Reviewerly/exHarmony huggingface dataset:
AraConv: Developing an Arabic Task-Oriented Dialogue System Using Multi-Lingual Transformer Model mT5

Task-oriented dialogue systems (DS) are designed to help users perform daily activities using natural language. Task-oriented DS for English language have demonstrated promising performance outcomes; however, developing such systems to support Arabic remains a challenge. This challenge is mainly due to the lack of Arabic dialogue datasets. This study introduces the first Arabic end-to-end generative model for task-oriented DS (AraConv), which uses the multi-lingual transformer model mT5 with different settings. We also present an Arabic dialogue dataset (Arabic-TOD) and used it to train and test the proposed AraConv model. The results obtained are reasonable compared to those reported in the studies of English and Chinese using the same mono-lingual settings. To avoid problems associated with a small training dataset and to improve the AraConv model’s results, we suggest joint-training, in which the model is jointly trained on Arabic dialogue data and data from one or two high-resource languages such as English and Chinese. The findings indicate the AraConv model performed better in the joint-training setting than in the mono-lingual setting. The results obtained from AraConv on the Arabic dialogue dataset provide a baseline for other researchers to build robust end-to-end Arabic task-oriented DS that can engage with complex scenarios.
Neural collaborative filtering with multicriteria evaluation data

Recommendation systems help consumers find useful items of information given a large amount of information while avoiding information overload. Nowadays, in addition to traditional evaluation information (such as individual reviewer ratings), information such as multicriteria ratings are available on the Web. In the work reported here, we investigated whether collaborative filtering methods using multicriteria evaluation data and deep learning are effective for abundant and sparse multicriteria evaluation data. We also investigated whether adaptability can be achieved by predicting aggregated ratings from the evaluations of a few users. Experimental results show that three proposed methods using deep learning are better than traditional methods for both recommendation and rating aggregation.
The Interplay Between Presence and Learning

The highly immersive Virtual reality (VR) headset is gaining popularity in multiple application domains. In the context of learning, it has been proposed to be beneficial by increasing presence and attention in noisy and distracting environments, both factors that are considered important for learning. Despite intensified research efforts in recent years, empirical knowledge of experimental research addressing the link between presence and learning in specific environmental contexts is still rather scarce. In this study following an experimental mixed-method approach, the link between presence and memorization as a particular form of learning is addressed by comparing memorization with a highly immersive VR headset to a less immersive system (desktop screen) in noisy and calm learning environments. Using a 2 (learning location) x 2 (learning device) between-subjects design, 63 participants interacted with one of the two devices in either of the two environments. As expected, VR headset users reported higher presence levels. While participants subjectively evaluated the VR headset as a better device for learning, the memorization test scores were higher for desktop screen users in both calm and noisy environments. Learning location did not show significant effects. Attention distraction and context-dependent learning are discussed with regard to the unexpected results, while implications for practice and future research are discussed.
RPkNN: An OpenCL-Based FPGA Implementation of the Dimensionality-Reduced kNN Algorithm Using Random Projection

Due to the so-called curse of dimensionality and increase in the size of databases, there is an ever-increasing demand for computing resources and memory bandwidth when performing the k-nearest neighbors (kNNs) algorithm, resulting in a slow-down to process large datasets. This work presents an OpenCL-based framework for accelerating the kNN algorithm on field-programmable gate arrays (FPGAs) benefiting from the random projection dimensionality reduction. The proposed RPkNN framework includes two compute modules implementing a throughput-optimized hardware architecture based on random projection and the kNN algorithm and a host program facilitating easy integration of the compute modules in the existing applications. RPkNN also utilizes a new buffering scheme tailored to random projection and the kNN algorithm. The proposed architecture enables parallel kNN computations with a single memory channel and takes advantage of the sparsity features of the input data to implement a highly optimized and parallel implementation of random projection. We employ a computation storage device (CSD) to directly access the high-dimensional data on non-volatile memory express (NVMe) solid state drive (SSD) and store and reuse the compressed and low-dimensional data on the FPGA dynamic random access memory (DRAM), hence eliminating data transfers to the host DRAM. We compare RPkNN implemented on the Samsung SmartSSD CSD with the kNN implementation of the scikit-learn library running on an Intel Xeon Gold 6154 CPU. The experimental results show that the proposed RPkNN solution achieves, on average, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$26\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$46\times $ </tex-math></inline-formula> higher performance across different dimensions per a single kNN computation for the SIFT1M and GIST1M databases, respectively. Finally, RPkNN is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> faster than the similar FPGA-based reference method.
A Bio-inspired VM Migration using Re-initialization and Decomposition Based-Whale Optimization

Virtual Machine (VM) migration has become quite prevalent in cloud computing. Numerous VM migration-oriented techniques are required for efficient VM allocation but continue to struggle due to incorrect energy usage in the cloud model. Bio-inspired R&D-WOA (Re-initialization and Decomposition-Whale Optimization Algorithm) is proposed by integrating WOA with NSGA-II algorithm to discover Pareto-optimal solutions. The proposed algorithm optimizes the allocation of the task to VM which diminishes the number of VM migrations, thereby lowering the migration cost and energy consumption. The efficiency of the Bio-inspired R&D-WOA is 67%, 7%, 1%, and 1.98% improved than the performance of existing WOA with regard to load, migration cost, energy utilized, and resource abundance respectively. At a similar level of load, it provided 24.80% efficiency in energy, 31.77% reduction in migration cost with maximal resource availability of 0.9917 compared to the ChicWhale algorithm.
From Less to More: Progressive Generalized Zero-Shot Detection With Curriculum Learning

Object detection, as one of the most important environment perception tasks for traffic safety in intelligent transportation systems, has been widely investigated recently. However, most of the researches focus on the fully supervised scenario, and inevitably lead to model failure. With the continuous development of Zero-Shot Learning (ZSL) models, Generalized Zero-Shot Detection (GZSD) has attracted great attention due to its ability of detecting unseen objects. Many researchers tend to map the detected visual features to semantic attributes and then separate seen and unseen domains during inference. But they have ignore that the generative methods generally have higher performance than these visual-semantic mapping methods, and they have been confirmed from previous GZSL methods. In order to make up for the vacancy of GZSD in the generative methods, we propose an idea of using curriculum learning to generate more precise unseen visual features. And with the excellent performance of WGAN-based method in sample synthesis, we realize the function of using semantics to generate visual features for unseen domains. In addition, we also adopt part of the idea of meta-learning to progressively correct the capability of the generator for better mitigating domain shift problem during the generation process. Through the above ideas, we can detect both seen and unseen bounding boxes and classify them accurately, by combining with the excellent detection ability of Faster-RCNN. Extensive experimental results on two popular datasets, i.e., MSCOCO and KITTI, show that our proposed method can outperform the state-of-the-art methods.
uTango: An Open-Source TEE for IoT Devices

Security is one of the main challenges of the Internet of Things (IoT). IoT devices are mainly powered by low-cost microcontrollers (MCUs) that typically lack basic hardware security mechanisms to separate security-critical applications from less critical components. Recently, Arm has started to release Cortex-M MCUs enhanced with TrustZone technology (i.e., TrustZone-M), a system-wide security solution aiming at providing robust protection for IoT devices. Trusted Execution Environments (TEEs) relying on TrustZone hardware have been perceived as safe havens for securing mobile devices. However, for the past few years, considerable effort has gone into unveiling hundreds of vulnerabilities and proposing a collection of relevant defense techniques to address several issues. While new TEE solutions built on TrustZone-M start flourishing, the lessons gathered from the research community appear to be falling short, as these new systems are trapping into the same pitfalls of the past. In this paper, we present uTango, the first multi-world TEE for modern IoT devices. uTango proposes a novel architecture aiming at tackling the major architectural deficiencies currently affecting TrustZone(-M)-assisted TEEs. In particular, we leverage the very same TrustZone hardware primitives used by dual-world implementations to create multiple and equally secure execution environments within the normal world. We demonstrate the benefits of uTango by conducting an extensive evaluation on a real TrustZone-M hardware platform, i.e., Arm Musca-B1. uTango will be open-sourced and freely available on GitHub in hopes of engaging academia and industry on securing the foreseeable trillion IoT devices.
Designing with the Immune System

The immune system is built from our cells, organs, proteins and tissue, and it is the sum of the whole that defends the body against illness. In this paper, we introduce the immune system as a site to explore more-than-human design. Specifically, we address the effects of chronic stress on the immune system to explore a set of speculative wearable designs that combine the microbial basis of the human body with that of more-than-human. We reflect on the relationships within living materials and discuss symbiosis and mutualistic care when designing alternative wearable artifacts and trackers.
Unsupervised Learning of Monocular Depth and Ego-Motion with Optical Flow Features and Multiple Constraints

This paper proposes a novel unsupervised learning framework for depth recovery and camera ego-motion estimation from monocular video. The framework exploits the optical flow (OF) property to jointly train the depth and the ego-motion models. Unlike the existing unsupervised methods, our method extracts the features from the optical flow rather than from the raw RGB images, thereby enhancing unsupervised learning. In addition, we exploit the forward-backward consistency check of the optical flow to generate a mask of the invalid region in the image, and accordingly, eliminate the outlier regions such as occlusion regions and moving objects for the learning. Furthermore, in addition to using view synthesis as a supervised signal, we impose additional loss functions, including optical flow consistency loss and depth consistency loss, as additional supervision signals on the valid image region to further enhance the training of the models. Substantial experiments on multiple benchmark datasets demonstrate that our method outperforms other unsupervised methods.
A Bayesian Approach To Distributed Anomaly Detection In Edge AI Networks

The challenge of anomaly detection is to obtain an accurate understanding of expected behaviour which is intensified when the data are distributed heterogeneously. Transmitting raw data to a central site incurs high communication overhead and raises privacy issues. The concept of Edge AI allows computation to be performed at the edge site allowing for quick decision making in mission critical scenarios such as self-driving cars. A model is learnt locally and its parameters are transmitted and aggregated. However, existing methods of aggregation do not account for variance and heterogeneous distribution of data. They also do not consider edge constraints such as limited computational, memory and communication capabilities of edge devices. In this work, a fully Bayesian approach is employed by means of a Bayesian Random Vector Functional Link AutoEncoder being incorporated with Expectation Propagation for distributed training. Our anomaly detection system operates without any transmission of raw data, is robust under inhomogeneous network densities and under uneven and biased data distributions. It allows for asynchronous updates to converge in a few iterations and is a relatively simple neural network addressing edge constraints without compromising on performance as compared to existing more complex models.
Applying augmented reality in a university English class: Learners’ perceptions of creativity and learning motivation

This study was conducted to explore the effects of an augmented-reality (AR) creative project on English L2 learners’ perceptions of creativity, as well as to investigate how to stimulate students’ learning motivation. The AR project was developed and integrated into the course syllabus of a university English class, and 39 Taiwanese freshmen completed the project step by step. Data for analysis primarily emerged from the Preference Scale, the Instructional Materials Motivation Survey (IMMS), and an AR student survey. Following a pretest-posttest design, this study showed that the participants demonstrated a higher level of favorable perceptions of creative thinking, such as preference for ideation and valuing new ideas, and a lower tendency for premature critical evaluation of ideas, although these differences were non-significant. However, working on the project also led to a significantly higher level of self-awareness of being too busy for new ideas. According to the results of the IMMS, attention yields significantly positive relationships with relevance, confidence, and satisfaction. This finding suggests that the students who were more engaged tended to see the project as relevant to their needs; they were also more confident and had a more satisfying AR experience. Based on the results, pedagogical implications are discussed.
A Detailed Understanding of Underwater Image Enhancement using Deep Learning

In the last few years underwater image processing has attracted much attention because of its importance in marine engineering and aquatic robotics. Various algorithms have been proposed for underwater image enhancement, but for their assessment either synthetic datasets or few selected real images were used. In this literature review paper we discussed few latest underwater image enhancement method based on deep learning. These methods not only enhance the images but also provide better results as compared to enhancement and restoration method. These deep learning based methods increasing the variety in underwater imaging, enhancing underwater image quality and providing wider scope in terms of improvisation. This paper mainly focuses on latest algorithms, their datasets and their evaluation criteria based on various parameters. At last we compare the results of these algorithms.
A Cooperative Neural Information Retrieval Pipeline with Knowledge Enhanced Automatic Query Reformulation

This paper presents a neural information retrieval pipeline that integrates cooperative learning of query reformulation and neural retrieval models. Our pipeline first exploits an automatic query reformulator to reformulate the user-issued query and then submits the reformulated query to the neural retrieval model. We simultaneously optimize the quality of reformulated queries and ranking performance with an alternate training strategy where query reformulator and neural retrieval model learn from the feedback of each other. Besides, we incorporate knowledge information into automatic query reformulation. The reformulated queries are further improved and contribute to a better ranking performance of the following neural retrieval model. We study two representative neural retrieval models KNRM and BERT in our pipeline. Experiments on two datasets show that our pipeline consistently improves the retrieval performance of the original neural retrieval models while only increases negligible time on automatic query reformulation.
Exploring Counterfactual Explanations for Classification and Regression Trees

The problem of counterfactual explanations is that of minimally adjusting attributes in a source input instance so that it is classified as a target class under a given classifier. They answer practical questions of the type “what should my annual income be for my loan to be approved?”, for example. We focus on classification and regression trees, both axis-aligned and oblique (having hyperplane splits), and formulate the counterfactual explanation as an optimization problem. Although this problem is nonconvex and nondifferentiable, an exact solution can be computed very efficiently, even with high-dimensional feature vectors and with both continuous and categorical features. We also show how the counterfactual explanation formulation can answer a range of important practical questions, providing a way to query a trained tree and suggest possible actions to overturn its decision, and demonstrate it in several case studies. The results are particularly relevant for finance, medicine or legal applications, where interpretability and counterfactual explanations are particularly important.
Smart Cities: Emerging technologies and Potential solutions to the Cyber security threads

Smart city development with proper use of emerging technologies greatly reduces security issues, reduces energy waste, saves time, and increases human safety in urban areas during urbanization. But, vulnerabilities in those technologies questioning the smart city’s security. This study explored the cyber security issues in smart city technologies and find potential solutions. There are various technologies engaged with smart city development but the Internet of Things, sensor technologies, and cloud computing are the most use by the smart industries. Cyber adversaries targeting to weakening the organization’s database breach, which increases financial losses and reduces human safety; Daniel of service attack was the major cyber-attack face by the smart cities. It is strongly suggested to implement Blockchain technologies while developing smart city developments. In addition to that, it is also suggested to implement strong cyber security policies in terms of confidentiality, integrity, and authentication within the organization.
Local Image Denoising Using RAISR

Digital images are frequently degraded by Gaussian noise while capturing photos. This paper proposes a rapid and high accurate Gaussian noise removal method by applying the learned linear filter used in RAISR for super-resolution. The denoising methods are classified into local, nonlocal methods and deep-learning-based methods. The conventional local processing has a problem that high-frequency components of the original image are lost while reducing the noise. The nonlocal and deep-learning-based methods achieve higher denoising performance but take a long time for training and implementation. To solve these problems, we apply a super-resolution method to the local denoising method as post-processing because it can efficiently recover the high-frequency components. The super-resolution method uses a learned linear filter according to the feature of patches. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The proposed algorithm is a rapid local denoising method and can achieve comparable performance to the high-accurate nonlocal denoising methods. Experimental results show that our proposed method provides accurate denoising performance with a low computational cost compared to nonlocal processing like BM3D.
Forecasting topic activity with exogenous and endogenous information signals in Twitter

Modeling social media activity has numerous practical implications such as designing and testing intervention techniques to mitigate disinformation or delivering critical information during disaster relief operations. In this paper we propose a modeling technique that forecasts topic-specific daily volume of social media activities by using both exogenous signals, such as news or armed conflicts records, and endogenous data from the social media platform we model. Empirical evaluations with real datasets from Twitter on two different contexts each composed of multiple interrelated topics demonstrate the effectiveness of our solution.
Bio-Inspired Agents for a Distributed NLP-Based Clustering in Smart Environments

Due to the high number and extreme dynamism of the entities involved in last-generation smart environments, often, traditional data management approaches aren’t particularly appropriate. In order to better meet users and services requirements, the objects, belonging to an intelligent environment, have to be able to interact and maintain relationships that require efficient management mechanisms. Clustering approaches, for example, have proven to be a valuable technique to extract useful information in data. This paper proposes a multiagent algorithm to perform distributed clustering in Smart environments through Swarm Intelligence (SI) techniques that leverage Natural Language Processing (NLP) concepts. By exploiting the Doc2Vec model, a word embedding tool that allows representing text documents with high-dimensional vectors, also capturing the semantic context, it is possible to describe the intelligent objects - advanced devices or services - through real-valued vectors. These vectors are assigned to the agents that change their position in a virtual space according to a bio-inspired model. The agents follow simultaneously simple and local movement rules obtaining a global and intelligent organization that enables a proper and useful positioning (clustering). Preliminary experimental evaluations have shown the validity of the approach.
Teaching Methods in Software Engineering: A Systematic Review

This study presents the results of a meta-analysis on teaching methods used in software engineering (SE) courses. The results summarize the most common teaching methods and can be used by both educators and practitioners in better alignment of SE courses with industry-relevant knowledge, tools, and practices.