Reviewerly/exHarmony huggingface dataset:
Validated tests for language research with university students whose native language is English: Tests of vocabulary, general knowledge, author recognition, and reading comprehension

We present five studies aimed at developing an L1 vocabulary test for English-speaking university students. Such a test is useful as an indicator of crystallized intelligence and because vocabulary size correlates well with reading comprehension. In the first study, we tested 100 written words with four answer alternatives, based on Nation's Vocabulary Size Test. Analysis suggested two factors, which we interpreted as the possible existence of two types of difficult words: unknown words for general knowledge and unknown words for specialized knowledge. In Study 2, we attempted to develop a vocabulary test for each type of word, and these tests were then validated in Study 3. Since the test for general words proved too easy for the target population, we improved it in a fourth study by creating and testing more difficult items. Finally, a fifth study was conducted to validate the new test. Unexpectedly, Study 5 found a high correlation (r = .82) between the general knowledge vocabulary test and the specialized knowledge vocabulary test, suggesting that they measure the same latent factor, contrary to our initial assumption. Both tests have high reliability (r > .85) and correlate well (r > .4) with general knowledge, author recognition, and reading comprehension. In addition, a collection of other language tests was used and improved to verify the validity of the vocabulary tests. An exploratory factor analysis of all tests identified three factors (text comprehension, crystallized intelligence, and reading speed), with the vocabulary tests loading on the factor crystallized intelligence, which in turn correlates with reading comprehension. Structural equation modeling confirmed the interpretation.
A rapid and accurate computer vision system for measuring the volume of axi-symmetric natural products based on cubic spline interpolation

The volume contains prominent information used to evaluate the visual quality of natural products and can be measured using a computer vision system. The computer vision system provides an alternative method for measuring the volume of natural products, especially for axi-symmetric natural products. The volume of axi-symmetric natural products can be obtained using integral for the volume of solid of revolution by rotating the boundary curve of the upper half object cross-section. This study proposes a computer vision system for measuring the volume of axi-symmetric natural products based on cubic spline interpolation. A piecewise cubic polynomial was constructed using cubic spline interpolation to approximate the boundary curve of the upper half object cross-section based on the boundary points of the object extracted from the image of the object. The polynomial was integrated to obtain the volume of the object. The experimental result reveals that the proposed system achieved an accurate volume measurement result with a mean absolute relative error of 1.03% compared to the water displacement method result.
Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation

Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge to solve the cold-start problem in recommender systems. Most of the existing CDR models assume that both the source and target domains share the same overlapped user set for knowledge transfer. However, only few proportion of users simultaneously activate on both the source and target domains in practical CDR tasks. In this paper, we focus on the Partially Overlapped Cross-Domain Recommendation (POCDR) problem, that is, how to leverage the information of both the overlapped and non-overlapped users to improve recommendation performance. Existing approaches cannot fully utilize the useful knowledge behind the non-overlapped users across domains, which limits the model performance when the majority of users turn out to be non-overlapped. To address this issue, we propose an end-to-end Dual-autoencoder with Variational Domain-invariant Embedding Alignment (VDEA) model, a cross-domain recommendation framework for the POCDR problem, which utilizes dual variational autoencoders with both local and global embedding alignment for exploiting domain-invariant user embedding. VDEA first adopts variational inference to capture collaborative user preferences, and then utilizes Gromov-Wasserstein distribution co-clustering optimal transport to cluster the users with similar rating interaction behaviors. Our empirical studies on Douban and Amazon datasets demonstrate that VDEA significantly outperforms the state-of-the-art models, especially under the POCDR setting.
A hybrid-supervision learning algorithm for real-time un-completed face recognition

It is still an important and challenging problem for face recognition with occlusion, small sample size, various expressions, and poses, called un-completed face recognition. So we design a simple but effective hybrid-supervision learning frame by fusing the advantages of supervised and unsupervised features. In the supervised branch, we propose an effective feature learning method: HMMFA. In the unsupervised branch, we improve the PCANet to extract more effective local information. In the fusion stage, we further extract the discriminant features contained in the hybrid features and then take SVM as the final classifier. Because the proposed method requires no auxiliary set and has less parameter number than that of deep learning methods, it has a low storage requirement, which makes it more economical and practical for small communities. Experiments on four databases show the effectiveness and efficiency of our method.
Going Beyond Deterrence: A Middle-Range Theory of Motives and Controls for Insider Computer Abuse

Reports indicate that employees are willing to share sensitive information under certain circumstances, and one-third to half of security breaches are tied to insiders. These statistics reveal that organizational security efforts, which most often rely on deterrence-based sanctions to address the insider threats to information security, are insufficient. Thus, insiders’ computer abuse (ICA)—unauthorized and deliberate misuse of organizational information resources by organizational insiders—remains a significant issue for industry. We present a motive–control theory of ICA that distinguishes among instrumental and expressive motives and internal and external controls. Specifically, we show that organizational deterrents (e.g., sanctions) do not create motives for ICA, but weaken existing motives (e.g., financial benefits). Conversely, financial benefits and psychological contract violations create motives to perform ICA, and insiders’ self-control diminishes the influence of these motives. The implications for practice are threefold: (1) organizations should make efforts to reduce psychological contract breach for employees by increasing the congruence between expectations and reality to reduce expressive motives for ICA; (2) organizations should seek maintain personnel with adequate self-control to diminish the impact of harmful ICA motives should they arise; and (3) organizations should develop targeted sanctions for committing ICA to control the harmful influence of financial motives.
A systematic literature review of requirements engineering education

Requirements engineering (RE) has established itself as a core software engineering discipline. It is well acknowledged that good RE leads to higher quality software and considerably reduces the risk of failure or budget-overspending of software development projects. It is of vital importance to train future software engineers in RE and educate future requirements engineers to adequately manage requirements in various projects. To this date, there exists no central concept of what RE education shall comprise. To lay a foundation, we report on a systematic literature review of the field and provide a systematic map describing the current state of RE education. Doing so allows us to describe how the educational landscape has changed over the last decade. Results show that only a few established author collaborations exist and that RE education research is predominantly published in venues other than the top RE research venues (i.e., in venues other than the RE conference and journal). Key trends in RE instruction of the past decade include involvement of real or realistic stakeholders, teaching predominantly elicitation as an RE activity, and increasing student factors such as motivation or communication skills. Finally, we discuss open opportunities in RE education, such as training for security requirements and supply chain risk management, as well as developing a pedagogical foundation grounded in evidence of effective instructional approaches.
HELCFL: High-Efficiency and Low-Cost Federated Learning in Heterogeneous Mobile-Edge Computing

Federated Learning (FL), an emerging distributed machine learning (ML), empowers a large number of embedded devices (e.g., phones and cameras) and a server to jointly train a global ML model without centralizing user private data on a server. However, when deploying FL in a mobile-edge computing (MEC) system, restricted communication resources of the MEC system, heterogeneity and constrained energy of user devices have a severe impact on FL training efficiency. To address these issues, in this article, we design a distinctive FL framework, called HELCFL, to achieve high-efficiency and low-cost FL training. Specifically, by analyzing the theoretical foundation of FL, our HELCFL first develops a utility-driven and greedy-decay user selection strategy to enhance FL performance and reduce training delay. Subsequently, by analyzing and utilizing the slack time in FL training, our HELCFL introduces a device operating frequency determination approach to reduce training energy costs. Experiments verify that our HELCFL can enhance the highest accuracy by up to 43.45 %, realize the training speedup of up to 275.03%, and save up to 58.25% training energy costs compared to state-of-the-art baselines.
A comparative analysis of transformer based models for figurative language classification

Efficient and effective methods are required to construct a model to rapidly extractdifferent sentiments from large volumes of text. To augment the performance of the models, contemporary developments in Natural Language Processing (NLP) have been utilized by researchers to work on several model architecture and pretraining tasks. This work explores several models based on transformer architecture and analyses its performance. In this work, the researchersusea dataset to answer the question of whether or not transformers work significantly well for figurative language and not just literal language classification. The results of various models are compared and have come up as a result of research over time. The studyexplains why it is necessary for computers to understand the occurrence of figurative language, why it is yet a challenge and is being intensively worked on to date, and how it is different from literal language classification. This research also covers how well these models train on a specific type of figurative language and generalize on a few other similar types.
JGPR: a computationally efficient multi-target Gaussian process regression algorithm

Multi-target regression algorithms are designed to predict multiple outputs at the same time, and allow us to take all output variables into account during the training phase. Despite the recent advances, this context of machine learning is still an open challenge for developing a low-cost and high accurate algorithm. The main challenge in multi-target regression algorithms is how to use different targets’ information in the training and/or test phases. In this paper, we introduce a low-cost multi-target Gaussian process regression (GPR) algorithm, called joint GPR (JGPR) that employs a shared covariance matrix among the targets during the training phase and solves a sub-optimal cost function for optimization of hyperparameters. The proposed strategy reduces the computational complexity considerably during the training and test phases and simultaneously avoids overfitting of the multi-target regression algorithm upon the targets. We have performed extensive experiments on both simulated data and 18 benchmark datasets to assess the proposed method compared with other multi-target regression algorithms. Experimental results show that the proposed JGPR outperforms the state-of-the-art approaches on most of the given benchmark datasets.
COVID-19 malicious domain names classification

Due to the rapid technological advances that have been made over the years, more people are changing their way of living from traditional ways of doing business to those featuring greater use of electronic resources. This transition has attracted (and continues to attract) the attention of cybercriminals, referred to in this article as "attackers", who make use of the structure of the Internet to commit cybercrimes, such as phishing, in order to trick users into revealing sensitive data, including personal information, banking and credit card details, IDs, passwords, and more important information via replicas of legitimate websites of trusted organizations. In our digital society, the COVID-19 pandemic represents an unprecedented situation. As a result, many individuals were left vulnerable to cyberattacks while attempting to gather credible information about this alarming situation. Unfortunately, by taking advantage of this situation, specific attacks associated with the pandemic dramatically increased. Regrettably, cyberattacks do not appear to be abating. For this reason, cyber-security corporations and researchers must constantly develop effective and innovative solutions to tackle this growing issue. Although several anti-phishing approaches are already in use, such as the use of blacklists, visuals, heuristics, and other protective solutions, they cannot efficiently prevent imminent phishing attacks. In this paper, we propose machine learning models that use a limited number of features to classify COVID-19-related domain names as either malicious or legitimate. Our primary results show that a small set of carefully extracted lexical features, from domain names, can allow models to yield high scores; additionally, the number of subdomain levels as a feature can have a large influence on the predictions.
Object detection using depth completion and camera-LiDAR fusion for autonomous driving

Autonomous vehicles are equipped with complimentary sensors to perceive the environment accurately. Deep learning models have proven to be the most effective approach for computer vision problems. Therefore, in autonomous driving, it is essential to design reliable networks to fuse data from different sensors. In this work, we develop a novel data fusion architecture using camera and LiDAR data for object detection in autonomous driving. Given the sparsity of LiDAR data, developing multi-modal fusion models is a challenging task. Our proposal integrates an efficient LiDAR sparse-to-dense completion network into the pipeline of object detection models, achieving a more robust performance at different times of the day. The Waymo Open Dataset has been used for the experimental study, which is the most diverse detection benchmark in terms of weather and lighting conditions. The depth completion network is trained with the KITTI depth dataset, and transfer learning is used to obtain dense maps on Waymo. With the enhanced LiDAR data and the camera images, we explore early and middle fusion approaches using popular object detection models. The proposed data fusion network provides a significant improvement compared to single-modal detection at all times of the day, and outperforms previous approaches that upsample depth maps with classical image processing algorithms. Our multi-modal and multi-source approach achieves a 1.5, 7.5, and 2.1 mean AP increase at day, night, and dawn/dusk, respectively, using four different object detection meta-architectures.
coxHE: A software-hardware co-design framework for FPGA acceleration of homomorphic computation

Data privacy becomes a crucial concern in the AI and big data era. Fully homomorphic encryption (FHE) is a promising data privacy protection technique where the entire computation is performed on encrypted data. However, the dramatic increase of the computation workload restrains the usage of FHE for the real-world applications. In this paper, we propose an FPFA accelerator design framework for CKKS-based HE. While the KeySwitch operations are the primary performance bottleneck of FHE computation, we propose a low latency design of KeySwitch module with reduced intra-operation data dependency. Compared with the state-of-the-art FPGA based key-switch implementation that is based on Verilog, the proposed high-level synthesis (HLS) based design reduces the operation latency by 40%. Furthermore, we propose an automated design space exploration framework which generates optimal encryption parameters and accelerators for a given application kernel and the target FPGA device. Experimental results for a set of real HE application kernels on different FPGA devices show that our HLS-based flexible design framework produces substantially better accelerator design compared with a fixed-parameter HE accelerator in terms of security, approximation error, and overall performance.
Do FDI Inflows and ICT Affect Economic Growth? An Evidence from Arab Countries

This article aims to examine the dynamic relationships between foreign direct investment inflows, information and communication technologies, and economic growth in a sample of 15 Arab countries over the period 1995–2019 by employing a panel ARDL approach. The results of estimation of the panel ARDL model reveal that ICT and FDI have positive and significant effects on economic growth in the long run, and ICT indicators have also positive impact on FDI inflows in the long run in the selected sample of Arab countries. From an empirical point of view, this study may have an important contribution. Its findings could be very interesting for better future management of ICT in Arab countries. Therefore, the Arab countries should further improve information and communication technology as an important infrastructure for receiving more foreign direct investment inflows and for better economic growth.
An English Blended Teaching Model under the Background of Education Informatization

The continuous development of information technology (IT) has brought great changes in people’s lives and the education sector has also gone through significant reforms because of the advancement of IT, in the recent years. With the improvement of education informatization, the traditional teaching systems cannot fulfill the demands of talent in the modern era. The rapid growth of educational informatization has introduced new requirements for the teaching methods and techniques. The development of new teaching modes that integrates the Internet, big data, and traditional teaching methods has become a major focus of the modern education. Computer system, particularly IT, is playing a key role in the education sector and is very essential for the development of students’ educational and professional careers/skills. At the same time, under the background of education informatization, the development of IT based hybrid teaching modes has attained promising teaching results. Keeping the significance of computer information in the field of education in consideration, this study first analyzes the current situation of English blended teaching in the context of education informatization and then explains the shortcomings of the traditional computer blended teaching models. In this study, I investigate the aims and content of comprehensive English courses using the questionnaire research method to identify the students’ learning requirements. Finally, this paper puts the way forward to implement the English blended teaching model in the context of education informatization and then improve the quality of teaching and promote students’ comprehensive computer ability. According to the results of this study, almost 100% of teachers and students hope that the platform supports the uploading of learning materials and assignments and also has the functions of discussion and communication. The proposed model presents a novel and improved method for the education sector and is expected that the proposed model will help the students and teaching staff to learn/teach in an effective manner.
Hyperspectral Data Compression Using Fully Convolutional Autoencoder

In space science and satellite imagery, better resolution of the data information obtained makes images clearer and interpretation more accurate. However, the huge data volume gained by the complex on-board satellite instruments becomes a problem that needs to be managed carefully. To reduce the data volume to be stored and transmitted on-ground, the signals received should be compressed, allowing a good original source representation in the reconstruction step. Image compression covers a key role in space science and satellite imagery and, recently, deep learning models have achieved remarkable results in computer vision. In this paper, we propose a spectral signals compressor network based on deep convolutional autoencoder (SSCNet) and we conduct experiments over multi/hyperspectral and RGB datasets reporting improvements over all baselines used as benchmarks and than the JPEG family algorithm. Experimental results demonstrate the effectiveness in the compression ratio and spectral signal reconstruction and the robustness with a data type greater than 8 bits, clearly exhibiting better results using the PSNR, SSIM, and MS-SSIM evaluation criteria.
A novel human activity recognition architecture: using residual inception ConvLSTM layer

Abstract Human activity recognition (HAR) is a very challenging problem that requires identifying an activity performed by a single individual or a group of people observed from spatiotemporal data. Many computer vision applications require a solution to HAR. To name a few, surveillance systems, medical and health care monitoring applications, and smart home assistant devices. The rapid development of machine learning leads to a great advance in HAR solutions. One of these solutions is using ConvLSTM architecture. ConvLSTM architectures have recently been used in many spatiotemporal computer vision applications.In this paper, we introduce a new layer, residual inception convolutional recurrent layer, ResIncConvLSTM, a variation of ConvLSTM layer. Also, a novel architecture to solve HAR using the introduced layer is proposed. Our proposed architecture resulted in an accuracy improvement by 7% from ConvLSTM baseline architecture. The comparisons are held in terms of classification accuracy. The architectures are trained using KTH dataset and tested against both KTH and Weizmann datasets. The architectures are also trained and tested against a subset of UCF Sports Action dataset. Also, experimental results show the effectiveness of our proposed architecture compared to other state-of-the-art architectures.