Reviewerly/exHarmony huggingface dataset:
Quality Evaluation Fuzzy Method of Automated Control Systems on the LMS Example

Software products are progressing rapidly and they are serve humans virtually in all spheres of life. Education is facing the same process. Consequently, evaluation of quality of a software product has become an important task for prevalent number of organizations. Several models have been proposed to help different types of users with quality problems. Development of software construction methods has influenced the creation of models for evaluating the quality. This paper describes a graph of the development of software quality models, as well as newly determined characteristics for creating a new model. The given paper proposes a new hierarchical four-level fuzzy model for evaluating software quality. A new method for evaluating the quality of the software is also described in the example of automated control systems or ERP systems (on the example of learning management systems). The proposed new model for evaluating software quality was software implemented with use of fuzzy logic. Through the number of experiments, we scrutinized quality evaluation of various software products. The proposed method should solve the problem of achieving quality in complex of development systems.
Device-independent certification of tensor products of quantum states using single-copy self-testing protocols

Self-testing protocols are methods to determine the presence of shared entangled states in a device independent scenario, where no assumptions on the measurements involved in the protocol are made. A particular type of self-testing protocol, called parallel self-testing, can certify the presence of copies of a state, however such protocols typically suffer from the problem of requiring a number of measurements that increases with respect to the number of copies one aims to certify. Here we propose a procedure to transform single-copy self-testing protocols into a procedure that certifies the tensor product of an arbitrary number of (not necessarily equal) quantum states, without increasing the number of parties or measurement choices. Moreover, we prove that self-testing protocols that certify a state and rank-one measurements can always be parallelized to certify many copies of the state. Our results suggest a method to achieve device-independent unbounded randomness expansion with high-dimensional quantum states.
How Does Information Transmission Influence the Value Creation Capability of a Digital Ecosystem? An Empirical Study of the Crypto-Digital Ecosystem Ethereum

With the advancement of digitization, digital ecosystems are playing an increasingly important role in value creation. The mechanism by which digital ecosystems create value, however, has been generally deemed to be a mixed effect due to various factors. On the basis of signaling theory, this paper explores the effect of information transmission on the value creation capability of a digital ecosystem from two dimensions: the scale and sustainability of value creation. Taking a sample of weekly transaction data from Ethereum during August 2015–August 2018, our research proposes an integrated framework of information transmission in value creating, and discusses the diffusion process of the network effect within the digital ecosystem. As a generally accepted exchange medium, digital currency traffic acts as an observable proxy of information flow in a crypto-digital ecosystem, where the effects of heterogeneity in transaction attributes are filtered. Empirical results show that information transmission positively influences the scale and sustainability of value creation activities in a digital ecosystem by affecting user number and transaction frequency. Further research reveals that user number is the initial driving force of the network effect and a critical factor for the overall ecosystem market capitalization. This research provides a new insight into the design of sustainable value creation mechanisms under digital circumstances.
Lower Limb Locomotion Activity Recognition of Healthy Individuals Using Semi-Markov Model and Single Wearable Inertial Sensor

Lower limb locomotion activity is of great interest in the field of human activity recognition. In this work, a triplet semi-Markov model-based method is proposed to recognize the locomotion activities of healthy individuals when lower limbs move periodically. In the proposed algorithm, the gait phases (or leg phases) are introduced into the hidden states, and Gaussian mixture density is introduced to represent the complex conditioned observation density. The introduced sojourn state forms the semi-Markov structure, which naturally replicates the real transition of activity and gait during motion. Then, batch mode and on-line Expectation-Maximization (EM) algorithms are proposed, respectively, for model training and adaptive on-line recognition. The algorithm is tested on two datasets collected from wearable inertial sensors. The batch mode recognition accuracy reaches up to 95.16%, whereas the adaptive on-line recognition gradually obtains high accuracy after the time required for model updating. Experimental results show an improvement in performance compared to the other competitive algorithms.
Toward Forward Secure SSE Supporting Conjunctive Keyword Search

The primitive of Searchable Symmetric Encryption (SSE) allows a server to perform search directly over encrypted documents.Recently, forward secure SSE (FSSE) has attracted significant attention due to its secure property for document update.That is, it can prevent the server from learning whether the newly updated document contains previously queried keywords.Most of the existing FSSE schemes are designed for single keyword search.For the case of conjunctive keyword search, prior dedicated FSSE constructions either require inefficient tools or suffer from significant leakage (i.e., it leaks the documents matching each queried keywords).To deal with this challenge, we propose a general framework to design efficient conjunctive keyword FSSE schemes without significant leakage: a basic scheme FOXT-B and an enhanced one FOXT-E.Specifically, in our basic scheme, we combine XSet structure in the Oblivious Cross-Tags protocol (CRYPTO 2013) with a single keyword FSSE to support conjunctive keyword search.However, this simple combination is limited to support document update.That is, the user cannot perform search until all the keyword-document pairs in a document have been updated.To support flexible keyword-document update, we design a new XSet for our enhanced scheme, in which the user can perform search at any time.Finally, we present a formal security analysis and implement our schemes on a real-world database, which demonstrates that our proposed schemes can achieve the desired security goals with practical efficiency.
GABAC: an arithmetic coding solution for genomic data

In an effort to provide a response to the ever-expanding generation of genomic data, the International Organization for Standardization (ISO) is designing a new solution for the representation, compression and management of genomic sequencing data: the Moving Picture Experts Group (MPEG)-G standard. This paper discusses the first implementation of an MPEG-G compliant entropy codec: GABAC. GABAC combines proven coding technologies, such as context-adaptive binary arithmetic coding, binarization schemes and transformations, into a straightforward solution for the compression of sequencing data.We demonstrate that GABAC outperforms well-established (entropy) codecs in a significant set of cases and thus can serve as an extension for existing genomic compression solutions, such as CRAM.The GABAC library is written in C++. We also provide a command line application which exercises all features provided by the library. GABAC can be downloaded from https://github.com/mitogen/gabac.Supplementary data are available at Bioinformatics online.
Aggregated Residual Dilation-Based Feature Pyramid Network for Object Detection

In order to tackle the issue of multi-scale object detection, recent detectors usually adopt hierarchical feature pyramids which are generated by naive combinations of top-down features and lateral features. Considering the limited effective receptive fields of the methods for top-down features augmentation, the generated regions are only associated with the fixed areas of the coarser features. Meanwhile, noisy features are introduced by irrelevant regions inevitably since the finer features in relation to rigid coarser regions. Thus, the pyramidal features with strong semantics are difficult to be obtained via simply enlarging the top-down features. In this paper, we present the Aggregated Residual Dilation based Feature Pyramid Network (ARDFPN) to exploit the inherent correlation of regions in feature pyramid. The network is designed by stacking a building block that aggregates a set of dilated convolutions with the same topology. We show that carefully adding additional transformation stages into feature pyramid enables a potential way for further multi-scale feature generation. As an intuitive extension of Feature Pyramid Network (FPN), we conduct an exhaustive study to evaluate the model performance by replacing FPN with the proposed ARDFPN in both object detection and instance segmentation tasks. With Residual network in Faster R-CNN and Mask R-CNN framework, ARDFPN outperforms the prevalent detection module - FPN on the challenging COCO dataset without bells and whistles. In particular, ARDFPN exhibits a superior performance, especially for the small and middle objects.
Feature Extraction of Bearing Fault Based on Improved Switching Kalman Filter

摘要: 提出了一种基于改进开关卡尔曼滤波的滚动轴承故障特征提取新方法,与传统卡尔曼滤波算法相比,该方法每次迭代只需当前监测数据测量值和上一时刻最优估计值,计算效率高,具有较强实时性。首先将故障轴承振动信号分为故障冲击振动和正常振动两种成分;其次,针对故障冲击振动和正常振动两种状态,分别建立基于轴承质量-弹簧-阻尼系统动力学脉冲响应的卡尔曼滤波器及线性卡尔曼滤波器模型;然后,应用基于贝叶斯估计的开关卡尔曼滤波算法对振动信号进行状态估计;最终,通过时域迭代滤波,滤除噪声并识别故障冲击成分,实现轴承故障特征提取。仿真和试验信号分析结果表明了所提方法的可行性和有效性。
Assessing the Potential of Classical Q-learning in General Game Playing

After the recent groundbreaking results of AlphaGo and AlphaZero, we have seen strong interests in deep reinforcement learning and artificial general intelligence (AGI) in game playing. However, deep learning is resource-intensive and the theory is not yet well developed. For small games, simple classical table-based Q-learning might still be the algorithm of choice. General Game Playing (GGP) provides a good testbed for reinforcement learning to research AGI. Q-learning is one of the canonical reinforcement learning methods, and has been used by (Banerjee & Stone, IJCAI 2007) in GGP. In this paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe, Connect Four, Hex), to allow comparison to Banerjee et al. We find that Q-learning converges to a high win rate in GGP. For the $$\epsilon $$ -greedy strategy, we propose a first enhancement, the dynamic $$\epsilon $$ algorithm. In addition, inspired by (Gelly & Silver, ICML 2007) we combine online search (Monte Carlo Search) to enhance offline learning, and propose QM-learning for GGP. Both enhancements improve the performance of classical Q-learning. In this work, GGP allows us to show, if augmented by appropriate enhancements, that classical table-based Q-learning can perform well in small games.
Virtual Display System of Geological Body Based on Key Algorithms of Three-dimensional Modelling

In order to study the virtual display system of geological body, the key algorithm of three-dimensional modelling is used to analyze it, and the virtual display scheme of geological body is realized. Three-dimensional modelling of geological bodies is a frontier field of current geoscience research, and also a hot issue in computer science. Its research can complement and improve the theoretical basis of virtual reality technology and expand its application fields. Geological body three-dimensional modelling software can meet the needs of geoscientists to study geological problems more effectively from three-dimensional space, and has considerable practical value. In the network environment, users' information needs are diversified and individualized, and special attention is paid to the breadth, novelty and timeliness of information. Faced with massive information resources, the information users need is a drop in the ocean in the huge and disorderly network information space. Users need to spend a lot of time and energy to find valuable information in all kinds of uniform systems. Traditional system information services can no longer help users effectively get rid of information overload and information lost. Therefore, the concept of personalized service system has become a hot spot of people' s attention as soon as it is put forward. In recent years, personalized systems have emerged in endlessly. In the early stage of formulation and implementation of personalized service strategy, the existing personalized service generally lacks the attention to context information such as user' s environment. There are weak links in personalization and intellectualization of service, which damages user experience. Therefore, Shepard algorithm and Kriging algorithm of three-dimensional modelling are used to study the virtual display system of geological bodies. The results show that these two key algorithms play a very important role in the research process of geological virtual display system, and also provide a new idea for further research on the popularization of geological knowledge.
A Heterogeneous Parallel Genetic Algorithm Based on SW26010 Processors

The genetic algorithm is a kind of evolutionary algorithms, which is widely used to solve optimization problems. It still faces the challenge of computing efficiency when dealing with large-scale optimization problems, which motivates the research on parallel genetic algorithm (PGA) to accelerate it. Sunway TaihuLight system is the first supercomputer with a peak performance of over 125 PFlops, which is powered by SW26010 heterogeneous many-core processors. Among the common PGA models, the hierarchical model fits the SW26010 best due to its heterogeneity. In this paper, we propose a heterogeneous parallel genetic algorithm (swGA) on Sunway TaihuLight system, which implements process-level and thread-level parallelism. In the process-level parallelism, we propose an island model based on a migration pool, which preserves the elitists and provides the globally stochastic individuals to be immigrated for islands. In addition, an interleaving emigration scheme and a dynamic immigration scheme are proposed to reduce the communication overhead of migration. In the thread-level parallelism, we propose the island/master-slave, island/cellular, and island/island models based on the unique mechanisms of SW26010. We optimize these models by using some optimization methods such as direct memory access, register-level communication, double buffering, vectorization. The experimental results show that the island model of swGA has a better search ability compared with a directed migration based island model and a serial GA. Based on the island model, the three hierarchical models all have satisfied search ability and acceleration effect. Among them, the island/island model has the best search ability, and the island/cellular model has the best acceleration effect, which achieves a speedup up to 3603.13× on 64 CGs.
RSLIME: An Efficient Feature Importance Analysis Approach for Industrial Recommendation Systems

iQIYI represents one of the largest scale and most sophisticated video recommendation systems in China. In this paper, we focus on the model architecture as well as feature interpretation of our short video recommendation system. We firstly describe the short video recommendation system of iQIYI at a high level, which follows a three-stage information retrieval dichotomy. A Recommendation System Boosted Local Interpretable Model-Agnostic Explanations (RSLIME) is then proposed for real-time feature interpretation and importance evaluation. Lastly, comprehensive online and offline experiments are conducted to demonstrate the effectiveness of RSLIME and prove its unique advantages in the task of feature importance analysis and feature selection on large scale industrial recommendation systems.
ELECTRICITY AVAILABILITY, HUMAN CAPITAL INVESTMENT AND SUSTAINABLE ECONOMIC GROWTH CAUSALITY IN SUB SAHARA AFRICA: REVISITED EVIDENCES

The paper examines the impact of electricity availabilities proxy by electricity supply, human capital development and the role of institutional qualities on economic growth in Sub-Sahara African countries using a panel data spanning from 1980 to 2016. The study utilized various dynamic panel models (Dynamic Ordinary Least Square, DOLS, PMG, DFE and FMOLS) and several panel unit roots model were adopted to check the degree of integration of the variables. Our empirical results found that electricity consumption, electricity availability and control of corruption have a positive impact on economic growth in SSA countries. However, human capital development has a negatively significant impact on economic growth in SSA countries. A deregulated energy sector is likely to yield smaller net benefits than a regulated market due to nature of African economies due to the level of corruption in the region. We therefore suggest a policy to improving their institutions qualities and build a good political structure to utilities government expenditure on electricity supply and human capital development to ensure economic growth in SSA countries.Keywords: Electricity availability, Institutional qualities, Human Capital development and Sub-Sahara Africa.JEL Classifications: K32 H52, F43, R11DOI: https://doi.org/10.32479/ijeep.8201
A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation

In this paper, we proposed and validated a novel joint 3D+2D fully convolutional framework for segmenting subcortical structures from magnetic resonance images (MRIs). A 2D Attention U-net (AU-net) following a multi-atlas guided 3D fully convolutional network (MF-net) is constructed in the proposed framework. A novel multi-atlas based encoding block for learning both prior expert information and MRI intensity profile, and a novel attention block for learning structural boundary information are respectively proposed in the 3D MF-net and the 2D AU-net. In the joint 3D+2D framework, the to-be-segmented image and the 2D probability maps for each structure of interest (obtained from the 3D MF-net) were sliced into a 2D image set at each of the three orthogonal views (axial, sagittal, coronal) and then fed into three trained 2D AU-nets, which yields superior segmentation performance. Validation experiments were performed on two datasets respectively contain 16 and 18 T1-weighted MRIs. Compared to several existing state-of-the-art segmentation methods including a multi-atlas joint label fusion method and three representative fully convolutional network methods, the proposed method performed significantly better for a majority of the 12 subcortical structures, with the overall mean Dice scores being respective 0.917 and 0.865 for the two datasets.
The Grammatical Awareness of Student Teachers: The Case of an English Education Study Program in Indonesia

The aims of this study were to find out student teachers' grammatical awareness and their perception towards grammatical awareness.This study employed a mixed method research.The participants were 129 English Education Study Program's student teachers at one state Islamic University in Indonesia.Purposeful sampling technique was applied to choose the participants.To collect the quantitative data, grammatical test was used and interview was used to obtain qualitative data.129 student teachers took the test and 12 of them were willing to participate in the interview.The results indicated that student teachers had four types of grammatical awareness, namely (1) metalanguage recognition, (2) metalanguage production, (3) identification and grammatical error correction, and (4) grammatical rules explanation.The student teachers lacked of grammatical awareness.It could be seen from their ability to explain grammatical mistakes.Rules in grammar and explanation tasks were the most difficult for them whereas the easiest one was a metalanguage recognition task.The major factor of their weakness was the complexity of the rules of tenses.
Methamphetamine drug abuse and addiction: Effects on face asymmetry

The abnormal aging mechanism associated with drug abuse results in poor performance of face recognition systems on illicit drug addicts (mainly methamphetamine). Consequently, the high correlation between drug addiction and crime exaggerates the urge for further investigations to originate and overcome this problem. Concurrently, face asymmetry was found to play a significant role in face recognition and age estimation. Therefore, facial asymmetry assessment for meth-addicts is highly serviceable, acknowledging how meth addiction accelerates biological aging and causes severe face distortion. In this work, we address facial asymmetry for meth-addicts compared with ordinary people. We assess facial asymmetry by employing the most credible state-of-the-art tools for local and global two-dimensional (2D) methods. More specifically, we use a classical bilateral-based metric for local analysis, combined with a proposed global approach, that we refer to as the Area Mismatch metric, to give a vivid overview of geometrical facial asymmetry. Finally, we construct a metric for textural facial asymmetry assessment by employing the Structural Similarity Index (SSIM) for dual regions in a given face. We apply the aforementioned metrics on two databases, a recently collected meth-addicted database and a regular aging database (FERET). Statistical analysis indicated a significant increment of facial asymmetry for meth addicts while aging, three to five times more than ordinary people. This study definitively answers the question regarding the correlation between meth abuse and addiction and the increase of facial asymmetry. Also, it confirms previous findings concerning aging and increased facial asymmetry.
Thinking Like a Fraudster: Detecting Fraudulent Transactions via Statistical Sequential Features

Aiming at the increasing threat of fraud in electronic transactions, so far researchers have already proposed many different models. However, few previous studies take advantage of the sequential characteristics of fraudulent transactions. In this paper, by statistical analysis on a real dataset, we discover that partial-order sequential features are able to reflect the intrinsic motivation of fraudsters, e.g., stealing the money as quickly as possible before being intercepted. Based on the sequential features, we propose a novel model, SeqFD (Sequential feature boosting Fraud Detector), to detect fraudulent transactions real-timely. SeqFD applies a sliding time window strategy to aggregate the historical transactions. In specific, statistical sequential features are computed based on the transactions within the time window. Thus, the raw dataset can be transformed into a feature set. Several classification models are evaluated on the feature set, and finally, XGBoost is validated to be a fast, accurate and robust classifier which fits well with SeqFD. The experiments on real dataset show that the proposed model reaches a 97.2% TPR (True Positive Rate) when FPR (False Positive Rate) is less than 1%. Furthermore, the average time for giving a prediction is 1.5 ms, which meets the real-time requirement in the industry.