Reviewerly/exHarmony huggingface dataset:
Evolving Deep DenseBlock Architecture Ensembles for Image Classification

Automatic deep architecture generation is a challenging task, owing to the large number of controlling parameters inherent in the construction of deep networks. The combination of these parameters leads to the creation of large, complex search spaces that are feasibly impossible to properly navigate without a huge amount of resources for parallelisation. To deal with such challenges, in this research we propose a Swarm Optimised DenseBlock Architecture Ensemble (SODBAE) method, a joint optimisation and training process that explores a constrained search space over a skeleton DenseBlock Convolutional Neural Network (CNN) architecture. Specifically, we employ novel weight inheritance learning mechanisms, a DenseBlock skeleton architecture, as well as adaptive Particle Swarm Optimisation (PSO) with cosine search coefficients to devise networks whilst maintaining practical computational costs. Moreover, the architecture design takes advantage of recent advancements of the concepts of residual connections and dense connectivity, in order to yield CNN models with a much wider variety of structural variations. The proposed weight inheritance learning schemes perform joint optimisation and training of the architectures to reduce the computational costs. Being evaluated using the CIFAR-10 dataset, the proposed model shows great superiority in classification performance over other state-of-the-art methods while illustrating a greater versatility in architecture generation.
Teaching Machine Comprehension with Compositional Explanations

Advances in machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated examples in the form of (question, paragraph, answer) triples.In contrast, humans are typically able to generalize with only a few examples, relying on deeper underlying world knowledge, linguistic sophistication, and/or simply superior deductive powers.In this paper, we focus on "teaching" machines reading comprehension, using a small number of semi-structured explanations that explicitly inform machines why answer spans are correct.We extract structured variables and rules from explanations and compose neural module teachers that annotate instances for training downstream MRC models.We use learnable neural modules and soft logic to handle linguistic variation and overcome sparse coverage; the modules are jointly optimized with the MRC model to improve final performance.On the SQuAD dataset, our proposed method achieves 70.14% F1 score with supervision from 26 explanations, comparable to plain supervised learning using 1,100 labeled instances, yielding a 12x speed up 1 .
Wearable on-device deep learning system for hand gesture recognition based on FPGA accelerator

Gesture recognition is critical in the field of Human-Computer Interaction, especially in healthcare, rehabilitation, sign language translation, etc. Conventionally, the gesture recognition data collected by the inertial measurement unit (IMU) sensors is relayed to the cloud or a remote device with higher computing power to train models. However, it is not convenient for remote follow-up treatment of movement rehabilitation training. In this paper, based on a field-programmable gate array (FPGA) accelerator and the Cortex-M0 IP core, we propose a wearable deep learning system that is capable of locally processing data on the end device. With a pre-stage processing module and serial-parallel hybrid method, the device is of low-power and low-latency at the micro control unit (MCU) level, however, it meets or exceeds the performance of single board computers (SBC). For example, its performance is more than twice as much of Cortex-A53 (which is usually used in Raspberry Pi). Moreover, a convolutional neural network (CNN) and a multilayer perceptron neural network (NN) is used in the recognition model to extract features and classify gestures, which helps achieve a high recognition accuracy at 97%. Finally, this paper offers a software-hardware co-design method that is worth referencing for the design of edge devices in other scenarios.
An Image Enhancing Pattern-Based Sparsity for Real-Time Inference on Mobile Devices

Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms. However, most of the pruning techniques are essentially trade-offs between model accuracy and regularity which lead to impaired inference accuracy and limited on-device acceleration performance. To solve the problem, we introduce a new sparsity dimension, namely pattern-based sparsity that comprises pattern and connectivity sparsity, and becoming both highly accurate and hardware friendly. With carefully designed patterns, the proposed pruning unprecedentedly and consistently achieves accuracy enhancement and better feature extraction ability on different DNN structures and datasets, and our pattern-aware pruning framework also achieves pattern library extraction, pattern selection, pattern and connectivity pruning and weight training simultaneously. Our approach on the new pattern-based sparsity naturally fits into compiler optimization for highly efficient DNN execution on mobile platforms. To the best of our knowledge, it is the first time that mobile devices achieve real-time inference for the large-scale DNN models thanks to the unique spatial property of pattern-based sparsity and the help of the code generation capability of compilers.
Semantic Similarity Analysis for Corpus Development and Paraphrase Detection in Arabic

Paraphrase detection allows determining how original and suspect documents convey the same meaning. It has attracted attention from researchers in many Natural Language Processing (NLP) tasks such as plagiarism detection, question answering, information retrieval, etc., Traditional methods (e.g., Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Latent Semantic Analysis (LSA)) cannot capture efficiently hidden semantic relations when sentences may not contain any common words or the co-occurrence of words is rarely present. Therefore, we proposed a deep learning model based on Global Word embedding (GloVe) and Recurrent Convolutional Neural Network (RCNN). It was efficient for capturing more contextual dependencies between words vectors with precise semantic meanings. Seeing the lack of resources in Arabic language publicly available, we developed a paraphrased corpus automatically. It preserved syntactic and semantic structures of Arabic sentences using word2vec model and Part-Of-Speech (POS) annotation. Overall experiments shown that our proposed model outperformed the state-of-the-art methods in terms of precision and recall
SemEval-2020 Task 6: Definition Extraction from Free Text with the DEFT Corpus

Research on definition extraction has been conducted for well over a decade, largely with significant constraints on the type of definitions considered.In this work, we present DeftEval, a SemEval shared task in which participants must extract definitions from free text using a termdefinition pair corpus that reflects the complex reality of definitions in natural language.Definitions and glosses in free text often appear without explicit indicators, across sentences boundaries, or in an otherwise complex linguistic manner.DeftEval involved 3 distinct subtasks: 1) Sentence classification, 2) sequence labeling, and 3) relation extraction.
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack

Zero-shot slot filling has widely arisen to cope with data scarcity in target domains.However, previous approaches often ignore constraints between slot value representation and related slot description representation in the latent space and lack enough model robustness.In this paper, we propose a Contrastive Zero-Shot Learning with Adversarial Attack (CZSL-Adv) method for the cross-domain slot filling.The contrastive loss aims to map slot value contextual representations to the corresponding slot description representations.And we introduce an adversarial attack training strategy to improve model robustness.Experimental results show that our model significantly outperforms state-of-the-art baselines under both zero-shot and few-shot settings.
Development of Engineering Students Competencies Based on Cognitive Technologies in Conditions of Industry 4.0

Industry 4.0 and Society 5.0 concepts are actively developing all over the world. The accelerating transition to Industry 4.0 and Society 5.0 sets new requirements for the university education system in qualifications and competencies of engineering universities graduates. The article reveals the possibilities of using cognitive models in the professional training of research engineers for new industries. Authors used the modeling method for creating a cognitive and metacognitive model of the process. It can be used for the development of forming the optimal structure of higher professional engineering education. The article substantiates that the main tasks of modernization of pedagogical approaches in modern education, is to establish the compliance of educational products with the labor market requirements and transform the structure of vocational education, providing training for professional specialists required by specific employers. Conclusions are drawn about the important role of soft skills for engineering education in Industry 4.0. The results obtained in the study can be used for the engineering category of students.
Few-shot Font Style Transfer between Different Languages

In this paper, we propose a novel model FTransGAN that can transfer font styles between different languages by observing only a few samples. The automatic generation of a new font library is a challenging task and has been attracting many researchers' interests. Most previous works addressed this problem by transferring the style of the given subset to the content of unseen ones. Nevertheless, they only focused on the font style transfer in the same language. In many tasks, we need to learn the font information from one language and then apply it to other languages. It's difficult for the existing methods to do such tasks. To solve this problem, we specifically design our network into a multi-level attention form to capture both local and global features of the style images. To verify the generative ability of our model, we construct an experimental font dataset which includes 847 fonts, each of them containing English and Chinese characters with the same style. Experimental results show that compared with the state-of-the-art models, our model generates 80.3% of all user preferred images.
Pengembangan Sistem Informasi Geografis Untuk Pendonor Darah Tetap di Bandar Lampung dengan Algoritma Dijkstra berbasis Android

Palang Merah Indonesia (PMI) is a government agency whose job is to provide blood for the needy.PMI has a relationship with registered hospitals, the aim is to make it easier to provide blood stocks that patients need.The problem is the availability of blood stock that the patient needs when it runs out, so the solution is that the patient must find blood donors independently to meet blood needs.The difficulty of the patient in finding the required blood stock is a problem that will be discussed in this study.The contribution in this research is to design an information system that can find blood donors in the Bandar Lampung area with gelocation technology and using the dijkstra algorithm in determining the closest route to build using the Android platform.The system built can recommend donors to patients who need blood according to the patient's qualifications.The result obtained is a geographic information system of blood donors using the dijkstra algorithm which has the ability to map the location of permanent blood donors based on the access's location.Based on the test results, this system can map regular blood donors and make recommendations for blood donors for those in need. Testing the system using the ISO 9126 standard using two test characteristics, the results obtained are: in the aspect of functionality, the result is 100%, on the usability aspect the result is 85%. @font-face{ font-family:Times New Roman; } @font-face{ font-family:宋体; } @font-face{ font-family:SimSun; } @font-face{ font-family:Calibri; } p.MsoNormal{ mso-style-name:Normal; mso-style-parent:; margin-bottom:12,0000pt; text-align:justify; text-justify:inter-ideograph; font-family:'Times New Roman'; mso-fareast-font-family:Calibri; } p.MsoHeader{ mso-style-name:Header; mso-style-noshow:yes; margin-bottom:0,0000pt; text-align:justify; text-justify:inter-ideograph; font-family:'Times New Roman'; mso-fareast-font-family:Calibri; } span.msoIns{ mso-style-type:export-only; mso-style-name:; text-decoration:underline; text-underline:single; color:blue; } span.msoDel{ mso-style-type:export-only; mso-style-name:; text-decoration:line-through; color:red; } } div.Section0{page:Section0;}
Super resolution convolutional neural network based pre-processing for automatic polyp detection in colonoscopy images

Colonoscopy is the most common methodology used to detect polyps on the colon surface. Increasing the image resolution has the potential to improve the automatic colonoscopy based diagnosis and polyp detection and localization. In this study, we proposed a pre-processing approach that uses convolutional neural network based super resolution method (SRCNN) to increase the resolution of the training colonoscopy images before the localization of polyps. We also investigated the use of CNN based models such as the Single Shot MultiBox Detector (SSD) and Faster Regional CNN (RCNN) for real-time polyp detection and localization. Our results showed that using SRCNN method before the training process provides better results in terms of accuracy in both models compared to the low-resolution cases. Furthermore, we reached an F2 score of 0.945 for the correct localization of colon polyps using Faster RCNN with ResNet-101 feature extractor.
Model-Based Offline Planning

Offline learning is a key part of making reinforcement learning (RL) useable in real systems. Offline RL looks at scenarios where there is data from a system's operation, but no direct access to the system when learning a policy. Recent work on training RL policies from offline data has shown results both with model-free policies learned directly from the data, or with planning on top of learnt models of the data. Model-free policies tend to be more performant, but are more opaque, harder to command externally, and less easy to integrate into larger systems. We propose an offline learner that generates a model that can be used to control the system directly through planning. This allows us to have easily controllable policies directly from data, without ever interacting with the system. We show the performance of our algorithm, Model-Based Offline Planning (MBOP) on a series of robotics-inspired tasks, and demonstrate its ability leverage planning to respect environmental constraints. We are able to find near-optimal polices for certain simulated systems from as little as 50 seconds of real-time system interaction, and create zero-shot goal-conditioned policies on a series of environments.
Efficient Feature Selection for Static Analysis Vulnerability Prediction

Common software vulnerabilities can result in severe security breaches, financial losses, and reputation deterioration and require research effort to improve software security. The acceleration of the software production cycle, limited testing resources, and the lack of security expertise among programmers require the identification of efficient software vulnerability predictors to highlight the system components on which testing should be focused. Although static code analyzers are often used to improve software quality together with machine learning and data mining for software vulnerability prediction, the work regarding the selection and evaluation of different types of relevant vulnerability features is still limited. Thus, in this paper, we examine features generated by SonarQube and CCCC tools, to identify those that can be used for software vulnerability prediction. We investigate the suitability of thirty-three different features to train thirteen distinct machine learning algorithms to design vulnerability predictors and identify the most relevant features that should be used for training. Our evaluation is based on a comprehensive feature selection process based on the correlation analysis of the features, together with four well-known feature selection techniques. Our experiments, using a large publicly available dataset, facilitate the evaluation and result in the identification of small, but efficient sets of features for software vulnerability prediction.
Having Our “Omic” Cake and Eating It Too?: Evaluating User Response to Using Blockchain Technology for Private and Secure Health Data Management and Sharing

This paper reports on end users' perspectives on the use of a blockchain solution for private and secure individual “omics” health data management and sharing. This solution is one output of a multidisciplinary project investigating the social, data, and technical issues surrounding application of blockchain technology in the context of personalized healthcare research. The project studies potential ethical, legal, social, and cognitive constraints of self-sovereign healthcare data management and sharing, and whether such constraints can be addressed through careful design of a blockchain solution.
Linear Last-iterate Convergence in Constrained Saddle-point Optimization

Optimistic Gradient Descent Ascent (OGDA) and Optimistic Multiplicative Weights Update (OMWU) for saddle-point optimization have received growing attention due to their favorable last-iterate convergence. However, their behaviors for simple bilinear games over the probability simplex are still not fully understood --- previous analysis lacks explicit convergence rates, only applies to an exponentially small learning rate, or requires additional assumptions such as the uniqueness of the optimal solution. In this work, we significantly expand the understanding of last-iterate convergence for OGDA and OMWU in the constrained setting. Specifically, for OMWU in bilinear games over the simplex, we show that when the equilibrium is unique, linear last-iterate convergence is achievable with a constant learning rate, which improves the result of (Daskalakis & Panageas, 2019) under the same assumption. We then significantly extend the results to more general objectives and feasible sets for the projected OGDA algorithm, by introducing a sufficient condition under which OGDA exhibits concrete last-iterate convergence rates with a constant learning rate. We show that bilinear games over any polytope satisfy this condition and OGDA converges exponentially fast even without the unique equilibrium assumption. Our condition also holds for strongly-convex-strongly-concave functions, recovering the result of (Hsieh et al., 2019). Finally, we provide experimental results to further support our theory.
Evaluating cybersecurity attitudes and behaviors in Portuguese healthcare institutions

The growing digitization of healthcare institutions and its increasing dependence on Internet infrastructure has boosted the concerns related to data privacy and confidentiality. These institutions have been challenged with specific issues, namely the sensitivity of data, the specificity of networked equipment, the heterogeneity of healthcare professionals (nurses, doctors, administrative staff and other) and the IT skills they have. In this paper we present the results obtained with a study made with healthcare professionals on evaluating their awareness level with the information security, namely by assessing their attitudes and behaviours in cybersecurity. The methodology consisted in translating, adjusting and applying two previously validated and already published Likert-type response scales, in a healthcare institution in Portugal, namely "Centro Hospitalar Barreiro Montijo" (CHBM). The scales used were cybersecurity risky behaviour (RScB) and cybersecurity and cybercrime in business attitudes (ATC-IB). Although there were no significant statistical differences between the sociodemographic factors and the scores obtained on both scales, the results showed a relationship between acquired behaviours and the attitudes of involvement with work and organizational commitment, establishing a bridge for the quantification in awareness.
Neuroscope: An Explainable AI Toolbox for Semantic Segmentation and Image Classification of Convolutional Neural Nets

Trust in artificial intelligence (AI) predictions is a crucial point for a widespread acceptance of new technologies, especially in sensitive areas like autonomous driving. The need for tools explaining AI for deep learning of images is thus eminent. Our proposed toolbox Neuroscope addresses this demand by offering state-of-the-art visualization algorithms for image classification and newly adapted methods for semantic segmentation of convolutional neural nets (CNNs). With its easy to use graphical user interface (GUI), it provides visualization on all layers of a CNN. Due to its open model-view-controller architecture, networks generated and trained with Keras and PyTorch are processable, with an interface allowing extension to additional frameworks. We demonstrate the explanation abilities provided by Neuroscope using the example of traffic scene analysis.
Whispering Devices: A Survey on How Side-channels Lead to Compromised Information

While operating, information processing devices or communication systems may emit unwanted signals (or alter existing ones) through electromagnetic waves, light, sound or power drain. These side-channels can be intercepted by anyone with scientific or technical knowledge and appropriate equipment, leading to a potentially high risk of security breaches. This survey focuses on these emanation side-channels and provides an extensive literature review. To provide an in-depth analysis despite the variety of attacks, we propose to classify the side-channels based on their intentionality, the type of attackers and the physical medium. Illustrative use-cases are presented and serve as a basis to infer individual threats. Particular attention is paid to electromagnetic side-channels which exhibit the highest criticality and have therefore been used in the most recent attacks. The main characteristics of the side-channels revealed by state-of-the-art papers are summarized, and recommendations on countermeasures are provided to protect any sensitive equipment.