Reviewerly/exHarmony huggingface dataset:
Experimental Perspective of Artificial Intelligence Technology in Human Resources Management

This experimental study investigates the use of artificial intelligence (AI) technology and its role in improving human resources management, particularly the selection and recruitment process. The importance of this study lies in the using AI technologies within the business environment has significantly increased because of the contentious technological developments and the revolution of the internet of things that have been imposed in business environments, which made it imperative for organizations to keep pace with these developments and work with them to create an innovative and competitive business model. The study focuses on the artificial intelligence dimensions represented in technological skills, automation, and expert systems in improving the effectiveness of human resources recruitment and selection. Due to the nature of the study, a qualitative methodology was found to be more appropriate to achieve the objectives of the study. Accordingly, semi-structured interviews were conducted with a sample of twenty-five specialists in human resources management at Zain Telecom Company in Kingdom of Bahrain. The findings reveal that AI technology had achieved a high level of effectiveness in Zain Telecom Company, in addition to the presence of a positive qualitative role for AI technology in its dimensions (technological skills, automation, and expert systems) to improve the effectiveness of selection and staffing at an entrepreneurial organization operating in Bahrain (i.e. Zain Telecom Company). It has been concluded that AI gives promising answers for scouts to advance ability procurement by assuming control over the long run, burning-through redundant undertakings, for example, sourcing and screening candidates, to improve the nature of the recruiting cycle and kill human predispositions. Enlarged knowledge will be utilized broadly and progressively to create better and more successful outcomes; subsequently, routine authoritative positions will be supplanted by smart AI innovations and will slowly vanish.
Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory Systems

Humans and other intelligent animals evolved highly sophisticated perception systems that combine multiple sensory modalities. On the other hand, state-of-the-art artificial agents rely mostly on visual inputs or structured low-dimensional observations provided by instrumented environments. Learning to act based on combined visual and auditory inputs is still a new topic of research that has not been explored beyond simple scenarios. To facilitate progress in this area we introduce a new version of VizDoom simulator to create a highly efficient learning environment that provides raw audio observations. We study the performance of different model architectures in a series of tasks that require the agent to recognize sounds and execute instructions given in natural language. Finally, we train our agent to play the full game of Doom and find that it can consistently defeat a traditional vision-based adversary. We are currently in the process of merging the augmented simulator with the main ViZDoom code repository. Video demonstrations and experiment code can be found at https://sites.google.com/view/sound-rl.
Dynamical quantum phase transitions in the spin-boson model

We study dynamical quantum phase transitions in a 2-qubit system interacting with a transverse field and a quantized bosonic environment in the context of open quantum systems. By applying the stochastic Schrödinger equation approach, the model with a spin-boson type of coupling can be solved numerically. It is observed that the dynamics of the rate function of the Loschmidt echo in a 2-qubit system within a finite size of Hilbert space exhibit nonanalyticity when the direction of the transverse field coupled to the system is under a sudden quench. Moreover, we demonstrate that the memory time of the environment and the coupling strength between the system and the transverse field can jointly impact the dynamics of the rate function. We also supply a semi-classical explanation to bridge the dynamical quantum phase transitions in many-body systems and the non-Markovian dynamics of open quantum systems.
Type III solar radio burst detection: A deep learning approach

Solar Radio Bursts (SRBs) are generally observed in dynamic spectra and have five major spectral classes, labelled Type I to Type V depending on their shape and extent in frequency and time. Due to their complex characterization, a challenge in solar radio physics is the automatic detection and classification of such radio bursts. Classification of SRBs has become fundamental in recent years due to large data rates generated by advanced radio telescopes such as the LOw-Frequency ARray, (LOFAR). Current state-of-the-art algorithms implement the Hough or Radon transform as a means of detecting predefined parametric shapes in images. These algorithms achieve up to 84% accuracy, depending on the Type of radio burst being classified. Other techniques include procedures that rely on Constant-False-Alarm-Rate detection, which is essentially detection of radio bursts using a de-noising and adaptive threshold in dynamic spectra. It works well for a variety of different Types of radio bursts and achieves an accuracy of up to 70%. In this research, we are introducing a Convolutional Neural Network (CNN) named You Only Look Once v2 (YOLOv2) for solar radio burst classification. By using Type III simulation methods we can train the algorithm to classify real Type III solar radio bursts in real-time at an accuracy of 82.63% with a maximum 77 frames per second (fps).
Practical Principle of Least Privilege for Secure Embedded Systems

Many embedded systems have evolved from simple bare-metal control systems to highly complex network-connected systems. These systems increasingly demand rich and feature-full operating-systems (OS) functionalities. Furthermore, the network connectedness offers attack vectors that require stronger security designs. To that end, this paper defines a prototypical RTOS API called Patina that provides services common in featurerich OSes (e.g., Linux) but absent in more trustworthy μ -kernel based systems. Examples of such services include communication channels, timers, event management, and synchronization. Two Patina implementations are presented, one on Composite and the other on seL4, each of which is designed based on the Principle of Least Privilege (PoLP) to increase system security. This paper describes how each of these μ -kernels affect the PoLP based design, as well as discusses security and performance tradeoffs in the two implementations. Results of comprehensive evaluations demonstrate that the performance of the PoLP based implementation of Patina offers comparable or superior performance to Linux, while offering heightened isolation.
Formal Synthesis of Filter Components for Use in Security-Enhancing Architectural Transformations

Safety- and security-critical developers have long recognized the importance of applying a high degree of scrutiny to a system’s (or subsystem’s) I/O messages. However, lack of care in the development of message-handling components can lead to an increase, rather than a decrease, in the attack surface. On the DARPA Cyber-Assured Systems Engineering (CASE) program, we have focused our research effort on identifying cyber vulnerabilities early in system development, in particular at the Architecture development phase, and then automatically synthesizing components that mitigate against the identified vulnerabilities from high-level specifications. This approach is highly compatible with the goals of the LangSec community. Advances in formal methods have allowed us to produce hardware/software implementations that are both performant and guaranteed correct. With these tools, we can synthesize high-assurance “building blocks” that can be composed automatically with high confidence to create trustworthy systems, using a method we call Security-Enhancing Architectural Transformations. Our synthesis-focused approach provides a higherleverage insertion point for formal methods than is possible with post facto analytic methods, as the formal methods tools directly contribute to the implementation of the system, without requiring developers to become formal methods experts. Our techniques encompass Systems, Hardware, and Software Development, as well as Hardware/Software Co-Design/CoAssurance. We illustrate our method and tools with an example that implements security-improving transformations on system architectures expressed using the Architecture Analysis and Design Language (AADL). We show how message-handling components can be synthesized from high-level regular or context-free language specifications, as well as a novel specification language for self-describing messages called Contiguity Types, and verified to meet arithmetic constraints extracted from the AADL model. Finally, we guarantee that the intent of the message processing logic is accurately reflected in the application binary code through the use of the verified CakeML compiler, in the case of software, or the Restricted Algorithmic C toolchain with ACL2-based formal verification, in the case of hardware/software co-design.
Blockchain Based Unified Authentication with Zero-knowledge Proof in Heterogeneous MEC

The next generation wireless network will be a heterogeneous network with multiple access networks, and Mobile Edge Computation (MEC) is oriented to heterogeneous users with various security authentication mechanisms. However, compared to the central servers, the lightweight and the limited computing power make MEC servers hard to support multiple security authentication mechanisms at the same time. To resolve this issue, this paper introduces a blockchain based unified authentication with Zero-knowledge Proof (ZPB authentication) system. Blockchain is adopted to recorder the authentication results, so there is no need to perform secondary authentication in HetNet handoff. A non-interactive Schnorr Zero-knowledge Proof scheme is used to avoid privacy issues caused by blockchain, and it can calculate parameters in advance to reduce real-time overhead of MEC. At the same time, the mechanism of anonymous code updating periodically also makes the system more secure. Through performance analysis and simulation, it can be seen that the authentication system proposed in this paper can achieve both security and effectiveness, and is suitable for HetNet access authentication in MEC scenario.
Bell‐GHZ Measurement‐Device‐Independent Quantum Key Distribution

Abstract In this work, a one‐sided measurement‐device‐independent quantum key distribution protocol that uses Bell and Greenberger–Horne–Zeilinger (GHZ) states is proposed. The protocol considers three parties: Alice and Bob, who will share the key at the end of the protocol; and Charlie, who plays the role of facilitating the key distribution without having access to the key itself. Alice and Charlie are trusted users, while Bob is uncharacterized. The protocol is asymmetrical, meaning that Alice sends one qubit while Bob sends two; this allows Bob to identify himself via a previously assigned ID; if this authentication fails, the key distribution also will.
Knowledge-driven fuzzy consensus model for team formation

The correct allocation of human resources is of utmost importance for any kind of enterprise and organization. Many approaches have been defined so far to support team formation leveraging on different techniques, from knowledge engineering to operational research and computational intelligence. Unfortunately, these approaches are often specifically thought for large organizations owning the right set of technological assets and human resources able to manage and use these approaches. In this work, we propose an original approach to team formation, namely the KnowMIS-Team approach, specifically designed for knowledge-intensive small and medium enterprises. This is a lightweight hybrid approach that combines three different techniques: a knowledge-driven technique for finding the most competent team for a given project based on a lightweight semantic model of knowledge, skills and attitudes; a top-down, leader-selected approach wherein the competent members selected in the previous phase can propose their candidate teams; a bottom-up fuzzy consensus-based mechanism in which the employees of the organization can express their preferences on the candidate teams. A conceptual architecture of an intelligent system implementing the approach is also presented. The KnowMIS-Team approach is the overall result of many years of experience in team formation and management for a research center and embeds all the best practices therein adopted, and it has been experimented in the same center and in other university spin-offs for many years, contributing to the realization of successful projects.
We Dare You

In this article, we present a lifecycle study of We Dare You, a Substitutional Reality (SR) installation that combines visual and tactile stimuli. The installation is set up in a center for architecture, and invites visitors to explore its facade while playing with vertigo, in a visual Virtual Reality (VR) environment that replicates the surrounding physical space of the installation. Drawing on an ethnographic approach, including observations and interviews, we researched the exhibit from its opening, through the initial months plagued by technical problems, its subsequent success as a social and playful installation, on to its closure, due to COVID-19, and its subsequent reopening. Our findings explore the challenges caused by both the hybrid nature of the installation, as well as the visitor' playful use of the installation which made the experience social and performative - but also caused some problems. We also discuss the problems We Dare You faced in light of hygiene demands due to COVID-19. The analysis contrasts the design processes and expectations of stakeholders with the audience's playful appropriation, which led the stakeholders to see the installation as both a success and a failure. Evaluating the design and redesign through use on behalf of visitors, we argue that an approach that further opens up the post-production experience to a process of continuous redesign based on the user input - what has been termed "design-after-design" - could facilitate the design of similar experiences in the museum and heritage sector, supporting a participatory agenda in the design process, and helping to resolve the tension between stakeholders' expectations and visitors' playful appropriations.
Encrypted Medical Records Search with Supporting of Fuzzy Multi-keyword and Relevance Ranking

Medical records are highly private and sensitive. To protect the information security of patients and medical institutes, medical records and data should be encrypted before outsourcing to the cloud storage. However, the retrieval of the encrypted data is currently a crucial issue of medical big data security. The existing searchable encryption methods can hardly satisfy the goals of fuzzy multi-keywords search, relevance ranking, access pattern protection and high retrieval efficiency simultaneously. Thus, in this paper, we propose an encrypted medical records searching scheme that can achieve those goals. A new spelling correction algorithm is proposed to support fuzzy multiple input keywords. A new relevance score encryption and calculation algorithm is introduced for ranking medical records with privacy preserving. In addition, a queue-based query procedure is also applied in this scheme to protect the access pattern from statistical attack and file-injection attack. Our proposed scheme achieves fuzzy matching without expending index table or sacrificing computational efficiency, and can support dynamic file updating. The theoretical analysis and experiment results show that our scheme is secure, accurate, error-tolerant and very efficient.
A survey on the scope of cloud computing

Distributed computing is an accelerated innovation that has resulted in major transformations and opportunities in India in different fields. This is an inevitable approximation of how to change the knowledge system and management that is increasingly interested in the use of distributed computing in the field of training. This study seeks to survey an applied distributed model of computation between the academic network and the society for a composite effort. This paper presents distributed electronic computing, with points: its functions, its organisational structure, its goals and its problems. This document represents an intelligent evaluation of the work of distributed computer processing in the field of advice for working companies. The requisite evaluation was finished with the key partners of the specific preparation systems, which are performed for informative use. The course was conducted through the use of distributed computing and the right subjective approach in the exam classroom. Approximately 8 evaluation tasks were perceived following a thorough examination of open writing and addressed the significance of cloud in the school environment and the ease of significant usage. The examination considers and evaluates that the main accomplices in planning should be used. The comprehensive research indicates that familiarization with board preparation is practical in order to make the desirable circumstances clearer.
Panoramic Image Reflection Removal

This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes. Although a partial view of the reflection scene is included in the panoramic image, it cannot be utilized directly due to its misalignment with the reflection-contaminated image. We propose a two-step approach to solve this problem, by first accomplishing geometric and photometric alignment for the reflection scene via a coarse-to-fine strategy, and then restoring the transmission scene via a recovery network. The proposed method is trained with a synthetic dataset and verified quantitatively with a real panoramic image dataset. The effectiveness of the proposed method is validated by the significant performance advantage over single image-based reflection removal methods and generalization capacity to limited-FoV scenarios captured by conventional camera or mobile phone users.
A lightweight framework for function name reassignment based on large-scale stripped binaries

Software in the wild is usually released as stripped binaries that contain no debug information (e.g., function names). This paper studies the issue of reassigning descriptive names for functions to help facilitate reverse engineering. Since the essence of this issue is a data-driven prediction task, persuasive research should be based on sufficiently large-scale and diverse data. However, prior studies can only be based on small-scale datasets because their techniques suffer from heavyweight binary analysis, making them powerless in the face of big-size and large-scale binaries.
Enhancing Fireworks Algorithm in Local Adaptation and Global Collaboration

In order to improve the performance of fireworks algorithm, this paper carries out a comprehensive enhancement for its framework. Locally, the basic explosion operator is replaced by an efficient adaptation method in CMA-ES. Globally, the explosion range of all fireworks is effectively collaborated by search space partition. On the one hand, the proposed algorithm can quickly adapt to local landscape and improve the local exploitation efficiency significantly. On the other hand, it can collaborate the search ranges of multiple fireworks to form a seamless and non-overlapping partition of the search space, thereby ensuring the global search ability. Since the proposed framework evaluates one batch of a fixed large number of solutions in each iteration, it also achieves better computational efficiency in modern parallel hardware. The proposed algorithm is tested on the CEC 2020 benchmark functions with three different dimensions. The experimental results prove that those strategies improve fireworks algorithm significantly.
Improving Recommendations for Online Retail Markets Based on Ontology Evolution

The semantic web is considered to be an extension of the present web. In the semantic web, information is given with well-defined meanings, and thus helps people worldwide to cooperate together and exchange knowledge. The semantic web plays a significant role in describing the contents and services in a machine-readable form. It has been developed based on ontologies, which are deemed the backbone of the semantic web. Ontologies are a key technique with which semantics are annotated, and they provide common comprehensible foundation for resources on the semantic web. The use of semantics and artificial intelligence leads to what is known to be “Smarter Web”, where it will be easy to retrieve what customers want to see on e-commerce platforms, and thus will help users save time and enhance their search for the products they need. The semantic web is used as well as webs 3.0, which helps enhancing systems performance. Previous personalized recommendation methods based on ontologies identify users’ preferences by means of static snapshots of purchase data. However, as the user preferences evolve with time, the one-shot ontology construction is too constrained for capturing individual diverse opinions and users’ preferences evolution over time. This paper will present a novel recommendation system architecture based on ontology evolution, the proposed subsystem architecture for ontology evolution. Furthermore, the paper proposes an ontology building methodology based on a semi-automatic technique as well as development of online retail ontology. Additionally, a recommendation method based on the ontology reasoning is proposed. Based on the proposed method, e-retailers can develop a more convenient product recommendation system to support consumers’ purchase decisions.
Lightweight and fine‐grained access control for cloud–fog‐based electronic medical record sharing systems

Summary The electronic medical record (EMR) sharing systems promote medical services' efficiency, save human resources, and reduce energy consumption. A number of patients are ready to store their medical records in cloud. Nevertheless, owing to the medical record's privacy sensitivity, security problems are of critical importance in EMR sharing systems. This work presents a lightweight and fine‐grained access control with data security for EMR sharing by combining fog and cloud using symmetric encryption algorithm and attribute‐based encryption algorithm. Data owners encrypt the medical records with symmetric encryption and store them in cloud. The symmetric key is protected by ciphertext‐policy attribute‐based encryption and stored in fog. The keyword extracted from medical record is encrypted by data owner with key‐policy attribute‐based keyword search encryption and sent to fog. The requesters who satisfy with the keyword ciphertext's access policy can search for the keyword. They are capable of accessing the symmetric key if their attributes satisfy with the symmetric key ciphertext's access policy. The key management method and access control scheme guarantee the medical record's security and privacy. Security analysis demonstrates that our work is able to achieve privacy protection, secure search, fine‐grained access control, and collusion resistance.