Reviewerly/exHarmony huggingface dataset:
Face Recognition using Haar Cascade and Local Binary Pattern Histogram in OpenCV

One of the most unique features that a human body can possess is the Face. This feature can be used to create a system that uniquely differentiates among different people. Face Recognition is one such system that detects a particular face by facial features. In contrast to the traditional methods of collecting attendance by calling out students' names by the teachers in a university/school or marking it in the registers at the main gate of any organization, this one consumes less time, effort, is more efficient, and also is a contactless method of doing the same. In this paper, we worked on a model that uses facial recognition technique to mark students&#x2019; attendance in an automated attendance management system using the Haar cascade classifier and LBPH algorithm. This one-time generation of dataset and face detection from the existing recognized images in this proposed system, is a more accurate and more improved system to collect attendance, thus leaving behind the tedious manual task.
Constructing Models

The literature on Futures Studies shows the need to give the process of inference of the future, guidelines that help to face turbulence and recognize the emerging properties that belong to the dynamics of complex social systems. These systems are always in a state of non-equilibrium. Therefore, from a prospective point of view, a system must have means to monitor and understand the changes that occur in its environment, which in many cases express mega trends, often in conflict with each other. To respond to the challenges, the proposal called Meta-Prospective allows to combine Soft Computing with prospective strategic methods, providing the opportunity to develop strategic intelligence capabilities based on prospective thinking and modeling, but prioritizing the process on the methods to turns the proposal into a humanized model. This chapter develops the proposed called Meta-Prospective.
Differential Whale Optimization Algorithm

The whale optimization algorithm (WOA) is a natural-inspired effective optimization algorithm by imitating the behavior of whales rounding up their prey. Due to the high capacity of WOA in terms of exploitation, it is likely to fall into a local optimum as individuals of WOA lack communication. In this paper, we innovatively enlarge the search range of WOA by performing a differential search for each individual in the population, thus enabling the proposed differential whale optimization algorithm (DWOA) to possess an ability of jumping out of the local optimum and meanwhile accelerating its convergence speed. Experimental results based on IEEE CEC2017 benchmark functions demonstrate the superiority of DWOA in terms of solution quality, population diversity, and convergence speed.
Embedded system implementation of an evolutionary algorithm for circle detection on programmable devices

Programmable devices combine powerful processing systems with a rich infrastructure of general-purpose and specific logic blocks, making it possible the efficient implementation of embedded systems to perform complex tasks by facilitating hardware acceleration of critical stages to improve their performance. Based on these characteristics, a hardware implementation of a genetic algorithm for circle detection in digital images is described in this paper. The detection system has been designed for Xilinx Zynq-7000 and Zynq UltraScale+ family devices and implemented on two low-cost development boards that reach acceleration factors of 33.12 and 37.3, respectively, when compared to the fully software implementation. Detection results from both development boards have been compared using synthetic and real images from different scenarios. The accuracy and performance achieved demonstrate the suitability of this proposal to design embedded systems with restricted size, resources and energy consumption for applications in Internet of Things, Industry 4.0 and other related paradigms.
Spoken Language Identification for Native Indian Languages Using Deep Learning Techniques

In this paper, we present a Spoken Language Identification System (LID) for native Indian languages. LID task aims to determine the spoken language in a speech utterance of an individual. In this system, ‘Resemblyzer’ is used for feature extraction, which derives a high-level representation of a voice in the form of a summary vector of 256 values. Experimentation is done on ‘IndicTTS Database’, developed by IIT Madras which comprises 13 languages, and ‘Open-source Multi-speaker Speech Corpora’ database developed by the European Language Resources Association which consists of 7 languages. The work consists of training Deep Neural Networks (DNN), Recurrent Neural Networks with Long Short-Term Memory (RNN–LSTM) and Gaussian Mixture Model (GMM) on each database for 1.5 and 5 s feature lengths and comparing their performances with each other and in each scenario.
Dual Modal Meta Metric Learning for Attribute-Image Person Re-identification

Attribute-image person re-identification (AIPR) aiming to retrieve persons from massive images via an attribute query is a meaningful but challenging cross-modal retrieval task. Because there is a huge modal difference between person images and attributes, e.g., on the image modal one subject usually contains of varying instances, but on the attribute modal, one subject only contains an explicit instance. Unlike most existing AIPR methods focusing on shrinking feature differences crossing modals, we propose a dual modal meta metric learning (DM<sup>3</sup>L) method for AIPR in this paper. Specifically, in each episode, we sample a subset as a new task and split the training data into a single-modal support set of person images and a dual modal query set consisting of both person images and attributes. Based on the single-modal support set and the dual modal query set, our DM<sup>3</sup>L learns not only attribute-image cross-modal metrics but also learns image-image intra-modal metrics. Therefore, our DM<sup>3</sup>L method encourages data on both attribute and image modalities are discriminate to improve AIPR. Experiments show that our DM<sup>3</sup>L outperforms state-of-the-art approaches on Market-1501 Attribute and PETA datasets.
Analysis Of Various Neural Network Architectures for Automatic Modulation Techniques

Wireless Communication plays a paramount role in modern communication systems. Modulation classification as a phase in the middle of demodulation and signal detection, is gaining interest. The main aim of modulation classification is to differentiate the type of modulation from the signal that is received. Here, transmitters choose freely between the different modulation types of signals. It has applications, like CR, electronics reconnaissance. The modulation recognition system consists of 3 steps, which are, signal pre-processing, feature extraction and selection of modulation algorithm. This paper reviews the different existing automatic modulation classification techniques with primary focus on the deep learning-based schemes. In this paper, different modulation techniques are taken like CNN 2-layer and 4-layer, LSTM and CLDNN. The best epoch level for training is found and the different modulation techniques are compared to see which gives the best accuracy.
OCR and Speech Recognition System Using Machine Learning

Image processing and Speech synthesis is a complex and growing field of computer science and is being developed by many researchers. An image is a map of several pixels with values assigned according to what they are representing. These images are captured through various methods and for the common people it is easy to identify the characters in the images. For the visually impaired and illiterate people, identification of character in the image is impossible. Therefore, this paper presents a model for a system where the characters/text in the images can be read out loud by the system. The system is composed of two main subsystems: a model for Optical Character Recognition and a model for speech synthesis. The Optical Character Recognition model recognizes the characters/text in the images and converts into editable text through various process such as preprocessing, segmentation and classification. The editable text is fed as input to the speech synthesis model, which generates speech signal from the input text. Both the models are trained with machine learning and deep learning neural networks. The models have an accuracy of about 90&#x0025; and the speech generate sounds similar to the speech used to train the model.
Research on the Application of Big Data in the Evaluation System of Music Teaching in Chinese Colleges and Universities

The teaching effect of music courses in Colleges and universities is closely related to the teaching quality of teachers. Starting with the application of big data in the teaching quality evaluation system of colleges and universities, this paper objectively summarizes the application of big data in the teaching quality evaluation system of music courses in Colleges and universities. Finally, Put forward the countermeasures to strengthen the application of big data in the teaching quality evaluation system of music courses in Colleges and Universities: gradually optimize the teaching quality evaluation system and enrich the evaluation content; Optimize the display form of teaching quality evaluation and highlight the role of evaluation; At the same time, improve students' enthusiasm for participation, mobilize students' enthusiasm for participating in the evaluation, and improve the quality of the course.
Mineral chemistry-driven protocol to unravel the complex paragenesis of the Klaza (Yukon, Canada) superimposed porphyry-epithermal system

The distribution of precious metals in intermediate sulfidation (IS) epithermal systems remains a subject of limited understanding and, as such, contributes a possible metallurgical risk during its processing and recovery. A combination of empirical (field observations, drill core logging, petrography) and quantitative analytical (LA-ICP-MS element mapping of sulfides) methods are used to investigate ore genesis and sulfide modification at the well-preserved Late Cretaceous Klaza deposit in Yukon, Canada. A workflow utilizing unsupervised machine learning methods (K-means clustering and principal component analysis (PCA)) is established to interrogate raster datasets generated from the (often underutilized) laser ablation maps and to quantify and assess elemental relationships in the data. This workflow was successful in: (1) automating the filtration of pixels unrelated to the mineral of interest from the element maps; (2) differentiating between primary growth-related features from secondary late features (PC1); and (3) producing “feature maps” that depict these textural relationships for comparison with the element maps. Results suggest four distinct stages: (1) primary Au was deposited as solid-solution (Au1+) in early arsenopyrite and arsenian pyrite during cooling of a magmatic fluid, whereas Ag was deposited later (with Zn-Pb sulfides) as a result of further cooling and dilution of this fluid with its concentration in sulfosalts and galena; (2) a later, higher temperature, chalcophile element-rich (Ag-Cu-Bi-Pb-Sn-Sb-As), and higher-sulfidation state fluid of likely magmatic origin overprinted the earlier Klaza system; (3) addition of Cu and liberation of earlier lattice-hosted Au (and other impurities) from the arsenian pyrite and arsenopyrite to form particulate Au (Au0); and (4) purification of the sulfides with coupled re-concentration of Au-Ag-Bi-Pb as inclusions during zone refining in the epithermal realm. The results of this study have significant impacts on the understanding of metal deportment, ore formation, and the economics of IS epithermal systems.
Arabic sign Language Recognition: Towards a Dual Way Communication System Between Deaf and Non-Deaf People

One key perspective when communicating with deaf people is sign language recognition. Many experts agree upon the fact that using a system communication is a key for bridging the gap between deaf and non deaf people since ordinary people can not exchange using the sign language. As a result, researchers, deaf people, parents and deaf-mute community are striving to have a bidirectional communication system based on translation with lower costs. The importance of the research is related to its goal of helping these categories of unvoiced people communicate with others and enhance their contributions to growth and capacity building and vice versa.This paper gives an overview of the most used techniques and technologies (gloves, android application, image processing, &#x2026;) in order to translate sign language to written or spoken language. Furthermore, this paper provides a critical and comparative analysis of the studied approaches and stands out major challenges to overcome their limits. Finally, we propose in this paper a dual way communication system ensuring arabic sign language translation into spoken language based on image processing and deep learning.
Design and Simulation of Dual-Axis Solar Tracking Systems

Photovoltaic (PV) devices are now increasingly being deployed all over the globe. However, a fixed PV module is usually used in installations, utilizing pre-specified angles obtained through geographical positioning. Thus, due to the variance in solar energy as the day and the seasons a year changes, the power produced by PV systems drops dramatically. This paper suggests the design, simulation of a dual-axis solar tracker where the solar module easily moved on two (2) axis of rotation to monitor the sun's progress from east to west and from north to south in order to optimize solar energy generation. The tracking system is configured as an adaptive tracking system based on closed-loop monitoring, and the use of Light Dependent Resistor (LDR) sensors as device inputs, servo motors as module adjusters, Arduino Uno as the brain of the system. Voltage, current and power readings collected at alternate time of the day from the dual-axis Solar Tracker and compared with readings obtained from stationary solar module at same time when the reading of the other module was taken. The results showed that the method of automated solar tracking is accurate and effective. The electricity produced by the proposed tracking device has an average increase of approximately 26-27% more than the fixed PV system.
ARES: Persistently Secure Non-Volatile Memory with Processor-transparent and Hardware-friendly Integrity Verification and Metadata Recovery

Emerging byte-addressable Non-Volatile Memory (NVM) technology, although promising superior memory density and ultra-low energy consumption, poses unique challenges to achieving persistent data privacy and computing security, both of which are critically important to the embedded and IoT applications. Specifically, to successfully restore NVMs to their working states after unexpected system crashes or power failure, maintaining and recovering all the necessary security-related metadata can severely increase memory traffic, degrade runtime performance, exacerbate write endurance problem, and demand costly hardware changes to off-the-shelf processors. In this article, we designed and implemented ARES, a new FPGA-assisted processor-transparent security mechanism that aims at efficiently and effectively achieving all three aspects of a security triad—confidentiality, integrity, and recoverability—in modern embedded computing. Given the growing prominence of CPU-FPGA heterogeneous computing architectures, ARES leverages FPGA’s hardware reconfigurability to offload performance-critical and security-related functions to the programmable hardware without microprocessors’ involvement. In particular, recognizing that the traditional Merkle tree caching scheme cannot fully exploit FPGA’s parallelism due to its sequential and recursive function calls, we (1) proposed a Merkle tree cache architecture that partitions a unified cache into multiple levels with parallel accesses and (2) further designed a novel Merkle tree scheme that flattened and reorganized the computation in the traditional Merkle tree verification and update processes to fully exploit the parallel cache ports and to fully pipeline time-consuming hashing operations. Beyond that, to accelerate the metadata recovery process, multiple parallel recovery units are instantiated to recover counter metadata and multiple Merkle sub-trees. Our hardware prototype of the ARES system on a Xilinx U200 platform shows that ARES achieved up to 1.4× lower latency and 2.6× higher throughput against the baseline implementation, while metadata recovery time was shortened by 1.8 times. When integrated with an embedded processor, neither hardware changes nor software changes are required. We also developed a theoretical framework to analytically model and explain experimental results.
Designing with Alganyl: A Hands-on Exploration of Biodegradable Plastics

In this studio we explore designing with Alganyl, a sustainable biomaterial, made of food-grade ingredients, that can be fully recycled (zero-waste) or biodegraded in soil. Specifically, Alganyl is a bioplastic formulated to mimic the flexibility and strength of vinyl fabric. This studio will bring together researchers with diverse expertise that will explore embedding color, texture and sensing in Alganyl as well as a variety of fabrication techniques. Participants will have the opportunity to gain hands-on experience through making wearables that can sense ultraviolet light or temperature, as well as explore their own creative applications. Participants will also learn about material-centered design methodologies and sustainability within the context of HCI. Lastly participants will be encouraged to share their experiences with designing with Alganyl as well as discuss the impact of biomaterials in sustainable HCI research.
Automatic Classification of Breast Cancer Histopathological Images Based on a Discriminatively Fine-Tuned Deep Learning Model

Automatic classification of breast cancer histopathology images is of great significance in breast cancer diagnosis. Convolutional Neural Networks (CNN) requires the right hyper-parameters to efficiently and accurately do these classification, usually based on expertise and extensive trial and error, and also factored by the dataset. In this paper, the veracity of discriminative fine-tuned algorithm on ResNet and DenseNet Models, which optimally sets a range of hyper-parameters, using cyclical learning rate policy per iteration in training. The proposed method was tested on the Public BreakHis Dataset of Magnification 100X and 400X. Experimental results based on Accuracy metric (Densenet (100X): 95.33%, Densenet (400X): 94.34%, Resnet (100X): 96.56%, Resnet (400X): 96.3%) proves the method of optimization is efficient for breast cancer histopathology image classification in clinical settings.
A Hybrid Deep Learning Model for Face Sketch Recognition

This paper introduces a hybrid deep learning model which integrates particle swarm optimization (PSO) with VGG-face deep learning network for face sketch recognition problem. Particularly, the proposed hybrid model incorporates PSO into VGG-face to find the best filters of the last layer that have the highest contribution in face sketch recognition. In addition, PSO performs fine-tuning for the selected filter to enhance recognition rate accuracy. To assess the performances of the proposed hybrid model, LFW face sketch benchmark images are used in this study. Reported results show that PSO can reduce VGG- face model complexity and increase recognition accuracy to 76% on LFW benchmark images.
Sexist Hate Speech and the International Human Rights Law: Towards Legal Recognition of the Phenomenon by the United Nations and the Council of Europe

Abstract For many women and girls sexist and misogynistic language is an everyday experience. Some instances of this speech can be categorized as ‘sexist hate speech’, as not only having an insulting or degrading character towards the individuals to whom the speech is addressed, but also resonating with the entire group, contributing to its silencing, marginalization and exclusion. The aim of this article is to examine how sexist hate speech is handled in international human rights law. The argument derives from the claim than that legal discourse should distinguish between ‘sexist speech’ and ‘sexist hate speech’, and that the later may be qualified as a form of violence against women. Then it analyses the approaches of two human rights protection systems—the United Nations and the Council of Europe system—towards the phenomenon of sexist hate speech, by taking their position towards hate speech in general as a point of reference. In both systems, sexist hate speech is being addressed more eagerly under the gender equality framework, including counteracting violence against women, than in their interpretation of hate speech under general human rights law. The article argues the importance of recognizing and addressing sexist hate speech both within the framework of gender equality instruments and anti-hate speech framework established by international human rights bodies.
Personality Classification Based on Bert Model

Personality classification is the process of analyzing and summarizing the relevant emotional information in the text, so as to infer the personality traits in the text. In view of the fact that traditional machine learning methods need to manually label to extract features when dealing with personality classification problems, which leads to poor per-formance of classification results. In this paper, we propose a deep learning method based on the BERT model. The model adopts the Transformer two-way coding structure, which can extract features more effectively than traditional methods. Finally, the Softmax classifier is used to classify the extracted text feature vectors. Qur experiment compares several classical models such as SVM, CNN and LSTM, and the experimental results show that the multi-classification effect of the BERT model is better than other models. It is proved that the BERT model can effectively improve the effect of personality classification.