Reviewerly/exHarmony huggingface dataset:
Beyond Explicit Transfers: Shared and Managed Memory in OpenMP

OpenMP began supporting offloading in version 4.0, almost 10 years ago. It introduced the programming model for offload to GPUs or other accelerators that was common at the time, requiring users to explicitly transfer data between host and devices. But advances in heterogeneous computing and programming systems have created a new environment. No longer are programmers required to manage tracking and moving their data on their own. Now, for those who want it, inter-device address mapping and other runtime systems push these data management tasks behind a veil of abstraction. In the context of this progress, OpenMP offloading support shows signs of its age. However, because of its ubiquity as a standard for portable, parallel code, OpenMP is well positioned to provide a similar standard for heterogeneous programming. Towards this goal, we review the features available in other programming systems and argue that OpenMP expand its offloading support to better meet the expectations of modern programmers. The first step, detailed here, augments OpenMP’s existing memory space abstraction with device awareness and a concept of shared and managed memory. Thus, users can allocate memory accessible to different combinations of devices that do not require explicit memory transfers. We show the potential performance impact of this feature and discuss the possible downsides.
A Deep Reinforcement Learning-based Task Scheduling Algorithm for Energy Efficiency in Data Centers

Cloud data centers provide end-users with a wide range of application scenarios, including scientific computing, smart grids, etc. The number and size of data centers have rapidly increased in recent years, which causes severe environmental problems and colossal power demand. Therefore, it is desirable to use a proper scheduling method to optimize resource usage and reduce energy consumption in a data center. However, it is rather difficult to design an effective and efficient task scheduling algorithm because of the dynamic and complex environment of data centers. This paper proposes a task scheduling algorithm, WSS, to optimize resource usage and reduce energy consumption based on a model-free deep reinforcement learning framework inspired by the Wolpertinger architecture. The proposed algorithm can handle the scheduling problem on a sizeable discrete action space, improve decision efficiency, and save the training convergence time. Meanwhile, the proposed algorithm based on Soft Actor-Critic is designed to improve the stability and exploration capability of WSS. Experiments based on real-world traces prove that WSS can reduce energy consumption by nearly 25% compared with the Deep Q-network task scheduling algorithm. Moreover, WSS can provide a short time of training convergence without increasing the average waiting time of tasks and achieve stable performance.
Maximality of bi-intuitionistic propositional logic

Abstract In the style of Lindström’s theorem for classical first-order logic, this article characterizes propositional bi-intuitionistic logic as the maximal (with respect to expressive power) abstract logic satisfying a certain form of compactness, the Tarski union property and preservation under bi-asimulations. Since bi-intuitionistic logic introduces new complexities in the intuitionistic setting by adding the analogue of a backwards looking modality, the present paper constitutes a non-trivial modification of the previous work done by the authors for intuitionistic logic (Badia and Olkhovikov, 2020, Notre Dame Journal of Formal Logic, 61, 11–30).
Energy‐aware autoscaling for scientific workflow in cloud environment

Summary The devices used for information and communication purpose consume an increasing amount of energy. As a result, the datacenters consume a lot of energy. Due to the expanding use of cloud services, there is considerable interest in reducing the energy consumption of datacenters. Effective resource utilization techniques and reduction in energy consumption are two crucial metrics in today's cloud computing environment. Many issues come along with contradicting objectives, viz., minimizing energy consumption raising the expenses of service supply. Autoscaling features of the cloud can bundle these issues. The major hurdle for effective resource scalability is the lack of proper management of energy supply to devices of servers in the datacenter. The main objective of the article is to supply energy to devices as per the allocated job‐size on the interaction of complex energy, to increase the energy utilization of servers. Also, using renewable energy (RE) to supplement the operational energy of the cloud datacenter. In this direction, the article presents a predator‐prey‐based mathematical model for energy scaling that leverages the accessibility of RE source to decrease the high energy demands of the datacenter. It is formalized using ecological concepts of metabolism and allometric scaling. An allometric energy scaling (AES) algorithm is presented. The performance and evaluation of the proposed algorithm are implemented with the Cloudsim 4.0 simulator. The results are compared with the HEFT algorithm, and AES algorithm gives slightly better results in the case of optimum energy consumption and processing cost compared with the HEFT algorithm.
Development and Implementation of Information Resource Platform for Business Administration Specialty Based on Industry Education Integration Mode

The integration of existing information resources is difficult to be achieved, resulting in the single form of information resources and incomplete knowledge framework. To solve such problem, under the background of the integration of production and education, this paper puts forward a design scheme of information resource management platform based on MVC mode, taking the teaching of Business Administration major as an example. Firstly, on the basis of clear platform requirements, the principle process of the system is determined by systematically learning ontology, service-oriented architecture and web service integration theory. Then, with the support of J2EE technology, through the data analysis and tracking design of teaching resource database, various resource display forms are realized according to the different requirements of users with different permissions. Similarly, the search engine toolkit for full-text retrieval of teaching resource database are called. Finally, the prototype system of basic data resource platform based on service integration is developed and tested. The results show that the scheme can effectively improve the teaching efficiency and teaching quality, which also has potential application value.
Research on the Methods of Panoramic Video Projection Mapping

In recent years, panoramic video has gradually emerged in the multimedia industry. As an important part of the panoramic video processing process, the choice of projection mapping method will affect the efficiency of panoramic video coding and decoding, transmission mode and user experience. There have been a lot of researches on the projection method from the spherical surface to the plane, and its important purpose is to reduce the distortion of the spherical video in the projection and improve the performance of panoramic video encoding. At present, projection methods are mainly divided into uniform quality projection and non-uniform quality projection. This paper compares several common projection mapping methods, discusses their respective advantages and disadvantages, and makes related summary at last.
VML-HP: Hebrew Paleography Dataset

This paper presents a public dataset, VML-HP, for Hebrew paleography analysis. The VML-HP dataset consists of 537 document page images with labels of 15 script sub-types. Ground truth is manually created by a Hebrew paleographer at a page level. In addition, we propose a patch generation tool for extracting patches that contain an approximately equal number of text lines no matter the variety of font sizes. The VML-HP dataset contains a train set and two test sets. The first is a typical test set, and the second is a blind test set for evaluating algorithms in a more challenging setting. We have evaluated several deep learning classifiers on both of the test sets. The results show that convolutional networks can classify Hebrew script sub-types on a typical test set with accuracy much higher than the accuracy on the blind test.
Application of the Adaptive Double-layer Ant Colony Algorithm in UAV Trajectory Planning

To solve UAV trajectory planning in a complex environment, an adaptive double-layer ant colony algorithm based on elitist strategy (ADAS) is proposed in this paper. Firstly, a mathematical model of rugged terrain is established, a multi-factor heuristic value is designed, and an adaptive pheromone update strategy is proposed. Secondly, the double-layer ant colony structure based on sorting is presented. The exploration factor is set up to carry out local exploration, which effectively prevents the algorithm from falling into the local optimum. Then, the trajectory points are selected by calculating transition probability and roulette strategy. The fitness value is used to evaluate the quality of the track and make an elite backup of excellent trajectory. Finally, an improved moving average algorithm (IMA) is designed to smooth and correct the optimal trajectory. The experimental results show that the algorithm can increase the convergence speed, avoid falling into the local optimum and get a better solution. The algorithm is applied to an example of UAV trajectory planning, and the effectiveness of the algorithm is proved.
Collection, Services, and Infrastructure available for access EResources in the Panjab University Library, Chandigarh: A Study

Nowadays availability and access of e-resources are very common in the academic libraries in India. E-resources are essentials part of the academic community for their works. The matter of fact is that proper and maximum utilisation of e-resources by the users is the need of the hour. The paper examines the satisfaction of e-resources related to the collection, services, and infrastructure available in the library of Panjab University, Chandigarh. The study also highlights the availability of computers and internet connection for accessing e-resources.
Incorporating Common Knowledge and Specific Entity Linking Knowledge for Machine Reading Comprehension

Machine comprehension of texts often requires external common knowledge and coreference resolution in the passage. However, most current machine reading comprehension models only incorporate external common knowledge. We propose CoSp model, which incorporates both common knowledge and specific entity linking knowledge for machine reading comprehension. It employs an attention mechanism to adaptively select relevant commonsense and lexical common knowledge from knowledge bases, then it leverages the relational-GCN for reasoning on the entity graph, which is constructed by the entity coreference and co-occurrence for each passage. Hence we obtain knowledge-aware and coreference-aware contextual word representation for answer prediction. Experimental results indicate that CoSp model offers significant and consistent improvements over BERT, outperforming competitive knowledge-aware models on ReCoRD and SQuAD1.1 benchmarks.
Privacy‐preserving convolutional neural network prediction with low latency and lightweight users

Convolutional neural networks (CNNs) have excellent and extensive applications in image recognition. With the continuous exploitation of data value and the proliferation of machine learning-as-a-service, convolutional neural network prediction schemes on privacy preservation have been introduced one after another, which makes much more attention focused on the privacy leakage and services offered to be efficient and light. Therefore, how to improve the convolutional neural prediction scheme on the premise of privacy preservation turns out to be an imperative research issue. In this paper, we propose a privacy-preserving convolutional neural network prediction scheme (PCP-LL) that supports low latency and lightweight users. The scheme starts from the perspective of lossless accuracy from underlying networks. First, we construct a secure activation function computing protocol (SActF) utilizing a commodity-based secure comparison protocol, which reduces the complexity and latency during the activation function computing under ciphertexts compared with common schemes. Second, to further support lightweight users, we introduce a secure output layer protocol (SOut) that enables users to obtain the prediction results without extra decryption after simple operations. Then, the scheme adopts the distributed two trapdoors public-key cryptosystem (DT-PKC) to achieve both data and model security, which well avoids security issues especially such as wiretapping by semi-honest participants commonly in secret sharing schemes. Finally, through relevant evaluations, the scheme not only achieves privacy preservation and low latency, but also supports lightweight users.
Reducing the Human Effort in Text Line Segmentation for Historical Documents

Labeling the layout in historical documents for preparing training data for machine learning techniques is an arduous task that requires great human effort. A draft of the layout can be obtained by using a document layout analysis (DLA) system that later can be corrected by the user with less effort than doing it from scratch. We research in this paper an iterative process in which the user only supervises and corrects the given draft for the pages automatically selected by the DLA system with the aim of reducing the required human effort. The results obtained show that similar DLA quality can be achieved by reducing the number of pages that the user has to annote and that the accumulated human effort required to obtain the layout of the pages used to train the models can be reduced more than \(95\%\).
Application of blockchain in automotive industry, waste management, and seed traceability

Blockchain technology gained prominence because it guarantees secure transactions. It offers solutions to create distributed, trust-free systems. The enhancing availability of trust-free, transparent, highly secure, and self-programmable blockchains are used in other applications beyond cryptocurrencies as well. The main characteristics of transparency, immutability, and decentralization paves the way to use blockchain in a wide variety of application such as the automotive industry, waste management, seed traceability, asset management, insurance claims processing, smartphones, health care, smart appliances, and identity applications. In all these applications, the blockchain ledger provides security to protect things, systems, smart information, or ownership and transferability. Hence, the blockchain could become a powerful tool for economic change, fair trade, democratization of the global economy, and support for more transparent and egalitarian communities.
Analysis of Records Management Maturity Level for Data Collection of Network Assets in Indonesian Telecommunication Industry

PT.XYZ is a state-owned company in telecommunications and information technology. In developing its business, PT.XYZ has served more than 2.200 corporate customers and 22.760 retail customers by having a lot of network assets. The large number of network assets owned means that the company must be able to carry out good data collection to find out information on the distribution and capacity of assets that are still available. The most common problems encountered at PT.XYZ is about data records management of network assets. PT.XYZ already has a system for data collection of network assets, but it’s still not running well. This occurs because there are no procedures and clarity of accountability related to records management, so there were many data collection processes that were still manually. Based on the problem, the purpose of this study is to conduct an assessment of records management level at PT.XYZ and provide recommendations to increase the level. The assessment process and recommendations will be based on ARMA GARP Framework. This was chosen because it’s a comprehensive approach that widely used in managing the information assets of companies or organizations. The results of maturity level assessment of this study are at level 2 for accountability, transparency, compliance, availability, retention, and also level 1 for integrity, protection, and disposition. These results also indicate that the company needs to improve several processes in the GAPR Principles of Integrity, Protection, and Disposition because there is a large gap between the current level and the expected target level.
Systematic Survey on Cryptographic Methods Used for Key Management in Cloud Computing

PurposeCloud computing is a new technology that aims to be the utility computing of the future. However, it is not without its share of threats and vulnerabilities. One of these threats is the security and privacy of the platform. One of the ways security is maintained is through cryptography, which includes the management of keys used for encryption and decryption. The data stored in the platform are encrypted, and the user must decrypt data when he needs it. This management is very important because once keys are compromised an attacker can hack and retrieve data very easily.MethodsThe current problem in the key management of various encryption algorithms/frameworks is studied through existing literatures. For each method, the parameters like current key management methods, their limitations, and some new approaches to tackle the issue are listed and compared in the paper.ResultsThis survey paper gives an account of the current key management methods, their limitations, and some new approaches to tackle the issue. It also presents the analysis performed on some cryptographic algorithms, which are used in cloud computing. The paper also looks at the proposed frameworks for the platform and how they aim to mitigate the issues in cloud computing. Some of the new approaches are feasible and can be deployed into the cloud system, while some are not yet fully tested and require improvements.ConclusionThe paper gives a systematic review of various cryptographic methods, which are available for key management in cloud computing. The paper also gives a short overview of various evaluation parameters to be used to compare the existing cryptographic methods for key management in cloud computing.
Finite Block Length Analysis on Quantum Coherence Distillation and Incoherent Randomness Extraction

We give the first systematic study on the second order asymptotics of the operational task of coherence distillation with and without assistance. In the unassisted setting, we introduce a variant of randomness extraction framework where free incoherent operations are allowed before the incoherent measurement and the randomness extractors. We then show that the maximum number of random bits extractable from a given quantum state is precisely equal to the maximum number of coherent bits that can be distilled from the same state. This relation enables us to derive tight second order expansions of both tasks in the independent and identically distributed setting. Remarkably, the incoherent operation classes that can empower coherence distillation for generic states all admit the same second order expansions, indicating their operational equivalence for coherence distillation in both asymptotic and large block length regime. We then generalize the above line of research to the assisted setting, arising naturally in bipartite quantum systems where Bob distills coherence from the state at hand, aided by the benevolent Alice possessing the other system. More precisely, we introduce a new assisted incoherent randomness extraction task and establish an exact relation between this task and the assisted coherence distillation. This strengthens the one-shot relation in the unassisted setting and confirms that this cryptographic framework indeed offers a new perspective to the study of quantum coherence distillation. Likewise, this relation yields second order characterizations to the assisted tasks. As by-products, we show the strong converse property of the aforementioned tasks from their second order expansions.
Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that SVM based RF approach generating better results for CBIR. Therefore, this paper focused on SVM based RF approach. To enhance the performance of SVM based RF approach this research work applied Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) before applying SVM on user feedback. The main objective of using these meta-heuristic was to increase the positive image sample size from SVM. Firstly steps PSO is applied by incorporating the user feedback and secondly GA is applied on the result generated through PSO, finally SVM is applied using the positive sample generated through GA. The proposed technique is named as Particle Swarm Optimization Genetic Algorithm- Support Vector Machine Relevance Feedback (PSO-G A-SVM-RF). Precisions, recall and F-score are used as performance metrics for the assessment and validation of PSO-GA-SVM-RF approach and experiments are conducted on coral image dataset having 10908 images. From experimental results it is proved that PSO-GA-SVM-RF approach outperformed then various well known CBIR approaches.
Performance of Adder Architectures on Encrypted Integers

The fully Homomorphic encryption scheme is corner stone of privacy in an increasingly connected world. It allows to perform all kinds of computations on encrypted data. Although, time of computations is bottleneck of numerous applications of real life. In this paper, a brief description is made on the homomorphic encryption scheme TFHE of Illaria Chillota and the others. TFHE, implemented in c language in a library, improves the bootstrapping execution time of the FHEW scheme to 13 milliseconds. TFHE performs homomorphic processing on a multitude of logic gates. This variety made it possible to construct, implement five adder architectures and compare them in terms of the execution time of the bootstrapping per logic gate. In a singleprocessor computing environment, the Carry Look-ahead Adder completed a two-integer addition in 90 seconds, whereas the Ripple carry Adder did the same processing in 109 seconds. An improvement in processing time of 15% is observed. And, the same ratio of about 15% was obtained on four integers, respectively for 279 seconds for the first adder and 320 seconds for Wallace's dedicated adder. While in the dual-processor environment, a 50% improvement was seen on all adders in the same processing on integers. The Carry Look-ahead Adder saw his handling improved by the sum of two numbers from 90 seconds to 46 seconds and four numbers from 279 seconds to 139 seconds, respectively.