Reviewerly/exHarmony huggingface dataset:
Bidirectional parallel echo state network for speech emotion recognition

Speech is an effective way for communicating and exchanging complex information between humans. Speech signal has involved a great attention in human-computer interaction. Therefore, emotion recognition from speech has become a hot research topic in the field of interacting machines with humans. In this paper, we proposed a novel speech emotion recognition system by adopting multivariate time series handcrafted feature representation from speech signals. Bidirectional echo state network with two parallel reservoir layers has been applied to capture additional independent information. The parallel reservoirs produce multiple representations for each direction from the bidirectional data with two stages of concatenation. The sparse random projection approach has been adopted to reduce the high-dimensional sparse output for each direction separately from both reservoirs. Random over-sampling and random under-sampling methods are used to overcome the imbalanced nature of the used speech emotion datasets. The performance of the proposed parallel ESN model is evaluated from the speaker-independent experiments on EMO-DB, SAVEE, RAVDESS, and FAU Aibo datasets. The results show that the proposed SER model is superior to the single reservoir and the state-of-the-art studies.
ProofChain: An X.509-compatible blockchain-based PKI framework with decentralized trust

Public Key Infrastructure (PKI) is the most widely accepted cryptography protocol to enable secure communication over the web. PKI comprises digital certificates managed by the certificate authorities (CAs) to verify the user’s identity, thus providing secure communication channels. However, the security of PKI is profoundly reliant on the reliability of these third-party CAs, which serves as a single point of failure for PKI. Over the past, there have been several incidents of popular CA breaches, where the centralized operation model of CAs caused numerous targeted attacks due to the spread of rogue certificates. In this paper, we aim to make the CA pool completely decentralized and concurrently build our decentralized solution cooperative with established PKI standards (i.e., X.509) for effective real-world integration. In particular, we harness blockchain technology to propose a decentralized PKI framework named ProofChain, which provides complete trust among a decentralized group of CAs. Our proposed solution provides all the traditional X.509 PKI operations (i.e., registration, validation, verification, and revocation), making it compatible with existing PKI standards. We have also evaluated ProofChain against popular security standards (i.e., the CIA triad model) and PKI adversarial attacks. Besides, to demonstrate the practicality of our proposed system, we have also evaluated the performance of the ProofChain by implementing it on the private testbed of the Ethereum network across various real-world PKI scenarios.
Interfacing of Online and Offline Voting System with an E-Voting Website

This paper presents the interfacing of online and offline voting systems with an E-Voting website. As per the bylaw of the Constitution of India, the Election Commission of India (ECI) has been driven to conduct elections honestly and autonomously at regular intervals. For this, from the last decade onwards they are implementing advanced technologies in the election process to ensure efficacy, less time consumption, and cost. Right now, the ECI effectively utilizing the Voter-Verified Paper Audit Trail (VVPAT) with an Electronic Voting Machine (EVM) to ensure each individual votes. However, still, the ECI struggling to control malpractice that exists in the election process while verifying voters with an electoral list. To overcome these issues, a face recognition device is embedded with the EVM. The ECI trying to achieve a more than ninety-five percent polling rate in a democratic country. At present, the average polling rate in all types of elections has not reached a mere seventy percent. The ECI is unable to achieve its target due to people migrating from state to state and abroad for employment. The main objective of interfacing online and offline voting systems are to provide opportunities for migrated people to complete vote during elections in their respective constituencies. In this regard, an online website is used for voting which can update the information of voters and their voting status which are acquired through both online and offline voting. The offline voting system implements the usage of the raspberry pi for face recognition. Test results on developed online and offline voting systems with an E-Voting website are found to be satisfactory.
Defending Adversarial Attacks against DNN Image Classification Models by a Noise-Fusion Method

Adversarial attacks deceive deep neural network models by adding imperceptibly small but well-designed attack data to the model input. Those attacks cause serious problems. Various defense methods have been provided to defend against those attacks by: (1) providing adversarial training according to specific attacks; (2) denoising the input data; (3) preprocessing the input data; and (4) adding noise to various layers of models. Here we provide a simple but effective Noise-Fusion Method (NFM) to defend adversarial attacks against DNN image classification models. Without knowing any details about attacks or models, NFM not only adds noise to the model input at run time, but also to the training data at training time. Two l∞-attacks, the Fast Gradient Signed Method (FGSM) and the Projected Gradient Descent (PGD), and one l1-attack, the Sparse L1 Descent (SLD), are applied to evaluate defense effects of the NFM on various deep neural network models which used MNIST and CIFAR-10 datasets. Various amplitude noises with different statistical distribution are applied to show the defense effects of the NFM in different noise. The NFM also compares with an adversarial training method on MNIST and CIFAR-10 datasets. Results show that adding noise to the input images and the training images not only defends against all three adversarial attacks but also improves robustness of corresponding models. The results indicate possibly generalized defense effects of the NFM which can extend to other adversarial attacks. It also shows potential application of the NFM to models not only with image input but also with voice or audio input.
Body-Centric NFC: Body-Centric Interaction with NFC Devices Through Near-Field Enabled Clothing

NFC (Near-Field Communication) has been widely applied for human-computer interaction (HCI). However, the short sensing distance of NFC requires the users to initiate the tasks with extra effort mostly using their hands, so it is inconvenient to use NFC in hands-busy scenarios. This paper presents an investigation of body-centric interactions between the NFC device users and their surroundings. The exploration is based on the recent development of near-field enabled clothing, which can passively extend an NFC-enabled device's reading distance to users' body landmarks. We present an accessible method for fabricating flexible, extensible, and scalable NFC extenders on clothing pieces, and an easy-to-use toolkit for facilitating designers to realize the interactive experiences. The method and toolkit were tested in technical experiments and in a co-creation workshop. The elicited design outcomes and the further exploratory makings generated knowledge for future research and embodied interaction design opportunities.
Data Anonymization: An Experimental Evaluation Using Open-Source Tools

In recent years, the use of personal data in marketing, scientific and medical investigation, and forecasting future trends has really increased. This information is used by the government, companies, and individuals, and should not contain any sensitive information that allows the identification of an individual. Therefore, data anonymization is essential nowadays. Data anonymization changes the original data to make it difficult to identify an individual. ARX Data Anonymization and Amnesia are two popular open-source tools that simplify this process. In this paper, we evaluate these tools in two ways: with the OSSpal methodology, and using a public dataset with the most recent tweets about the Pfizer and BioNTech vaccine. The assessment with the OSSpal methodology determines that ARX Data Anonymization has better results than Amnesia. In the experimental evaluation using the public dataset, it is possible to verify that Amnesia has some errors and limitations, but the anonymization process is simpler. Using ARX Data Anonymization, it is possible to upload big datasets and the tool does not show any error in the anonymization process. We concluded that ARX Data Anonymization is the one recommended to use in data anonymization.
A Multi-Dimensional Analysis of English tweets

This paper applies Multi-Dimensional Analysis (MDA) to a corpus of English tweets to uncover the most common patterns of linguistic variation. MDA is a commonly applied method in corpus linguistics for the analysis of functional and/or stylistic variation in a particular language variety. Notably, MDA is an approach aimed at identifying and interpreting the frequent patterns of co-occurring linguistic features across a corpus, such as a corpus of spoken and written English registers (Biber, 1988). Traditionally, MDA is based on a factor analysis of the relative frequencies of numerous grammatical features measured across numerous texts drawn from that variety of language to identify a series of underlying dimensions of linguistic variation. Despite its popularity and utility, traditional MDA has an important limitation – it can only be used to analyse texts that are long enough to allow for the relative frequencies of many grammatical forms to be estimated accurately. If the texts under analysis are too short, then few forms can be expected to occur sufficiently frequently for their relative frequency to be accurately estimated. Tweets are characteristically short texts, meaning that traditional MDA cannot be used in the present research. To overcome this problem, this paper introduces a short-text version of MDA and applies it to a corpus of English tweets. Specifically, rather than measure the relative frequencies of forms in each tweet, the approach analyses their occurrence. This binary dataset is then aggregated using Multiple Correspondence Analysis (MCA), which is used much like factor analysis in traditional MDA – to return a series of dimensions that represent the most common patterns of linguistic variation in the dataset. After controlling for text length in the first dimension, four subsequent dimensions are interpreted. The results suggest that there is a great deal of linguistic variation on Twitter. Notably, the results show that Twitter is commonly used for self-commodification, as people manage their identities, engaging in practices of self-branding through stance-taking, self-reporting, promotion and persuasion, as well as broadcasting their message beyond their followership, distributing news and expressing opposition, and this often occurs in order to attract attention. Additionally, the results show that interaction is common, suggesting that Twitter is also used for social and interpersonal gain.
An In-Depth Review on Blockchain Simulators for IoT Environments

Simulating blockchain technology within the IoT has never been as important. Along with this comes the need to find suitable blockchain simulators capable of simulating blockchain networks within an IoT environment. Despite there being a wide variety of blockchain simulators, not all are capable of simulating within an IoT environment and not all are suitable for every IoT environment. This article will review previously published works and present a list of suitable blockchain simulators as well as a few untested simulators that have the potential to simulate blockchain networks within an IoT environment. A total of 18 blockchain simulators are presented and discussed in this paper. In addition, a comprehensive list of the advantages and limitations of each simulator is presented to demonstrate the best situation in which simulators should be used. Finally, recommendations are made on when each simulator should be used and in what situation it should be avoided.
Lukewarm serverless functions

Serverless computing has emerged as a widely-used paradigm for running services in the cloud. In serverless, developers organize their applications as a set of functions, which are invoked on-demand in response to events, such as an HTTP request. To avoid long start-up delays of launching a new function instance, cloud providers tend to keep recently-triggered instances idle (or warm) for some time after the most recent invocation in anticipation of future invocations. Thus, at any given moment on a server, there may be thousands of warm instances of various functions whose executions are interleaved in time based on incoming invocations.
Low-resolution face recognition based on feature-mapping face hallucination

The image recognition approaches based on Convolutional Neural Network (CNN) have already achieved tremendous performance on super-resolution face images. However, there exist several challenges in face recognition field. For example, in very low-resolution face recognition (LRFR) environments, the accuracy will drop drastically. To address the issue, this paper proposes a novel face hallucination and recognition model for low resolution face images ground on feature-mapping. In the proposed model, a new loss function named identity-aware loss is also proposed. The proposed loss function is combined with the feature loss and image-content loss to jointly train models. The proposed model is evaluated on Labeled Faces in the Wild (LFW) dataset, which compared to progressive competing models. A large number of experimental results indicate that this model observably enhances the performance of recognition particularly when face images are very low-resolution. In addition, our model can perform high resolution face image reconstruction which is comparable to advanced approaches based on super-resolution in the field of visual quality, and achieve identity preservation of corresponding low resolution probe image.
Higher Education Teaching Quality Evaluation Model Based on Improved RBF Neural Network

In order to improve the reliability of higher education quality evaluation, this paper improves the RBF neural network algorithm based on the characteristics of higher education teaching data. Using RBF neural network technology, the predictive model established after learning from samples of actual teaching data can be used to evaluate teaching quality. Moreover, this paper combines the improved RBF neural network to construct a higher education teaching quality evaluation model. The model built in this article needs the support of a support vector machine (SVM) to build a sentiment analysis module and a teaching quality evaluation module. After the model is constructed, the performance of the model built in this article is evaluated, and the performance of the model is displayed by combing statistics. The teaching quality assessment of the higher education model is between 80% and 85%.The test results show that the higher education teaching quality appraise model depended on the improved RBF neural network put forward in this paper has certain effects.
Object Detection for Inventory Stock Counting Using YOLOv5

The study has successfully created a program to detect cellphone boxes namely, Cherry Aqua S9 and Cherry Flare S8 in any orientation. Successful detection was made possible by building the datasets where the target objects will capture using a camera in different scenarios. A total of 1,623 images were collected and automatically split by the Roboflow application. In this study, the best background light source to be used is 9Watts. Based on the data gathered were, both target objects had a perfect count of thirty-two (32) and had a 0.96 average accuracy for S8 and a 0.95 average accuracy for S9. For the 7W lighting source, the S9’s did not detect the side view 180° orientation; for the accuracy test, a 0.95 average accuracy for S8 and a 0.90 average accuracy for S9. Lastly, using 5W, eight (8) misdetections in the system, having a total count of only Twenty-four (24) for S8 and forty (40) for S9, for the accuracy-test a 0.70 average accuracy for S8 and a 0.93 for S9. The You Only Look Once v5 (YOLOv5) algorithm was successfully applied to identify and count the target objects and display the result in the touch display. Based on the outcome for noise reduction, placing different kinds of boxes and boxes with the same size and dimension of the target objects will not be detected. YOLO is the best algorithm for object detection that recognizes only the trained objects. The results show the accuracy, which offers a high precision and high recall curve and decreases all lost when the dataset added more pictures, leading to higher accuracy.
How Much Entanglement Do Quantum Optimization Algorithms Require?

ADAPT-QAOA, a novel problem-tailored version of quantum approximate optimization algorithm, speeds up convergence using entangling operators while reducing the total number of CNOTs. We explore how much entanglement is required to speed up optimization algorithms.
Meta-YOLO: Meta-Learning for Few-Shot Traffic Sign Detection via Decoupling Dependencies

Considering the low coverage of roadside cooperative devices at the current time, automated driving should detect all road markings relevant to driving safety, such as traffic signs that tend to be of great variety but are fewer in number. In this work, we propose an innovative few-shot object detection framework, namely Meta-YOLO, whose challenge is to generalize to the unseen classes by using only a few seen classes. Simply integrating the YOLO mechanism into a meta-learning pipeline will encounter problems in terms of computational efficiency and mistake detection. Therefore, we construct a two-stage meta-learner ℱ model that can learn the learner initialization, the learner update direction and learning rate, all in a single meta-learning process. To facilitate deep networks with learning, the fidelity features of the targets improve the performance of meta-learner ℱ, but we also design a feature decorrelation module (FDM), which firstly transforms non-linear features into computable linear features based on RFF, and secondly perceives and removes global correlations by iteratively saving and reloading the features and sample weights of the model. We introduce a three-head module to learn global, local and patch correlations with the category detection result outputted by the aggregation in meta-learner ℱ, which endows a multi-scale ability with detector ϕ. In our experiments, the proposed algorithm outperforms the three benchmark algorithms and improves the mAP of few-shot detection by 39.8%.
Evaluation of Hybrid Collaborative Filtering Approach with Context-Sensitive Recommendation System

This study aims to evaluate the recommendation system we have implemented. The test was carried out using student profile data with (1) adding contextual information and (2) without contextual information. The evaluation was carried out using three predictive methods, user collaborative filtering, item collaborative filtering, and hybrid. We use the correlation coefficient to determine which method has the best correlation coefficient between the predicted and actual values. In this experiment, the best method for predicting the actual value is to use a user-based collaborative filtering method. A low correlation coefficient indicates that a machine learning model is needed to learn the predictive formula of the predictor features and their actual values. The test results using these three methods show that the correlation coefficient between the actual value and the predicted value using user-based collaborative filtering is the highest. Meanwhile, the lowest correlation coefficient between the actual value and predicted values using item collaborative filtering is the lowest. The results of this study prove that contextual information as an additional feature of student profiles increases the correlation coefficient between actual and predicted scores using a user collaborative filter.
Revolutionizing E-Commerce Using Blockchain Technology and Implementing Smart Contract

The days of storing data manually are behind us. We are opting for the online form of data storage and transfer. The new era of data digitization comes with its own perks and detriments. Cybersecurity is still a crucial concern today. As more data transfer occurs through an online medium, the risks of a breach and cyberattacks are inevitable. The whole foundation of e-commerce is based on the online transfer of goods and transactions without the need to travel. Transferring transactional data and transactions in e-commerce are prone to cyber threats. Our research’s major objective is to develop a system that protects against such mishaps, especially during the transfer of transactional data, and also implement an automated system that ensures these transactions occur without any errors. To implement this, we are taking advantage of new emerging technologies called blockchain and smart contract. Blockchain allows a decentralized, immutable digital ledger to safely store and transfer data across the network. Blockchain technology is used in e-commerce to transfer transactions in a safe, secure, and faster way. Blockchain enables a peer-to-peer transaction system and data encryption that enables the safe transfer of transactional data. Blockchain is used to transfer transactional data. A smart contract is a special program that enables, verifies, and enforces the terms of a contract digitally. It provides transactional security as the contact is in place. The blockchain, coupled with smart contracts, will revolutionize the future of e-commerce. We have combined blockchain technology to ensure data security and user privacy with smart contracts to ensure that the protocol for the transaction is maintained. The results are presented by building and implementing the proposed system that provides the solution for transactional data privacy.
Cascading structured pruning

Performance and efficiency of running modern Deep Neural Networks (DNNs) are heavily bounded by data movement. To mitigate the data movement bottlenecks, recent DNN inference accelerator designs widely adopt aggressive compression techniques and sparse-skipping mechanisms. These mechanisms avoid transferring or computing with zero-valued weights or activations to save time and energy. However, such sparse-skipping logic involves large input buffers and irregular data access patterns, thus precluding many energy-efficient data reuse opportunities and dataflows. In this work, we propose Cascading Structured Pruning (CSP), a technique that preserves significantly more data reuse opportunities for higher energy efficiency while maintaining comparable performance relative to recent sparse architectures such as SparTen. CSP includes the following two components: At algorithm level, CSP-A induces a predictable sparsity pattern that allows for low-overhead compression of weight data and sequential access to both activation and weight data. At architecture level, CSP-H leverages CSP-A's induced sparsity pattern with a novel dataflow to access unique activation data only once, thus removing the demand for large input buffers. Each CSP-H processing element (PE) employs a novel accumulation buffer design and a counter-based sparse-skipping mechanism to support the dataflow with minimum controller overhead. We verify our approach on several representative models. Our simulated results show that CSP achieves on average 15× energy efficiency improvement over SparTen with comparable or superior speedup under most evaluations.