Reviewerly/exHarmony huggingface dataset:
The Use of Electronic Library Resources in Nigerian Universities

A number of studies have focused on the use of e-libraries, drawing numerous conclusions about the use and challenges facing e-libraries. This article therefore presents a review of the literature on the use of e-libraries in universities with a particular interest in Nigerian institutions. The article examines the concepts and the antecedents of electronic libraries. The review shows that a number of approaches have been used to assess the use of e-libraries, and that computer self-efficacy has an impact on the performance while technological innovation, by extension, enhances the satisfactory use of electronic library systems. In addition, the review underscores the fact that there are few studies that pay attention to the satisfactory usage of electronic libraries in Nigerian universities. The article thus concludes by restating the important roles those e-libraries play in information service delivery. Recommendations for further studies on the mode and modalities with which effective training is conducted especially in the area of information search skills are made.
Hybrid Discrete Event and Agent Based Simulation Modeling of Career Development. Part 3: Testing a Model and Analysis of Simulation Results

This part of article is dedicated to testing and analysis of the modeling and simulation results of career development processes. The simulation algorithm with some experimental data related to agent individual behavior characteristics, which was presented in the previous parts of this article [1,2], is built on a multimethod platform of Discrete Events and Agent Based modeling. The model takes into consideration various phenomena and processes that accompany the career struggle. Among them, the emergence, development and disintegration of coalitions of agents, the inertia of changes in agent activity, the agent's tendency to fluctuations in activity and the agent's growth potential. The model includes the use of various agent selection schemes and the participation of VIP and super active agents in the career struggle. The simulation provided data to analyze careers of agents depending on work experience and to track the influence of leadership in a coalition on the career history of the agent. Thus, a detailed and complete picture of career processes in the hierarchical structure of the organization is created. The model can be considered as a tool for assessing the effectiveness of the personnel policy of an organization on the basis of an analysis of the ratio of the contribution of employees to the amount of employee support costs.
A Security-By-Distribution Approach to Manage Big Data in a Federation of Untrustworthy Clouds

In recent years, Cloud Computing has drastically changed IT-Architectures in enterprises throughout various branches and countries. Dynamically scalable capabilities like CPUs, storage space, virtual networks, etc. promise cost savings, as huge initial infrastructure investments are not required anymore. This development shows that Cloud Computing is also a promising technology driver for Big Data, as the storage of unstructured data when no concrete and defined data schemes (variety) can be managed with upcoming NoSQL architectures. However, in order to fully exploit these advantages, the integration of a trustworthy 3rd party public cloud provider is necessary. Thus, challenging questions concerning security, compliance, anonymization, and privacy emerge and are still unsolved. To address these challenges, this work presents, implements and evaluates a security-by-distribution approach for NoSQL document stores that distributes data across various cloud providers such that every provider only gets a small data chunk which is worthless without the others.
AN OPTIMIZED ENCRYPTION BASED STEGANOGRAPHY MODEL TO SECURE MEDICAL IMAGES USING METAHEURISTIC TECHNIQUE.

The implementation of a secret data sharing algorithm along with water marking, steganography and cryptography can have various applications besides medical data privacy. It can be used for improving the authentication ability of confidential data too, so the demand of this type of approaches increases rapidly. We know that, Steganography is a scientific technique that is used to provide safe communication through multimedia carrier, for example, a combination of confidential information might be in the form of images, audio, and video files. If this feature is visible, the attack point is open, so the goal here is always to hide the existence of relevant information. Steganography has a variety of useful applications. But like any science, it can be used for bad intentions. In this research, medial image steganography model is designed to provide the security while transmitting the information in the form of a medical image by utilizing the concept of Discrete Wavelet transformation (DWT) as a decomposition approach with Modified Jamal Encryption Algorithm (MJEA) encryption. In addition the concept of Particle Swarm Optimization (PSO) as an optimization technique used to find out the better hiding location in the medical images. To provide high security different processes are implemented such as pre-processing that is used to resize and conversion of the image with image decomposition. At last, the performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), entropy and correlation coefficients are measured and compared with the existing work to validate the proposed model.
Products of Interest

July 27 2020 Products of Interest Author and Article Information Online Issn: 1531-5169 Print Issn: 0148-9267 © 2021 Massachusetts Institute of Technology.2021Massachusetts Institute of Technology Computer Music Journal (2020) 44 (2-3): 167–187. https://doi.org/10.1162/comj_r_00566 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Products of Interest. Computer Music Journal 2020; 44 (2-3): 167–187. doi: https://doi.org/10.1162/comj_r_00566 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsComputer Music Journal Search Advanced Search Grid is a set of portable, modular MIDI controllers from Hungarian company Intech Studio (see Figure 1). There are four controllers currently available with plans to expand the offerings in the future. The PO16 is a 4 × 4 grid of 16 potentiometers. The BU16 module features a 4 × 4 grid of tactile buttons, which can be set to toggle or momentary action. The EN16A is a 4 × 4 grid of 16 push-button encoders. The PBF4 controller is a mixing module with four 30-mm faders, four potentiometers, and four buttons. Each of the GRID controllers measure 106.6 × 106.6 × 32 mm and weigh 250 g. They have an aluminum front panel with programmable RGB LEDs for all controls. Each module has a USB-C connector for power and data. They also have magnetic connectors on all four sides of the module that are used to connect them... You do not currently have access to this content.
An Approach to Data Annotation for Internet of Things

The increasing number of connected devices which use Internet of Things (IoT) technology and variety of associated services is a real challenge to interoperability. There is a need of an abstraction layer which hides the technology heterogeneity. The paper presents an approach to data modeling in the domain of home energy saving which extends existing solutions with context-ware concepts and relationships. Requirements to data annotation are identified by use cases. Problem-solving principle is applied to data annotation which defines the vocabulary related to a certain task or activities. An observation and actuation models which show how data annotation works are proposed and formally verified. As a proof of concept, the server model includes web services that illustrate the usage of the proposed data annotation. The potential of home appliance power control is estimated.
The combinator M and the Mockingbird lattice

Abstract We study combinatorial and order theoretic structures arising from the fragment of combinatory logic spanned by the basic combinator ${{\mathbf{M}}}$ . This basic combinator, named as the Mockingbird by Smullyan, is defined by the rewrite rule ${{\mathbf{M}}} \mathsf{x}_1 \to \mathsf{x}_1 \mathsf{x}_1$ . We prove that the reflexive and transitive closure of this rewrite relation is a partial order on terms on ${{\mathbf{M}}}$ and that all connected components of its rewrite graph are Hasse diagram of lattices. This last result is based on the introduction of new lattices on duplicative forests, which are sorts of treelike structures. These lattices are not graded, not self-dual, and not semi-distributive. We present some enumerative properties of these lattices like the enumeration of their elements, of the edges of their Hasse diagrams, and of their intervals. These results are derived from formal power series on terms and on duplicative forests endowed with particular operations.
CacheRewinder: Revoking Speculative Cache Updates Exploiting Write-Back Buffer

Transient execution attacks are critical security threats since those attacks exploit speculative execution which is an essential architectural solution that can improve the performance of out-of-order processors significantly. Such attacks change cache state by accessing secret data during speculative executions, then the attackers leak the secret information exploiting cache timing side-channels. Even though software patches against transient execution attacks have been proposed, the software solutions significantly slow down the performance of a system. In this paper, we propose CacheRewinder, an efficient hardware-based defense mechanism against transient execution attacks. CacheRewinder prevents leakage of secret information by revoking the cache updates done by speculative executions. To restore the cache state efficiently, CacheRewinder exploits the underutilized write-back buffer space as the temporary storage for victimized cache blocks evicted during speculative executions. Hence, when speculation fails CacheRewinder can quickly restore the cache state using the victim blocks held in the write-back buffer. Our evaluation exhibits CacheRewinder can effectively defend against transient execution attacks. The performance overhead by CacheRewinder is only 0.6%, which is negligible compared to the unprotected baseline processor. CacheRewinder also requires minimal storage cost since it exploits unused write-back buffer entries as storage for evicted cache blocks.
Text Mining Analysis of Korean University Students’ Academic Coaching Intake Session Reports

Academic coaching has been emphasized in Korean universities as an effective measure to assist students' academic achievement and success. To better assess the needs of the students, the current study investigated academic coaching intake session reports archived at a Korean university from January 2017 to August 2021 and examined students' descriptions of their academic concerns and barriers. The intake session reports were categorized according to (1) students' affiliated department tracks, namely Humanities and Social Science (HSS) and Science, Technology, Engineering, and Math (STEM) tracks, and (2) the time the coaching sessions took place, i.e., before and after the outbreak of COVID-19. Text mining analysis was conducted to calculate the frequency of keywords, their degree of centrality, and the frequency of bigrams, or the sets of two adjacent words, for each category. Wordclouds and word networks were also visualized. The results indicated that the word study was dominant in both categories, reflecting the education culture in Korea. Similarities and differences between the two categories were also reported. Based on the results, practical implications for academic coaches, educators, and university administrators were proposed, and limitations were discussed.
American literature in the library of I.S. Turgenev

The article is devoted to the study of a particular aspect of the dialogue between Ivan Turgenev and the literature of the United States of America. The piece of research focuses on the Russian writer’s book collection, namely that part of it, which contains the works of American authors in English and French. According to the existing catalogs and inventories all the surviving copies of Turgenev’s library have been identified with maximum details. As a result, the “American Foundation” today consists of four representatives of romanticism: J. Cooper, W. Irving, N. Hawthorne and E. Poe. All discovered books of American literature are carefully described. The object of the description is the publication history of the book, the features of its external design and, which is especially important, the traces left by the reader on the pages, i. e. nail marks and underlines and notes made by a pencil. Thus, the earliest by the date of publication in Turgenev’s library is the novel by J. Cooper “The Pioneers”, published in French as part of the fourth volume of the massive series of Cooper’s works. Several nail marks were left on the pages of the novel, which certainly belong to Turgenev’s reading style. In addition, in the process of studying Turgenev’s library, it was possible to establish that the collection of fantastic fiction stories by E. Poe, translated by S. Baudelaire, which stands farthest in the chronology of publication, did not really belong to a Russian writer and could not be the subject of his reading. As a result, the works of American authors preserved in Turgenev’s library make it possible to reproduce a picture of the direct acquaintance of the Russian writer with the literature of the United States, and the marks and notes on particular books open up the prospect of setting and solving new problems (“I. Turgenev and N. Hawthorne”, “I. Turgenev and J. Cooper”) in the aspects of both the reader’s acquaintance and subsequent creative reflection.
DASC: A DRAM Data Mapping Methodology for Sparse Convolutional Neural Networks

The data transferring of sheer model size of CNN (Convolution Neural Network) has become one of the main performance challenges in modern intelligent systems. Although pruning can trim down substantial amount of non-effective neurons, the excessive DRAM accesses of the non-zero data in a sparse network still dominate the overall system performance. Proper data mapping can enable efficient DRAM accesses for a CNN. However, previous DRAM mapping methods focus on dense CNN and become less effective when handling the compressed format and irregular accesses of sparse CNN. The extensive design space search for mapping parameters also results in a time-consuming process. This paper proposes DASC: a DRAM data mapping methodology for sparse CNNs. DASC is designed to handle the data access patterns and block schedule of sparse CNN to attain good spatial locality and efficient DRAM accesses. The bank-group feature in modern DDR is further exploited to enhance processing parallelism. DASC also introduces an analytical model to facilitate fast exploration and quick convergence of parameter search in minutes instead of days from previous work. When compared with the state-of-the-art, DASC decreases the total DRAM latencies and attains an average of 17.1x, 14.3x, and 23.3x better DRAM performance for sparse AlexNet, VGG-16, and ResNet-50 respectively.
Generating Visualizations of Ontologies in the Logic Graphs Language

We analyze the existing ontology visualization tools. Our analysis shows that there is no tool that represents the semantics of logical operations in ontologies, as Ch. S. Pierce’s existential graphs do for first order logic. Previously, we developed Logic Graphs – a semantic-oriented visual language for ontologies, complete with respect to OWL DL. The goal of this research is to develop a tool for generating ontology visualizations in Logic Graphs. This tool parses an ontology to extract classes and their properties, converts ontology classes to their descriptions in DOT format, and builds graphical representations of ontology classes from DOT descriptions. Ontology visualization in Logic Graphs facilitates the users perception of ontologies, and provides the possibility of diagrammatic reasoning.
A Systematic Review of Tools Available in the Field of Augmented Reality

Augmented Reality interfaces have been extensively researched throughout the past few decades, with many user studies being conducted. This paper examines the landscape of research on augmented reality. We summarise the overall contribution of each field and will then present examples of influential user studies. We identify other areas of research that would be advantageous to possible future studies. There is a trend toward hands-free applications and most user testing is carried out in the laboratory. This research will also help researchers learning the best practices when conducting AR user studies.
A Bayesian latent class model for predicting gestational age in health administrative data

Health administrative data are oftentimes of limited use in epidemiological study on drug safety in pregnancy, due to lacking information on gestational age at birth (GAB). Although several studies have proposed algorithms to estimate GAB using claims database, failing to incorporate the unique distributional shape of GAB, can introduce bias in estimates and subsequent modeling. Hence, we develop a Bayesian latent class model to predict GAB. The model employs a mixture of Gaussian distributions with linear covariates within each class. This approach allows modeling heterogeneity in the population by identifying latent subgroups and estimating class-specific regression coefficients. We fit this model in a Bayesian framework conducting posterior computation with Markov Chain Monte Carlo methods. The method is illustrated with a dataset of 10,043 Rhode Island Medicaid mother-child pairs. We found that the three-class and six-class mixture specifications maximized prediction accuracy. Based on our results, Medicaid women were partitioned into three classes, featured by extreme preterm or preterm birth, preterm or" early" term birth, and" late" term birth. Obstetrical complications appeared to pose a significant influence on class-membership. Altogether, compared to traditional linear models our approach shows an advantage in predictive accuracy, because of superior flexibility in modeling a skewed response and population heterogeneity.
UNITS: Unsupervised Intermediate Training Stage for Scene Text Detection

Recent scene text detection methods are almost based on deep learning and data-driven. Synthetic data is commonly adopted for pre-training due to expensive annotation cost. However, there are obvious domain discrepancies between synthetic data and real-world data. It may lead to suboptimal performance to directly adopt the model initialized by synthetic data in the fine-tuning stage. In this paper, we propose a new training paradigm for scene text detection, which introduces an UNsupervised Intermediate Training Stage (UNITS) that builds a buffer path to real-world data and can alleviate the gap between the pre-training stage and fine-tuning stage. Three training strategies are further explored to perceive information from real-world data in an unsupervised way. With UNITS, scene text detectors are improved without introducing any parameters and computations during inference. Extensive experimental results show consistent performance improvements on three public datasets.
Choice of discount rate in reinforcement learning with long-delay rewards

In the world, most of the successes are results of long-term efforts. The reward of success is extremely high, but before that, a long-term investment process is required. People who are “myopic” only value short-term rewards and are unwilling to make early-stage investments, so they hardly get the ultimate success and the corresponding high rewards. Similarly, for a reinforcement learning (RL) model with long-delay rewards, the discount rate determines the strength of agent's “farsightedness”. In order to enable the trained agent to make a chain of correct choices and succeed finally, the feasible region of the discount rate is obtained through mathematical derivation in this paper firstly. It satisfies the “farsightedness” requirement of agent. Afterwards, in order to avoid the complicated problem of solving implicit equations in the process of choosing feasible solutions, a simple method is explored and verified by theoreti cal demonstration and mathematical experiments. Then, a series of RL experiments are designed and implemented to verify the validity of theory. Finally, the model is extended from the finite process to the infinite process. The validity of the extended model is verified by theories and experiments. The whole research not only reveals the significance of the discount rate, but also provides a theoretical basis as well as a practical method for the choice of discount rate in future researches.
The Interface of Competition law and blockchain Technology: a global Perspective

The rule of law does not govern all human interactions. There are times when the state bypasses legal constraints, as documented by the World Justice Project. Other times, jurisdictions may be mutually unfriendly and refuse to enforce foreign laws. Blockchains create trust between contracting parties at the individual level, enabling them to transact freely and increase consumer welfare. Blockchains can only supple-ment antitrust if the legal constraints do not impede their development. The law should thus support the decentralization of blockchains so that blockchain-based mechanisms may take over (even if imperfectly) where the law does not apply. With that in mind, we justify the attractiveness of that approach by showing that blockchain causes an increase in the number of transactions by creating trust (Part 1), and that it may overall increase the decentralization of economic transactions (Part 2). The law should take into account where it applies (Part 3). We conclude afterward (Part 4).
Circuits for Measurement Based Quantum State Preparation

In quantum computing, state preparation is the problem of synthesizing circuits that initialize quantum systems to specific states. It has been shown that there are states that require circuits of exponential size to be prepared (when not using measurements), and consequently, despite extensive research on this problem, the existing computer-aided design (CAD) methods produce circuits of exponential size. In this paper, we show how CAD based state preparation can be made scalable by using techniques that are unique to quantum computing: measurements, and the resulting state collapses. With this approach, we are able to produce wide classes of states in polynomial time, resulting in an exponential improvement over existing CAD methods.