Reviewerly/exHarmony huggingface dataset:
Construction of English Resource Database Based on Virtual Reality Technology

The construction of digital campus of higher vocational education is in full swing, and the construction and application of English resource database has unique advantages due to its simplicity, efficiency and high sharing characteristics. This paper uses virtual reality technology to realize the application scene reproduction, and uses the interactive function to realize human-human interaction and interpersonal interaction, and finally realize the ultimate goal of skill training and ability improvement. In this paper, through in-depth investigation and analysis of the author’s own teaching philosophy, teaching characteristics, and teaching mode of the higher vocational colleges, the system needs analysis and the needs of each functional module of the teaching resource database platform of the higher vocational colleges are analyzed, and a method is proposed. A teaching resource library platform model integrating shared resource library, high-quality course modules, and network teaching modules. In the construction process, the UML language is used to design the system’s functional modules and databases in detail. After the system is built, it will run on the stable, reliable, efficient and safe operating system Linux, and adopt the B/S mode for management and maintenance. Eclipse is used for code compilation, modification, optimization, and debugging during the development phase. Question 6 rose from 1.6 points to 3.12 points for the first time, the most significant change, which also directly shows that English learning has changed in the minds of students. The construction of the English resource database can facilitate the teaching and learning of teachers and students, and better improve the efficiency of teachers.
Adoption of cryptocurrencies for remittances in the UAE: the mediation effect of consumer innovation

Purpose This study investigated the internal factors that influence the adoption of cryptocurrencies for remittance transactions in the United Arab Emirates (UAE) by examining the relationships between behavioural intention (BI) and perceived risk (PR), as well as the mediating effect of consumer innovation (CI). Design/methodology/approach The authors developed a structural model using scales from the literature. The authors distributed an online questionnaire, evaluated by five cryptocurrency experts, using a snowball approach and collected 270 responses. Findings The results revealed that CI mediates the relationship between PR and BI. Also, CI enhances intentions to use cryptocurrencies for remittance transactions. However, PR has a negative impact on BI. Research limitations/implications This research adds to the body of knowledge by examining the acceptance and implementation of cryptocurrencies in the UAE and by developing and evaluating new constructs based on current notions. The study also contributes to the current understanding of cryptocurrencies and blockchain adoption. This article focusses on the mediating impact of CI on intentions to employ cryptocurrency instruments for international money transfers. Practical implications The conclusions of the research give advice for marketers on how to boost the commercialisation of cryptocurrencies in the UAE remittance market and may pave the way for other studies to assist impending developments in the UAE cryptocurrency industry. Originality/value This research offers novel insights into CI as a significant predictor of bitcoin product uptake in the remittance business.
Finding Deviated Behaviors of the Compressed DNN Models for Image Classifications

Model compression can significantly reduce the sizes of deep neural network (DNN) models and thus facilitate the dissemination of sophisticated, sizable DNN models, especially for deployment on mobile or embedded devices. However, the prediction results of compressed models may deviate from those of their original models. To help developers thoroughly understand the impact of model compression, it is essential to test these models to find those deviated behaviors before dissemination. However, this is a non-trivial task, because the architectures and gradients of compressed models are usually not available. To this end, we propose Dflare , a novel, search-based, black-box testing technique to automatically find triggering inputs that result in deviated behaviors in image classification tasks. Dflare iteratively applies a series of mutation operations to a given seed image until a triggering input is found. For better efficacy and efficiency, Dflare models the search problem as Markov Chains and leverages the Metropolis-Hasting algorithm to guide the selection of mutation operators in each iteration. Further, Dflare utilizes a novel fitness function to prioritize the mutated inputs that either cause large differences between two models’ outputs or trigger previously unobserved models’ probability vectors. We evaluated Dflare on 21 compressed models for image classification tasks with three datasets. The results show that Dflare not only constantly outperforms the baseline in terms of efficacy but also significantly improves the efficiency: Dflare is 17.84×–446.06× as fast as the baseline in terms of time; the number of queries required by Dflare to find one triggering input is only 0.186–1.937% of those issued by the baseline. We also demonstrated that the triggering inputs found by Dflare can be used to repair up to 48.48% deviated behaviors in image classification tasks and further decrease the effectiveness of Dflare on the repaired models.
Global optimality analysis and solution of the $ \ell_0 $ total variation signal denoising model

The total variation regularizer is diffusely emerged in statistics, image and signal processing to obtain piecewise constant estimator. The ℓ0 total variation (L0TV) regularized signal denoising model is a nonconvex and discontinuous optimization problem, and it is very difficult to find its global optimal solution. In this paper, we present the global optimality analysis of L0TV signal denoising model, and design an efficient algorithm to pursuit its solution. Firstly, we equivalently rewrite the L0TV denoising model as a partial regularized (PL0R) minimization problem by aid of the structured difference operator. Subsequently, we define a P-stationary point of PL0R, and show that it is a global optimal solution. These theoretical results allow us to find the global optimal solution of the L0TV model. Therefore, an efficient Newton-type algorithm is proposed for the PL0R problem. The algorithm has a considerably low computational complexity in each iteration. Finally, experimental results demonstrate the excellent performance of our approach in comparison with several state-of-the-art methods.
Quantum algorithm for evaluating operator size with Bell measurements

Operator size growth describes the scrambling of operators in quantum dynamics and stands out as an essential physical concept for characterizing quantum chaos. Important as it is, a scheme for direct measuring operator size on a quantum computer is still absent. Here, we propose a quantum algorithm for direct measuring the operator size and its distribution based on Bell measurement. The algorithm is verified with spin chains and meanwhile, the effects of Trotterization error and quantum noise are analyzed. It is revealed that saturation of operator size growth can be due to quantum chaos itself or be a consequence of quantum noises, which make a distinction between quantum integrable and chaotic systems difficulty on noisy quantum processors. Nevertheless, it is found that the error mitigation will effectively reduce the influence of noise, so as to restore the distinguishability of quantum chaotic systems. Our work provides a feasible protocol for investigating quantum chaos on noisy quantum computers by measuring operator size growth.
Role of Artificial Intelligence and Machine Learning in Schizophrenia—A Survey

Schizophrenia (SZ) is a neurological condition that begins before birth and usually manifests itself in preadolescence. It exhibits a range of pre-morbid and pro-dromal symptoms, progressing to a full-blown psychotic syndrome. Patients with SZ require continual integrated healthcare, yet many drop out. To achieve an accurate diagnosis of SZ, several artificial intelligence (AI) approaches have been used. AI in health care uses machine learning (ML) programs to simulate human intellect for the access of large/complicated data. SZ is a severe psychiatric health situation that impacts many individuals across the world, producing cognitive and behavioral impairments that can be extremely burdensome in daily life. New treatment approaches have the potential to improvise patient health and act as a useful aid in clinical trial of sick person selection. Despite the fact that ML approaches may recognize patient groups, they are usually “unexplainable” due to difficult programming that do not replicate doctor's regular decision-making. Existing researches have considered extensive time periods (in months and years) for their studies. Even then, the mechanisms that control SZ's genesis, relapsing, somatic symptoms, and therapy remain a mystery. ML may become a useful method to explore the mechanisms that cause SZ as a result of a number of techniques aiming at improving model interpretability and causal reasoning. Our study covers over 85 existing research articles and provides a detailed summary of the achievements, as well as the future scope of AI and ML in the detection and diagnosis of SZ. An attempt has also been made, towards the end of the chapter, to identify the pertinent research gaps in the research of SZ where AI and ML can play a more significant role.
A Systematic Review of Cost, Effort, and Load Research in Information Search and Retrieval, 1972–2020

During the information search and retrieval (ISR) process, user-system interactions such as submitting queries, examining results, and engaging with information impose some degree of demand on the user’s resources. Within ISR, these demands are well recognised, and numerous studies have demonstrated that the cost, effort, and load (CEL) experienced during the search process are affected by a variety of factors. Despite this recognition, there is no universally accepted definition of the constructs of CEL within the field of ISR. Ultimately, this has led to problems with how these constructs have been interpreted and subsequently measured. This systematic review contributes a synthesis of literature, summarising key findings relating to how researchers have been defining and measuring CEL within ISR over the past 50 years. After manually screening 1,109 articles, we detailed and analysed 91 articles which examine CEL within ISR. The discussion focuses on comparing the similarities and differences between CEL definitions and measures before identifying the limitations of the current state of the nomenclature. Opportunities for future research are also identified. Going forward, we propose a CEL taxonomy that integrates the relationships between CEL and their related constructs, which will help focus and disambiguate future research in this important area.
Impact of entrepreneurial orientation and risk sharing on organizational performance influencing role of news media and public opinion

An entrepreneurial orientation is a method of implementing a strategy that makes use of a variety of resources that are owned by organizations. Entrepreneurial orientation is one of the primary factors that led to the company's founding. Risk sharing is a useful tactic that can be implemented by businesses in order to mitigate the amount of risk to which they are exposed. As a consequence of this, the objective of the research is to ascertain how the performance of an enterprise can be affected by the presence of both an entrepreneurial orientation and shared risk. The proliferation of news media has led to modifications in the ways in which businesses carry out their day-to-day operations, which in turn has an effect on the overall success of the organization. As a direct consequence of this, the research looked into the function that the news media play as a moderator of the connections that exist between entrepreneurial orientation, risk sharing, and the level of performance achieved by organizations. Even for enormous, well-known businesses that are active on a global scale, damaging publicity has the potential to lower the value of their companies. The aim of this study was to investigate the impact of entrepreneurial orientation and risk sharing on organizational performance with the mediating role of news media and moderating role of public opinion. In order to achieve the objective of the study, a quantitative research approach was utilized. Data were collected from 450 managers of SMEs with the help of a questionnaire that was adapted from previous studies. A simple random sampling technique was used to collect data. The findings of the study showed that the relationship between entrepreneurial orientation, risk sharing, and organizational performance is positive and significant. The findings also showed that news media significantly mediated this relationship and public opinion moderated the relationship between news media and organizational performance. The current study has some practical and managerial implications which help SMEs to increase their performance.
Research on Unmanned Vehicle Path Planning based on Improved Bat Algorithm

Vehicle driving technology has developed in the direction of informatization and intelligence, and the research hotspot is unmanned driving and path planning <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> . This paper proposes an improved bat algorithm (Improved Bat Algorithm, IBA). First, a weighted speed update mechanism is introduced in BA, so that with the increase of the number of iterations, the speed changes adaptively to avoid the algorithm falling into local extreme values prematurely. Then, in the subsequent iterations, the quadratic differential change mechanism is used to maintain the diversity of the bat population; and under the static road network, the optimal route is planned through the MAT LAB simulation experiment, which verifies that the IBA algorithm has better performance.
Author identity taxonomy: Capturing diversity for the books supply chain

Key Points A metadata standard or taxonomy for tracking author identity is highly desired by stakeholders in the book industry. Achieving an authoritative standard for author self‐identification comes with numerous practical, legal, and ethical challenges for the scholarly publishing industry. Until this happens, publishers can engage with librarians and booksellers, with marketing campaigns of their own, or by providing materials to support marketing campaigns within libraries and bookstores, to spotlight authors of diverse backgrounds.
Speckle Removal Using Dictionary Learning and PnP-Based Fast Iterative Shrinkage Threshold Algorithm

Speckle is a multiplicative granular noise that naturally occurs in the images captured by coherent imaging sensors such as synthetic aperture radar (SAR). It visually degrades the underlying image information and has an impact on subsequent image analysis. This problem is addressed here by developing a sparse representation model and applying an alternating minimization scheme for SAR image despeckling. The proposed method directly deals with the multiplicative noise and the data model is formed by utilizing the speckle statistics. The similar patches are clustered together to adaptively learn the dictionary, and the sparse coefficients are updated using plug-and-play based fast iterative shrinkage threshold algorithm (PnP-FISTA). Finally, the clean image is estimated using Newton’s method. Experiments on simulated and practical SAR images signify that the proposed method performs better compared to the state-of-the-art methods in terms of performance metrics and visual assessment.
Review on Real-Time Object Detection Models using Deep Neural Networks

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Smart Glove With Fully Integrated Textile Sensors and Wireless Sensor Frontend for the Tactile Internet

In this letter, we present a smart glove for Tactile Internet applications. The individual finger motions are measured via resistive strain sensors. The strain sensors are directly integrated with the textile glove and are produced in an automated process. The sensor glove is integrated with sensor conditioning, controller, wireless frontend, and battery. We investigate the measured sensor data for a variety of gestures, demonstrating the good quality of the data allowing for easy and low-energy gesture recognition.
A Bin-Based Indexing for Scalable Range Join on Genomic Data

Range-join is an operation for finding overlaps in interval-form genomic data. Range-join is widely used in various genome analysis processes such as annotation, filtering and comparison of variants in whole-genome and exome analysis pipelines. The quadratic complexity of current algorithms with sheer data volume has surged the design challenges. Existing tools have limitations on algorithm efficiency, parallelism, scalability and memory consumption. This paper proposes BIndex, a novel bin-based indexing algorithm and its distributed implementation to attain high throughput range-join processing. BIndex features near-constant search complexity while the inherently parallel data structure facilitates exploitation of parallel computing architectures. Balanced partitioning of dataset further enables scalability on distributed frameworks. The implementation on Message Passing Interface shows upto 933.5x speedup in comparison to state-of-the-art tools. Parallel nature of BIndex further enables GPU-based acceleration with 3.72x speedup than CPU implementations. The add-in modules for Apache Spark provides upto 4.65x speedup than the previously best available tool. BIndex supports wide variety of input and output formats prevalent in bioinformatics community and the algorithm is easily extendable to streaming data in recent Big Data solutions. Furthermore, the index data structure is memory-efficient and consumes upto two orders-of-magnitude lesser RAM, while having no adverse effect on speedup.
Machine Learning as a Service (MLaaS)—An Enterprise Perspective

Machine learning (ML) algorithms due to their outstanding performances are being extensively used in applications covering several different domains. Recently, the increased growth of cloud services provided training infrastructures for complex ML models able to deal with big data, resulting in the enhancement of ML as a Service (MLaaS). Toward this end, ML applications have been deployed in systems, production models, and businesses. ML algorithms involve accessing data, which is often privacy sensitive. The latter may result in security and privacy risks. Toward this end, this work examines MLaaS and its incorporation into businesses, covering a wide range of different sectors. Companies that develop ML applications are reviewed, and trends in ML-related jobs are reported. Moreover, data protection privacy is discussed and the evolution of graphics processing units (GPUs) as a necessary supporting technology for ML applications is also considered.
An Efficient Multimodal Nature‐Inspired Unmanned Aerial Vehicle Capable of Agile Maneuvers

Unmanned Aerial Vehicles In article number 2200242, Shaohui Foong present a nature-Inspired Unmanned Aerial Vehicle (UAV). Drawing inspiration from maple seed autorotation, an efficient rotor-wing mode is added on top of a tail-sitter and fixed-wing mode, forming a versatile three-mode UAV which can seamlessly switch modes mid-air, unlike typical hybrid UAVs that only consist of two. This platform achieves efficient hover while retaining the flexibility to enter more agile states.
Validation of Labelling Algorithms for Abstract Argumentation Frameworks: The Case of Listing Stable Extensions

An abstract argumentation framework (af for short) is a pair (A, R) where A is a set of abstract arguments and $$R\subseteq A \times A$$ is the attack relation. Let $$H=(A,R)$$ be an af, $$S \subseteq A$$ be a set of arguments and $$S^+ = \{y \mid \exists x\in S \text { with }(x,y)\in R\}$$ . Then, S is a stable extension in H if and only if $$S^+ = A{\setminus } S$$ . In this paper, we present a thorough, formal validation of a known labelling algorithm for listing all stable extensions in a given af.
Relieving Pixel-Wise Labeling Effort for Pathology Image Segmentation with Self-training

Data scarcity is a common issue when training deep learning models for digital pathology, as large exhaustively-annotated image datasets are difficult to obtain. In this paper, we propose a self-training based approach that can exploit both (few) exhaustively annotated images and (very) sparsely-annotated images to improve the training of deep learning models for image segmentation tasks. The approach is evaluated on three public and one in-house dataset, representing a diverse set of segmentation tasks in digital pathology. The experimental results show that self-training allows to bring significant model improvement by incorporating sparsely annotated images and proves to be a good strategy to relieve labeling effort in the digital pathology domain.
Abstract Images Have Different Levels of Retrievability Per Reverse Image Search Engine

Much computer vision research has focused on natural images, but technical documents typically consist of abstract images, such as charts, drawings, diagrams, and schematics. How well do general web search engines discover abstract images? Recent advancements in computer vision and machine learning have led to the rise of reverse image search engines. Where conventional search engines accept a text query and return a set of document results, including images, a reverse image search accepts an image as a query and returns a set of images as results. This paper evaluates how well common reverse image search engines discover abstract images. We conducted an experiment leveraging images from Wikimedia Commons, a website known to be well indexed by Baidu, Bing, Google, and Yandex. We measure how difficult an image is to find again (retrievability), what percentage of images returned are relevant (precision), and the average number of results a visitor must review before finding the submitted image (mean reciprocal rank). When trying to discover the same image again among similar images, Yandex performs best. When searching for pages containing a specific image, Google and Yandex outperform the others when discovering photographs with precision scores ranging from 0.8191 to 0.8297, respectively. In both of these cases, Google and Yandex perform better with natural images than with abstract ones achieving a difference in retrievability as high as 54% between images in these categories. These results affect anyone applying common web search engines to search for technical documents that use abstract images.