Reviewerly/exHarmony huggingface dataset:
Language-agnostic Age and Gender Classification of Voice using Self-supervised Pre-training

Extracting speaker-dependent paralinguistic information out of a person’s voice, provides an opportunity for adaptive behaviour related to speaker information in speech processing applications. For instance, in audio-based conversational applications, adapting responses to the attributes of the correspondent is an integral part in making the conversations effective. Two speaker attributes that humans can estimate quite well, based solely on hearing a person speak, is the gender and age of that person. However, in the field of speech processing, age and gender classification are relatively unexplored tasks, especially in a multilingual setting. In most cases, hand-crafted features, such as MFCCs, have been used with some success. However, recently large transformer networks, utilizing self-supervised pre-training, have shown promise in creating general speech embeddings for various speech processing tasks. We present a baseline for gender and age detection, in both monolingual and multilingual settings, for multiple state-of-the-art speech processing models, fine-tuned for age classification. We created four different datasets with data extracted from the Common Voice project to compare monolingual and multilingual performances. For gender classification, we could reach a macro average F1 score of ~96% in both a monolingual and multilingual setting. For age classification, using classes with a size of 10 years, we obtained a macro average mean absolute class error (MACE) of 0.68 and 0.86 on monolingual and multilingual datasets, respectively. For the English TIMIT dataset, we improve upon the previous state of the art for both age regression and gender classification. Our fine-tuned WavLM model reaches a mean absolute error (MAE) of 4.11 years for males and 4.44 for females in age estimation and our fine-tuned UniSpeech-SAT model reaches an accuracy of 99.8% for gender classification. All the models were deemed fast enough on a GPU to be used in real-time settings, and accurate enough, using only a small amount of speech, to be applicable in multilingual speech processing applications.
CL-ETC: A Contrastive Learning Method for Encrypted Traffic Classification

In the fields of network management and cyber security, encrypted network traffic classification is a critical task. Although Deep Learning (DL) models have been used in this field, they lack explicit control over data feature extraction, resulting in the retention of low-value features, which confuses the training and negatively impacts the classification performance. In this paper, we design a Contrastive Learning (CL) based encoder for extracting robust representation vectors with valuable features from unlabeled data. We create multiple augmentation samples for each input data by a unique augmenter. By narrowing representation vectors among similar augmentation samples and alienating them among dissimilar ones, the encoder can capture valuable features. We propose CL-ETC, a semi-supervised method based on the encoder. In CL-ETC, a well-trained encoder can be utilized to guide supervised classifier training to increase classification performance and training speed. We conduct experiments on three datasets, and the findings reveal that CL-ETC outperforms other models in a variety of metrics, including accuracy, precision, recall, F1-score, and classifier training convergence speed.
A 28-nm 25.1 TOPS/W Sparsity-Aware CNN-GCN Deep Learning SoC for Mobile Augmented Reality

This work presents the first CNN-GCN SoC for diverse AI vision computations on mobile augmented reality (AR). A CNN engine utilizes the channel-wise feature sparsity with a specialized processing element to achieve an up to 8× higher throughput and 6.1× energy efficiency. A GCN engine is implemented for graph-based action recognition. The computational complexity and memory usage are minimized by lever-aging matrix and graph properties. The proposed SoC achieves 25.1 TOPS/W energy efficiency for CNN inference, outperforming prior designs by 2.0×. It delivers 72 action/s on action recognition, exceeding prior art by 18× in latency.
The “Great Beauty” of Diversity: Smart Totems to Promote Gender Uniqueness

Nowadays, Artificial Intelligence, Sensors Technology, and, above all Internet-of-Things affect people's behavior in everyday life. Actually, technology in general, cannot yet be considered gender neutral: it is enough to consider that smartphones are primarily designed by males for the male hand size. Besides, neuroscience evidence has shown that the minds and behavior of men and women are deeply different. This gender uniqueness, if valued, may constitute opportunities for the whole society. Hence the need to consider it in the technology life cycle. This paper presents an Internet-of-Things solution represented by smart totems for advertisement and wayfinding services within advanced ICT-based shopping malls, that are conceived as a sentient space. To take into account the specificity of final users and to increase awareness of the gender uniqueness in the design of Internet-of-Things, a User-Centered Design methodology has been used. Scenarios and Personas have been created to consciously seek gender biases. This identification is then exploited to make Artificial Intelligence algorithms ecological, i.e., adapted to the real context of use, which is currently characterized by gender balance.
DISARM: a Framework for Analysis of Disinformation Campaigns

State actors, private influence operators and grassroots groups are exploiting the openness and reach of the Internet to manipulate populations at a distance. They are extending a decades-long struggle for “hearts and minds” via propaganda, influence operations and information warfare, often in the form of coordinated incidents that are part of longer-timescale narrative-based campaigns. Our work on cognitive security extends information security principles, practices, and tools, to the detection and management of information harms including disinformation and disinformation. Specifically, we have adapted and extended frameworks used to describe information security incidents, to create the DISARM series of frameworks for understanding and responding to organized disinformation incidents; these have been in continuous use since 2019 for analysis, simulations, training, and country-level disinformation risk assessments. In this paper, we describe how and why the DISARM frameworks were created, and discuss their components and uses, including analysis of ways, means, and ends to achieve influence goals.
Synergy in Multi-Level Reasoning

This paper explores inference patterns within and across the data fusion levels. Methods and examples are presented for synergistic reasoning across diverse entities and situations in a dynamic, complex, and uncertain world. The series of JDL fusion levels often provides a useful and natural sequence for inferencing by component-wise composition: reasoning from measurements to features to states of individuals to relationships, situations, and scenarios. However, there are instances in which other inference sequences are preferable. These include cases where contextual information, provided by the encompassing situation and scenario, can provide expectations or to resolve ambiguities. Inference patterns are identified whereby fusion nodes output data that are at the same, or higher, or lower level than their inputs. A counter-piracy application is examined to illustrate methods for synergistic reasoning within and across fusion levels and the interplay between higher-level fusion and response management in dynamic threat situations.
Using Eye Tracking to Investigate Interaction Between Humans and Virtual Agents

Situation Awareness (SA) and Internet of Things (IoT) can successfully be adopted with the aim of developing “smart” assistive technologies able to understand and interact with users' actions, adapting to several situations. A peculiar type of assistive technology is meant to promote seniors' active aging, providing assistance, companionship, and entertainment. The study presented in this work was carried out in the context of a European project named “EMPATHIC” aimed at developing virtual coaches for senior users. More specifically, the study analysed seniors' gaze behaviour while interacting with a virtual agent, to investigate whether users' attentional engagement would have changed over the interaction and accordingly to the conversational topics. Results highlight senior users' considerable engagement during the interaction and an overall positive attitude toward the proposed virtual agents.
Quantum networking

This chapter discusses how to build a quantum network. Basic concepts of quantum communication networks and the quantum Internet are introduced, followed by quantum teleportation and relay concepts. Then entanglement distribution, a key technique that enables quantum networking by employing quantum teleportation concepts, is described, starting with a brief description of how to generate entangled states. Entanglement swapping, Bell state measurements, and the Hong–Ou–Mendel effect are described, followed by continuous-variable (CV) quantum teleportation and then quantum network coding basics. The section after that is devoted to the engineering of entangled states by photon addition and subtraction. After describing photon subtraction and addition concepts, we discuss how to apply them to build a hybrid CV–discrete-variable (DV) quantum network. Hybrid quantum networks are described in terms of hybrid DV–CV entangled states as well as state teleportation and entanglement swapping through entangling measurements. The generation of entangled macroscopic light states is then discussed, given that macroscopic light states are much more tolerant of channel loss than discrete-variable states. The section ends with noiseless amplification as a possible approach to extend transmission distances between quantum nodes. The focus of the chapter then moves to cluster state-based quantum networking. The cluster states concept is introduced first, followed by cluster state processing. The same section describes cluster state-based quantum networking as both quantum Internet and distributed quantum computing enabling technology. The section that follows describes surface code-based and quantum low-density parity-check code-based quantum networking concepts. The final section relates to entanglement-assisted communication networks.
Cross-Domain Aspect Detection and Categorization using Machine Learning for Aspect-based Opinion Mining

There is an increase in the development of social media and electronic commerce sites day by day. In order to express their opinions about the products purchased user's write comments, messages and reviews. The reviews present in the e-commerce sites are also increasing. Users find difficulty in getting appropriate information about the right topic from this large data. Aspect-based Opinion Mining (ABOM) helps users in this regard. In many real-world applications ABOM is used to get the details about the aspects of entities, where the opinion is expressed for those aspects and entities. One of the key elements of ABOM is Aspect extraction. Unsupervised Machine Learning approach has been used to extract aspects from the reviews as it does not require pre-labelled data. In this regard Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) are two most commonly used unsupervised Topic Modeling approaches. The topics are extracted from three different datasets such as Amazon Mobile Reviews, Hotel Reviews and IMDb Movie Reviews using LDA and LSA algorithms. These extracted topics are aspects of our interest. The results of topic modeling algorithms are quite difficult to be interpreted by the common user. The different visualization methods are used to display the results of topic modeling algorithms in an interactive way. Two different multi-class classifiers such as Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM) have been constructed for aspect categorization. These classifiers are evaluated by considering the evaluation measures such as Precision, Recall and F1 score. As a result, SVM classifier has good performance than MNB classifier for aspect categorization task of aspect-based opinion mining.
Sentiment Analysis of Tweets Using Naïve Bayes, KNN, and Decision Tree

Making use of social media for analyzing the perceptions of the masses over a product, event, or a person has gained momentum in recent times. Out of a wide array of social networks, the authors chose Twitter for their analysis as the opinions expressed there are concise and bear a distinctive polarity. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. The paper elaborately discusses three supervised machine learning algorithms—naïve bayes, k-nearest neighbor (KNN), and decision tree—and compares their overall accuracy, precision, as well as recall values, f-measure, number of tweets correctly classified, number of tweets incorrectly classified, and execution time.
Application of Sentiment Analysis in Movie reviews

Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured, or unstructured textual data. In this chapter, the authors try to focus the task of sentiment analysis on IMDB movie review database. This chapter presents the experimental work on a new kind of domain-specific feature-based heuristic for aspect-level sentiment analysis of movie reviews. The authors have devised an aspect-oriented scheme that analyzes the textual reviews of a movie and assign it a sentiment label on each aspect. Finally, the authors conclude that incorporating syntactical information in the models is vital to the sentiment analysis process. The authors also conclude that the proposed approach to sentiment classification supplements the existing rating movie rating systems used across the web and will serve as base to future researches in this domain.
Ooredoo Rayek

Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.
Deep Learning for Social Media Text Analytics

Text analytics is the process of extracting high quality information from the text. A set of statistical, linguistic, and machine learning techniques are used to represent the information content from various textual sources such as data analysis, research, or investigation. Text is the common way of communication in social media. The understanding of text includes a variety of tasks including text classification, slang, and other languages. Traditional Natural Language Processing (NLP) techniques require extensive pre-processing techniques to handle the text. When a word “Amazon” occurs in the social media text, there should be a meaningful approach to find out whether it is referring to forest or Kindle. Most of the time, the NLP techniques fail in handling the slang and spellings correctly. Messages in Twitter are so short such that it is difficult to build semantic connections between them. Some messages such as “Gud nite” actually do not contain any real words but are still used for communication.
A Literature Review on Cross Domain Sentiment Analysis Using Machine learning

Sentiment analysis is the field of NLP which analyzes the sentiments of text written by users on online sites in the form of reviews. These reviews may be either in the form of a word, sentence, document, or ratings. These reviews are used as datasets when applied to train a classifier. These datasets are applied in the annotated form with the positive, negative or neutral labels as an input to train the classifier. This trained classifier is used to test other reviews, either in the same or different domains to know like or dislike of the user for the related field. Various researches have been done in single and cross domain sentiment analysis. The new methods proposed are overcoming the previous ones but according to this survey, no methods best suit the proposed work. In this article, the authors review the methods and techniques that are given by various researchers in cross domain sentiment analysis and how those are compared with the pre-existing methods for the related work.
DE-ForABSA

Today, amongst the various forms of online data, user reviews are very useful in understanding the user's attitude, emotion and sentiment towards a product. In this article, a novel method, named as DE-ForABSA is proposed to forecast automobiles sales based on aspect based sentiment analysis (ABSA) and ClusFuDE [8] (a hybrid forecasting model). DE-ForABSA consists of two phases – first, extracted user reviews of an automobile are analysed using ABSA. In ABSA, the reviews are pre-processed; aspects are extracted &amp; aggregated to determine the polarity score of reviews. Second, uses of ClusFuDE consisting of clustering, fuzzy logical relationships and Differential Evolution (DE) to predict the sales of the automobile. DE is a population-based search method to optimize real values under the control of two operators: mutation &amp; crossover. Score from phase 1 is a parameter in differential mutation in phase 2. The proposed method is tested on reviews &amp; sales data of automobile. The empirical results show a Mean Square Error of 142.90 which indicates an effective consistency of the model
Stick Breaking, in Coalgebra and Probability

Stick breaking is an elementary operation that has been formulated and used within stochastic process theory. This paper extracts the essentials of stick breaking in terms of isomorphisms between discrete probability distributions (with full support) and sequences of numbers between zero and one. This works for both finite and infinite distributions. Stick breaking is a repetitive construction with a strong coalgebraic flavour. Indeed, it is shown that stick breaking turns discrete distributions with infinite full support on the natural numbers into a final coalgebra. Once isolated as a separate construction, the usefulness of stick breaking is illustrated in the description of various probability distributions, such as binomial & multinomial and beta & Dirichlet.
A Novel Group Teaching Optimization Algorithm Based Artificial Neural Network for Classification

Traditional techniques of training Artificial Neural Networks (ANNs) i.e. the Back Propagation algorithm (BPA), have high computational time and number of iterations and hence have been improved over the years with the induction of meta-heuristic algorithms that introduce randomness into the training process but even they have been seen to be prone to falling into local minima cost solutions at high dimensional search space and/or have low convergence rate. To cater for the inefficiencies of training such an ANN, a novel neural network classifier is presented in this paper using the simulation of the group teaching mechanism to update weights and biases of the neural network. The proposed network, the group teaching optimization algorithm based neural network (GTOA-NN) consists of an input layer, a single hidden layer of 10 neurons, and an output layer. Two University of California Irvine (UCI) database sample datasets have been used as benchmark for this study, namely ‘Iris’ and ‘Blood Transfusion Service Center’, for which the training accuracy is 97% and 77.9559% whereas the testing accuracy is 98% and 83.5341% respectively. Comparative analysis with PSO-NN and GWO-NN unveil that the proposed GTOA-NN outperforms by 0.4% and 4% in training accuracy and 2.6% and 6.8% in testing accuracy respectively.
Financial Data, Blockchain, Decision-Making and Quality of Experience

Blockchain is a technology that enables cryptocurrency, such as bitcoin (the most popular cryptocurrency). Cryptocurrency, in general, is a digital payment system that does not rely on banks as intermediary nodes to complete monetary transactions, but instead it is a peer-to-peer digital payment system that can enable anyone to participate in monetary transactions, e.g. to send and receive payments. Game theoretic philosophy is a core aspect of blockchain design. The chapter proposes a model for the process of mining in a cryptocurrency system. In the process of mining, miners have to create and publish blocks and within those steps, they can decide to be honest or dishonest about their progress with the block validation. Game theory helps us explore motivations for different decisions.
Comprehensive Evaluation of College Students’ Physical Health and Sports Mode Recommendation Model Based on Decision Tree Classification Model

Nowadays, more and more college students' physical health is getting worse because of their living habits and self-consciousness. In order to improve the physical quality of college students as much as possible, the experiment uses the improved iterative dichotomiser III (ID3) decision tree to make decisions on the physical condition of some college students and the corresponding sports mode recommendation, and compares the results with the traditional ID3 algorithm. In the experimental results, the information entropy ratio of the improved ID3 algorithm is 89.5%, the operation information loss rate is 4.136%, and the accuracy of motion mode decision is 92.58%. The average relative time is 12.7, and the accuracy of physical health decision making is 90.02%. The above two values are not significant compared with the traditional ID3 algorithm. The experimental results show that the improved ID3 algorithm has significant optimization in the stability of information transmission and the accuracy of sports recommendation decision making, and can be applied to the physical health evaluation and sports recommendation of college students in a certain range.
Discovering the Emergence of Technical Sociology in Human Capital Systems and Technology-Driven Organizations

In 2019 the global cost of cyber-crime was over 2 trillion dollars. Current research literature outlines that 80-90% of security breaches are due to human-enabled errors in the U.S. and the U.K. Organizations encounter a barrage of cybersecurity threats that prey on the propensity of human error, human inaction, human behavior, and human misbehavior. As a result, there is an emergence of a new area of research development around technical sociology or digital sociology as a domain to explore the human capital perspectives, group dynamics, and social aspects of cybersecurity and technology management. The paper uses a relational content analysis of the literature as the research approach aimed to determine the presence and relationships of common themes and concepts. The results were creation of a concept matrix model of interrelated co-occurring concepts. The approach used was outlined by Krippendorff (1980) who asserts that concepts are "ideational grains;" these grains can be thought of as emblems which develop connotation through their connections to other emblems.