Reviewerly/exHarmony huggingface dataset:
A review on empowering women Via women network education

Advanced education aims to foster networking, learning, or, as a result, the exchange of ideas about shared interests amongst teachers, students, and others. Because learning fires psychosocial information processes by linking observer cognition, network learning strengthens the structure, content, and types of information processing across groups and cohorts. When women connect with learning groups, hidden possibilities emerge to break down obstacles to development. Learning became a collaborative activity as a result of virtual research settings, cloud computing, social networking sites, including blogs. Piaget's constructivist cognitive learning, Jean Lava's Situated learning, as well as Salomon and Perkins' Transfer of learning all support the network instructional approach paradigm. This study attempts to demonstrate the value of girl's network learning in enhancing schooling. Women's network learning improves education quality, progress, and hope in the near future. In our overprotective social organization, the experts' research efforts supplement the knowledge to form and elevate the ladies network learning as “quality stroke to education.
Unravelling Modern News Classification Methods: A Systematic Review

Nowadays, the news is being generated each second from every corner of the world, with millions of news articles generated every day. Some assume that at least 1.8 million articles are published yearly, in about 28,000 journals. It has become difficult to recognize what's fake and what's genuine due to the overflow of millions of articles every day. Not every person reads every news, so the classification of news according to people’s interests has become significant. 80% of the information is unstructured and “text” being the most common type of unstructured data has led to the emergence of numerous news classification methods. Some of the modern news classification methods stretch from using Machine Learning models like “SVM”, “Naive Bayes”, “Decision Tree” to the modern complex transformer and deep learning models like “BERT” and “LSTM”. So, the main aim of this review is to better understand the pipeline and shortcomings of the news classification processes aforementioned. This article will help researchers to distinguish between various models and choose the one best for their projects.
Transfers Takeaways from GDPR Enforcement in Cloud Computing &amp;amp; Beyond

What are the key takeaways from international transfers-related enforcement to date under the GDPR (EU General Data Protection Regulation), including in the cloud computing context? After recapping the background, this article summarises the main practical lessons from such enforcement, and ends with a discussion of some problematic points regarding the transfers restriction including the EDPB recommendations on supplementary measures, and related policy questions. It covers both court cases and action by GDPR supervisory authorities, summarised in the Appendix.
Anomaly detection on ICS(Industrial Control System) using deep learning method

4차 산업혁명 시대가 도래하면서 스마트 공장을 비롯한 산업제어 시스템에서의 제어 공정과 관련한 사이버 보안이 매우 중요해지고 있다. 본 논문에서는 사이버 공격으로 산업제어 시스템에서 발생할 수 있는 이상징후를 딥러닝 기법을 이용하여 탐지하는 모델을 개발하였다. 이상징후 탐지를 위해 수자원 시스템에 대한 공개 데이터 셋인 SWaT(Secure Water Treatment)를 사용하였다. 또한, 산업제어 시스템에서 발생하는 신호가 시계열 데이터인 점을 고려하여 RNN(Recurrent Neural Network), LSTM(Long Short-Term Memory), GRU(Gated Recurrent Unit) 딥러닝 모델을 개발하였다. 이상징후 탐지 성능을 평가하기 위해서는 기존 정밀도(Precision)와 재현율(Recall) 평가 지표뿐만 아니라 이를 보완한 RP(Range-based Precision), RR(Range-based Recall), TaP(Time-Series Aware Precision), TaR(Time-Series Aware Recall)을 구현하여 비교하였다. 이상징후 탐지 실험 결과, 기존 모델과 비교하여 TaR 관련 지표에서는 LSTM이, TaP 관련 지표에서는 GRU가 우수한 성능을 나타냄을 확인하였다.
Large extra dimensional limit of gravity subject to proton decay

D(p) Branes can range from any number of spatial dimensions extending from 1 to 10. Most of the force and matter fields are concentrated along the wall of the brane as a open strings thereby permitted to travel in higher dimensions however a typical type of bosonic force field called the gravitons extend beyond the boundary of our 4 dimensions and extend to (4 + n) dimensions with the value of (n ≤ 10). This peculiar property of gravitons exist as because there are always a closed strings with no ends attached to a D(p) branes. The energy required to detect gravity is at a close proximity of 10^16 GeV to 10^19 GeV making GUT or grand unified theories. However, the LED or large extra dimensions model reduce the energy scale to 1000 GeV or 1 TeV. However in this paper I will show that not only energy required equals to 1 TeV but an energy of much less than 1 TeV that is 10 GeV. Therefore, the gravity would be accessible at a much lower energy provided the dimension of the LED is equal to 1 mm. During a head to head collision of proton bombardment in LHC 'gravitons' appeared but remains undetected as they quickly appeared to higher dimensions at (4 + n) with (4 &amp;lt; ≤ 10). However when dimensions = 6 then a Calabi-Yau manifold is formed but here there is SED or 'small extra dimensions' instead of LED. Now, an open question that I want to ask to my readers is that, if gravity of 1 mm which is sufficiently small one a scale = 1 where the force actually disappears then if it got except in higher dimensions then the higher dimensions must have a value of D = 2mm &amp;gt; r = 1mm. So, the throat must be larger than the minimal length of gravity to swallow it whole. Therefore, if extra dimensions exist then they must be equal to or greater than 1mm. Another important thing to note is the proton decay where a proton decays into a neutral pions and a positron which ultimately decays into 2 gamma rays and the half-life time of the proton decay is around 10^34 years which is impossible to observe for the humans. Now, a proton having a mass parameter of 1800 times greater than the electrons have the effect of gravity comparatively significant than that of the lepton 'electron'. And that proton decay has a connection with GUT via magnetic monopoles provided the D(p)-1 Branes are extremely long. However, an alternative approach of the 'magnetic monopole' can be found from the Gauss's law of magnetism as (∇ • B = 4πρ) where magnetic charge density that is ρ.
News Recommendation Method Based on Topic Extraction and User Interest Transfer

Contemporary news reader faces the problem of information explosion which has led to the difficulty for users to acquire information they really need. News recommendation system plays a significant role in screening interested news for users, but traditional news recommendation methods often face cold-start problem and are difficult to reflect the user personalized differences. This paper proposes a news recommendation method based on topic extraction and user interest transfer (NRTU). The proposed model extracts news topic tags by analyzing semantic information from texts which alleviates cold-start problem of news and applies Long-Short Term Memory (LSTM) model to represent user interest transfer. Extensive experiments on a real-world dataset validate the effectiveness of our approach.
Research on an Improved Driver-response Chaotic Synchronization Voice Encryption Technology

An improved method of voice encryption based on drive-responsive chaotic synchronization is proposed to solve the problem of voice encryption communication with different energy. Based on the drive-responsive chaotic synchronization, a gain link K is added before the drive system and the other gain link <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1/K$</tex> is placed after the responsive system to improve the concealment and quality of signal communication. The system is tested with the standard pronunciation of 26 English alphabets, the best value of parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> is obtained by calculating the correlation coefficient, and the new system is verified by the voice signals of different energy. The results show that the improved method effectively solves the effect of amplitude and overcomes the limitation of the drive-responsive chaotic synchronization. The new system accurately restores the originally given voice signal and increases the confidentiality of the signal transmission.
Recognition of Key Injuries in Winter Sports Events Based on Rough Set and Cellular Genetic Algorithm

The normal operation of winter sports events is faced with the challenge of many security incidents, which will inevitably cause harm to people when they turn into disasters. Taking winter sports events as the scene, this paper combs the main disasters, injuries and treatment measures, constructs a decision table with multiple decision attributes, and reduces the attributes of injuries based on rough set theory and cellular genetic algorithm, so as to find four key injuries. On the basis of finding the four key injuries, it can provide guidance for the next step of specific research for each injury, and also provide a reference for the actual medical measures of winter sports events to a certain extent.
Demand Response Analysis for Different Residential Personas in a Comfort-Driven Behavioral Context

Abstract Low demand response (DR) participation and high program drop-out rates continue to impede DR goals that could save up to $13 billion in annual grid expansion and electricity demand costs. Yet, the literature lacks a thorough understanding of how different residential customer segments enrolled in DR programs respond to utility signals in view of occupant comfort considerations. The objective of this study is to gain a clear understanding of the effects of four different customer personas on residential DR. Given current data limitations, this work developed an array of hypothetical personas with varied priorities, activity levels, and comfort thresholds based on demographic variables that have been found in previous studies to influence energy consumption. A BEopt™ DR model for a reference residential single-family building located in Colorado was built to isolate the effect of differences in buildings or climate. The results provide useful evidence on how persona-comfort differences lead to significant deviations in DR goals (especially peak demand reduction), ranging from 0.1% to 20%. This work presents a novel framework representing comfort preferences in DR models. The data generated, albeit synthetic, and the results could inform DR program design considerations of how different people respond to different comfort priorities.
Bilateral Access Control with Traceability and Revocability in Secure Cloud-Fog Computing Framework

In the Cloud-Fog computing infrastructure, the Fog layer is located between the end device and the cloud and this improves efficiency by performing local tasks at Fog layer. A number of security algorithms and cryptographic tools are used to provide confidentiality and protect the data from untrustworthy users and adversaries. Due to the large number of end devices connecting to the fog node, tracking the source of the malicious activity and revoking access to the services provided by the fog node become a tedious task. We extend the existing Fine-Grained Bilateral Access Control system to provide traceability and revocability by introducing a central authority in each fog node. The central authority is a fully trusted entity that monitors the communication channel between the end node and the fog node. It detects anomalies in the data traffic to identify various attacks and malicious nodes. The proposed approach has been implemented using iFog simulator.
Conclusion

Although we’ve reached the end of this book, it’s more than likely that you’re still at the beginning of your Zero Trust journey. We’ve covered a great deal of material, spanning topics that are conceptual, technical, strategic, and organizational, and yet, despite the breadth of topics, we acknowledge that we weren’t able to cover everything. Zero Trust is very broad in scope—essentially as broad as enterprise IT—and is fast-moving. New technologies, platforms, and solutions arise seemingly every day. Not to mention, each and every enterprise has assembled IT and security components in unique combinations to meet their specific needs. As such, we remain confident that there is plenty of remaining future work in this area.
The Effect of Picture and Pictures Model in Teaching Narrative Text For The Tenth Grade of SMAN 20, Bekasi

This study aims to determine whether or not there is an effect of the picture and pictures model in teaching narrative text for the tenth graders of SMAN 20 Kota Bekasi. The method used in this research is the True Experimental Design method. In this method, the researcher uses two classes, namely the experimental class, and the control class. The instrument used in this class is a test of the ability to write narrative text using different learning models. The experimental class was given a picture and pictures learning model and the control class was given a conventional model. The population of this study was students of class X SMAN 20 Bekasi City. Sampling was done by random sampling, namely students in class X IPS 1 and class X IPS 2. For the experimental class, class X IPS 2 and control class X IPS 1 with a total of 34 students. Test the normality of the test results (posttest) using the Liliefors test. In the experimental class, it was obtained that Lcount = 0.104 &lt; 0.206 = Ltable at a significant level of 0.05 and the control class Lcount = 0.0158 &lt; 0.206 = Ltable at a significant level of 0.05. In both samples, it turns out that Lcount &lt; Ltable, it can be concluded that the two samples are normally distributed. The homogeneity test of the test results using the Havley test, obtained – 0.429 0.295 2.33., it turns out – Ftable left Fcount Ftable right, it can be concluded that the sample is homogeneous. Furthermore, hypothesis testing using t-test obtained tcount = 12.06 &gt; 1.69 = ttable at a significant level of 0.05, it turns out that tcount &gt; ttable, then H0 is rejected and H1 is accepted. That is, there is an Influence of Picture and Pictures Model in Narrative Text Learning on students' writing ability. These results state that there is an influence of Picture and Pictures Models in Narrative Text Learning in class X SMAN 20 Bekasi City.
Adaptive Kernel Transform for Face Recognition Under Uneven Illumination Conditions

Face recognition plays an important role in person identification from a human face image. Existing approaches can extract facial features, but their performance is insufficiently accurate in uneven illumination conditions, especially in low light intensity. Therefore, an adaptive kernel transform (AKT) is proposed for recognizing face images under uneven illumination conditions. The proposed method provides horizontal-vertical detail components excluding illumination components by means of lighting component decomposition. Then it extracts robust features based on adaptive maximum histogram thresholding with matrix multiplication. The obtained features are composed of the important key facial regions without background noise and are robust in uneven illumination. From the Extended Yale-B face database, our AKT method yields 96.14%, which is higher than existing methods. The AKT method can adaptively extract robust features, thereby improving the effectiveness of face recognition under uneven illumination conditions.
Self-Calibrating Sparse Far-Field Photometric Stereo With Collocated Light

Photometric stereo is one common surface normal estimation technique, which can be utilized for various industrial tasks such as 3-D surface detail reconstruction and defect inspection. To further satisfy the requirement of the industrial inspection, the uncalibrated photometric stereo is needed. This article proposes a collocated light method for uncalibrated far-field photometric stereo to achieve accurate surface normal estimation under a small number of lights. Collocated light is the light close to the camera, with the angle between the lighting direction of the collocated light and the viewing direction of the camera being less than 10°. The image under the collocated light could be utilized to extract features related to the shadings and the shadows that are the key to the light self-calibration. Thus, the collocated light is introduced to realize the light self-calibration, with the accuracy being less influenced by the number of lights. Then, the normal estimation network with collocated light is integrated to give precise surface normal estimations under a small number of lights. Both synthetic and real experiments validate that the proposed method can achieve the state-of-the-art performance.
Educational Problems in Russian Universities in the Context of Society Digitalization

The work investigates the introduction of digital technologies and digital tools into the educational process. The influence of digitalization on the problem field of higher education is noticeable, which can be considered not only from the positive side, but also from the point of view of the threats that digitalization brings to educational institutions, including higher education, as well as educational problems that express situations of uncertainty in changing the configuration of relations between the subjects of the new educational reality. The subject of the study is the possible directions of the development of higher education at the stage of expanding digitalization in society. The research purpose is to study the problematic processes in the transformation of higher education under the influence of the digital environment. Under the influence of the digitalization of society, the role of the teacher and the student is changing, the field of telecommunications technologies in the managerial and social spheres is expanding. It is necessary to calculate the possible negative consequences of increasing digitalization in the educational process of Russian universities. Research methods include theoretical: analysis, synthesis, comparative, generalization, bibliometric analysis, empirical: development of research tools for questionnaire survey (quantitative method), observation.
Effects of Broca’s Area tDCS on Acquisition of Concrete and Abstract Words

Many software systems today are highly configurable. They can produce a potentially large variety of related programs (variants) by selecting suitable configuration options (features) at compile time. Recently, specialized variability-aware (lifted, family-based) static analyses based on abstract interpretation have been developed. They allow analyzing all variants of a program family (or, any other configurable software system), simultaneously, in a single run without generating any of the variants explicitly. In effect, they produce precise analysis results for all individual variants. The elements of the underlying lifted analysis domain represent tuples (i.e. disjunction of properties), which maintain one property from an existing single-program analysis domain per variant. Nevertheless, explicit property enumeration in tuples, one by one for all variants, immediately yields to combinatorial explosion given that the number of variants can grow exponentially with the number of features. Therefore, such lifted analyses may be too costly or even infeasible for program families with a large number of variants.In this work, we propose a more efficient lifted static analysis of program families with Boolean features, where sharing is explicitly possible between analysis elements corresponding to different variants. This is achieved by giving a symbolic representation of the lifted analysis domain, which can efficiently handle disjunctive properties in program families. The elements of the new lifted domain are binary decision diagrams, where decision nodes are labeled with Boolean features and leaf nodes belong to an existing single-program analysis domain. The lifted domain is parametric in the choice of the abstract (property) domain for leaf nodes. To illustrate the potential of this representation, we have implemented a lifted static analyzer that uses a combination of forward and backward analyses for inferring numerical invariants and necessary preconditions of C program families. It uses APRON and BDDAPRON libraries for implementing the new lifted analysis domain. The APRON library, used for the leaves, is a widely accepted API for numerical abstract domains (e.g. polyhedra, octagons, intervals), while the BDDAPRON is an extension of APRON which adds the power domain of Boolean formulae and any APRON domain. An empirical evaluation on C benchmarks taken from SV-COMP and BusyBox indicates that our binary decision diagram-based approach is effective and outperforms the baseline tuple-based approach.
SHORT-TERM FORECASTING OF TIME SERIES BASED ON EVOLUTIONAL ALGORITHMS

The paper proposes a method of short-term forecasting based on the identification of the spanning algebraic sequence c using evolutionary algorithms. This method is especially effective in the case of short time series. When there is not enough data for training standard models, the developed method still allows to extract the maximum available information on the process behavior in order to extrapolate it to future points in time.
Simulation of Wave Propagation in Biomimetic Porous Scaffold Using Artificial Neural Network

Abstract The study of wave propagation in biomimetic porous scaffold requires the inclusion of some complex physics such as the interaction of the ultrasonic wave with pore fluid, solid phase, and porous material. Also, due to viscous interactions between the pore fluid and skeletal frame, the dynamic tortuosity as a fractional function of frequency in the clinically relevant ultrasound frequency range is considered. The bone scaffold here is simulated using a porous slab whose two dimensions are infinite. The Biot-JKD theory used for wave propagation in porous media is conditioned with many physical parameters. Solving such governing equations for complex multi-physics problems is computationally expensive. Therefore, developing efficient tools and numerical methods to address multi-physics problems is appealing. Artificial Neural Network (ANN) can efficiently solve convoluted-parametric problems. The purpose of this research is to propose a physics-aware ANN to simulate wave propagation in bone scaffold filled with a viscous fluid. A set of data including porosity, viscosity, tortuosity, viscous characteristics length, Poisson’s ratio, and elastic modulus which are sensitive to the transmission and reflection signals are applied to the ANN as inputs and the reflection and transmission signals are obtained as outputs. The reflected and transmitted waves for different porosities are considered and the results show an excellent agreement with the proposed analytical theory and experimental data found in the literature.
A Variational Quantum Algorithm for Ordered SVD

Singular value decomposition (SVD) is fundamentally important and broadly useful in both quantum and classical computing. There are several quantum algorithms known for SVD. Variational quantum algorithms with parametric quantum circuits (PQC) are the most promising approach to find SVD with near-term quantum computers. This paper reports a new method for quantum SVD (QSVD) that finds the singular vectors ordered by the magnitude of singular values by adding extra CNOT gates and a new local cost function design. This ordering is important because the singular vectors with the larger singular values is more representative for the given information. The ordering property of this approach is investigated with the standard Iris dataset by numerical simulations of 4-qubit states. Methods for choosing appropriate choices for the cost function hyperparameter and cost function terms as well as an application example of a quantum encoder are also discussed.