Reviewerly/exHarmony huggingface dataset:
Over‐optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results

In recent years, the need for neutral benchmark studies that focus on the comparison of methods from computational sciences has been increasingly recognised by the scientific community. While general advice on the design and analysis of neutral benchmark studies can be found in recent literature, certain amounts of flexibility always exist. This includes the choice of data sets and performance measures, the handling of missing performance values and the way the performance values are aggregated over the data sets. As a consequence of this flexibility, researchers may be concerned about how their choices affect the results or, in the worst case, may be tempted to engage in questionable research practices (e.g. the selective reporting of results or the post-hoc modification of design or analysis components) to fit their expectations or hopes. To raise awareness for this issue, we use an example benchmark study to illustrate how variable benchmark results can be when all possible combinations of a range of design and analysis options are considered. We then demonstrate how the impact of each choice on the results can be assessed using multidimensional unfolding. In conclusion, based on previous literature and on our illustrative example, we claim that the multiplicity of design and analysis options combined with questionable research practices lead to biased interpretations of benchmark results and to over-optimistic conclusions. This issue should be considered by computational researchers when designing and analysing their benchmark studies and by the scientific community in general in an effort towards more reliable benchmark results.
Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis

Abstract Nowadays, it is important for buyers to know other customer opinions to make informed decisions on buying a product or service. In addition, companies and organizations can exploit customer opinions to improve their products and services. However, the Quintilian bytes of the opinions generated every day cannot be manually read and summarized. Sentiment analysis and opinion mining techniques offer a solution to automatically classify and summarize user opinions. However, current sentiment analysis research is mostly focused on English, with much fewer resources available for other languages like Persian. In our previous work, we developed PerSent, a publicly available sentiment lexicon to facilitate lexicon-based sentiment analysis of texts in the Persian language. However, PerSent-based sentiment analysis approach fails to classify the real-world sentences consisting of idiomatic expressions. Therefore, in this paper, we describe an extension of the PerSent lexicon with more than 1000 idiomatic expressions, along with their polarity, and propose an algorithm to accurately classify Persian text. Comparative experimental results reveal the usefulness of the extended lexicon for sentiment analysis as compared to PerSent lexicon-based sentiment analysis as well as Persian-to-English translation-based approaches. The extended version of the lexicon will be made publicly available.
Feature Selection by Hybrid Binary Ant Lion Optimizer with COVID-19 dataset

Metaheuristic optimization is becoming increasingly popular as a way for addressing complicated issues that are difficult to tackle using standard approaches. The capacity to solve issues that may occur as a result of the interactions of basic information processing units is known as swarm intelligence. Swarm intelligence is a type of artificial intelligence methods, that is based on indirect communications between individual from population. The ant lion optimizer algorithm is recently proposed swarm intelligence algorithm. However, basic ant lion version suffers from some deficiencies which are addressed in this study by performing hybridization with well-known firefly algorithm. Proposed hybrid approach was adapted for solving important feature selection problem from machine learning domain. Hybrid metaheuristics was first validated against 10 UCL datasets and afterwards it was applied to a novel COVID-19 dataset. Moreover, comparative analysis with similar methods tested under the same condition was presented. Obtained experimental results prove the efficiency of proposed hybrid ant lion optimizer for tackling feature selection challenge.
Freire 2.0: Pedagogy of the digitally oppressed

This paper reinvents Freire’s concepts of ‘banking education’ and ‘literacy’ within the context of the exponential growth of digital instruction in the 21st century. We argue that digital learning (i.e. online or technology enhanced) undoubtedly increases access to education globally, but also can intensify some of the worst problems described in Freire’s banking model. Accordingly, we draw from postdigital theory to scrutinize the specific structures and functions of common digital Learning Management Systems (LMSs) used by schools (i.e. Blackboard and Google Classroom) to reveal a type of learning that further exacerbates the teacher-student dichotomy without liberating either party in a Freirean sense. We then use a Foucauldian lens to bring an awareness to how the accelerated use of these systems at scale, in part caused by the COVID-19 pandemic, can further entrench a data-driven, dehumanized educational experience which increases corporate profitability perhaps over the needs of students. Finally, we use these insights to modernize Freire’s concept of ‘literacy’ by building on Critical Medial Literacy (CML) in order to help educators address LMSs, (mis)information facilitated by digital content, and schooling in a (post)pandemic and postdigital world.
A systematic review and meta-analysis on the use of tactile stimulation in vection research

Vection is classically defined as the illusory perception of self-motion induced via visual stimuli. The utility of vection research lies in its potential to enhance simulation fidelity, as measured through presence, and reduce the probability that motion sickness symptoms occur. Studies have shown a multimodal interaction of various sensory systems in facilitating vection, and the utility of co-stimulating some of these sensory systems along with the presentation of visual stimuli have been reviewed. However, a review on the use of tactile stimulation in vection research appears to be missing from literature. The purpose of this review was to evaluate the current methodologies, and outcomes, of tactile stimulation in vection research. We searched for articles through EBSCOHost, Scopus and Web of Science. Studies were included only if they detailed an experiment on the effect of tactile stimulation on vection. Twenty-four studies were obtained and distilled in tabular form. Eighteen studies contained sufficient information to be included in a meta-analysis. We identified that tactile stimulation has mostly been applied in the form of vibrational stimulation to the feet. Furthermore, tactile stimulation is most effective when it is presented in a temporally congruent manner to other sensory cues, whereas tactile stimulation as a unisensory stimulus does not appear to be effective in eliciting vection. We discuss the need for more qualitative research to reduce methodological inhomogeneities and recommend future research in tactile-mediated vection to investigate stimulation to the torso and investigate the use of forces as a tactile stimulus.
Multi-person multi-camera tracking for live stream videos based on improved motion model and matching cascade

Multiple live videos are streamed to the security room in the video surveillance system of a museum, shopping mall, plaza, etc. For public safety and crowd research, multi-person tracking is essential in these small-scale distributed multi-camera systems. This paper presents a multi-person multi-camera tracking framework for live stream videos. It is compatible with both overlapping and non-overlapping views. Since the tracking framework cannot consistently achieve real-time performance, live stream videos are probably sub-sampled. This sub-sampled issue will enlarge the error of every tracked person’s predicted bounding box at each frame. A time-based motion model studying the precise time intervals among sub-sampled frames is proposed. Besides, cameras have different orientations and exposures, resulting in false matches when tracked pedestrians cross the camera boundaries. When a tracked person is out of a camera’s view, its motion model will fail, and its tracking identity will probably switch when reentering the same camera. An improved multi-person matching cascade scheme is proposed to solve these problems. It can increase the accuracy of inter-camera person re-identification (Re-ID) by taking advantage of association priorities. Experiments are implemented with the EPFL dataset and live stream videos. Results show that the proposed method has robust performances in different situations.
The effect of perceived organizational culture on employees’ information security compliance

Purpose This paper aims to investigate the connection between different perceived organizational cultures and information security policy compliance among white-collar workers. Design/methodology/approach The survey using the Organizational Culture Assessment Instrument was sent to white-collar workers in Sweden ( n = 674), asking about compliance with information security policies. The survey instrument is an operationalization of the Competing Values Framework that distinguishes between four different types of organizational culture: clan, adhocracy, market and bureaucracy. Findings The results indicate that organizational cultures with an internal focus are positively related to employees’ information security policy compliance. Differences in organizational culture with regards to control and flexibility seem to have less effect. The analysis shows that a bureaucratic form of organizational culture is most fruitful for fostering employees’ information security policy compliance. Research limitations/implications The results suggest that differences in organizational culture are important for employees’ information security policy compliance. This justifies further investigating the mechanisms linking organizational culture to information security compliance. Practical implications Practitioners should be aware that the different organizational cultures do matter for employees’ information security compliance. In businesses and the public sector, the authors see a development toward customer orientation and marketization, i.e. the opposite an internal focus, that may have negative ramifications for the information security of organizations. Originality/value Few information security policy compliance studies exist on the consequences of different organizational/information cultures.
Conceptual Framework—Artificial Intelligence and Better Entrepreneurial Decision-Making: The Influence of Customer Preference, Industry Benchmark, and Employee Involvement in an Emerging Market

Purpose: Technology initiatives are now incorporated into a wide range of business domains. The objective of this paper is to explore the possible effects that Artificial intelligence systems have on entrepreneurs’ decision-making, through the mediation of customer preference and industry benchmark. Design/methodology/approach: This is a non-empirical review of the literature and the development of a conceptual model. Searches were conducted in key academic databases, such as Emerald Online Journals, Taylor and Francis Online Journals, JSTOR Online Journals, Elsevier Online Journals, IEEE Xplore, and Directory of Open Access Journals (DOAJ) for papers which focused on Artificial intelligence (AI), Entrepreneurial decision-making, Customer preference, Industry benchmarks, and Employee involvement. In total, 25 articles met the predefined criteria and were used. Findings: The study proposes that Artificial intelligence systems can facilitate better decision-making from the entrepreneurial perspective. In addition, the study demonstrates that employees, as stakeholders, can moderate the relationship between Artificial intelligence systems and better decision-making for entrepreneurs with their involvement. Moreover, the study demonstrates that customer preference and industry benchmark can mediate the relationship between Artificial intelligence systems and better entrepreneur decision-making. Research limitations/implications: The study assumes a perfect ICT environment for the smooth operation of Artificial intelligence systems. However, this might not always be the case. The study does not consider the personal disposition of entrepreneurs in terms of ICT usage and adoption. Practical implications: This study proposes that entrepreneurial decision-making is enriched in an environment of Artificial intelligence systems, which is complemented by customer preference, industry benchmark, and employee involvement. This finding provides entrepreneurs with a possible technological tool for better decision-making, highlighting the endless options offered by Artificial intelligence systems. Social Implications: The introduction of AI in the business decision-making process comes with many social issues in relation to the impact machines have on humans and society. This paper suggests how this new technology should be used without destroying society. Originality/value: This conceptual framework serves as a valuable organizational spectrum for entrepreneurial development. In addition, this study makes a valuable contribution to entrepreneurial development through Artificial intelligence systems.
RNSiM: Efficient Deep Neural Network Accelerator Using Residue Number Systems

In this paper, we propose an efficient convolutional neural network (CNN) accelerator design, entitled RNSiM, based on the Residue Number System (RNS) as an alternative for the conventional binary number representation. Instead of traditional arithmetic implementation that suffers from the inevitable lengthy carry propagation chain, the novelty of RNSiM lies in that all the data, including stored weights and communication/computation, are performed in the RNS domain. Due to the inherent parallelism of the RNS arithmetic, power and latency are significantly reduced. Moreover, an enhanced integrated intermodulo operation core is developed to decrease the overhead imposed by non-modular operations. Further improvement in systems' performance efficiency is achieved by developing efficient Processing-in-Memory (PIM) designs using various volatile CMOS and non-volatile Post-CMOS technologies to accelerate RNS-based multiplication-and-accumulations (MACs). The RN-SiM accelerator's performance on different datasets, including MNIST, SVHN, and CIFAR-10, is evaluated. With almost the same accuracy to the baseline CNN, the RNSiM accelerator can significantly increase both energy-efficiency and speedup compared with the state-of-the-art FPGA, GPU, and PIM designs. RNSiM and other RNS-PIMs, based on our method, reduce the energy consumption by orders of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$28-77\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$331-897\times$</tex> compared with the FPGA and the GPU platforms, respectively.
A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation

Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metaheuristics in 37 out of 48 cases based on cost function evaluation, 26 of 48 cases based on feature similarity index, and 20 of 32 cases based on Dice similarity. The obtained results demonstrate that population-based metaheuristics can be successfully applied to entropy-based image thresholding and that strengthening both exploitation and exploration strategies, as performed in ME-GDEAR, is crucial for designing such an algorithm.
Marvel: A Data-Centric Approach for Mapping Deep Learning Operators on Spatial Accelerators

A spatial accelerator’s efficiency depends heavily on both its mapper and cost models to generate optimized mappings for various operators of DNN models. However, existing cost models lack a formal boundary over their input programs (operators) for accurate and tractable cost analysis of the mappings, and this results in adaptability challenges to the cost models for new operators. We consider the recently introduced Maestro Data-Centric (MDC) notation and its analytical cost model to address this challenge because any mapping expressed in the notation is precisely analyzable using the MDC’s cost model. In this article, we characterize the set of input operators and their mappings expressed in the MDC notation by introducing a set of conformability rules . The outcome of these rules is that any loop nest that is perfectly nested with affine tensor subscripts and without conditionals is conformable to the MDC notation. A majority of the primitive operators in deep learning are such loop nests. In addition, our rules enable us to automatically translate a mapping expressed in the loop nest form to MDC notation and use the MDC’s cost model to guide upstream mappers. Our conformability rules over the input operators result in a structured mapping space of the operators, which enables us to introduce a mapper based on our decoupled off-chip/on-chip approach to accelerate mapping space exploration. Our mapper decomposes the original higher-dimensional mapping space of operators into two lower-dimensional off-chip and on-chip subspaces and then optimizes the off-chip subspace followed by the on-chip subspace. We implemented our overall approach in a tool called Marvel , and a benefit of our approach is that it applies to any operator conformable with the MDC notation. We evaluated Marvel over major DNN operators and compared it with past optimizers.
Comparison of YOLO Versions for Object Detection from Aerial Images

Many different disciplines use deep Learning algorithms for various purposes. In recent years, object detection by deep learning from aerial or terrestrial images has become a popular research area. In this study, object detection application was performed by training the YOLOv2 and YOLOv3 algorithms in the Google Colaboratory cloud service with the help of Python software language with the DOTA dataset consisting of aerial photographs. 43 aerial photographs containing 9 class objects were used for evaluation. Accuracy analyzes of these two algorithms were made according to Recall, Precision and F-score for 9 classes, and the results were compared accordingly. YOLOv2 gave better results in 5 out of 9 classes, while YOLOv3 gave better results in recognizing small objects. While YOLOv2 can detect objects in an average photograph in 43 seconds, YOLOv3 has achieved superior performance in terms of time by detecting objects in an average of 2.5 seconds.
An <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2617" altimg="si276.svg"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math>-overlapping group sparse total variation for impulse noise image restoration

Total variation (TV) based methods are effective models in image restoration. For eliminating impulse noise, an effective way is to use the ℓ1-norm total variation model. However, the TV image restoration always yields staircase artifacts, especially in high-density noise levels. Additionally, the ℓ1-norm tends to over penalize solutions and is not robust to outlier characteristics of impulse noise. In this paper, we propose a new total variation model to effectively remove the staircase effects and eliminate impulse noise. The proposed model uses the ℓ0-norm data fidelity to effectively remove the impulse noise while the overlapping group sparse total variation (OGSTV) acts as a regularizer to eliminate the staircase artifacts. Since the proposed method requires solving an ℓ0-norm and an OGSTV optimization problem, a formulation using the mathematical program with equilibrium constraints (MPEC) and the majorization–minimization (MM) method are respectively used together with the alternating direction method of multipliers (ADMM). Experiments demonstrate that our proposed model performs better than several state-of-the-art algorithms such as the ℓ1 total generalized variation, ℓ0 total variation, and the ℓ1 overlapping group sparse total variation in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM).
Research on the Evaluation Model of Educational Management Theory Based on Data Mining from the Perspective of Neural Network

The improvement of the theoretical quality of education management is an indispensable part of a country’s education modernization. However, the existing research on the evaluation of educational management theory is still relatively small, and there is a lack of scientific educational management theory evaluation model. Designing a comprehensive and accurate educational management theory evaluation model has important theoretical value and practical significance. It is possible to process a lot of information in parallel using the artificial neural network method. By optimizing the artificial neural network, data mining of characteristic information data can be realized. Therefore, this paper uses neural network to conduct data mining on education management theory and conduct a comprehensive system evaluation of education management theory. At the same time, the traditional BP algorithm is improved. To train a neural network with large amounts of data, the BP algorithm uses a lot of gradient calculation, which takes a long time and often results in training going to extremes in the local area. BP neural networks are trained using the particle swarm optimization algorithm, and the backward propagation process in the BP algorithm is replaced with particle swarm iteration. To improve algorithm execution efficiency and speed up neural network training, a large number of gradient operations can be avoided. This can help overcome the limitations of the BP algorithm when dealing with large amounts of data. The improved BP algorithm is applied to the evaluation system of education management theory, and the quality evaluation prediction of management education theory is realized.