Reviewerly/exHarmony huggingface dataset:
Do Wider Neural Networks Really Help Adversarial Robustness

Adversarial training is a powerful type of defense against adversarial examples. Previous empirical results suggest that adversarial training requires wider networks for better performances. However, it remains elusive how neural network width affects model robustness. In this paper, we carefully examine the relationship between network width and model robustness. Specifically, we show that the model robustness is closely related to the tradeoff between natural accuracy and perturbation stability, which is controlled by the robust regularization parameter $\lambda$. With the same $\lambda$, wider networks can achieve better natural accuracy but worse perturbation stability, leading to a potentially worse overall model robustness. To understand the origin of this phenomenon, we further relate the perturbation stability with the network's local Lipschitzness. By leveraging recent results on neural tangent kernels, we theoretically show that wider networks tend to have worse perturbation stability. Our analyses suggest that: 1) the common strategy of first fine-tuning $\lambda$ on small networks and then directly use it for wide model training could lead to deteriorated model robustness; 2) one needs to properly enlarge $\lambda$ to unleash the robustness potential of wider models fully. Finally, we propose a new Width Adjusted Regularization (WAR) method that adaptively enlarges $\lambda$ on wide models and significantly saves the tuning time.
Building a digital capacitor model for diagnostic and predictive analytics applications

The article discusses one of the possible ways to solve the problem of reducing the efficiency of electric power facilities during their operation through the development and implementation of digital twins. The features of the APCS of the lower level are analyzed and the priority parameters are selected for implementation in the modeling system. The result of the work is the development of a mathematical model of the condensing unit, as well as the software implementation of the simulation model of the condensing unit of the CHP power station in the high-level programming language Python. Fragments and results of the developed program are presented.
Representing Implicit Positive Meaning of Negated Statements in AMR

Meaning Representation (AMR) has become popular for representing the meaning of natural language in graph structures.However, AMR does not represent scope information, posing a problem for its overall expressivity and specifically for drawing inferences from negated statements.This is the case with so-called "positive interpretations" of negated statements, in which implicit positive meaning is identified by inferring the opposite of the negation's focus.In this work, we investigate how potential positive interpretations (PPIs) can be represented in AMR.We propose a logically motivated AMR structure for PPIs that makes the focus of negation explicit and sketch an initial proposal for a systematic methodology to generate this more expressive structure.
Framework for enhancing students’ smartphone learning ability: a case study of Nigerian public Universities

Purpose The importance of smartphones in enhancing students learning, research and development is well-established in many published studies. Nevertheless, due to numerous challenges, Nigerian students are yet to reap from the benefits of smartphones in terms of accessing vital information for learning and development. Therefore, this study aims to develop a framework that will enhance Nigerian students’ ability to use smartphones for learning. Design/methodology/approach The SERVQUAL Theory Framework is adapted to actualize the research aim. Moreover, a qualitative (in-depth interview) research approach was used to interview 60 students from 3 public universities in Nigeria. Purposive sampling technique was used to select the interviewees, and the interview data were analysed using thematic content analysis. Findings The findings identified the critical constraints that deter Nigerian students from reaping the full potential of smartphones for learning. The constraints are categorized into three groups which include telecommunication companies’ hiccups, such as high cost of internet, poor network services and coverage; government obstacles, such as high tax policies, insufficient electricity supply and lack of basic infrastructure; and university barriers, such as unavailability of Wi-Fi connection for students, lack of equipped university libraries and poor management. Originality/value Thus, a holistic Framework for Enhancing Students Smartphones Learning Ability was established to provide fundamental recommendations to the Nigerian Government, telecommunication companies in Nigeria and Nigerian universities to tackle the pressing issues hindering Nigerian students from using their smartphones for learning. This will enhance the smartphone learning experience of Nigerian students.
A Hybrid K-Means-GRA-SVR Model Based on Feature Selection for Day-Ahead Prediction of Photovoltaic Power Generation

In order to ensure that the large-scale application of photovoltaic power generation does not affect the stability of the grid, accurate photovoltaic (PV) power generation forecast is essential.A short-term PV power generation forecast method using the combination of K-means++, grey relational analysis (GRA) and support vector regression (SVR) based on feature selection (Hybrid Kmeans-GRA-SVR, HKGSVR) was proposed.The historical power data were clustered through the multi-index K-means++ algorithm and divided into ideal and non-ideal weather.The GRA algorithm was used to match the similar day and the nearest neighbor similar day of the prediction day.And selected appropriate input features for different weather types to train the SVR model.Under ideal weather, the average values of MAE, RMSE and R 2 were 0.8101, 0.9608 kW and 99.66%, respectively.And this method reduced the average training time by 77.27% compared with the standard SVR model.Under nonideal weather conditions, the average values of MAE, RMSE and R 2 were 1.8337, 2.1379 kW and 98.47%, respectively.And this method reduced the average training time of the standard SVR model by 98.07%.The experimental results show that the prediction accuracy of the proposed model is significantly improved compared to the other five models, which verify the effectiveness of the method.
Block-Based Compression and Corresponding Hardware Circuits for Sparse Activations

In a CNN (convolutional neural network) accelerator, to reduce memory traffic and power consumption, there is a need to exploit the sparsity of activation values. Therefore, some research efforts have been paid to skip ineffectual computations (i.e., multiplications by zero). Different from previous works, in this paper, we point out the similarity of activation values: (1) in the same layer of a CNN model, most feature maps are either highly dense or highly sparse; (2) in the same layer of a CNN model, feature maps in different channels are often similar. Based on the two observations, we propose a block-based compression approach, which utilizes both the sparsity and the similarity of activation values to further reduce the data volume. Moreover, we also design an encoder, a decoder and an indexing module to support the proposed approach. The encoder is used to translate output activations into the proposed block-based compression format, while both the decoder and the indexing module are used to align nonzero values for effectual computations. Compared with previous works, benchmark data consistently show that the proposed approach can greatly reduce both memory traffic and power consumption.
Recognition of Tablet using Blister Strip for Visually Impaired using SIFT Algorithm

Objectives: To create a tablet recognition system that recognizes and gives an audio output of the name of the tablet so that the visually impaired person may recognize it.Methods: In this, to create a user friendly system, such that a blind person does not require the help of another person to use it.These systems give medical assistive instrument to take the drug at the accurate time as per the doctor prescription and removes dependency on others for the visually challenged people.Various models were technologically advanced to compact with misidentification of remedies but are incompetent of formative exact pill picked by the person.Findings: The name of the medicine is obtained from the image.Then the name of the medicine is converted from text to speech using Google text to speech (gTTS).The proposed system will be helpful for visually challenged people, SQLite Database management system has been used along with Scale Invariant Feature Transform algorithm (SIFT) to identify and pronounce local features in the image.The image of the medicine given by the user is matched with the images in the database.Novelty: Developed a user-friendly voice based tablet recognition system for visually impaired people and this system is fast and accurate.
RootLogChain: Registering Log-Events in a Blockchain for Audit Issues from the Creation of the Root

Logging system activities are required to provide credibility and confidence in the systems used by an organization. Logs in computer systems must be secured from the root user so that they are true and fair. This paper introduces RootLogChain, a blockchain-based audit mechanism that is built upon a security protocol to create both a root user in a blockchain network and the first log; from there, all root events are stored as logs within a standard blockchain mechanism. RootLogChain provides security constructs so as to be deployed in a distributed context over a hostile environment, such as the internet. We have developed a prototype based on a microservice architecture, validating it by executing different stress proofs in two scenarios: one with compliant agents and the other without. In such scenarios, several compliant and non-compliant agents try to become a root and register the events within the blockchain. Non-compliant agents simulate eavesdropper entities that do not follow the rules of the protocol. Our experiments show that the mechanism guarantees the creation of one and only one root user, integrity, and authenticity of the transactions; it also stores all events generated by the root within a blockchain. In addition, for audit issues, the traceability of the transaction logs can be consulted by the root.
Towards Better Standard Cell Library

In this paper, we cryptographically optimize the constructions of3-input gates or multi-output gates using Fully Homomorphic Encryption (FHE) including Half Adder, Full Adder and AOI21. We implemented the gates over Torus Fully Homomorphic Encryption(TFHE). These gates achieve their respective functionalities with less computational cost than conventional constructions. By using the proposed gates, 16-bit adders and 16-bit multipliers can be evaluated about 5× and 2.5× faster, respectively, compared to the conventional constructions.
Is Virtual Reality Streaming Ready for Remote Medical Education? Measuring Latency of Stereoscopic VR for Telementoring

Telementoring in healthcare education has been used successfully to teach technical skills and clinical reasoning when in-person instruction is not feasible; however, previous technology for telementoring had limitations such as narrow field-of-view and high latency. Novel virtual reality (VR) livestreaming technology may address issues in traditional 2-dimensional (2D) systems by expanding the field of view while streaming with low latency. Low latency streaming of video and audio is necessary for smooth communication between a medical specialist and remote trainees. If latency is low between the instructor and the remote trainees, conversations can be held without a noticeable delay, supporting synchronous instruction and collaboration. This research reports the first latency test results of a novel VR system that livestreams stereoscopic video and audio to remote VR headsets. Results showed the one-way audio and video latency was less than half a second, confirming the viability of live VR for medical telementoring.
Review on Mapping of Tasks to Resources in Cloud Computing

Resource allocation and scheduling algorithms are the two essential factors that determine the satisfaction of cloud users. The major cloud resources involved here are servers, storage, network, databases, software and so on based on requirements of customers. In the competitive scenario, each service provider tries to use factors like optimal configuration of resources, pricing, Quality of Service (QoS) parameters and Service Level Agreement (SLA) in order to benefit cloud users and service providers. Since, many researchers have proposed different scheduling algorithms and resource allocation strategies, it becomes a cumbersome task to conclude which ones really benefit customers and service providers. Hence, this paper analyses and presents the most relevant considerations that would help the cloud researchers in achieving their goals in terms of mapping of tasks to cloud resources.
Web-mapping of solar energy potential in Yakutia

It is cost effective and environmentally rational to develop renewable energy source in sthe territories of decentralized energy supply, since it allows replacing diesel power generation. Yakutia has a high potential for the development of solar energy and 64 % of its area is in the zone of decentralized energy supply. The features of web mapping—interactivity, multiscale and availability—are useful for assessment of big multi-temporal data on solar resources at the regional scale. Web mapping shows one parameter for Yakutia—all sky insolation incident on a horizontal surface per day per square meter. The parameter is used to assess the potential of solar energy resources in the territory. Initial data comes from the Surface meteorology and Solar Energy archive of the NASA Prediction of Worldwide Energy Resource—SSE NASA POWER project. It includes monthly and annual average global grids for each year from 1984 to 2018. The web application allows users to map the data by grid points, by administrative units and by watersheds. Users can interactively set a period with an accuracy of a month and a territorial division for drawing up a map. The web-based mapping application allows users to create about 50,000 web maps in total. The number based on query combinations available to the user. A large amount of data used for web mapping requires the development of an information system for the dynamic delivery of data at the user’s request. Data preprocessing algorithm helps efficiently aggregate data “on-the-fly” for various territorial units. The DeckGL cartographic library gives high-performance visualization of big spatial data in the browser. We use PostgreSQL and Flask software to develop the information system. Web mapping is useful for the assessment of solar energy resources specifically at the regional level. Interactive tools provided by the web-based mapping applications deepen the analytical content of the cartographic work. There are notable changes in the data preparation through the design of web-based mapping applications in comparison with regular maps.
Nonclassical kernels in continuous-variable systems

Kernel methods are ubiquitous in classical machine learning, and recently their formal similarity with quantum mechanics has been established. To grasp the potential advantage of quantum machine learning, it is necessary to understand the distinction between non-classical kernel functions and classical kernels. This paper builds on a recently proposed phase space nonclassicality witness [Bohmann, Agudelo, Phys. Rev. Lett. 124, 133601 (2020)] to derive a witness for the kernel's quantumness in continuous-variable systems. We discuss the role of kernel's nonclassicality in data distribution in the feature space and the effect of imperfect state preparation. Furthermore, we show that the non-classical kernels lead to the quantum advantage in parameter estimation. Our work highlights the role of the phase space correlation functions in understanding the distinction between classical machine learning from quantum machine learning.
Piercing the Digital Veil: A Case Study for a DAO Legal Framework under Swiss Law

Blockchain technology is associated with the emergence of decentralised applications such as smart contracts and Decentralised Autonomous Organisations (DAO) as self-governing and software-based agents. The concept of a blockchain-based peer-to-peer vending machine serving both as a testing ground for the design of a marketplace for physical goods, and a speculative artefact has been posited and analysed from an economic perspective by a group of scholars at the Center for Innovative Finance of the University of Basel, Switzerland. Building on this particular case study, this paper provides for a legal analysis under Swiss law. In Part A, the economic analysis of the initiative is briefly described. In Part B, the proposed concept is analysed from a de lege lata perspective, taking into account foremost liability questions both from Swiss private law (tort and contractual) and public law (criminal and tax law) perspectives, by building on literature and applicable case law. For this, the authors propose a hypothetical scenario upon which a legal analysis is applied. As a result of the analysis, a conclusion is drawn highlighting the status quo in Swiss legal framework, whereby the authors argue in favour of a possible reform for the purpose of enhancing legal certainty. In Part C, the authors then examine, from a de lege ferenda perspective, the question of whether the Swiss legislative body would require introducing a bespoke legal framework for DAOs. For this, a reference is made to relevant foreign legislation such as the State of Wyoming DAO Bill, without essentially taking a comparative approach.
Writing Motivation Sources of Teacher Candidates: A Phenomenological Study

Writing motivation is effective on writing behavior and writing habits. The aim of this study is to determine the factors that motivate pre-service teachers to write and thus support their writing habits. For this purpose, the research was designed in accordance with the phenomenological pattern from qualitative research designs. In the research, interviews were conducted with pre-service teachers through the semi-structured interview form prepared by the researcher. The data obtained from these interviews were analyzed by content analysis technique. Research findings showed that there were internal (internal) and external (external) factors motivating teacher candidates to write. According the findings, the main internal factors that motivated pre-service teachers to write were positive thoughts and beliefs about writing and writing skills, goals expected to be achieved by writing, and lastly, interest and desire for writing. Requirements and environmental expectations for writing, appreciation and support from family and readers, and the environment which is a positive role model in writing were external factors that motivated pre-service teachers to write. In the study, these factors and student views under these factors were discussed and what could be done to increase their motivation for writing was evaluated.
اثربخشی درمان‌ تحولی شنیداری با رویکرد پردازش اطلاعات شناختی در درمان نارساخوانی شناختی

Purpose:The purpose of this study was to evaluate the effectiveness of audio transformation therapy with a cognitive information processing approach on improvement of children with cognitive dyslexia.Methods:This study was a quasi-experimental with a pre-test-post-test design and a control group. To select study population, 140 students of 7-9 years old with special learning disabilities who had referred to Yeganeh center of Savojbolagh in Alborz province in the academic year of 1397-98 were used. Among these students, 60 patients with dyslexia were selected using the available sampling method, from which 20 students had auditory learning style. Then they were randomly assigned into two examination (10) and control (10) groups. Wechsler IV test, VAK Questionnaire form made by Lilinfeld (2011), reading disorder test made by the researcher and case history form were used in this research. Thereafter, the training sessions were held within two months (15 sessions of 90 minutes) for examination group using ISHINR, Cognitive Information Processing (CIP), Cattell- Horn- Carroll (CHC) and Auditory Learning Style while the control group did not receive any training. At the end of the treatment sessions and 8 weeks after that, the students in both groups were re-evaluated. Data were analyzed using repeated measures analysis of variance and Bonferroni post hoc test at a level of 5% error using version 22nd of SPSS software.Results:The results showed that auditory transformation therapy with a cognitive information processing approach caused a significant increase in symbol reading, image reading, comprehension and verbal expression in the post-test and follow-up in comparison with the pre-test. Also, after performing auditory transformational therapy, symbol reading, image reading, comprehension and verbal expression in the examination group was significantly greater than the control group. While before treatment, there were not significant differences.Conclusions:Auditory transformation therapy with a cognitive information processing approach is effective in treating cognitive dyslexia.
Hybrid big bang–big crunch with ant colony optimization for email spam detection

Electronic mails (emails) have been widely adapted by organizations and individuals as efficient communication means. Despite the pervasiveness of alternate means like social networks, mobile SMS, electronic messages, etc. email users are continuously growing. The higher user growth attracts more spammers who send unsolicited emails to anonymous users. These spam emails may contain malware, misleading information, phishing links, etc. that can imperil the privacy of benign users. The paper proposes a self-adaptive hybrid algorithm of big bang–big crunch (BB–BC) with ant colony optimization (ACO) for email spam detection. The BB–BC algorithm is based on the physics-inspired evolution theory of the universe, and the collective interaction behavior of ants is the inspiration for the ACO algorithm. Here, the ant miner plus (AMP) variant of the ACO algorithm is adapted, a data mining variant efficient for the classification. The proposed hybrid algorithm (HB 3 C-AMP) adapts the attributes of B 3 C (BB–BC) for local exploitation and AMP for global exploration. It evaluates the center of mass along with the consideration of pheromone value evaluated by the best ants to detect email spam efficiently. The experiments for the proposed HB 3 C-AMP algorithm are conducted with the Ling Spam and CSDMC2010 datasets. Different experiments are conducted to determine the significance of the pre-processing modules, iterations, and population size on the proposed algorithm. The results are also evaluated for the AM (ant miner), AM2 (ant miner2), AM3 (ant miner3), and AMP algorithms. The performance comparison demonstrates that the proposed HB 3 C-AMP algorithm is superior to the other techniques.