Reviewerly/exHarmony huggingface dataset:
Extended Irreducible Binary Sextic Goppa Codes

Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n (&gt;3)$ </tex-math></inline-formula> be a prime number and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}}_{2^{n}}$ </tex-math></inline-formula> a finite field of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n}$ </tex-math></inline-formula> elements. Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L ={\mathbb {F}}_{2^{n}}\cup \{\infty \}$ </tex-math></inline-formula> be the support set and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$g(x)$ </tex-math></inline-formula> an irreducible polynomial of degree 6 over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}}_{2^{n}}$ </tex-math></inline-formula> . In this paper, we obtain an upper bound on the number of extended irreducible binary Goppa codes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Gamma (L, g)$ </tex-math></inline-formula> of degree 6 and length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n}+1$ </tex-math></inline-formula> .
Application of VR technology in the aircraft cabin design process

This paper addresses issues currently present in the aircraft cabin design process. It focuses on making the design process more time and cost efficient, while altogether involving the end-users (passengers and cabin crew) in the development process in its earliest stages. By understanding the underlying issues and reasons the cabin is developed according to the current approach, new methods are established and adapted to suit the needs of such a complex process. In this paper, the preposition is made that Virtual Reality is the key technology for achieving the following goals: shortening the initial cabin design process (from sketch to concept design) and including the end-users and their wishes and ideas into the ideation phase. Through cooperation with an external design agency, a Virtual Reality tool is implemented and tested to ensure the theory behind the established design methodology can also be put into practice.
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning

We introduce a novel binary latent space autoen-coder architecture to rehearse training samples for the continual learning of neural networks. The ability to extend the knowledge of a model with new data without forgetting previously learned samples is a fundamental requirement in continual learning. Existing solutions address it by regularizing network weights, adjusting its architecture, or retraining with past data samples, regenerated from memory or reconstructed with generative models. Unfortunately, recreating past data from memory requires an infinite buffer, while the reconstructions of generative models tend to miss details of individual samples when generalizing beyond the training set. In this paper, we aim to overcome these limitations and introduce a novel generative rehearsal approach called BinPlay. Its main objective is to find a quality-preserving encoding of past samples into precomputed binary codes living in the autoencoder's binary latent space. Since we parametrize the formula for precomputing the codes only on the training samples' chronological indices, the autoencoder is able to compute the binary codes of rehearsed samples on the fly without the need to keep them in memory. Evaluation on three benchmark datasets shows up to a twofold accuracy improvement of BinPlay versus competing generative replay methods.
A Framework for Sustainable Federated Learning

Potential environmental impact of machine learning in large-scale wireless networks is a major challenge for the sustainability of next-generation intelligent systems. Federated learning is a recent framework for communication-efficient training of machine learning models over the data collected, stored, and processed by millions of wireless devices. In this paper, we introduce a sustainable machine learning framework for federated learning, using rechargeable devices that can collect energy from the ambient environment. In particular, we propose a practical federated learning framework that utilizes intermittent energy arrivals for training, with provable convergence guarantees. Our framework can be applied to both cross-device and cross-silo federated learning settings, including federated learning in wireless edge networks and the Internet-of-Things. Our experiments demonstrate that the proposed framework can provide significant performance improvement over the benchmark energy-agnostic federated learning settings.
Transformed Dynamic Feature Pyramid for Small Object Detection

The low resolution and less feature information of small targets make it difficult to recognize and locate, which greatly hinders the improvement of object detection accuracy. In this paper, an object detection model (TDFP) based on CNN and transformer was established, which combines local and global context to establish the connection between features. In the proposed transformed dynamic feature pyramid network, a transformer module was designed to dynamically transform and fuse the multi-scale features generated by the backbone to generate a transformed feature pyramid with richer multi-scale features and context information. In this transformation process, gate block is used to dynamically select single-scale transformation or cross-scale transformation to achieve optimal style of transformation and fusion of multi-scale features. The experimental results show that the model improves the small targets detection accuracy based on CNN and transformer. Based on the backbone ResNeXt-101, TDFP achieves 46.2% AP and 26.3% APS on MS COCO, and takes the amount of computation as a loss constraint to achieve a better balance between detection accuracy and computational complexity.
A Mobile Tool that Helps Nonexperts Make Sense of Pretrained CNN by Interacting with Their Daily Surroundings

Current research on explainable AI (XAI) is primarily aimed at expert users (data scientists or AI developers). However, there is an increasing emphasis on making AI more understandable to non-experts who are expected to use AI techniques but have limited knowledge about AI. We propose a mobile application to help non-experts understand convolutional neural networks (CNN) in an interactive way; it allows users to taking pictures of surrounding objects and use pre-trained CNN to recognize it. We use the latest XAI (Class Activation Map) technology to visualize the model decision (the most important image area leading to a specific result). This playful learning tool was implemented in college courses and found to help design students gain a vivid understanding of the functions and limitations of pre-trained CNN in the real world. We thereby contribute an online tool that could be used for twofold purposes: first, it could help non-experts interactively learn how a pre-trained CNN works. Second, it can be used by researchers to probe and characterize the non-experts’ process of sensemaking, which could contribute insights into explainable AI design beyond expert users.
Cognitive Patterns for Semantic Presentation of Natural-Language Descriptions of Well-Formalizable Problems

The paper suggests and develops a method for creating cognitive patterns for well-formalizable problems described in a natural language. The method takes into account the full set of human cognitive abilities: the understanding of a natural language in the light of a well-formalizable problem, its formalization for an automatic solution, a graphical display of the solution, and its psychological aspects. The method is focused on a holistic approach to applied artificial intelligence systems considered in terms of interdisciplinarity. We have conducted an experimental study of the method capabilities on a set of geometric problems. Our experiment has included the modification of the source text at the level of morphology, syntax, and significant objects of a geometric problem followed by the preparation of a drawing. We have suggested an extension of the experiment to the problems with physical content.
Joint stiffness identification of industrial serial robots using 3D digital image correlation techniques

Industrial robots have been widely used in manufacturing for advantages of flexibility and high efficiency, while there exists a critical problem of low stiffness. Measuring the stiffnesses of joints accurately have a positive effect on optimizing the stiffness through compensation or posture adjustment. This study proposed a new method for stiffness identification of serial industrial robots using 3D digital image correlation (3D-DIC) techniques, which exhibits high accuracies. External forces are applied to the robot end and its 6-dimensional displacements are recorded with a 3D-DIC system. The values of joint stiffness are evaluated from the data of robot configurations, displacements and forces. The proposed method is implemented on the KUKA KR600-2830 robot experimentally and the average absolute value of relative error is 5.8%, which demonstrates that the proposed method provides much improved accuracy compared to the traditional method.
A Formal Security Analysis of the W3C Web Payment APIs: Attacks and Verification

Payment is an essential part of e-commerce. Merchants usually rely on third-parties, so-called payment processors, who take care of transferring the payment from the customer to the merchant. How a payment processor interacts with the customer and the merchant varies a lot. Each payment processor typically invents its own protocol that has to be integrated into the merchant’s application and provides the user with a new, potentially unknown and confusing user experience.Pushed by major companies, including Apple, Google, Master-card, and Visa, the W3C is currently developing a new set of standards to unify the online checkout process and “streamline the user’s payment experience”. The main idea is to integrate payment as a native functionality into web browsers, referred to as the Web Payment APIs. While this new checkout process will indeed be simple and convenient from an end-user perspective, the technical realization requires rather significant changes to browsers.Many major browsers, such as Chrome, Firefox, Edge, Safari, and Opera, already implement these new standards, and many payment processors, such as Google Pay, Apple Pay, or Stripe, support the use of Web Payment APIs for payments. The ecosystem is constantly growing, meaning that the Web Payment APIs will likely be used by millions of people worldwide.So far, there has been no in-depth security analysis of these new standards. In this paper, we present the first such analysis of the Web Payment APIs standards, a rigorous formal analysis. It is based on the Web Infrastructure Model (WIM), the most comprehensive model of the web infrastructure to date, which, among others, we extend to integrate the new payment functionality into the generic browser model.Our analysis reveals two new critical vulnerabilities that allow a malicious merchant to over-charge an unsuspecting customer. We have verified our attacks using the Chrome implementation and reported these problems to the W3C as well as the Chrome developers, who have acknowledged these problems. Moreover, we propose fixes to the standard, which by now have been adopted by the W3C and Chrome, and prove that the fixed Web Payment APIs indeed satisfy strong security properties.
Development of an Indoor Exergame Based on Moving-Target Hitting Task for COVID-19 Epidemic: A Comparison Between AR and VR Mode

The COVID-19 has led to home quarantine in global scale, which increased people’s need for indoor fitness activities in order to relieve stress/anxiety and learn knowledge well during the prevention. In this article, an augmented reality (AR) indoor exergame is developed, mainly a kind of moving target hitting task with Lomachenkos headwear speedball as the prototype. Then, a comparative experiment is performed to test the effect of the exergame in relieving anxiety, as well as the differences between AR and virtual reality modes (using HTC Vive Pro) on user experience (flow experience and motion sickness) and interaction efficiency. Results from 28 participants show that the anxiety level of participants significantly reduced after using the exergame. Although the difference on flow experience is not significant, there is a marginally significant difference on nausea level, a dimension of motion sickness. Moreover, interaction efficiency between the two modes is significantly different. In the task of moving target hitting, AR mode performed better in avoiding motion sickness and improving interaction performance.
Analysis of Josephson Junction Lifetimes for the Detection of Single Photons in a Thermal Noise Background

This work deals with the numerical analysis of the zero-voltage state lifetimes distribution of an underdamped Josephson junction used for the detection of single microwave photons in the presence of thermal noise. The analysis considers the switching probabilities of a JJ subjected to a train of current pulses, which simulates a weak photon field. To characterize the detection, we take advantage of a statistic tool, the Kumar-Carroll (KC) index, which is a good proxy of the signal-to-noise-ratio. It can be, therefore, exploited to identify the proper device fabrication parameters and the optimal operation point of the junction.
BanglaLM: Data Mining based Bangla Corpus for Language Model Research

Natural language processing (NLP) is an area of machine learning that has garnered a lot of attention in recent days due to the revolution in artificial intelligence, robotics, and smart devices. NLP focuses on training machines to understand and analyze various languages, extract meaningful information from those, translate from one language to another, correct grammar, predict the next word, complete a sentence, or even generate a completely new sentence from an existing corpus. A major challenge in NLP lies in training the model for obtaining high prediction accuracy since training needs a vast dataset. For widely used languages like English, there are many datasets available that can be used for NLP tasks like training a model and summarization but for languages like Bengali, which is only spoken primarily in South Asia, there is a dearth of big datasets which can be used to build a robust machine learning model. Therefore, NLP researchers who mainly work with the Bengali language will find an extensive, robust dataset incredibly useful for their NLP tasks involving the Bengali language. With this pressing issue in mind, this research work has prepared a dataset whose content is curated from social media, blogs, newspapers, wiki pages, and other similar resources. The amount of samples in this dataset is 19132010, and the length varies from 3 to 512 words. This dataset can easily be used to build any unsupervised machine learning model with an aim to performing necessary NLP tasks involving the Bengali language. Also, this research work is releasing two preprocessed version of this dataset that is especially suited for training both core machine learning-based and statistical-based model. As very few attempts have been made in this domain, keeping Bengali language researchers in mind, it is believed that the proposed dataset will significantly contribute to the Bengali machine learning and NLP community.
Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study

Histopathological diagnosis of bone tumors is challenging for pathologists. We aim to classify bone tumors histopathologically in terms of aggressiveness using deep learning (DL) and compare performance with pathologists.A total of 427 pathological slides of bone tumors were produced and scanned as whole slide imaging (WSI). Tumor area of WSI was annotated by pathologists and cropped into 716,838 image patches of 256 × 256 pixels for training. After six DL models were trained and validated in patch level, performance was evaluated on testing dataset for binary classification (benign vs. non-benign) and ternary classification (benign vs. intermediate vs. malignant) in patch-level and slide-level prediction. The performance of four pathologists with different experiences was compared to the best-performing models. The gradient-weighted class activation mapping was used to visualize patch's important area.VGG-16 and Inception V3 performed better than other models in patch-level binary and ternary classification. For slide-level prediction, VGG-16 and Inception V3 had area under curve of 0.962 and 0.971 for binary classification and Cohen's kappa score (CKS) of 0.731 and 0.802 for ternary classification. The senior pathologist had CKS of 0.685 comparable to both models (p = 0.688 and p = 0.287) while attending and junior pathologists showed lower CKS than the best model (each p < 0.05). Visualization showed that the DL model depended on pathological features to make predictions.DL can effectively classify bone tumors histopathologically in terms of aggressiveness with performance similar to senior pathologists. Our results are promising and would help expedite the future application of DL-assisted histopathological diagnosis for bone tumors.
Adaptive Task Offloading in Coded Edge Computing: A Deep Reinforcement Learning Approach

In this letter, we consider a Coded Edge Computing (CEC) network in which a client encodes its computation subtasks using the Maximum Distance Separable (MDS) code before offloading them to helpers. The CEC network is heterogeneous in which the helpers are different in computing capacity, wireless communication stability, and computing price. Thus, the client needs to determine a desirable size of MDS-coded subtasks and selects proper helpers such that the computation latency is within the deadline and the incentive cost is minimal. This problem is challenging since the helpers are generally dynamic and random in the computing, communication, and computing price. We thus propose to adopt a Deep Reinforcement Learning (DRL) algorithm that allows the client to learn and find optimal decisions without any prior knowledge of network environments. The experiment results reveal that the proposed algorithm outperforms the standard Q-learning and baseline algorithms in both terms of computation latency and incentive cost.
Fuzzy vector quantization with a step-optimizer to improve pattern classification

The paper proposes a novel supervised algorithm for data classification based on fuzzy vector quantization. The novelty here lies in including an optimization procedure to determine the step size of the quantization process. Adoption of fuzzy space for data exploration eliminates the problem of class-overlapping, while dealing with high dimensional non-linear data. In the proposed approach, the input data are first been transformed from the Euclidean space to the fuzzy space with three membership grades. Next, we have used uniform manifold approximation algorithm (UMAP), for projecting the data in visual space by selection of the most contrasting features from the fuzzy vectors. Then these features are passed through a quantization step with a novel step-optimization technique. Optimizing the quantization step and making it independent of data sets significantly speeds up the process. As the class information of all the feature vectors obtained by UMAP is known, majority voting principle has been used to locate the class-centroids in the subsequent step which in turn represent the class labels of the test samples. In test phase, after obtaining the test vectors, the Hyperspherical Direction Cosines (HDC) between the test vectors and the previously obtained class-centroids are evaluated. The test sample is finally assigned that class label where the sum of absolute differences (SAD) of these direction cosines is minimum. We have validated our classifier on various benchmark data sets and achieved higher accuracy with significantly low computation time than the existing state-of-the-art algorithms.
Identification of facial expression using a multiple impression feedback recognition model

Facial expression recognition (FER) plays a vital role in the automatic detection of human emotions with intelligent machine. Since the FER is an interdisciplinary technique involving biology, computer science and even psychology, more challenges will be encountered as we pursue a high recognition accuracy of facial expressions. Inspired by the progressive enhancing procedure of face recognition of human, we proposed a multiple impression feedback recognition model (MIFR) for the identification of facial expression. Different from current deep learning techniques, the MIFR realizes a quick facial expression recognition through cascade feedback recognition cycles. Multiple impression features of an FER image are firstly obtained by the discrete wavelet decomposition (DWT). Each recognition cycle is implemented by inputting wavelet impression features in a specific decomposition scale into a classifier set of parallel Support Vector Machines (SVMs). In terms of coarse-to-fine wavelet features on multiple scales, the recognition results are gradually improved through integrating classification probability vectors of multiple cycles. In order to validate the performance of the MIFR_SVM for face expression recognition, we also conducted an experiment of facial multi-view expression with occlusion (FMEO) in the laboratory. Three traditional schemes and seven deep learning schemes have been chosen for the FER comparison of three public datasets and one lab dataset FMEO. The classification results show that the MIFR_SVM is not only superior than traditional schemes, but also performs better than deep learning techniques, including both neural-network-based ones and random-forest-based ones. The average recognition accuracy of MIFR_SVM reaches to 92.31% for four datasets. Furthermore, the MIFR_SVM has fewer parameters, less complexity and adaptable cascade structure, which is more suitable for the image datasets with small size or with diverse qualities.
Application of Horse Herd Optimization Algorithm for medical problems

A vast impact concerning cancer research has been made by researchers analyzing the expression of thousands of genes at the same time and relating them to clinical phenotypes by DNA microarray technology. The large dimensions of gene expression microarray datasets, combined with a lack of samples and irrelevant or noisy genes, make classification tasks more difficult The purpose of gene selection is to identify the most informative genes in order to enhance prediction results. The Horse Herd Optimization Algorithm (HOA) is a novel swarm intelligence algorithm for solving continuous optimization problems, which simulate the behavior of a herd of horses. This study introduces a binary version of the Horse Herd Optimization Algorithm (BHOA) for solving discrete problems in biological data. Moreover, this paper introduces a novel hybrid gene selection framework that combines BHOA and a minimum Redundancy Maximum Relevance (mRMR) method. mRMR is applied as a filter approach to reduce noisy and irrelevant genes as well as to reduce the high computational cost that swarm intelligence algorithms generally suffer. The proposed approach’s performance was examined using four well-known datasets: DLBCL, Colon, SRBCT, and Leukemia. According to the experimental results, the hybrid approach performed better than all the other methods regarding both accuracy and the number of selected genes.