Reviewerly/exHarmony huggingface dataset:
Genetic improvement of data gives double precision invsqrt

CMA-ES plus manual code changes rapidly transforms 512 Newton-Raphson start points from a GNU C library table driven version of sqrt into a double precision reciprocal square root function. The GI x-1/2 is far more accurate than Quake's InvSqrt, Quare root.
Quantum witness of a damped qubit with generalized measurements

Abstract We evaluate the quantum witness based on the no-signaling-in-time condition of a damped two-level system for nonselective generalized measurements of varying strength. We explicitly compute its dependence on the measurement strength for a generic example. We find a vanishing derivative for weak measurements and an infinite derivative in the limit of projective measurements. The quantum witness is hence mostly insensitive to the strength of the measurement in the weak measurement regime and displays a singular, extremely sensitive dependence for strong measurements. We finally relate this behavior to that of the measurement disturbance defined in terms of the fidelity between pre-measurement and post-measurement states.
Spartan-6 FPGA Implementation of AES Algorithm

Due to advancement of Information and Technology, Artificial Intelligence and Internet of things, Space Technology and E-Commerce world that changes the life of human being as because of that it is possible to interact with people by electronic means to everybody, every time, everywhere. It is possible to have seamless fabric of electronic connection with the help of electronic devices. The data which transmitted ceaselessly through electronic media which provides more assurance on cryptography to secure the data and communication. AES algorithm design is Implemented in VHDL and Simulated and Synthesized in Integrated Environment synthesis Xilinx 14.2 version software, then after actual result of AES Operation is validated on hardware in the form of Spartan 6 FPGA. From the work completed in this paper it is observed that AES Algorithm which designed and Implemented with less FPGA Hardware, Less Power consumption and Better Throughput of the Cryptosystem. Hence AES Algorithm which implemented on Spartan-6 FPGA can Give the possibility of portability, applications Compatibility and Improvement of security levels.
Accelerated Inference Framework of Sparse Neural Network Based on Nested Bitmask Structure

In order to satisfy the ever-growing demand for high-performance processors for neural networks, the state-of-the-art processing units tend to use application-oriented circuits to replace Processing Engine (PE) on the GPU under circumstances where low-power solutions are required. The application-oriented PE is fully optimized in terms of the circuit architecture and eliminates incorrect data dependency and instructional redundancy. In this paper, we propose a novel encoding approach on a sparse neural network after pruning. We partition the weight matrix into numerous blocks and use a low-rank binary map to represent the validation of these blocks. Furthermore, the elements in each nonzero block are also encoded into two submatrices: one is the binary stream discriminating the zero/nonzero position, while the other is the pure nonzero elements stored in the FIFO. In the experimental part, we implement a well pre-trained sparse neural network on the Xilinx FPGA VC707. Experimental results show that our algorithm outperforms the other benchmarks. Our approach has successfully optimized the throughput and the energy efficiency to deal with a single frame. Accordingly, we contend that Nested Bitmask Neural Network (NBNN), is an efficient neural network structure with only minor accuracy loss on the SoC system.
DESIGN OF KNOWLEDGE MANAGEMENT IMPLEMENTATION IN ISLAMIC UNIVERSITIES

Purpose: The objective of the present study is to investigate the design of knowledge management (KM) implementation in State Islamic Universities, which possess the status of a Public Service Agency (BLU) and have implemented the remuneration system. KM is an approach to understand how knowledge is produced, stored, and distributed to improve the academic productivity.&#x0D; Methodology: It is a qualitative research with a phenomenological approach, which is conducted to reveal the KM implementation by exploring the knowledge management-related activities in Islamic universities in Indonesia. Data were collected through observation, in-depth interviews, documentation and focus group discussions (FGDs). Data analysis was done through data reduction, data display, conclusions, and verification.&#x0D; Main Findings: The present study indicates that KM is designed by including: First, knowledge production sourced from the outcomes of research carried out by lecturers and academic staff members with various research schemes; Second, knowledge storage is done in offline and online modes (Offline storage includes printouts, journals, and proceedings while online storage is in the form of repositories, websites, and online journals); Third, knowledge distribution is done through printouts, electronic systems, conventional lectures, and references. The KM implementation will be optimal with the support of relevant policies from the top management.&#x0D; Applications: The present study provides an overview of KM design that can be used as a framework by the actors and policy makers in educational institutions.&#x0D; Novelty / Originality: The novelty of the present study is on the description and the KM flow design in State Islamic Universities in Indonesia.
Making the Last Iterate of SGD Information Theoretically Optimal

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 23 September 2019Accepted: 06 January 2021Published online: 13 April 2021Keywordsstochastic gradient descent, convex optimization, gradient descent, last iterate guaranteesAMS Subject Headings90C25, 90C15Publication DataISSN (print): 1052-6234ISSN (online): 1095-7189Publisher: Society for Industrial and Applied MathematicsCODEN: sjope8
Education of the Post-Industrial Era: Setting on Person and Intelligence

The article discusses theoretical background and empirical premises for supplementing the goals of education with orientation to individual actor as the “assemblage point” of the educational result in a constantly changing reality. The article has an interdisciplinary modus, its theoretical basis is the D. Bell’s concepts of the post-industrial society, the “liquid modernity” of Z. Bauman and the idea of C.S. Dweck about the ability of personality’s sets “plants for growth” or “for granted” to influence intellect and achievement of goals. The work proceeds from the understanding of the fact that the “spreading” state of education is due to the specifics of the transition to some post-state of the society that is not yet clear. It is characterized by the fundamental ambiguity of circumstances and tendencies when centralized management of complex systems is difficult or erroneous, and this makes it necessary to reduce the level of decision making to self-organizing local social forms, giving them greater autonomy, in this case – to an individual and his autonomy. However, in our culture we have a lack of confidence to the individual and his ability to make wise choices, as well as, wittingly or unwittingly, reduce the setting for growth and primacy. But if students are offered well-organized research practices instead of imitations of scientific work, then they form the true key competencies necessary for modern society – a willingness to be in search, continuously learn and create new solutions for new tasks, contribute to the development of good conceptual language, cooperation in group work, courage in posing problems, readiness to work with complex problems. The article presents a kind of a case analysis of such work. The article also poses the problem of exploiting the cognitive status and well-being of teachers in super-formalized labor practices.
Pragmatics in the interpretation of scope ambiguities

Abstract This paper presents a corpus study of pragmatic factors involved in interpreting potentially ambiguous sentences with negation and universal quantifiers, as demonstrated by the Russian sentence Oni ne uspejut vsjo eto sdelat’ ‘They won’t have time to do all this.’ Ambiguity in such sentences results from potential differences in scope assignment. If negation scopes over the quantifier, we get the interpretation of partial negation: ‘They will manage to do some of these things, but not everything.’ If negation scopes over the verb, we get total negation: ‘They won’t manage to do anything.’ This study is based on Russian and English data extracted from a variety of corpora. We demonstrate that while syntactic conditions where scope ambiguity is possible are different for Russian and English, in situations when both languages allow it, speakers rely on the same pragmatic mechanisms for disambiguation that are based on Gricean cooperation principle and shared background knowledge. Disambiguation is facilitated by lexical markers, different for verb-negated and quantifier-negated readings, and similar in Russian and English. We show that the interpretation of the quantifier is pragmatically different for verb-negated and quantifier-negated readings (emphatic in the former case and quantificational in the latter), and lexical markers of each reading are semantically and pragmatically consistent with this difference. Namely, verb-negated readings occur primarily in the context of demonstrative pronouns in their pragmaticalized meaning of negative assessment and negatively connoted nouns, while quantifier-negated readings occur in the context of verbs with quantitative semantics and quantitative implicatures that consolidate the interpretation of quantification.
Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code

Software defect prediction has extensive applicability thus being a very active research area in Search-Based Software Engineering. A high proportion of the software defects are caused by violated couplings. In this paper, we investigate the relevance of semantic coupling in assessing the software proneness to defects. We propose a hybrid classification model combining Gradual Relational Association Rules with Artificial Neural Networks, which detects the defective software entities based on semantic features automatically learned from the source code. The experiments we have performed led to results that confirm the interplay between conceptual coupling and software defects proneness.
A Fuzzy-Based Approach and Adaptive Genetic Algorithm in Multi-Criteria Recommender Systems

Recommender Systems (RSs) are termed as web-based applications that make use of filtering methods and several machine learning algorithms to suggest relevant user objects.It can be said that some techniques are usually adopted or trained to develop these systems that generate lists of suitable recommendations.Conventionally, RS uses a single rating approach to preference user recommendation over an item.Recently, multi-criteria technique has been identified as a new approach of recommending user items based on several attributes or features of user items.This new technique of item recommendation has been adopted to solve several recommendation problems compared to the single rating approach.Furthermore, the predictive performance of the multi-criteria technique when tested proves to be further efficient as compared to the traditional single ratings approach.This paper gives a comparative study between two models that are based on the features and architecture of fuzzy sets system and adaptive genetic algorithm.Genetic Algorithms (GAs) are robust and stochastic search techniques centered on natural selection and evaluation that are often applied when encountering optimization problems.Fuzzy logic (FL) on the other hand, is known for its wide application in diverse fields in science.This study aims to evaluate, analyze, and compare the predictive performance of both methods and present their results.The study has been accomplished using Yahoo!Movies dataset, and the results of the performance of each model have been presented in this paper.The results proved that both techniques have significantly enhanced the system's accuracy.
Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA

We explore the extent to which neural networks can learn to identify semantically equivalent sentences from a small variable dataset using an end-to-end training.We collect a new noisy non-standardised user-generated Algerian (ALG) dataset and also translate it to Modern Standard Arabic (MSA) which serves as its regularised counterpart.We compare the performance of various models on both datasets and report the best performing configurations.The results show that relatively simple models composed of 2 LSTM layers outperform by far other more sophisticated attention-based architectures, for both ALG and MSA datasets.
A Stochastic Quasi-Newton Method with Nesterov’s Accelerated Gradient

Incorporating second order curvature information in gradient based methods have shown to improve convergence drastically despite its computational intensity. In this paper, we propose a stochastic (online) quasi-Newton method with Nesterov’s accelerated gradient in both its full and limited memory forms for solving large scale non-convex optimization problems in neural networks. The performance of the proposed algorithm is evaluated in Tensorflow on benchmark classification and regression problems. The results show improved performance compared to the classical second order oBFGS and oLBFGS methods and popular first order stochastic methods such as SGD and Adam. The performance with different momentum rates and batch sizes have also been illustrated.
A framework for software requirement ambiguity avoidance

This research deals with software requirements ambiguity problems. Among these are incomplete, incorrect, improper, inaccurate and unambiguous requirements. Interestingly, published material related to Software Requirements Specifications (SRS) problems discusses ambiguity as one of the most conversed problems. This paper proposes a Software Requirement Ambiguity Avoidance Framework (SRAAF) to assist and support requirement engineers to write unambiguous requirements, by selecting correct elicitation technique based on the evaluation of various attributes and by applying the W6H technique. We explored existing theories and the outcomes of experimental research to construct the framework. On the basis of existing and inferred knowledge, we tried to justify proposed frameworks components. Our selection process focuses on various situational attributes. We added various situational attributes related to project, stakeholders and requirement engineer for the selection process. Newly devised approach chooses techniques other than traditional techniques or most common techniques and deals with ambiguity to capture the correct requirements information from stakeholders. The framework will be able to address the selection and ambiguity issues in a more effective way and can handle vagueness. New evidence related to attributes and adequacy matrix can be easily added to the framework without any inconvenience.
Sentiment Analysis for Multilingual Corpora

The paper presents a generic approach to the supervised sentiment analysis of social media content in foreign languages.The method proposes translating documents from the original language to English with Google's Neural Translation Model.The resulted texts are then converted to vectors by averaging the vectorial representation of words derived from a pretrained Word2Vec English model.Testing the approach with several machine learning methods on Polish, Slovenian and Croatian Twitter corpora returns up to 86 % of classification accuracy on the out-of-sample data.
Monitoring the Effectiveness of Master’s Programs Implementation

The paper highlights a mechanism for monitoring the effectiveness of master’s programs implementation. The authors dwell on the experience of M.K. Ammosov North-Eastern Federal University in the developing of such a mechanism. Five main blocks are considered as the monitoring criteria reflecting the effectiveness of educational programs implementation, such as: 1) demand, 2) resource provision, 3) quality control, 4) contingent of students, and 5) employment of educational program graduates. Each criterion contains three main indicators for which points are calculated. On the results obtained from the monitoring carried out using this methodology, the master programs are ranked according to their efficiency, taking into account the total number of points scored on all the criteria units. After analyzing the data obtained, it turns out which block or direction of work has lag in the implementation of educational program and administrative measures are taken to remedy the situation. The outcome of this monitoring should be the improvement of the quality and efficiency of master’s programs implementation at federal university.
An Introduction to Wearable Technology and Smart Textiles and Apparel: Terminology, Statistics, Evolution, and Challenges

Within the last 6 years, there has been a palpable surge in interest and investment into wearable technology and Smart Textiles & Apparel, manifested both in academia and in the wide array of aspirational products available on the market. Recent market research on wearables also forecasts further growth in the next 3 years. However, as the field becomes increasingly saturated, potential terminology confusion and misconceptions may arise particularly among those in the industry who may not deal directly with the technologies but have interest in applying it to their work. Therefore in an attempt to provide clarity on an increasingly popular field to those perhaps less familiar with it, this chapter delivers an introductory overview of wearable technology and Smart Textiles & Apparel, which clarifies terminology encompassed within the field, reviews recent statistics and maps out how developments have been evolving over time, and assesses some of the challenges confronting the field.
Adaptive Authorization Access Method for Medical Cloud Data Based on Attribute Encryption

The outsourcing storage of medical big data encrypted in the cloud can effectively alleviate the problem of privacy disclosure, but cipher text storage will lead to the inconvenience of data access, which brings new challenges to medical big data shared access. The existing flexible authorization solutions for encrypted data are mainly based on methods such as CP-ABE, which requires the data owner to define the data access strategy, while in reality, the patient is the data owner of the medical data. At the same time, the existing scheme does not support access control authorization in emergency scenarios, and in medical big data applications, when patients can not authorize data users to access cipher text medical data, it will lead to unpredictable consequences. According to the application requirements of encrypted medical big data shared service in cloud environment, an adaptive authorization access method based on attribute encryption is proposed to realize flexible and secure medical data access authorization in normal and emergency situations. Experimental results demonstrate that our scheme is efficient.
A Deep Learning Approach to Writer Identification Using Inertial Sensor Data of Air-Handwriting

To the best of our knowledge, there are a few researches on air-handwriting character-level writer identification only employing acceleration and angular velocity data. In this paper, we propose a deep learning approach to writer identification only using inertial sensor data of air-handwriting. In particular, we separate different representations of degree of freedom (DoF) of air-handwriting to extract local dependency and interrelationship in different CNNs separately. Experiments on a public dataset achieve an average good performance without any extra hand-designed feature extractions.
Effectiveness of Digital Note-Taking on Students’ Performance in Declarative, Procedural and Conditional Knowledge Learning

Note-taking is an ever-present learning activity in students’ daily lives, and an increasing number of mobile terminals have been integrated into curricu-lums. However, the effectiveness of the use of digital note-taking on mobile terminals on students’ learning has not been deeply explored. The purpose of this study is to evaluate how digital note-taking using mobile terminals af-fects student performance, with particular regard to declarative, procedural and conditional knowledge learning. A quasi-experiment was conducted for three months among 72 first-year high school students from a computer sci-ence (CS) course. In the study, students in the experimental group (n = 40) recorded notes digitally, whereas the students in the control group (n = 32) used the conventional approach (i.e., recording handwritten notes). The re-sults indicate that the students who recorded notes digitally scored signifi-cantly higher than those who recorded notes conventionally. The students who were designated as “excellent,” and those who were designated as “low-performing,” were most likely to benefit from this new method of note-taking.
Training Simulator for Manual Lathe Operation Using Motion Capture

Training systems for manual machine tools using virtual reality have been developed for high safety and economy. Using these systems, a trainee can safely work on virtual machines to manipulate input interfaces modeled after devices in real-world machines. However, a trainee cannot completely learn how to use the target machine because the interface lacks all the necessary devices. Because safety is ensured even if the trainee performs dangerous actions, these virtual systems cannot teach safety measure. Therefore, after training with these systems, a trainee needs instructors when operating actual machines. This study describes a training system that does not require instructors. By training with the proposed system, a trainee can safely work on virtual machines as the input interface to manipulate the actual lathe. This system constantly monitors the position of all operating devices, handles and levers on the actual lathe, and the user’s body parts (the head, both hands and upper body of the trainee) by motion capture. The behavior of the virtual lathe and the cutting of the virtual workpiece are simulated from the manipulated variables. This system can detect whether the trainee is in danger from the movement of his body parts.