Reviewerly/exHarmony huggingface dataset:
MOOCs in Blended English Teaching and Learning for Students of Technical Curricula

New educational standards of higher education and requirements for learning outcomes in non-linguistic universities require new approaches to the educational process. Modern realities lead to the development of effective teaching methods and the implementation of urgent information and communication technologies in the educational process of higher education institutions. This article explores the possibilities of using massive open online courses in blended teaching and learning of foreign language (English) for students majoring in engineering. The criteria and methods for selecting massive open online courses for integration into educational process of English classroom at tertiary level are presented. The comparison of the subject course content in foreign language and content of the Computer Science curricula is demonstrated. The model of face-to-face teaching and learning with the implementation of massive open online course in the framework of the discipline "Professional Foreign Language" is given. It is concluded that massive open online courses have positive opportunities for the development of students' foreign language professional competence while using them in blended teaching and learning activities.
A quasi-experiment on using guided mobile learning interventions in ESL classrooms: Time use and academic performance

This quasi-experimental study examined a guided learning approach towards the use of mobile devices and investigated the performance of language learners who were guided in the usage. A total of 419 students from two faculties were invited to participate in this 8-week intervention, 155 participants in the control group and 264 in the experimental group. In the experimental group, the researchers incorporated guided activities Module Intervention Model (MIM) using mobile devices into the ESL lessons whereas the control group lessons were without guided activities. Participants from both groups were asked to record their daily mobile device use for activities related to English language learning using an online form. These data were compared to the results of the tests conducted pre- and post-treatment. At the end of the study, students who received guided language activities utilizing the mobile devices had significantly higher levels of language performance than control group students. However, treatment group students who spent more time using mobile devices to learn the language did not display better performance compared to those who spent minimal time. This finding re-established the importance of guided activities as intervention to facilitate students' learning and points to the need for curricular modernization and faculty development in the instructional use of technology. Due to the increased need for online instruction precipitated by "social distancing and isolation' required to overcome the coronavirus pandemic, the need for faculty to acquire skills in guided use of mobile devices for school-related learning is anticipated to be greater than ever.
Improved Discrete Gaussian and Subgaussian Analysis for Lattice Cryptography

Discrete Gaussian distributions over lattices are central to lattice-based cryptography, and to the computational and mathematical aspects of lattices more broadly. The literature contains a wealth of useful theorems about the behavior of discrete Gaussians under convolutions and related operations. Yet despite their structural similarities, most of these theorems are formally incomparable, and their proofs tend to be monolithic and written nearly "from scratch," making them unnecessarily hard to verify, understand, and extend. In this work we present a modular framework for analyzing linear operations on discrete Gaussian distributions. The framework abstracts away the particulars of Gaussians, and usually reduces proofs to the choice of appropriate linear transformations and elementary linear algebra. To showcase the approach, we establish several general properties of discrete Gaussians, and show how to obtain all prior convolution theorems (along with some new ones) as straightforward corollaries. As another application, we describe a self-reduction for Learning With Errors (LWE) that uses a fixed number of samples to generate an unlimited number of additional ones (having somewhat larger error). The distinguishing features of our reduction are its simple analysis in our framework, and its exclusive use of discrete Gaussians without any loss in parameters relative to a prior mixed discrete-and-continuous approach. As a contribution of independent interest, for subgaussian random matrices we prove a singular value concentration bound with explicitly stated constants, and we give tighter heuristics for specific distributions that are commonly used for generating lattice trapdoors. These bounds yield improvements in the concrete bit-security estimates for trapdoor lattice cryptosystems.
Framework Study for Agile Software Development Via Scrum and Kanban

This paper provides a systematic comparison between two well-known Agile methodologies: Scrum, which is a framework of doing projects by allocating tasks into small stages called sprints, and Kanban, which is a scheduling system to manage the flow of work by means of visual signals. In this regard, both methodologies were reviewed to explore similarities and differences between them. Then, a focus group survey was performed to specify the preferable methodology for product development according to various parameters in the project environment including project complexity, level of uncertainty, and work size with consideration of output factors like quality, productivity, and delivery. Results show the flexibility of both methodologies in approaching Agile objectives, where Scrum emphasizes on the corporation of the customer and development teams with a focus on particular skills such as planning, organization, presentation, and reviewing which makes it ideal for new and complex projects where a regular involvement of the customer is required, whereas Kanban is more operative in continuous-flow environments with a steady approach toward a system improvement.
Minimal linear codes from Maiorana-McFarland functions

Minimal linear codes have important applications in secret sharing schemes and secure multi-party computation, etc. In this paper, we study the minimality of a kind of linear codes over GF(p) from Maiorana-McFarland functions. We first obtain a new sufficient condition for this kind of linear codes to be minimal without analyzing the weights of its codewords, which is a generalization of some works given by Ding et al. in 2015. Using this condition, it is easy to verify that such minimal linear codes satisfy wminwmax≤p−1p for any prime p, where wmin and wmax denote the minimum and maximum nonzero weights in a code, respectively. Then, by selecting the subsets of GF(p)s, we present two new infinite families of minimal linear codes with wminwmax≤p−1p for any prime p. In addition, the weight distributions of the presented linear codes are determined in terms of Krawtchouk polynomials or partial spreads.
Stepwise PathNet: a layer-by-layer knowledge-selection-based transfer learning algorithm

Abstract Some neural network can be trained by transfer learning, which uses a pre-trained neural network as the source task, for a small target task’s dataset. The performance of the transfer learning depends on the knowledge (i.e., layers) selected from the pre-trained network. At present, this knowledge is usually chosen by humans. The transfer learning method PathNet automatically selects pre-trained modules or adjustable modules in a modular neural network. However, PathNet requires modular neural networks as the pre-trained networks, therefore non-modular pre-trained neural networks are currently unavailable. Consequently, PathNet limits the versatility of the network structure. To address this limitation, we propose Stepwise PathNet, which regards the layers of a non-modular pre-trained neural network as the module in PathNet and selects the layers automatically through training. In an experimental validation of transfer learning from InceptionV3 pre-trained on the ImageNet dataset to networks trained on three other datasets (CIFAR-100, SVHN and Food-101), Stepwise PathNet was up to 8% and 10% more accurate than finely tuned and from-scratch approaches, respectively. Also, some of the selected layers were not supported by the layer functions assumed in PathNet.
Video trajectory analysis using unsupervised clustering and multi-criteria ranking

Abstract Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead.
Religious Nationalism and the Coronavirus Pandemic: Soul-Sucking Evangelicals and Branch Covidians Make America Sick Again

This article investigates the response to the coronavirus crisis by Evangelical Christian nationalists in the USA. The article outlines the curious mediaverse of religious nationalism—its post-truth and fake news aspects in particular—links religious nationalism to American exceptionalism, and analyzes conflicts between secular and religious authorities. Drawing upon some lessons from the past, the article addresses the wider implications of Christian nationalism on American politics, and capitalist ideology, as it has been played out virally in the corporate media. The article shows that the ideological underpinnings of evangelical Christianity prevent its proponents from understanding the virus in an historical and materialist manner and points toward more epistemically sound approaches to relationships between science and religion. It concludes that privatization, austerity capitalism, and 'gig economy' need to be replaced by socialist alternatives and seeks inspiration in theory and practice of Marxism and South American liberation theology.
Reliability assessment of tissue classification algorithms for multi-center and multi-scanner data

Gray and white matter volume difference and change are important imaging markers of pathology and disease progression in neurology and psychiatry. Such measures are usually estimated from tissue segmentation maps produced by publicly available image processing pipelines. However, the reliability of the produced segmentations when using multi-center and multi-scanner data remains understudied. Here, we assess the robustness of six publicly available tissue classification pipelines across images acquired from different MR scanners and sites. We used 90 T1-weighted images of a single individual, scanned in 73 sessions across 27 different sites to assess the robustness of the tissue classification tools. Variability in Dice similarity index values and tissue volumes was assessed for Atropos, BISON, Classify_Clean, FAST, FreeSurfer, and SPM12. BISON had the highest overall Dice coefficient for GM, followed by SPM12 and Atropos; while Atropos had the highest overall Dice coefficient for WM, followed by BISON and SPM12. BISON had the lowest overall variability in its volumetric estimates, followed by FreeSurfer, and SPM12. All methods also had significant differences between some of their estimates across different scanner manufacturers (e.g. BISON had significantly higher GM estimates and correspondingly lower WM estimates for GE scans compared to Philips and Siemens), and different signal-to-noise ratio (SNR) levels (e.g. FAST and FreeSurfer had significantly higher WM volume estimates for high versus medium and low SNR tertiles as well as correspondingly lower GM volume estimates). Our comparisons provide a benchmark on the reliability of the publicly used tissue classification techniques and the amount of variability that can be expected when using large multi-center and multi-scanner databases.
Sense of Presence in MaxWhere Virtual Reality

Presence is a psychological phenomenon, the sense of being in a virtual environment. Experiencing presence is not limited to the users of full immersive virtual realities, but it can be experienced also in desktop virtual realities. These VRs visualize the simulated 3D environment on two-dimensional displays. MaxWhere is a desktop virtual environment for education, learning and working. It can be described as a 3D browser because any web-page could be loaded on the predefined SmartBoards of the virtual space. Already many research has been done on the use of this software, but this is the first time when the sense of presence is considered. The study aimed to measure the sense of presence in MaxWhere virtual reality and investigate its relationship with individual navigational experience and prior knowledge of the VR. Navigational experience is how much the user feels natural and automatic the movements in the virtual space. The results showed that better navigational experience correlates with stronger sense of spatial presence. Furthermore, the more experienced users reported higher sense of presence and gave higher rating on experienced realism subscale.
Identifying financial statement fraud with decision rules obtained from Modified Random Forest

Purpose Financial statement fraud (FSF) committed by companies implies the current status of the companies may not be healthy. As such, it is important to detect FSF, since such companies tend to conceal bad information, which causes a great loss to various stakeholders. Thus, the objective of the paper is to propose a novel approach to building a classification model to identify FSF, which shows high classification performance and from which human-readable rules are extracted to explain why a company is likely to commit FSF. Design/methodology/approach Having prepared multiple sub-datasets to cope with class imbalance problem, we build a set of decision trees for each sub-dataset; select a subset of the set as a model for the sub-dataset by removing the tree, each of whose performance is less than the average accuracy of all trees in the set; and then select one such model which shows the best accuracy among the models. We call the resulting model MRF (Modified Random Forest). Given a new instance, we extract rules from the MRF model to explain whether the company corresponding to the new instance is likely to commit FSF or not. Findings Experimental results show that MRF classifier outperformed the benchmark models. The results also revealed that all the variables related to profit belong to the set of the most important indicators to FSF and that two new variables related to gross profit which were unapprised in previous studies on FSF were identified. Originality/value This study proposed a method of building a classification model which shows the outstanding performance and provides decision rules that can be used to explain the classification results. In addition, a new way to resolve the class imbalance problem was suggested in this paper.
Within-System and Cross-System Behavior-Based Biometric Authentication in Virtual Reality

In this paper, we perform the first analysis of behavior-based authentication within and across multiple virtual reality (VR) systems. The proliferation of VR in everyday applications such as healthcare, manufacturing, education, and remote teleoperation necessitates an understanding of operation over multiple VR systems as it is likely that a user will utilize several systems and requiring the user to re-train to a new system is time consuming. We collect the first multi-system dataset for VR biometrics consisting of 46 users performing a ball-throwing interaction using the Oculus Quest, HTC Vive, and HTC Vive Cosmos. Each user provides 10 training samples on the first day and 10 test samples on the second day for each of the three devices. We collect position and orientation data for devices such as the headset, left hand controller, and right hand controller, as well as trigger positions for the dominant hand controller. Our approach uses pairwise matches between trajectory features to represent high intra-user consistency and inter-user discriminative capacity. We analyze within-system and cross-system authentication accuracy for the 41 right-handed users in our 46-user dataset over varying combinations of matches for features such as point positions, device orientations, linear and angular velocities, and trigger grab or release for the right hand controller, left hand controller, and headset. Our approach provides maximum within-system authentication accuracy of 97%, 91%, and 91% when test trajectories are compared to training trajectories for the Vive, Quest, and Cosmos respectively. We provide maximum accuracies of 58% for testing with Cosmos and training with Quest, 70% for testing with Cosmos and training with Vive, and 85% for testing with Quest and training with Vive.
Practical Attacks on Deep Neural Networks by Memory Trojaning

Deep neural network (DNN) accelerators are widely deployed in computer vision, speech recognition, and machine translation applications, in which attacks on DNNs have become a growing concern. This article focuses on exploring the implications of hardware Trojan attacks on DNNs. Trojans are one of the most challenging threat models in hardware security where adversaries insert malicious modifications to the original integrated circuits (ICs), leading to malfunction once being triggered. Such attacks can be conducted by adversaries because modern ICs commonly include third-party intellectual property (IP) blocks. Previous studies design hardware Trojans to attack DNNs with the assumption that adversaries have full knowledge or manipulation of the DNN systems' victim model and toolchain in addition to the hardware platforms, yet such a threat model is strict, limiting their practical adoption. In this article, we propose a memory Trojan methodology that implants the malicious logics merely into the memory controllers of DNN systems without the necessity of toolchain manipulation or accessing to the victim model and thus is feasible for practical uses. Specifically, we locate the input image data among the massive volume of memory traffics based on memory access patterns and propose a Trojan trigger mechanism based on detecting the geometric feature in input images. Extensive experiments show that the proposed trigger mechanism is effective even in the presence of environmental noises and preprocessing operations. Furthermore, we design and implement the payload and verify that the proposed Trojan technique can effectively conduct both untargeted and targeted attacks on DNNs.
GMTA: A Geo-Aware Multi-Agent Task Allocation Approach for Scientific Workflows in Container-Based Cloud

Scientific workflow scheduling is one of the most challenging problems in cloud computing because of the large-scale computing tasks and massive data volumes involved. A cloud system is a distributed system that follows the on-demand resource provisioning and pay-per-use billing model. Therefore, practical scheduling approaches are essential for good workflow performance and low overheads. This paper proposes a novel workflow allocation approach, the Geo-aware Multiagent Task Allocation Approach (GMTA), which aims to optimize large-scale scientific workflow execution in container-based clouds. GMTA is an agent-based workflow allocation method that includes a market-like agent negotiation mechanism and a dynamic workflow restructuring strategy. It decreases workflow makespans and traffic overheads by reasonable task replications. Furthermore, the performance of GMTA is verified on real scientific workflows in the CloudSim environment.
Searching Better Architectures for Neural Machine Translation

Neural architecture search (NAS) has played important roles in the evolution of neural architectures. However, no much attention has been paid to improve neural machine translation (NMT) through NAS approaches. In this work, we propose a gradient-based NAS algorithm for NMT, which automatically discovers architectures with better performances. Compared with previous NAS work, we jointly search the network operations (e.g., LSTM, CNN, self-attention etc) as well as dropout rates to ensure better results. We show that with reasonable resources it is possible to discover novel neural network architectures for NMT, which achieve consistently better performances than Transformer [1], the state-of-the-art NMT model, across different tasks. On WMT'14 English-to-German translation, IWSLT'14 German-to-English translation and WMT'18 Finnish-to-English translation tasks, our discovered architectures could obtain 30.1, 36.1 and 26.4 BLEU scores, which are great improvement over Transformer baselines. We also empirically verify that the discovered model on one task can be transferred to other tasks.
A robust white-light interference signal leakage sampling correction method based on wavelet transform

White-light scanning interferometry has become a well-established tool for measuring the microscopic three-dimensional morphology of objects. However, it is sensitive to external disturbances and suffers from leakage sampling, which compromises the accuracy of the measurements. To avoid the error, a robust correction method is proposed, based on wavelet transform, to find leakage sampling point (LSP), and the cubic spline interpolation method is used to complete the interference signal to achieve the signal correction. Further, phase evaluation at each pixel provides a high vertical resolution to characterize the microstructure. In addition, the proposed method exhibits a good denoising performance. Simulations and comparison experiments demonstrate the effectiveness of the proposed method.
Convolutional Neural Network Framework for Encrypted Image Classification in Cloud-Based ITS

Internet of Things (IoT) and Cloud Computing (CC) technologies are becoming critical requirements to the advancement of intelligent transportation systems (ITSs).ITSs generally rely on captured images to evaluate the status of traffic and perform vehicle statistics.However, such images may contain confidential information, and thus, securing such images is paramount.Therefore, we propose in this paper an efficient framework for improving the security of CC-IoT based ITSs.The proposed framework allows extracting particular vehicle information without revealing any sensitive information.Towards this goal, a convolutional neural network is used to classify encrypted images, based on the vehicle type in real-time, obtained by cameras integrated into road-side units that are part of an ITS leaving sensitive information in all images hidden.Within the proposed framework, we develop a new image classification architecture that never fully decrypts the captured images, thus protecting drivers' personal information, such as location, license plate, and vehicle contents.In addition, the system does not require a fully decrypted image, which increases the system computational efficiency as compared to conventional systems.The obtained results show that the proposed partial decryption classification technique presents up to 18% reduction in average computational complexity when compared with a fully decrypted system.