Reviewerly/exHarmony huggingface dataset:
Feature Selection by Associativity for Sentiment Analysis

Sentiment analysis faces the biggest challenge of high-dimensional feature set. The current trends contemplate this intricate process through conventional feature selection/extraction. In this proposal, the statistical feature selection method, Wilcoxon sign rank score, is used for optimal feature selection. The proposal evaluates and compares the conventional filter method of feature selection with the Wilcoxon sign score for finding the optimal features. The features are defined by feature association between the terms and the sentiment lexicon. The identified optimal features using information gain (IG), chi square, and statistical method Wilcoxon sign score are then classified using the Adaboost classifier. The experiment was conducted on three datasets widely used for sentiment analysis. Result from the experiment demonstrate that the classification accuracy using the proposed method of optimal feature selection is much higher when compared to the contemporary models.
Explaining SOMA

The Self-Organizing Migrating Algorithm (SOMA) is enjoying a renewed interest of the research community, following recent achievements in various application areas and renowned performance competitions. In this paper, we focus on the importance and effect of the perturbation operator in SOMA as the perturbation is one of the fundamental inner principles of SOMA. In this in-depth study, we present data, visualizations, and analysis of the effect of the perturbation on the population, its diversity and average movement patterns. We provide evidence that there is a direct relation between the perturbation intensity (set by control parameter prt) and the rate of diversity loss. The perturbation setting further affects the exploratory ability of the algorithm, as is demonstrated here by analysing the parameter space coverage of the population. We aim to provide insight and explanation of the impact of perturbation in SOMA for future researchers and practitioners.
Federated Self-training for Semi-supervised Audio Recognition

Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets. Since data reside on devices such as smartphones and virtual assistants, labeling is entrusted to the clients or labels are extracted in an automated way. Specifically, in the case of audio data, acquiring semantic annotations can be prohibitively expensive and time-consuming. As a result, an abundance of audio data remains unlabeled and unexploited on users’ devices. Most existing federated learning approaches focus on supervised learning without harnessing the unlabeled data. In this work, we study the problem of semi-supervised learning of audio models via self-training in conjunction with federated learning. We propose FedSTAR to exploit large-scale on-device unlabeled data to improve the generalization of audio recognition models. We further demonstrate that self-supervised pre-trained models can accelerate the training of on-device models, significantly improving convergence within fewer training rounds. We conduct experiments on diverse public audio classification datasets and investigate the performance of our models under varying percentages of labeled and unlabeled data. Notably, we show that with as little as 3% labeled data available, FedSTAR on average can improve the recognition rate by 13.28% compared to the fully supervised federated model.
Recognizing Novice Learner’s Modeling Behaviors

Modeling is an important aspect of scientific problem-solving. However, modeling is a difficult cognitive process for novice learners in part due to the high dimensionality of the parameter search space. This work investigates 50 college students’ parameter search behaviors in the context of ecological modeling. The study revealed important differences in behaviors of successful and unsuccessful students in navigating the parameter space. These differences suggest opportunities for future development of adaptive cognitive scaffolds to support different classes of learners.
Language contact and morphological competition: Plurals in central Sicily

This paper explores the effects of language contact in the nominal morphology of central Sicilian dialects. In particular, this study is concerned with the contact-induced changes related to the distribution of three plural formatives that give rise to competition between different inflectional classes with respect to a number of lexemes. It is shown that sociolinguistic factors such as speaker age account for the distribution of the competing plural forms and the high degree of variation. As a consequence, a slow and gradual change is leading to the disappearance of the plural form that has no equivalent in the contact language, that is, in Italian.
Evaluation of Evolutionary Algorithms Under Frugal Learning Constraints for Online Policy Capturing

Decision making can be modeled in various ways for the design of decision-support systems. One strategy privileged for this purpose is policy capturing, i.e. using statistical techniques (and more recently machine learning) to model judgement policies. The Cognitive Shadow is a prototype tool suited for frugal learning that automatically learns a user's decision pattern in real time based on an ensemble of seven supervised learning algorithms. This tool can provide advisory warnings when the user decision is inconsistent with the predicted outcome. Evolutionary computation methods could reinforce the system's efficiency because of their ability to deal with computational complexity via evolution-inspired optimization mechanisms. The goal of this study was to assess the potential of evolutionary algorithms for frugal learning in an online policy capturing context. To do so, we tested three evolutionary algorithms on three different datasets (each split in three sizes), and compared both their prediction performance and training time with that of the other modeling techniques already implemented in the Cognitive Shadow system. Although all three evolutionary models were generally outperformed by non-evolutionary learning algorithms, one genetic programming method showed good prediction performance for the more complex use cases with the smaller datasets.
A Novel User Collusion-Resistant Decentralized Multi-Authority Attribute-Based Encryption Scheme Using the Deposit on a Blockchain

Recently, the concept of a decentralized data marketplace is getting much attention to exchange user data. Multi-authority attribute-based encryption (ABE), which can provide flexibility and user-centric access control, is previously widely used in decentralized data sharing applications and also becoming a foundation to build decentralized data trading applications. It is known that users in a multi-authority ABE system can collude by sharing their secret information for malicious purposes. To address this issue, the collusion-resistant multi-authority ABE model was introduced in which a unique global identifier (GID) is issued by the central authority (CA) to each user. Unfortunately, such approach cannot be used directly to build a decentralized data marketplace as (a) such intervention of the CA is directly against the main motivation of the decentralized trading platform and, mostly importantly, (b) the CA can exploit its full knowledge on users’ GID to launch various attacks against users. Motivated by these observations, this paper introduces a novel user collusion-resistant decentralized multi-authority ABE scheme for privacy preserving data trading systems. In the existing multi-authority ABE systems, users utilize his/her GID that is solely assigned by the CA to generate his/her secret keys throughout the collaboration with authorities and a user can compute multi-authority keys by combining the secret keys (stem from the same GID) in various ways. In the proposed system, the CA only has a partial knowledge of users’ GIDs, and thus, users’ privacy can be protected. On the other hand, we set the user’s own partial GID as a secret which can be used to withdraw his/her deposit to discourage any possible collusion among users.
Towards Effective Robot-Assisted Photo Reminiscence: Personalizing Interactions Through Visual Understanding and Inferring

In this work, we present a robot-assisted photo reminiscence system. Previously, we focused on the development of the question generation system through a social robot interface. Results showed that the proposed system was able to produce coherent, related and appropriate questions that would trigger the reminiscence process. In this study, using an iterative incremental model, based on feedback from the end-users (elderly and caregivers), we deployed an improved prototype robotic system on a senior center with elderly users and in a lab setting for independent living elderly. Results from our feasibility study show that participants proactively engage with the robot and find the prospect of reminiscence therapy both effective and enjoyable.
On detecting the novelties in metaphor-based algorithms

Following recent criticisms of the metaphor-based algorithms, several studies have reported that the basic core ideas of some nature-inspired algorithms are basically the same, so such methods do not bring any novelty at all. The majority of these papers dealt with the problem on the conceptual level. The aim of this study is to show how similar operations of these algorithms are on the operational level, where searching for the best solutions in the genotype space is affected by the exploration/exploitation components of the search process. In line with this, a new method is proposed for comparing the similarity of algorithms considering both levels.
Development of Face Recognition System by Using Deep Learning and Face-Net Algorithm in the Operations Processes

Biometry is a branch of science that studies the physical and behavioral characteristics of living things. Every human being has its physical characteristics. These features are unique features that distinguish people from others. Biometric systems are the systems established by examining the biometric properties of people to determine their identity. Facial recognition systems, a type of biometric system, using the facial features of individuals, identify snow-bodies, identify information at the distance between the eyes, nose, cheekbones, jawline, jaw and so on and individual. The real purpose here is to confirm for whom did the operation. With the development of technology, many organizations use biometric data as an authentication tool. Within the scope of our study, to check the accuracy of customers who make transactions by identification method during operational transactions. In this study, if the level of accuracy in the face detection system is above a certain rate, no process blocking is performed. In cases where the similarity is low, alert information is sent to the operator and re-authentication with different data is want to be required. This study has been proposed to overcome the difficulties arising from a new convolutional neural network (CNN) framework, matrix completion, and deep learning techniques. FaceNet algorithm, which is one of the deep face recognition algorithms that can be used in the recognition of face images, is the algorithm used in the study. FaceNet is a neural network that learns to map distances from face images to a compact Euclidean space that corresponds to the face similarity measure. In this study, we will explore how to develop a face detection system using the FaceNet algorithm to identify people from photographs. The picture of the customer taken during an operational transaction is converted into a matrix expression using a trained system using this algorithm. If the matrix value obtained is greater than the threshold in the learned system, the definition is considered positive. If the obtained matrix value is less than the threshold value in the learned system, the definition is considered negative. Our data and success rates show the applicability of the study.
Single Image Dehazing Using Adaptive Sky Segmentation

A new image dehazing algorithm based on adaptive sky region is proposed in this paper, which shows good fidelity in sky region and satisfying visual effect in non‐sky region. For robust sky segmentation, we propose a rough‐to‐fine method that can make a balance between efficiency and accuracy. Considering distribution of haze is inconsistent, we divide the input image into three parts and calculate their atmospheric lights respectively. To solve the problem of invalid dark channel prior, we make an improvement for the transmission estimation. Finally, image fusion is taken as a post processing that can solve the problem of partial darkness and ensure a visual pleasing result. The experimental results for both synthetic and natural hazy images demonstrate that our algorithm performs comparable or even better results than the state‐of‐the‐art methods in terms of various measurement indexes, such as the PSNR, SSIM, and so forth. Besides, the proposed algorithm can be also applied in FPGA platform due to the optimized performance. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
An Improved Stereo Matching Algorithm for Vehicle Speed Measurement System Based on Spatial and Temporal Image Fusion

This paper proposes an improved stereo matching algorithm for vehicle speed measurement system based on spatial and temporal image fusion (STIF). Firstly, the matching point pairs in the license plate area with obviously abnormal distance to the camera are roughly removed according to the characteristic of license plate specification. Secondly, more mismatching point pairs are finely removed according to local neighborhood consistency constraint (LNCC). Thirdly, the optimum speed measurement point pairs are selected for successive stereo frame pairs by STIF of binocular stereo video, so that the 3D points corresponding to the matching point pairs for speed measurement in the successive stereo frame pairs are in the same position on the real vehicle, which can significantly improve the vehicle speed measurement accuracy. LNCC and STIF can be used not only for license plate, but also for vehicle logo, light, mirror etc. Experimental results demonstrate that the vehicle speed measurement system with the proposed LNCC+STIF stereo matching algorithm can significantly outperform the state-of-the-art system in accuracy.
Learning Efficient Hash Codes for Fast Graph-Based Data Similarity Retrieval

Traditional operations, e.g. graph edit distance (GED), are no longer suitable for processing the massive quantities of graph-structured data now available, due to their irregular structures and high computational complexities. With the advent of graph neural networks (GNNs), the problems of graph representation and graph similarity search have drawn particular attention in the field of computer vision. However, GNNs have been less studied for efficient and fast retrieval after graph representation. To represent graph-based data, and maintain fast retrieval while doing so, we introduce an efficient hash model with graph neural networks (HGNN) for a newly designed task (i.e. fast graph-based data retrieval). Due to its flexibility, HGNN can be implemented in both an unsupervised and supervised manner. Specifically, by adopting a graph neural network and hash learning algorithms, HGNN can effectively learn a similarity-preserving graph representation and compute pair-wise similarity or provide classification via low-dimensional compact hash codes. To the best of our knowledge, our model is the first to address graph hashing representation in the Hamming space. Our experimental results reach comparable prediction accuracy to full-precision methods and can even outperform traditional models in some cases. In real-world applications, using hash codes can greatly benefit systems with smaller memory capacities and accelerate the retrieval speed of graph-structured data. Hence, we believe the proposed HGNN has great potential in further research.
On the Effect of the Key-Expansion Algorithm in Simon-like Ciphers

Abstract In this work, we investigate how the choice of the key-expansion algorithm and its interaction with the round function affect the resistance of Simon-like ciphers against rotational-XOR cryptanalysis. We observe that, among the key-expansion algorithms we consider, Simon is most resistant, while Simeck is much less so. Implications on lightweight ciphers design are discussed and open questions are proposed.
Digital Health Passes in the Age of COVID-19: Are 'Vaccine Passports' Lawful and Ethical?

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A Comparative Study on Breast Cancer Tissues Using Conventional and Modern Machine Learning Models

Recent advancement in analytical models, availability of hardware resources like GPU, TPU, and cloud infrastructure began to play a pivotal role in healthcare practices and research. It has numerous tools and techniques are available to archive, manage, analyze, and predict large volumes of structured, unstructured, and semi structured data. ‘Data Science’ plays a vital role in the medical field with the better support for diagnosis, early prediction and cure for the disease. In this research, a comparative study on conventional models and modern methods are carried out using 12 different machine learning models for classification. The response or target variable is the diagnostic observation having two classes viz benign (not cancerous) or malignant (cancerous). The dataset used is The Wisconsin Diagnostic Breast Cancer (WDBC) which is taken from the UCI machine learning repository. The performances of the models are analyzed using the validation matrices, precision, recall, accuracy, and F1 Score. In this research study, we have built the ensemble model using the base estimator as a Random Forest classification model, Principal Component Analysis (PCA), Extra Gradient Boosting (XGBoost) and bagging techniques. Among 11 conventional models, the highest accuracy is obtained for the combined model which is 98.8%. A modern method tSNE is used for dimensionality reduction along with the feature engineering method called k-best method and base model for the classification is Random Forest model is used. This combined approach resulted in the best accuracy 99%. We have obtained a little improved score using PCA and tSNE methods than the reference articles considered in this research.
Comparison of different similarity measures in hierarchical clustering

The management of datasets containing heterogeneous types of data is a crucial point in the context of precision medicine, where genetic, environmental, and life-style information of each individual has to be analyzed simultaneously. Clustering represents a powerful method, used in data mining, for extracting new useful knowledge from unlabeled datasets. Clustering methods are essentially distance-based, since they measure the similarity (or the distance) between two elements or one element and the cluster centroid. However, the selection of the distance metric is not a trivial task: it could influence the clustering results and, thus, the extracted information. In this study we analyze the impact of four similarity measures (Manhattan or L1 distance, Euclidean or L2 distance, Chebyshev or L∞ distance and Gower distance) on the clustering results obtained for datasets containing different types of variables. We applied hierarchical clustering combined with an automatic cut point selection method to six datasets publicly available on the UCI Repository. Four different clusterizations were obtained for every dataset (one for each distance) and were analyzed in terms of number of clusters, number of elements in each cluster, and cluster centroids. Our results showed that changing the distance metric produces substantial modifications in the obtained clusters. This behavior is particularly evident for datasets containing heterogeneous variables. Thus, the choice of the distance measure should not be done a-priori but evaluated according to the set of data to be analyzed and the task to be accomplished.
Construction of Diverse DropBlock Branches for Person Reidentification

In this article, we propose to use the data augmentation of batch drop-block with varying dropping ratios for constructing diversity-achieving branches in person reidentification (Re-ID). Since a considerable portion of input images may be dropped, this reinforces feature learning of the un-dropped region but makes the training process hard to converge. Hence, we propose a novel double-batch-split co-training approach for remedying this problem. In particular, we show that the feature diversity can be well achieved with the use of multiple dropping branches by setting individual dropping ratio for each branch. Empirical evidence demonstrates that the proposed method performs competitively on popular person Re-ID data sets, including Market-1501, DukeMTMC-reID, and CUHK03, and the use of more dropping branches can further boost the performance. Source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/AI-NERC-NUPT/DDB</uri> .
Application of Blockchain in Civil Aviation

In recent years, blockchain technology has increasingly attracted attention in the field of civil aviation, whose features such as decentralization, openness, transparency, traceability, and anonymity provide new ideas for solving problems in civil aviation. In this paper, from the perspective of the alliance chain, we design and analyze the application of blockchain technology in six scenarios, including passenger identity racking, baggage handling and indemnity, ticket selling, aircraft maintenance record, passenger privacy and security, and opaque examination and approval of airline resources.
Sensors for Assistive Robotic Drinking with Physical Contact

This paper proposes two sensor systems for human robot interaction in assistive drinking tasks for motion impaired people. The first sensor system uses a capacitive and resistive sensor to detect proximity and measure contact forces between the user and a regular drinking cup. The cup is held by a robot with a three finger gripper. Control strategies for the drinking process with a regular cup using both the capacitive and resistive measurements were developed and tested. The control strategies were rated by the subjects with a NASA RTLX questionnaire. Statistically significant differences between both strategies are not evident. The second sensor system uses a System on Chip (SoC) with four sensors. It enables motion impaired users to stop the robot without contact to the robot. This is accomplished with a robust detection of a human blowing on the sensor. A sensor fusion algorithm is used for detection. The results suggest the usage of both sensing methods in further robotic systems. However, the detection of a human blow should undergo further tests with motion impaired people.
Optimizing Parameters of Discrete Wavelet Transform for Noise Reduction in Fingerprint Images

Fingerprint recognition is an important way in person's identification. Noise reduction in fingerprint images increases the accuracy of minutiae detection. Wavelet transforms are widely used in noise reduction in image enhancement. Discreet Wavelet Transform is a method widely used in this field. It is important to find the appropriate filters and mother wavelet. Setting the appropriate parameters has impact in getting optimal results. The parameters analyzed here are the number of decomposition levels and the size of the filter window. The metric used to measure noise reduction is PSNR.