Reviewerly/exHarmony huggingface dataset:
Joint Scheduling of Participants, Local Iterations, and Radio Resources for Fair Federated Learning over Mobile Edge Networks

Federated learning (FL) provides a promising way to train a machine learning model among mobile devices without collecting their raw data to a central node. During training, proper devices are selected to participate in the training process to avoid model unfairness. In a mobile edge network, participant selection must be considered together with three factors: non-iid datasets possessed by devices, tunable local iterations on devices, and radio resource allocation to counter the impact of time-varying channel conditions on parameter transmissions. Since datasets of devices are given, to ensure model fairness and achieve fast convergence in the FL training process, participants, local iterations, and radio resources must be scheduled jointly in each iteration of FL training. In this paper, the joint scheduling problem is analyzed and formulated. Since it is NP-hard, a heuristic scheduling method called PALORA is designed to conduct joint scheduling of participants, local iterations, and radio resources. PALORA consists of three sequentially interactive function blocks: 1) a pointer network embedded deep reinforcement learning method to select participants, 2) an estimation algorithm to determine the numbers of local iterations, and 3) a breadth-first search method to allocate radio resources to the selected participants. PALORA is evaluated via extensive simulations based on real-world datasets. Results show that it significantly outperforms benchmark approaches.
Incentive Mechanism for AI-Based Mobile Applications with Coded Federated Learning

Federated learning (FL) has emerged as a highly-effective distributed learning framework for various AI-based mobile applications. However, in conventional FL, participating mobile users (MUs) may have limited computing resources to train their local data, which leads to learning quality degradation for the whole FL process. To address this problem, coded FL (codFL) has been recently introduced, allowing MUs to upload part of their coded data to a mobile application provider (MAP) before the learning process. As a result, codFL can not only deal with the MUs&#x0027; limited computing resources, but also provide more benefits for the MUs to participate in the learning process. Nonetheless, in practice, the MAP and MUs often belong to different parties who unilaterally aim to maximize their individual utility functions. Thus, in this paper, we propose an effective mechanism for the codFL process to incentivize all the participating MUs while improving the learning quality of the MAP. Specifically, we first design a codFL contract optimization problem leveraging a multi-principal one-agent (MPOA) approach in contract theory, under limited computing resources at the MAP and MUs as well as information asymmetry between them. To find the optimal contracts for MUs, we develop an iterative contract algorithm which can produce maximum utilities for all MUs while satisfying all the constraints of the MAP. Numerical results show that our framework can enhance the utilities of MUs up to 113&#x0025; and system performance in terms of social welfare up to 42&#x0025; compared with the baseline method.
Evaluation Method of Enterprise Environmental Accounting Information Disclosure Quality Based on Data Classification

Facing the problem that the currently used analytic hierarchy process and the evaluation method based on the fuzzy analytic hierarchy process are affected by a large amount of corporate environmental accounting information, resulting in inaccurate evaluation results, a data classification-based corporate environmental accounting information disclosure quality evaluation method is proposed. Through environmental reports, corporate environmental accounting information is divided into environmental accounting information in the annual reports of listed companies and environmental accounting information in independent reports. Integrate fuzzy theory and the analytic hierarchy process, analyze the set of each index in the two types of information, and obtain the fuzzy evaluation matrix of each subsystem. Using the synthetic operation of the fuzzy matrix, the comprehensive evaluation model of each subsystem is obtained, thereby completing the quality evaluation. Through the empirical analysis results, it can be seen that there is a maximum error of 0.003 between the evaluation results of this method and the statistical results, which has accurate evaluation results.
Self-supervised part segmentation via motion imitation

Given two images with the same appearance and different poses, the motion imitation is to warp the source image into the reference pose. Can the network learn the part information of the object in this process? In this paper, we take motion simulation as the pretext task to learn part information. It is based on the assumption that the generated image will be similar to the reference image only if the part information is sufficiently accurate. Different from the existing work, the key idea of this paper is to minimize the “creativity” of the motion imitation network to avoid that the network can generate an image similar to the reference image even if the part information is not learned. In particular, we investigate the complementarity of key point information and part information, and propose a joint learning module to make them benefit from each other. We constructed a multi-source fusion module to fuse missing information from multiple images to reduce the importance of the inpainting network with “creative” capabilities in the entire framework. And warping the image in the image space forces the network to directly use the original information to generate the image. In this way, the network can only move pixels based on the learned part information, not modify pixels. Compared with the existing self-supervised methods, the proposed method in this paper can produce more semantically consistent and meaningful parts without the utilization of any pre-computed information or pre-training weights. The effectivity and validity of the proposed method have verified through extensive experiments on Tai-Chi-HD and VoxCeleb datasets.
Edu-bot: An AI based Smart Chatbot for Knowledge Management System

The pandemic has brought in a lot of changes in various aspects of human life. Every sector has been affected by this and so is the educational sector. Organizing and accessing of documents was one of the challenges that students faced during the pandemic. To overcome this problem, we propose an AI system that is equipped with a chatbot. In this study, we develop a smart knowledge management system that authenticates the user via face recognition, then a chatbot helps the users in searching and accessing the required document easily. Face Recognition is implemented using libraries such as OpenCV, Tensorflow and Face_Recognition. Face_Recognition Library provided the highest accuracy so it was used in the smart knowledge management system. A chatbot was implemented to search and access the study materials such as notes, textbooks and question banks from a particular destined folder through the hyperlinks created. To make the system user friendly, GUI was designed using Tkinter library. This AI based smart knowledge management system provides access to the required document very effectively and effortlessly.
Research on Mask Wearing Detection Algorithm Based on YOLOv5

The current mask wearing detection device is affected by numerous factors, such as the large number of people in complex scenes, the easy obstruction of the gathering crowd, and the small size of the inspection target, which are prone to false detections and missing inspections. In order to solve the above problems, this journal proposed a mask-wearing detection algorithm based on YOLOv5 to realize real-time detection in complex scenes. Firstly, perform Mosaic data enhancement and other processes on the data set. Then, apply Focus process to retain more complete down sampling information of the images for subsequent feature extraction. Later, employ SPP to integrate multi-scale information to achieve feature enhancement and retain spatial information within Neck. Finally, CIoU Loss function is selected s to improve the positioning accuracy. And during the training process, a dynamic adjustment strategy is adopted for the learning rate.Experimental results show that the average accuracy of the improved algorithm reaches 99.1%.
Comparative Analysis of Six Programming Languages Based on Readability, Writability, and Reliability

In recent years, the development of programming languages has been centered around making them easily understandable and learnable to users. Hence, the readability, writability of languages is being constantly improved while trying to keep the performance reliable. These factors affect how many new users start to use a particular language and how many experienced programmers continue to use it reliably in real applications. Hence, this research has compared the readability, writability, and reliability of six mainstream programming languages, namely C, C++, Java, JavaScript, Python, and R, based on their theoretical characteristics. Furthermore, we conducted a survey determining the choice of a language among programmers and nonprogrammers, which complemented the results gathered from the study. We found that Python outperforms others in terms of its readability and writability, while Java is proven to be the most reliable of all. We reported our findings, insights, and a discussion on the future development of better evaluation metrics.
Serious Interactive Digital Narrative: Explorations in Personalization and Player Experience Enrichment

Serious Interactive Digital Narrative is the next step in the evolution of serious games: a deeply adaptive medium that applies player modeling at its core to provide seamless and justifiable personalized experience, which entrenches the impact of its serious discourse using the novel artificially intelligent methods. The objective of this exploratory paper is to re-explore the idea that narration has central role in serious games and interactiveness can arguably enrich the player experience. This is done through tracing relevant literature to highlight its importance and exploration into the arsenal of novel intelligent techniques as an attempt to elaborate on its realization, emphasizing on major functionality of interactive narrative in delegating agency and providing immersive experience for the player. To clarify, due to its exploratory nature, this study in its current stage is void of experimental results. Nevertheless, it concludes with a proposed prototype game design, outlining its key game design elements and stating possible outlooks.
A Transformer Based Encodings for Detection of Semantically Equivalent Questions in cQA

Abstract The probability of redundancy in questions has significantly increased due to the increasing influx of users on different cQA forums such as Quora, Stack overflow, etc. Because of this redundancy, the responses are scattered through various variations of the same question that results in unsatisfactory search results to a specific question. To address this issue, this work proposes the model for discovering the semantic similarity among the cQA questions. We followed two approaches (i) Feature-based: the question embedding is created using four forms of word embeddings and an ensemble of all four. Then Siamese LSTM (sLSTM) is used to find the semantic similarity among the questions. (ii) Fine-tuning: we fine-tuned BERT model on STS and SNLI data, which employs Siamese network architectures to generate semantically meaningful sentence embeddings. Then sBERT is used to assess the similarity between the questions. Experiments were carried out on Quora (QQP) and Stack Exchange cQA dataset with training sets of different sizes and word vectors of different dimensionalities. The model shows significant improvement over the state-of-the-artwork on sentence similarity tasks.
Multi-Frame Super-Resolution Algorithm Based on Attention Mechanism

In the field of computer vision, image super-resolution is a difficult task, which have many applications in remote sensing, military and so on. In this paper, we introduce the convolutional block attention module (CBAM) into super-resolution problem, proposing a novel method called multi-frame super-resolution (MFSR) algorithm based on attention mechanism. Our proposed MFSR algorithm uses a three-layer CNN as benchmark and cascades CBAM at the end of each CNN block. The proposed algorithm can deliver a high-resolution output corresponding to the center (3rd) input frame. The average PSNR and SSIM of our algorithm are 33.318dB and 0.906 respectively, which outperform other MFSR algorithm.
Mobile instant messaging for ELT

In this series, we explore technology-related themes and topics. The series aims to discuss and demystify what may be new areas for some readers and to consider their relevance for English language teachers. The use of mobile devices and, in particular, mobile instant messaging (MIM) applications in ELT classrooms has been the subject of research in recent years. Undoubtedly, classroom environments need to adapt to constant technological development in order to foster language teaching and learning opportunities, and to keep pace with a fast-evolving world. In this context, MIM appears to be a potential tool for language teaching, which can take advantage of existing social practices involving the exchange of messages, whether visual or aural, among individuals at different times and places. At the same time, the inherent characteristics of MIM applications (e.g. WhatsApp, WeChat, Line, Telegram, Kaotalk, Facebook Messenger) allow: instant delivery of messages through a pop-up mechanism; text,...
Two-Pass End-to-End ASR Model Compression

Speech recognition on smart devices is challenging owing to the small memory footprint. Hence small size ASR models are desirable. With the use of popular transducer-based models, it has become possible to practically deploy streaming speech recognition models on small devices [1]. Recently, the two-pass model [2] combining RNN-T and LAS modules has shown exceptional performance for streaming on-device speech recognition. In this work, we propose a simple and effective approach to reduce the size of the two-pass model for memory-constrained devices. We employ a popular knowledge distillation approach in three stages using the Teacher-Student training technique. In the first stage, we use a trained RNN-T model as a teacher model and perform knowledge distillation to train the student RNN-T model. The second stage uses the shared encoder and trains a LAS rescorer for student model using the trained RNN-T+LAS teacher model. Finally, we perform deep-finetuning for the student model with a shared RNN-T encoder, RNN-T decoder, and LAS rescorer. Our experimental results on standard LibriSpeech dataset show that our system can achieve a high compression rate of 55% without significant degradation in the WER compared to the two-pass teacher model.
Modeling and Simulation of Heterogeneous Resources in the Cloud: (Work in Progress)

Heterogenous cloud computing will take a significant role in addressing performance concerns at hyper-scale computing. New approaches are needed to manage the system complexity and to support an High-Performance Computing (HPC) in the cloud. The large-scale modeling is associated with challenges in transitioning to a heterogeneous cloud with an emphasis on resource management. In this paper, the complex cloud system infrastructure of five heterogeneous data centers is modeled and simulated. The simulation is performed using a CloudSim simulator under the parallel workload. The idea is to connect data centers using a central broker for assigning tasks to data centers depending on their predefined settings and job requirements. The operation and performance of the system execution under a high load are shown. We consider this model optimal for distribution regarding complex data infrastructures. All assigned tasks were successfully performed on cloud data centers. Guidelines for future work are given.
Bangla Printed Character Generation from Handwritten Character Using GAN

Bangla language contains a vast character set containing more than twelve thousand compound characters. These characters are the key to make words. A lot of words are now on the verge of extinction because Unicode contains only three hundred Bangla compound characters. Another option to save these words from extinction is improving the optical character recognition system. But, Optical Character Recognition (OCR) needs a lot of samples from the diversely distributed dataset for training. Making diverse datasets for OCR training is a challenging and tedious task to perform. In this paper, Generative Adversarial Net (GAN) based architecture capable of generating printed-character from handwritten Bangla character is proposed. GAN model works on the principle of an image to image translation based on image mapping to output. The architecture not only learns a mapping between handwritten-character image and printed-character image but also learn loss function, which is vital to generate printed-character from different probability distribution like Bangla script. In most of the cases, there is hardly any differences found between generated character using GAN and printed-character. The probability distribution of generated printed-character has diversity because of GAN loss. Generated printed-character can be used in training OCR because of variations in the data distribution. Hardcoded loss function or mapping function is no longer needed for handwritten-character to printed-character generation.
A Framework for Multi-party Skyline Query Maintaining Privacy and Data Integrity

Skyline query is well-known to find out the dominant objects from a large number of datasets. While multiple organizations want to analyze their combined dataset, skyline queries can assist in this regard. Maintaining privacy along with the data integrity of participating organizations’ datasets is important because their commercial success depends on the result of these queries. This paper proposes a new framework for the multi-party skyline query that encompasses both privacy and data integrity. To ensure the privacy of participants’ datasets, it adopts commutative encryptions by employing multiple independent entities. To support the data integrity, it combines encrypted unique tags (UTs) with the encrypted datasets of all participants. In addition, to retain the anonymity of participants’ encrypted data from anyone including authorities, it exploits the re-encryption. Although the proposed framework also practices homomorphic encryption, which usually sacrifices the data integrity, here due to the usage of UTs, it is maintained. This paper is a preliminary report of the proposed framework.
Analysis of Machine Learning Algorithms by Developing a Phishing Email and Website Detection Model

Machine Learning is a key branch of Artificial Intelligence that concentrates on the development of computational algorithms by creating models. It has caught major attention in the technological domain due to its various applications in speech recognition, recommendation engines, computer vision, automated stock trading etc. The model’s performance is dependent on the dataset provided and its accuracy can easily be enhanced by expanding the training dataset. Post Covid-19, it has been observed that phishing websites are appallingly on the rise, especially the phishing attacks. These attacks are caused by cybercriminals using PDF’s, Microsoft office documents and other attachments via emails. This paper focusses on discussion and comparison of different machine learning algorithms that are capable of detecting phishing emails and websites. The experiments have shown that that MultinomialNB attains the highest efficiency of 98.06% for phishing email detection and Decision Tree Classifier offers the maximum efficiency of 95.41% for phishing website detection.
SMILE - Smart eMaIl Link Domain Extractor

Abstract Phishing over email continues to be a significant threat, as such messages still end up in users’ inboxes. Several studies showed that users rarely check the URL in the statusbar before clicking a link and that they have difficulties reading URLs. To support users, we propose SMILE (Smart eMaIl Link domain Extractor), a novel approach that provides the relevant information to distinguish between legitimate and phishing emails checking the links in them. Once applied, SMILE modifies all links in an email to contain the domain and top-level-domain of the URL behind them, e.g., “ Click here ” in an legitimate Amazon email is modified to “Click here [ amazon.com ]”.
Comprehensive benefit evaluation method of energy Internet platform based on cloud model

The energy internet platform not only integrates multiple types of energy forms, but also involves the benefit distribution of multiple subjects during the construction and operation. How to evaluate the platform objectively and accurately is the foundation for the construction of the platform. To this end, the comprehensive benefit evaluation index system of energy internet platform is proposed. The index was weighted by the method of analytic hierarchy process and entropy weight combining subjectivity and objectivity. Then, the comprehensive benefit cloud chart of the energy internet platform is determined by constructing the fuzzy synthetic evaluation matrix based on the cloud model to generate digital characteristics of the cloud model of every index. Finally, an example is given to verify the rationality of the evaluation index system and the validity of the evaluation method.
Adversarial Attacks on Computation of the Modified Policy Iteration Method

Adversarial attacks on Markov decision processes (MDPs) and reinforcement learning (RL) have been studied in the literature in the context of robust learning and adversarial game theory. In this paper, we introduce a new notion of adversarial attacks on MDP and RL computation that is motivated by the emergence of edge computing. The large-scale computation of MDP and RL models in the form of value/policy iteration and Q-learning is being offloaded from agents to distributed servers, giving rise to edge reinforcement learning. By the inherently distributed nature of edge RL, the MDP/RL computation can be prone to adversarial attacks in different forms. We analyze a probabilistic model of adversarial attacks on the computation of the modified policy iteration method in which the principal contraction property of the Bellman operator is undermined with a certain probability in iterations of the policy evaluation step of the aforementioned method. This can result in luring the agent to search among suboptimal policies without improving the true values of policies. We prove that under certain conditions, the attacked modified policy iteration method can still converge to the vicinity of the optimal policy with high probability if the number of policy evaluation iterations is larger than a threshold that is logarithmic in the inverse of a desired precision. We also provide an upper bound on the number of iterations needed for the attacked modified policy iteration method to terminate, which holds with an associated confidence level.
Programming Models of Command Processing for Time-Series Data with Fault Tolerance to C++/CLI Graphics Codes

This paper is to propose an object-oriented framework with C++/CLI programming models that process the input commands for time series with fault tolerance to the graphics in an integrated development environment, e.g., Microsoft Visual Studio. In this approach, the collection of those opcodes (e.g., string array) can be systematically augmented. Also, with the predefined template of multiple-division commands, the proposed methods can be immune to format error or incomplete operand divisions. The experimental results reveal the effectiveness of the fault tolerance to graphing time series.