Reviewerly/exHarmony huggingface dataset:
Prediction of financial economic growth trend based on PVAR model

In order to solve the problem of low prediction accuracy of traditional financial and economic growth trend prediction methods, this paper proposes a prediction method of financial economic growth trend based on pvar model. According to the influence parameters of financial and economic growth, the pvar model is constructed, and the impulse response function is used to correct the influence of random disturbance in pvar model to reduce the model error. The ant colony algorithm is used to mine the data of financial economy and cluster the data. Input the above data into the pvar model, and the output of the model is the prediction result of the financial and economic growth trend. Experimental results show that the proposed method can effectively correct the model error, and the prediction results are basically consistent with the actual results. Therefore, the prediction performance of the proposed method is good.
A Reliable Machine Learning Approach applied to Single-Cell Classification in Acute Myeloid Leukemia.

Machine Learning research applied to the medical field is increasing. However, few of the proposed approaches are actually deployed in clinical settings. One reason is that current methods may not be able to generalize on new unseen instances which differ from the training population, thus providing unreliable classifications. Approaches to measure classification reliability could be useful to assess whether to trust prediction on new cases. Here, we propose a new reliability measure based on the similarity of a new instance to the training set. In particular, we evaluate whether this example would be selected as informative by an instance selection method, in comparison with the available training set. We show that this method distinguishes reliable examples, for which we can trust the classifier's prediction, from unreliable ones, both on simulated data and in a real-case scenario, to distinguish tumor and normal cells in Acute Myeloid Leukemia patients.
An Analysis of LIME for Text Data

Text data are increasingly handled in an automated fashion by machine learning algorithms. But the models handling these data are not always well-understood due to their complexity and are more and more often referred to as black-boxes. Interpretability methods aim to explain how these models operate. Among them, LIME has become one of the most popular in recent years. However, it comes without theoretical guarantees: even for simple models, we are not sure that LIME behaves accurately. In this paper, we provide a first theoretical analysis of LIME for text data. As a consequence of our theoretical findings, we show that LIME indeed provides meaningful explanations for simple models, namely decision trees and linear models.
A Survey of Lightweight Cryptography for Power-Constrained IoT Devices: Security Challenges and Issues

As the Internet of Things (IoT) is growing exponentially, the number of data shared between IoT devices is increasing at an exponential rate. Almost all IoT devices are battery concerned devices in which power is very low, and these devices are interconnected and communicated to perform certain tasks, and also to transmit confidential and sensitive data over a communication channel. The IoT now enables power-restricted systems for communication, processing, and communication decisions. In stratified IoT networks, there are many problems and challenges, including protection, cheap power usage in computers, restricted battery size, memory space, high efficiency, and minimal communication network latency. We address in this paper a state-of-the-art lightweight cryptographic algorithm that includes lightweight block ciphers, hash functions, stream ciphers, high-performance systems, and low power-constrained devices and IoT network tools in detail. The lightweight cryptography algorithms are evaluated based on key size, block size, round size, and structure. We also explore the security framework, challenges, and key solutions for the power-constrained device of the IoT system.
Construction of Knowledge Graph of Multi-Source Heterogeneous Distribution Network Systems

To solve the problems that the business data in state grid corporation cannot be crossed professionally, and the data resources cannot be intelligently analyzed and managed, this paper proposes a knowledge graph construction method oriented for heterogeneous data sources. On the basis of multi-source data from marketing management system (MBS), production management system (PMS), geographical information system (GIS) collected by the big data technology, the semantic annotation method is used to extract the knowledge entities, attributions and relations from the structured, semi-structured and unstructured data firstly. Secondly, the knowledge graph is constructed through the knowledge fusion technology based on ontology fusion and instance fusion algorithms proposed in this paper. Finally, the ontology model for multi-source data is constructed. Experiments show that the proposed algorithms improve the matching degree and F1 score, and have better visualization ability.
Towards Multi-label Feature Selection by Instance and Label Selections

In multi-label learning, feature and instance selection represent two effective dimensionality reduction techniques, which remove noise, irrelevant and redundant entries from original data for easy later analysis, such as clustering and classification. Label selection also plays a fundamental role in the pre-processing step since label-noises could negatively affect the performance of the underlying learning algorithms. The literature has been mainly limited to feature and/or instance selection, but has somewhat overlooked label selection. In this paper, we introduce, for the first time, a combination of the three selection techniques (feature, instance and label) for multi-label learning. We propose an efficient convex optimization based algorithm that evaluates the usefulness of features, instances and labels in order to select the most relevant ones, simultaneously. Experimental results on some known benchmark datasets are presented to demonstrate the performance of the proposed method.
Formal Security Analysis on dBFT Protocol of NEO

NEO is one of the top public chains worldwide. It adopts a new consensus algorithm called delegated Byzantine Fault Tolerance (dBFT). In this article, we formalize dBFT via the state machine replication model and point out its potential issues. Our theoretical analysis indicates that dBFT could guarantee neither liveness nor safety , even if the number of Byzantine nodes is no more than the threshold, which has contradicted the established security claim. Then, we identify two attacks and successfully simulate them. Finally, we provide recommendations. Notably, NEO official team has accepted our suggested fixes.
A RIGOROUS APPROACH TO PRIORITIZING CHALLENGES OF WEB-BASED APPLICATION SYSTEMS

Web innovations are unique advancements of software engineering. They are equipped with new apparatuses, novel systems, and revolutionary plans, ready to be used by web application developers and their users. According to their importance for web application improvement, there is a need to locate a fitting approach to adapt to these challenges. Therefore, the core objective of this study is to rank the challenges that were encountered by the developers and users of web-based applications. This study contributes to knowledge by proposing a hierarchical framework for identifying and categorizing the challenges appropriately. An exhaustive list of sixty-nine challenges was formulated, based on extensive literature review. Quantitative in nature, this study uses the survey questionnaire to collect data from 87 experts in the web innovation domain. Various statistical models like the Cronbach’s Alpha, Principal Component Analysis, Stepwise, and Logistics Regression were employed to analyze the data. The stepwise regression was deployed to formulate the hierarchy of the extracted challenges. The outcome of the proposed framework would lead to a more efficient development and utilization of web applications. The limitations and future directions of this study were also discussed.
A Contrastive Analysis of Pre-Service and In-Service EFL Teachers’ Levels of Technological, Pedagogical, and Content Knowledge

This study attempted to investigate pre-service and in-service EFL teachers’ levels of knowledge in content, pedagogy, and technology in relation to their gender, Internet access at school, and technology training in Saudi Arabia. The Technological, Pedagogical, and Content Knowledge (TPACK) framework was utilized to (1) examine pre-service and in-service EFL teachers’ levels of TPACK; (2) investigate the differences between pre-service and in-service EFL teachers’ levels of TPACK; (3) scrutinize whether gender, Internet access at school, and technology training had any significant effect on pre-service EFL teachers’ levels of TPACK; and (4) analyze whether gender, Internet access at school, and technology training had any significant effect on in-service EFL teachers’ levels of TPACK. The participants in this study were (111) EFL pre-service teachers and (106) EFL in-service teachers. The descriptive statistical analysis (mean and standard deviation) revealed that the pre-service teachers’ overall level of TPACK was moderate scoring highest in TK, TPK, and TCK and lowest in CK and PK respectively. On the other hand, the in-service teachers’ overall level of TPACK was moderate scoring highest in CK, PK, and PCK and lowest in TK, TPK, and TCK respectively. The inferential statistical analysis (t-test) indicated a statistically significant difference between pre-service and in-service teachers’ levels of TPACK. All the seven sub-domains of TPACK were significant, with pre-service teachers scoring higher in TK, TCK, and TPK while in-service teachers scoring higher in CK, PK, PCK, and TPACK. The inferential analysis also showed that gender, Internet access at school, and technology training had a significant effect on both pre-service and in-service EFL teachers’ levels of TPACK. The study concluded that both pre-service teacher education programs and in-service teacher training courses need to focus on the connections and interactions between content, pedagogy, and technology in order to help EFL teachers integrate technology effectively into their teaching process. The study recommended investigating other factors that might be predictive of teachers’ levels of TPACK.
Using a Correction Factor to Remove Machine Compliance in a Tensile Test on DP1000 Steel Validated with 2D Digital Image Correlation Technique

This paper presents the procedure for the machine compliance removal in the obtained force-displacement data from a tensile test in order to generate a correct stress-strain curve for any material. A tensile test has been conducted on a dual phase steel using an ATSM standard 12.5 mm flat-type tensile specimen geometry and the aim is to produce the correct stress-strain curve without the effect of machine compliance. A correction factor has been developed and is applied to the obtained force-displacement response of the tested DP1000 steel specimen. The strain result obtained from the stress-strain curve with applied machine stiffness correction factor is then validated against the strain result obtained using a virtual strain gauge of digital image correlation (DIC). Strain results obtained from both procedures, through applying correction factor to data and DIC technique are almost similar with a small difference of 0.21%. The implementation of the correction factor procedure in tensile response data are described in this contribution.
Large‐Scale Fabrication of Surface Ion Traps on a 300 mm Glass Wafer

Herein, a large‐scale fabrication of radio frequency (RF) surface ion traps on a 300 mm glass wafer using a standard foundry process is reported. Established wafer‐level packaging process of electroplated Cu with Au finish is used to fabricate the surface electrodes directly on the glass wafer substrate. A trap is tested by loading it with laser‐cooled 88 Sr + ions. The trap shows a stable operation with RF amplitudes in the range of 100–250 V at 33 MHz frequency. The ion lifetime is on the order of 30 min with laser cooling with a vacuum chamber pressure of ≈5 × 10 −11 mbar. These results demonstrate the potential of large‐size foundry glass substrates to realize scalable and integratable trapped ion‐based quantum devices.
THE COMPETENCE AND KNOWLEDGE APPROACHES: PHILOSOPHICAL AND EDUCATIONAL PROBLEMS OF UNDERSTANDING AND APPLICATION

The article considers historical aspects of the experience in the development and formation of digital education in the USA and Western Europe. It has been determined that the introduction of the latest information technologies in education has made it possible to advance the solution of quality, accessibility and effectiveness issues, revealing such problems as occurrence of computer addiction, fragmentation and unsystematic knowledge, redundancy of information, insufficient motivation and others. Tendencies in the development of digitalization in the Russian Federation are highlighted. The generalized definition is given of the information and communication educational environment as a purposefully organized system of technical and information resources, electronic didactic materials, telecommunication facilities which ensure the effective mastering of professional educational programs. The educational information system should be harmonized with the curriculum and work programs of disciplines and integrated with electronic library resources. The trends and possibilities of digital education in Russia are identified and briefly characterized from the point of view of modernization and solving problems of higher education quality. The main directions of developing the electronic communication system in teaching technical and general technical disciplines as a means of managing students» research activities are considered. The article also presents a generalized classification of e-learning implementation models for the disciplines under consideration. It is noted that the choice of optimal models is determined by the tasks being solved by educational goals. It was highlighted that modern theoretical training, within the framework of modernization trends, involves active use of modern computer technologies, interactive multimedia tools (webinars, presentations, video lectures) and exchange of information via telecommunication channels (online consultations). It was emphasized that it is desirable to carry out a laboratory workshop in a real environment, rather than a virtual one (on modern equipment). The conclusion is made that the digitalization of education in Russia will contribute to increasing the efficiency, accessibility and quality of education with the high quality of the components and subjects of the information and communication system.
Refining Automatic Speech Recognition System for Older Adults

Building a high quality automatic speech recognition (ASR) system with limited training data has been a challenging task particularly for a narrow target population. Open-sourced ASR systems, trained on sufficient data from adults, are susceptible on seniors’ speech due to acoustic mismatch between adults and seniors. With 12 hours of training data, we attempt to develop an ASR system for socially isolated seniors (80+ years old) with possible cognitive impairments. We experimentally identify that ASR for the adult population performs poorly on our target population and transfer learning (TL) can boost the system’s performance. Standing on the fundamental idea of TL, tuning model parameters, we further improve the system by leveraging an attention mechanism to utilize the model’s intermediate information. Our approach achieves 1.58% absolute improvements over the TL model.
A Mechanism for Verifying the Integrity and Immutability of Tuberculosis Data Using IOTA Distributed Ledger Technology

Background: Intensified research and innovation and rapid uptake of new tools, interventions, and strategies are crucial to fight Tuberculosis, the world’s deadliest infectious disease. The sharing of health data remains a significant challenge. Data consumers must be able to verify the consistency and integrity of data. Solutions based on distributed ledger technologies may be adequate, where each member in a network holds a unique credential and stores an identical copy of the ledger and contributes to the collective process of validating and certifying digital transactions. Objectives: This work proposes a mechanism and presents a use case in Digital Health to allow the verification of integrity and immutability of TB electronic health records. Methods: IOTA was selected as a supporting tool due to its data immutability, traceability and tamper-proof characteristics. Results: A mechanism to verify the integrity of data through hash functions and the IOTA network is proposed. Then, a set of TB related information systems was integrated with the network. Conclusion: IOTA technology offers performance and flexibility to enable a reliable environment for electronic health records.
Vulnerability Assessment in Heterogeneous Web Environment Using Probabilistic Arithmetic Automata

In the current scenario most of the business enterprises are running through web applications. But the major drawback is that they fail to provide a secure environment. To overcome this security issue in web applications, there are many vulnerability detection tools are available at present. But these tools are not proactive and consistent as it does not adapt to all kinds of recent updates and is unable to track new emerging vulnerabilities. For the long-term functioning of a business enterprise, statistical data with efficient analytics on vulnerabilities is required to enhance its security impacts. Predictive Analytics is a powerful solution to effectively arm the recent incident response to modern-day threats. Predictive Analytics provides a proactive and decision-making approach and insights into how well security programs are working. It can also help to identify problem areas and can warn about imminent or active attacks in heterogeneous web applications to enhance the former features and analyze the origin and pattern of the attack in a more effective manner. The pattern analyzed through research is given as an input to the Machine Learning techniques such as Deterministic Arithmetic Automata (DAA), Probabilistic Arithmetic Automata (PAA) to predict the probabilistic value as an output. From the obtained probabilistic values, we can detect the cause of an attack, prevent the heterogeneous web application of business enterprises from further impacts and find the penetration level of an attack from web application to web service.
The youth bulge

Chapter 18, by Zafiris Tzannatos, also examines the Arab Spring but from a different perspective. These tumultuous uprisings are often ascribed in the literature to the role played by the youth bulge in the demographic structure of the Arab countries. Tzannatos juxtaposes the case of the demographic youth bulge and high youth unemployment versus structural reasons as causes for these uprisings. He argues that the youth bulge was practically over at least a decade before the uprisings, youth unemployment had been reduced more than adult unemployment during the preceding two decades and discontent arising from the liberalising economic reforms pursued since the 1990s was generalised across the population, not just the youth. This despite the fact that the reforms reduced the deficit, debt and inflation while economic and employment rates accelerated from their low rates during the ‘lost’ decade of the 1980s. However, the reforms were implemented in a way that their benefits were captured by few insiders. Overall, evidence lends support to more complex economic, institutional and political explanations for the uprisings than demographic ones, especially those centring on the youth. Tzannatos argues that for policy purposes, future approaches should first focus on improving the functioning of the macroeconomy and its ability to create inclusive growth. Hence, youth and adult issues should be examined together, rather than routinely attributing the underperformance of labour markets to the low quality and relevance of education or school-to-work transition issues, both of which can always be improved and not only in the Arab countries.
Detection of clouds in multiple wind velocity fields using ground-based infrared sky images

To improve the performance of an intra-hour global solar irradiance forecasting algorithm, it is important to detect multiple layers of clouds. Horizontal atmospheric wind shear causes wind velocity fields to have different directions and speeds. In images of clouds acquired using ground-based sky imagers, clouds may be moving in different wind layers. The information provided by a solar forecasting algorithm is necessary to optimize and schedule the solar generation resources and storage devices in a smart grid. This investigation studies the performance of unsupervised learning techniques when detecting the number of cloud layers in infrared sky images. The images are acquired using an innovative infrared sky imager mounted on a solar tracker. Different mixture models are used to infer the distribution of the cloud features. Multiple Bayesian metrics and a sequential hidden Markov model are implemented to find the optimal number of clusters in the mixture models, and their performances are compared. The motion vectors are computed using a probabilistic implementation of the Lucas-Kanade algorithm. The correlations between the cloud motion vectors and temperatures are analyzed to discover the method that leads to the most accurate results. The findings point that a sequential hidden Markov model outperforms the detection accuracy of standard Bayesian model selection metrics.
Uniform Sampling on the Standard Simplex

Three methods for obtaining uniform sampling on the standard simplex are summarized and a new derivation of one method is provided, which only uses basic notions from calculus, difference equations, and differential equations.
Hardware for multi-superconducting qubit control and readout*

We have developed an electronic hardware system for the control and readout of multi-superconducting qubit devices. The hardware system is based on the design ideas of good scalability, high synchronization and low latency. The system, housed inside a VPX-6U chassis, includes multiple arbitrary-waveform generator (AWG) channels, analog-digital-converter (ADC) channels as well as direct current source channels. The system can be used for the control and readout of up to twelve superconducting transmon qubits in one chassis, and control and readout of more and more qubit can be carried out by interconnecting the chassis. By using field programmable gate array (FPGA) processors, the system incorporates three features that are specifically useful for superconducting qubit research. Firstly, qubit signals can be processed using the on-board FPGA after being acquired by ADCs, significantly reducing data processing time and data amount for storage and transmission. Secondly, different output modes, such as direct output and sequential output modes, of AWG can be implemented with pre-encoded FPGA. Thirdly, with data acquisition ADCs and controlAWGs jointly controlled by the same FPGA, the feedback latency can be reduced, and in ourtest a 178.4 ns latency time is realized. This is very useful for future quantum feedback experiments. Finally, we demonstrate the functionality of the system by applying the system to the control and readout of a 10 qubit superconducting quantum processor.