Reviewerly/exHarmony huggingface dataset:
Lower Secondary Students’ Arabic Speaking Anxiety: A Foreign Language Literacy Perspective

Speaking in a foreign language classroom can be extremely anxiety-provoking for some students. There are many studies on foreign language anxiety (FLA). The studies show that anxiety influences achievement and performance in speaking. However, most of the previous studies have investigated FLA in English-speaking contexts. Hence, this study aims to investigate speaking anxiety among Arabic Language learners in secondary schools from the Federal Territory of Kuala Lumpur, Malaysia. The students (n=40) were selected following the random sampling method. The established questionnaire of Foreign Language Classroom Anxiety Scale (FLCAS) developed by Horwitz et al. (1986) was used for collecting the data. This questionnaire consists of four factors namely test anxiety, communication apprehension, anxiety in the classroom and fear of negative evaluation. The researcher followed descriptive statistical methods including means and standard deviation values to address the research questions. The study shows that the process of learning the Arabic Language has significant impact on students in learning a foreign language, especially in daily secondary schools. The findings show several concerns exist among the students while speaking Arabic in the classroom. The results can help Arabic Language teachers to understand students’ anxiety in the process of teaching the language in the daily/non-boarding secondary schools which will contribute to literacy of Arabic Language at large.
SVM Classification for Novel Time Domain IMU and EMG fused features for control of 6-DOF industrial robot

Gesture recognition is an up and coming field with applications in the field of biomedical engineering, human computer interaction and other fields. Electro myogram sensors (EMG) and inertial measurement units (IMU) are often used to combine the vital information necessary for gesture recognition. This study provides a novel method to access the time domain features of IMU sensors and then fuses this information with the time domain features of the EMG sensors. Although various classification techniques are used to this end. This research uses the gesture recognized to control a virtual robot. This study presents the design of sensory system and collection of data. This study also deals with calculation of features for both EMG and IMU time series. This study visualizes the class separability using various visualization tools. The following classification methods are applied on these features, support vector machines (SVM)The results of these different methods are compared based on accuracy, precision, recall, f1-scores and ROC curves and area under ROC curves for each class of gestures. Finally, a JACO robot is controlled using the gestures in a virtual environment.
Generating Adversarial Texts for Recurrent Neural Networks

Adversarial examples have received increasing attention recently due to their significant values in evaluating and improving the robustness of deep neural networks. Existing adversarial attack algorithms have achieved good result for most images. However, those algorithms cannot be directly applied to texts as the text data is discrete in nature. In this paper, we extend two state-of-the-art attack algorithms, PGD and C&W, to craft adversarial text examples for RNN-based models. For Extend-PGD attack, it identifies the words that are important for classification by computing the Jacobian matrix of the classifier, to effectively generate adversarial text examples. For Extend-C&W attack, it utilizes \(\mathcal {L}_{1}\) regularization to minimize the alteration of the original input text. We conduct comparison experiments on two recurrent neural networks trained for classifying texts in two real-world datasets. Experimental results show that our Extend-PGD and Extend-C&W attack algorithms have advantages of attack success rate and semantics-preserving ability, respectively.
Comparing Machine Learning Algorithms to Predict Topic Keywords of Student Comments

Student comments as a kind of online teaching feedback in higher education organizations are becoming important which provides the evidence to improve the quality of teaching and learning. Effectively extracting useful information from the comments is critical. On the other hand, machine learning algorithms have achieved great performance in automatically extracting information and making predictions. This research compared the performance of three statistical machine learning algorithms and two deep learning methods on topic keyword extraction.
Traceback: Learning to Identify Website’s Landing URLs via Noisy Web Traces Passively

The current web has become a platform where different web resources are combined together. These resources span different URLs and often involve malicious and sensitive content or advertisements (ads). Much of the content is dynamically generated. Thus, diagnosing these complex HTTP URLs hosted on which website is a daunting challenge. Although many tracing methods exist, they are typically designed for specific kinds of websites. There is currently no tool for reconstructing a comprehensive view of identifying landing URLs which are requested by users from noisy URLs automatically fired by browsers. In this paper, we propose Traceback, a tracing framework that provides such a comprehensive view. We build per-user and multi-user chains from passively collecting traffic. Then we extract novel statistical features from graph structures, HTTP states, and semantics. We demonstrate that our methodology is very effective in accurately identifying landing URLs, with recall and precision values up to 95% and over 94% by cross-validation experiments on Random Forest in a real local area environment.
Paradigm Shift in Remote Eye Gaze Tracking Research: Highlights on Past and Recent Progress

Over the last two decades, researchers have demonstrated the potentials in using eye gaze data for various tasks across fields of human endeavor. In this light, Remote Eye Gaze Tracking Systems (REGTs) rose to popularity with the presentation of several hardware and software modules intended for the Point of Gaze (POG) estimation task. This paper presents a paradigm shift in REGTs research from past to recent progress point of view. Our findings and discussions are focused on hardware, software, and application areas of REGTs, which we hope will benefit current and future researchers in this field.
Resilient Machine Learning (rML) Ensemble Against Adversarial Machine Learning Attacks

Machine Learning (ML) algorithms have been widely used in many critical applications, including Dynamic Data Driven Applications Systems (DDDAS) applications, automated financial trading applications, autonomous vehicles, and intrusion detection systems for the decision-making process of users or automated systems. However, malicious adversaries have strong interests in manipulation the operations of machine learning algorithms to achieve their objectives in gaining financially, injecting injury or disasters. Adversaries against ML can be classified based on their capabilities and goals into two types: Adversary who has full knowledge of the ML models and parameters (white-box scenario) and one that does not have any knowledge and use guessing techniques to figure out the ML model and its parameters (black-box scenario). In both scenarios, the adversaries will attempt to maliciously manipulate model either during training or testing. Defending against these attacks can be successful by following three methods: 1) making the ML model robust to adversary, 2) validating and verifying input, or 3) changing ML architecture. In this paper, we present a resilient machine learning (rML) ensemble against adversarial attacks by dynamically changing the ML architecture and the ML models to be used such that the adversaries have no knowledge about the current ML model being used and consequently stop their attempt to manipulate the ML operations at testing phase. We evaluate the effectiveness of our rML ensemble using the benchmarking, zero-query dataset "DAmageNet" that contains both clean and adversarial image samples. We use three main neural networks in our ensemble that includes VGG16, ResNet-50, and ResNet-101. The experimental results show that our rML can tolerate the adversarial samples and achieve high classification accuracy with small execution time degradation.
Modular-Relatedness for Continual Learning

Deep Neural Network (NN) architectures often achieve super-human performance in many application domains. Recent models are made of up to billions of parameters (e.g. GPT2 and GPT3 for Natural Language Processing) and require massive training resources. How can these models be trained on sequences of tasks without negatively affecting each other? Continual Learning (CL) methods tackle the problem of incrementally updating NN models with new tasks while retaining the performance on previously learned tasks. In this paper, we propose a continual learning (CL) technique that is beneficial to sequential task learners by improving their retained accuracy and reducing catastrophic forgetting. The principal target of our approach is the automatic extraction of modular parts of the neural network (NN) and then estimating the relatedness between the tasks given these modular components. This technique is applicable to the CL family of rehearsal-based (e.g., the Gradient Episodic Memory) approaches where episodic memory is needed. Empirical results demonstrate remarkable performance gain (in terms of robustness to forgetting) for methods such as GEM based on our technique, especially when the memory budget is very limited.
Phrase-Based Statistical Machine Translation of Hindi Poetries into English

Statistical machine translation (SMT) is a variant of machine translation where the translations are handled with statistically defined rules. Numerous researchers have attempted to build the framework which can comprehend the different dialects to translate from one source language to another target language. However, the focus on the translation of poetry is less. Reliable and rapid transliteration of the poetry is very mandatory for the execution of the computer to translate the poem from one language to another. The existing approach has several issues such as time consumption, quality of the translation process, and matching of similar words. To overcome these issues, we propose a phrase-based statistical machine translation (PSMT) with special adherence to word sense disambiguation (WSD). Quality of the translation is increased by sensing the ambiguous words with WSD. The Hindi WordNet along with the Lesk algorithm identifies the ambiguous words and senses the exact meaning before the phrase extraction. Finally, the proposed method is compared with machine translation schemes such as rule-based machine translation and transfer-based machine translation. The experimental results suggest that the proposed method performed well with the inclusion of WSD in the PSMT technique.
Innovative Application of IoT Technologies to Improve Geotechnical Monitoring Tools and Early Warning Performances

The integration of Internet-of-Things principles, giving the possibility to connect different physical sensors through a communication network, is becoming increasingly important in several fields of application. Within the geotechnical framework, one of most productive application of the IoT approach is related to monitoring systems, which can significantly benefit from the possibility to exploit more proficient and reliable connections between sensors, control units and visualization devices. In particular, these advantages allow for the development of advanced Early Warning Systems (EWS) able to communicate with high efficiency the occurrence of a potentially critical event, getting even closer to a real-time approach. An innovative application of IoT technologies to geotechnical monitoring is presented, involving the integration of modern devices with data loggers that can be controlled from remote, dedicated software implementing machine-learning algorithms, and web-based visualization platforms. The result of these components integration is a fully automated and network-connected system, defined Internet of Natural Hazards (IoNH). One application deals with the installation of a mechanical trigger on a rockfall barrier, in order to detect an impact on the structure and consequently read all installed sensors, alerting those responsible of the monitoring activity. Another case study includes the monitoring of a building with tilt-based sensors, automatically identifying the overcoming of predefined alert thresholds. The software is able to disseminate automatically a message in case of an overcoming of these levels and to activate different alarm devices (e.g. sirens, traffic lights, etc.).
Cognitive diagnosis models and automated test assembly: an approach incorporating response times

Cognitive diagnosis models (CDMs) have been studied as a means of providing detailed diagnostic information about the skills that have been mastered, and the skills that have not, by examinees. Prior research has examined the use of automated test assembly (ATA) alongside CDMs; however, no previous study has investigated how to perform ATA when a CDM is employed and the total amount of time taken by the test must be controlled. The purpose of the current research was to develop an ATA procedure to select tests that are highly informative while simultaneously satisfying constraints on key parameters related to the total-time distribution. In a simulation study, the procedure successfully selected tests that met these dual goals.
Collection Development in the Field of Business Communication: Citation Analysis Based Guidance

This study analyzes citations from The International Journal of Business Communication to reveal characteristics of the interdisciplinarity of the field of business communication and provide insight and guidance for collection development in this area. A list of most cited journals and books is provided. Subject analysis is included to determine discipline distribution, and collection development recommendations are made. A snapshot analysis is undertaken to determine what percentage of top cited journals are available with full-text at present in commonly-held subscription databases. An annotated bibliography of a selection of more cited and/or seminal books is provided, of which some may lie outside the typical expertise of business and communication librarians.
Leverage Unlabeled Data for Abstractive Speech Summarization with Self-supervised Learning and Back-Summarization

Supervised approaches for Neural Abstractive Summarization require large annotated corpora that are costly to build. We present a French meeting summarization task where reports are predicted based on the automatic transcription of the meeting audio recordings. In order to build a corpus for this task, it is necessary to obtain the (automatic or manual) transcription of each meeting, and then to segment and align it with the corresponding manual report to produce training examples suitable for training. On the other hand, we have access to a very large amount of unaligned data, in particular reports without corresponding transcription. Reports are professionally written and well formatted making pre-processing straightforward. In this context, we study how to take advantage of this massive amount of unaligned data using two approaches (i) self-supervised pre-training using a target-side denoising encoder-decoder model; (ii) back-summarization i.e. reversing the summarization process by learning to predict the transcription given the report, in order to align single reports with generated transcription, and use this synthetic dataset for further training. We report large improvements compared to the previous baseline (trained on aligned data only) for both approaches on two evaluation sets. Moreover, combining the two gives even better results, outperforming the baseline by a large margin of +6 ROUGE-1 and ROUGE-L and +5 ROUGE-2 on two evaluation sets.
Electrolysis Ion Display on Wet Surfaces

We present a novel method to render color patterns using electrolysis applied onto open wet surfaces. By implementing electrodes within a wet object and electrifying them, electrolysis can occur and generate ions. Using color indicators reacting to such ions, we can create a color-forming display. By applying common techniques, such as a printed circuit board, arbitrary patterns can be displayed by computer control. By reversing the polarity of electrodes and varying the number of ions, it is possible to fade the existing pattern and display a contrasting color. The proposed method can be used to create a color-forming display using various objects more common in everyday life than those used in conventional substantial displays.
Research on Digital Image Processing and Recognition Technology of Weeds in Maize Seedling Stage Based on Artificial Intelligence

Abstract In today’s agricultural development, field weeds can be said to be a strong enemy of agricultural production. In the production of traditional agriculture, herbicides and other related pesticides are mainly used to ensure the normal production of crops, which will cost a lot of manpower, material resources, financial resources and a large number of herbicides, and also cause serious environmental pollution. The development prospect of modern agriculture is precision agriculture. How to realize the sustainable development of agriculture, reduce pollution and achieve the goal of precision weeding in the current era is the difficult problem of agricultural development. Therefore, in this paper, the corn seedling stage as a model, the artificial intelligence technology and other electronic technology are integrated into the agricultural management system, and the weeds in the corn seedling stage are identified by using the weed digital image processing and recognition technology, so as to accurately spray herbicides, so as to achieve the purpose of improving agricultural productivity. Through the analysis, it is found that the method proposed in this paper has important practical significance in the development of digital image processing and recognition technology of maize seedling Weeds Based on artificial intelligence technology.
Protecting Enclaves from Intra-Core Side-Channel Attacks through Physical Isolation

Systems that protect enclaves from privileged software must consider software-based side-channel attacks. Our system isolates enclaves on separate secure cores to stop attackers from running on the same core as the victim, which mitigates intra-core side-channel attacks. Redesigning the memory hierarchy based on enclave ownership protects enclaves against inter-core side-channel attacks. We implement this system and evaluate it in terms of communication performance, memory overhead and hardware area. Combining physical isolation and a redesigned memory hierarchy protects enclaves against all known software-based side-channel attacks.
Performance Characterization of Lattice-Based Cryptography Workloads

In the domain of lattice cryptography, an emerging field of post-quantum secure cryptography, very few accelerator designs exist that both sufficiently accelerate a particular lattice cryptosystem and provide enough flexibility to accommodate a variety of cryptographic schemes. In this work, we move towards such a design by analyzing lattice cryptography algorithms with respect to their architectural properties. Through our analysis on an Intel Core i7 processor, we show that key operations such as modulo are essential to realizing an efficient design and should be added to the hardware substrate. We also show that while the algorithms are amenable to SIMD (single instruction multiple data) hardware acceleration, they may be limited to ~64-lane designs before diminishing returns.
Recognition method of outdoor design scene based on support vector machine and feature fusion

Based on the impact of epidemic prevention and control, the floating population supervision department classifies and controls the floating population by industry. There are many personnel management and control points. When the computer-aided management system is used, the outdoor environment is complex and the data interference is large. Therefore, the recognition accuracy of outdoor scenery is required to be higher. In this paper, a convolutional neural network with adaptive weights is proposed. In this method, the feature fusion strategy is combined with the network, and the optimal feature weight is obtained by training the network. In addition, this paper uses multiple two classifiers instead of multiple classifiers to achieve accurate target classification. Experiments show that the method proposed in this paper has excellent performance in the detection of similar objects. The strategy of replacing multi classification network with multi classification network improves the accuracy and recall of target detection in known environment.
Joint feature weighting and adaptive graph-based matrix regression for image supervised feature Selection

Matrix regression (MR) is a regression model that can directly perform on matrix data. However, the effect of each element in matrix data on regression model is different. Taking into consideration the relevance of every original feature in the matrix data and their influence on the final estimation of the regression model, we introduce an unknown weight matrix to encode the relevance of feature in matrix data and propose a feature weighting and graph-based matrix regression (FWGMR) model for image supervised feature selection. In this model, the feature weight matrix is used to select some important features from the matrix data and preserve the relative spatial location relationship of elements in the matrix data. In addition, in order to effectively and reasonably preserve the local manifold structure of the training matrix samples, a regularization term in the model is used to adaptively learn a graph matrix on low-dimensional space. An optimization algorithm is devised to solve FWGMR model and to provide the closed-form solutions of this model in each iteration. Extensive experiments on some public datasets demonstrate the superiority of FWGMR.
Performing Realistic Workout Activity Recognition on Consumer Smartphones

Smartphones have become an essential part of our lives. Especially its computing power and its current specifications make a modern smartphone a powerful device for human activity recognition tasks. Equipped with various integrated sensors, a modern smartphone can be leveraged for lots of smart applications. We already investigated the possibility of using an unmodified commercial smartphone to recognize eight strength-based exercises. App-based workouts have become popular in the last few years. The advantage of using a mobile device is that you can practice anywhere at anytime. In our previous work, we proved the possibility of turning a commercial smartphone into an active sonar device to leverage the echo reflected from exercising movement close to the device. By conducting a test study with 14 participants, we showed the first results for cross person evaluation and the generalization ability of our inference models on disjoint participants. In this work, we extended another model to further improve the model generalizability and provided a thorough comparison of our proposed system to other existing state-of-the-art approaches. Finally, a concept of counting the repetitions is also provided in this study as a parallel task to classification.