-the-art methods on NUS-WIDE, MIR Flickr, and Wikipedia datasets.
A workload-based nonlinear approach for predicting available computing resources

Performance degradation or system resource exhaus- tion can be attributed to inadequate computing resources as a result of software aging.In the real world, the workload of a web server varies with time, which will cause a nonlinear aging phenomenon.The nonlinear property often makes analysis and modelling difficult.Workload is one of the important factors influencing the speed of aging.This paper quantitatively analyzes the workload-aging relation and proposes a framework for aging control under varying workloads.In addition, this paper proposes an approach that employs prior information of workloads to accurately forecast incoming system exhaustion.The workload data are used as a threshold to divide the system resource usage data into multiple sections, while in each section the workload data can be treated as a constant.Each section is described by an individual autoregression (AR) model.Compared with other AR models, the proposed approach can forecast the aging process with a higher accuracy.
Models and algorithms of privacy-preserving machine learning

The paper is devoted to the recent scientific problem of privacy-preserving machine learning. The problem actuality is determined by the growing need to use machine learning for personal data, as well as for data that make up commercial, medical, financial and other types of information protected by law. The aim of the study is to systematize the security models of machine learning, to identify algorithmic tools that can be used to ensure the privacy of the learning process and application of models, as well as to analyze the privacy-preserving machine learning systems. The paper presents the major concepts and definitions related to machine learning, provides a systematization of machine learning problems and methods of their solution, and pays attention to the modern and promising areas of development of machine learning. Among the tasks of machine learning are those for which it is important to ensure the privacy of data from training, test and work samples. Special cryptographic methods and protocols are correlated to the problems solved. A brief description of the known privacy-preserving machine learning systems is given. The machine learning methods supported by these systems, as well as the type of adversary that the system can resist, and the cryptographic primitives used for the implementation are described in the paper. Unsolved problems in the field of privacy-preserving machine learning and prospects for the development of this scientific field are considered.
Pengaruh Bimbingan Kelompok Melalui Media Audio Visual Terhadap Motivasi Belajar Siswa

The study aims: 1) to determine the conditions of vocational students 'learning motivation, 2) determine the effect of group guidance services through audio visual media on the motivation of vocational students' learning. This type of research is an experiment with one group pre-test and post-test design models. The population was 102 students and the sample of this study was 10th grade students of SMK N 1 Pejawaran with 10 students. Data analysis using the Wilcoxon test. The results of this study 1) the learning motivation of students of class X SMK N 1 Pejawaran Academic Year 2018/2019 is still low, based on the distribution of students' learning motivation scale with a low category of 33 students (32.4%), in the medium category of 69 students (67, 6%), 2) Group Guidance service using audio visual media has an effect in increasing student motivation in class X SMK N 1 of Academic Year 2018/2019, with a sig. = 0.008 &lt;0.05. The conclusions in this study are 1) Class X students learning motivation SMK N 1 Pejawaran still moderate, and 2) group guidance services using audio visual media influence the learning motivation of class X students of SMK N 1 Pejawaran.
Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy

In this paper, a reversible data hiding method in encrypted image (RDHEI) is proposed. Prior to image encryption, the embeddable pixels are selected from an original image according to prediction errors due to adjacent pixels with strong correlation. Then the embeddable pixels and the other pixels are both rearranged and encrypted to generate an encrypted image. Secret bits are directly embedded into the multiple MSBs (most significant bit) of the embeddable pixels in the encrypted image to generate a marked encrypted image during the encoding phase. In the decoding phase, secret bits can be extracted from the multiple MSBs of the embeddable pixels in the marked encrypted image. Moreover, the original embeddable pixels are restored losslessly by using correlation of the adjacent pixels. Thus, a reconstructed image with high visual quality can be obtained only when the encryption key is available. Since exploiting multiple MSBs of the embeddable pixels, the proposed method can obtain a very large embedding capacity. Experimental results show that the proposed method is able to achieve an average embedding rate as large as 1.7215 bpp (bits per pixel) for the BOW-2 database.
Reversible color video watermarking scheme based on hybrid of integer-to-integer wavelet transform and Arnold transform

&lt;p&gt;Unauthorized redistribution and illegal copying of digital contents are serious issues which have affected numerous types of digital contents such as digital video. One of the methods, which have been suggested to support copyright protection, is to hide digital watermark within the digital video. This paper introduces a new video watermarking system which based on a combination of Arnold transform and integer wavelet transforms (IWT). IWT is employed to decompose the cover video frames whereby Arnold transform is used to scramble the watermark which is a grey scale image. Scrambling the watermark before the concealment makes the transmission more secure by disordering the information. The system performance was benchmarked against related video watermarking schemes, in which the evaluation processes consist of testing against several video operations and attacks. Consequently, the scheme has been demonstrated to be perfectly robust.&lt;/p&gt;
Learning visual features under motion invariance

Humans are continuously exposed to a stream of visual data with a natural temporal structure. However, most successful computer vision algorithms work at image level, completely discarding the precious information carried by motion. In this paper, we claim that processing visual streams naturally leads to formulate the motion invariance principle, which enables the construction of a new theory of learning that originates from variational principles, just like in physics. Such principled approach is well suited for a discussion on a number of interesting questions that arise in vision, and it offers a well-posed computational scheme for the discovery of convolutional filters over the retina. Differently from traditional convolutional networks, which need massive supervision, the proposed theory offers a truly new scenario for the unsupervised processing of video signals, where features are extracted in a multi-layer architecture with motion invariance. While the theory enables the implementation of novel computer vision systems, it also sheds light on the role of information-based principles to drive possible biological solutions.
A Global Study on ICT Project Performance

There is a perception of low performance in the information communications technology (ICT) industry with various differing opinions as to the source of the low performance. As technology becomes more integrated within all aspects of society the need for high performance is critical. This research focuses on understanding project performance in the ICT industry. Through literature research an analysis and compilation was performed with 56 publications related to project performance factors, reported performance statistics, and solutions to improve ICT project performance. The research findings establish a definition of performance, identify the current level of performance within the ICT industry, identify existing solutions to the ICT industry and a prioritized listing of 25 factors of project performance.
EFL Teachers’ Attitudes towards the Use of Mobile Devices in Learning English at A University in Vietnam

Among the sharp development of modern technology, mobile learning (M-learning) appears as the significantmark of technological revolution 4.0.It has seen that the exponential expansionof mobile devices accompanied their smart functions to support learners' learning everywhere and every time.Thus, mobile-assisted language learning (MALL) is believed to serve learners' English knowledge.Although learning through mobile devices is expected to apply appropriately toteaching and learning, teachers'attitudestowardsmobile learning needto be assessedseriously.This writing is to present EFL learners' psychological preparation to perceive English mobile learning application at University X through a survey on 69 EFL teachers byquestionnaire,including tenstatements within 10 minutes.The investigationis carried out in the second semester of the school year 2018-2019.The qualitative approach was used to collect and analyze data, and findings showed that teachers have positive attitudes towards M-learning and its application at University X can be accepted firmly.
DLS Magician

In this work, we present a prototype of an intelligent system that can automate the UI design process via converting text descriptions into interactive design prototypes. We conducted user research in an international oilfield services company, and found that product owners prefer to validate their hypotheses via visual mockups rather than text descriptions; however, many of them need assistance from designers to produce the visual mockups. Based on this finding and after exploring multiple possibilities using design thinking, we chose a solution that uses natural language processing (NLP) to automate the visual design process. To validate the answer, we conducted user tests via means and iterated the solution. In the future, we expect the work can be fully deployed in a working environment to help product owners initiate their projects faster.