Reviewerly/exHarmony huggingface dataset:
Human Interactable Method through A Custom Voice-chat Application: LiveTalk

Cell phone communication has become an integral part of everyday life. The advent of cell phones has reinforced the need for people to connect without tying knots. Communication is important, but current methods are limited to simple contracts such as telephone, and smaller communication methods such as messaging. LiveTalk is an archive audio chat app that attempts to address current restrictions on mobile communications by exploring verbal communication methods as a flexible Internet Protocol (IP) communication method. LiveTalk acts as a program server within a mobile audio framework that supports a variety of compatible and non-compatible communication channels, archiving chat archives, and new ways to browse speech. A new shift between compatible and compatible communications, as well as integrated channels supporting both methods, has been explored, which is a debate over new mobile communication opportunities.
Big Data Image Processing Based on Coefficient 3D Reconstruction Model

In the history of human society, information is an indispensable part of development, and extracting useful data from the huge amount of data can effectively solve some real-life problems. At the same time, with the continuous improvement of modern technology and computer hardware and equipment, the demand for information subjects is getting higher and higher. The field of human research is gradually moving in the direction of multiscientific exploration and deepening of related work. The related information disciplines have been studied by more scholars and experts in the field to a greater extent and put forward more demanding theories and methods. In the process of human social development, things are constantly changing and updating, which makes data mining technology more and more attention. This article first describes the relevant basic theoretical knowledge of 3D reconstruction, and secondly, it analyzes the big data platform technology, which mainly includes the analysis of the big data platform architecture, the description of the Hadoop distributed architecture, and the analysis of the HBase nonrelational database. Finally, it studies the video image feature extraction technology of video content and uses this to study the design of a big data image processing platform for 3D reconstruction models.
Environmental domain embedding for far-field speaker verification

Speaker embedding based on neural network has achieved significant performance in speaker verification. However, this method results maladaptation of far-field speech. To fix this problem, this paper proposes applying environmental domain embedding to far-field speaker verification. By mapping speech environment to domain embedding and combining it with speaker features, the model is guided to detect the information of environment and speaker identity, thus improves the performance of far-field recognition. Comparing to traditional single domain and single embedding depth method, we explore the effects of domain type and embedding depth from gender, speech recording distance and noise. Furthermore, embeddings are applied to convolution through reshaping rather than vanilla fully-connected layers. The improved embedding method allows better combination. Experiments show the EER (Equal Error Rate) of gender-based, distance-based and noise-based model, comparing to baseline (ResNet-34, EER=1.28%), respectively decrease to 1.12%, 0.92% and 0.88%. And further comparison tests discover that carrying out combination in the second or third model stage achieves better performance.
Accelerated Algorithm for Counting the Amount of Numbers in Specified Ranges

An accelerated algorithm for calculating the number of array numbers that fit in each of the specified ranges is proposed. The algorithm assumes that the number of ranges is a multiple of the power of two, the bit depth of the maximum number of the array does not exceed the bit depth of the memory cell used to store it. The acceleration of counting numbers is based on the conversion of the analyzed number of the source array to the address of the memory cell corresponding to the range of the number. This conversion reduces the time for counting the number of numbers in the source array that fit into the specified ranges by eliminating memory accesses to read the boundary values of the addresses of the ranges. The time estimates of the algorithm execution are given, the prospects of using the algorithm in solving problems are noted.
Assessing the Sub-Dimensions of Symbolism in the User Interfaces of Interactive Products

Products are often valued for what they mean to people. The theoretical dimension that describes the meaning of products is referred to as symbolism. Symbolism plays an important role in product evaluations in pre-use conditions. This paper suggests a framework for the analysis of this dimension in product evaluations. Symbolism comprises varying mixtures of pragmatic, product, and user symbolism. Pragmatic symbolism, which includes the product's instrumental benefits, affects product symbolism, or the overall impression of the product. Product symbolism, in turn, affects user symbolism, which refers to the self-image of the product's user. Our findings, based on exploratory and confirmatory factor analyses, suggest that while symbolic value formation often occurs in a bottom-up process, from pragmatic considerations upon which the product's image is built that transfer to the user, it can also be in a top-down process for specific types of products whose design elements directly affect the user's self-image.RESEARCH HIGHLIGHTSSymbolism is not a unified dimension, but comprises three sub-dimensions.Perceived usefulness and functional affordance positively affect the symbolic value of products.For most product types, the image of the product positively affects the image of its user.For most product types, the effect of product usefulness on user image is mediated by product image.While symbolism is usually formed in a bottom-up process, it can also be formed in a top-down process.
APT: The master-copy-free training method for quantised neural network on edge devices

Quantisation is a commonly applied technique to improve the efficiency of a neural network on edge devices. Many applications also require that a machine learning model extend its learning cycle into the field. Training a quantised neural network on edge devices is a non-trivial task since the resource available on the edge is limited. Most of the quantisation-aware training methods maintain a quantised model, as well as an extra full-precision model, which is used to prevent large accuracy losses. The keep of the full precision model is based on the assumption that there are sufficient memory and energy supply to the machine for training. The assumption is contradictory to the situation of edge AI. In this paper, we propose the Adaptive Precision Training method (APT), which only keeps a quantised model. The challenge is that a quantised model has difficulty in learning, due to the quantisation underflow issue. APT employs a metric called G a v g to quantify the learning ability of each layer and dynamically adjusts per-layer bitwidth to ensure the model can learn effectively. Experiments on image classification and text classification tasks suggest that APT trains quantised models effectively with limited accuracy loss. Compared with the 8-bit traditional QAT method, APT saves 60-72.5% memory space for model parameters. We investigate the bitwidth necessary for effective training and gain preliminary insights into the relationship between architecture and its learning ability. • Adaptive Precision Training (APT) trains a quantised network without a master copy. • APT saves 60-72.5% memory for model parameter in training with limited accuracy loss. • Insights into the characteristics of building blocks in neural network architectures.
STROE: An Ungrounded String-Based Weight Simulation Device

We present STROE, a new ungrounded string-based weight simulation device. STROE is worn as an add-on to a shoe that in turn is connected to the user&#x2019;s hand via a controllable string. A motor is pulling the string with a force according to the weight to be simulated. The design of STROE allows the users to move more freely than other state-of-the-art devices for weight simulation. It is also quieter than other devices, and is comparatively cheap. We conducted a user study that empirically shows that STROE is able to simulate the weight of various objects and, in doing so, increases users&#x2019; perceived realism and immersion of VR scenes.
Seven Staged Identity Recognition System Using Kinect V.2 Sensor

By employing artificial intelligence techniques and algorithms such as color and depth image processing, signal processing, machine learning, evolutionary algorithms and fuzzy systems, an identity recognition expert system with approximate recognition accuracy of 99% is proposed. Available identity recognition systems mostly are in three stages which may lead to some security problems, so it is decided to make a robust system. Proposed system uses Kinect Version 2 sensor in order to conduct 7 main stages of recognition. The system includes following stages of recognition and estimation which are, face and voice recognition, finger print recognition, iris recognition, gesture recognition, sex detection and age estimation. By adding macro lens to the sensor, recognition accuracy for fingerprint and iris increases significantly. All efforts on this project were to achieve the highest potential out of available techniques. The system is learning based and has high precision and could be well used in industrial purposes. By installing macro lens on Kinect sensor, the system could compete with other expensive identification systems. It has to be mention that proposed system works well in the pure darkness ass Kinect sensor supports the infrared spectrum.
Fuzzy Cognitive Map Ensembles to Solve Regression and Time Series Tasks

The most popular tasks in the data analysis field are regression and time series forecasting. The basic idea is to find relations between factors. There are many different models could solve this task, but the main problem is explainability. On the other hand, fuzzy cognitive map models have quite enough explainability of decision making. This paper shows its own implementation of cognitive map mechanics based on actor models.
Hybrid multi-document text summarization via categorization based on BERT deep learning models

Text summarization is the process of employing a system to shorten a document or a collection of documents into brief paragraphs or sentences using various approaches. This paper presents text categorization using BERT to improve summarization task which is a state-of-the-art deep learning language processing model that performs significantly better than all other previous language models. Multi-document summarization (MDS) has got its bottleneck due to lack of training data and varied categories of documents. Aiming in this direction, the proposed novel hybrid summarization B-HEATS (Bert based Hybrid Extractive Abstractive Text Summarization)framework is a combination of extractive summary via categorization and abstractive summary using deep learning architecture RNN-LSTM-CNN to fine-tune BERT which results in the qualitative summary for multiple documents and overcomes out of vocabulary (OOV). The output layer of BERT is replaced using RNN-LSTM-CNN architecture to fine tune which improves the summarization model. The proposed automatic text summarization is compared over the existing models in terms of performance measures like ROUGE metrics achieves high scores as R1 score 43.61, R2 score 22.64, R3 score 44.95 and RL score is 44.27 on Benchmark DUC datasets.
Advancing tangible augmented game objects for use in a golf swing, self-service training environment: Report of Work-in-Progress with a Multidisciplinary Emphasis

We report our work-in-progress to advance the use of tangible augmented game objects for use in a golf swing, self-service training environment. Created in 2006 using a virtual reality environment and a hardwired sensor tracking system, this paper presents our transition to augmented reality using wearable sensors and mobile technologies. This paper shares our multidisciplinary approach to define a strategic pathway to address the technical challenges and user adoption.
Gun Detection: Comparative Analysis using Transfer Learning in Single Stage Detectors

Every year, a lot of people around the world suffer from gun-related violence. A solution for this could be using a Single stage detector to detect such incidents quickly. They provide accurate and fast detection. Normally in single stage detectors YOLOv3tiny provides fast detection than YOLOv3 but with less accuracy. But in this paper when transfer learning is applied to both the versions with the small dataset having new class as gun then tiny version improves with accuracy by 4&#x0025; than that of v3. When YOLOv3 and tiny version are trained on 3000 and 2500 respectively then we have got that point as a threshold where both gave same accuracy. Their performances were also evaluated using criteria such as precision, recall, F1 score. The key takeaway from this is YOLOv3 tiny performed best in terms of accuracy and F1 score than that of YOLOv3 in case of transfer learning.
Web User Behavior Analysis Using Pre-Processing of Web Documents to Create Effective Web Designs

The interactions with web systems is huge because of COVID-19, where every system is run through online interactions. The world wide web is continuously expanding, and users' interactions with websites generate a vast quantity of data. Web usage mining is the use of data mining techniques to extract important and hidden information about users. It allows you to see the most frequently visited sites, imagine user navigation, and track the progress of your website's structure, among other things. The web mining techniques help us to analyze the user's behavior and accordingly create the required web designs, which will appear in the relevant searches of the users. In this scenario, one of the important processes is web document preprocessing, which will help us to extract the particular quality data inputs for analyzing the behaviors which helps in effective web design. Here, the authors discuss preprocessing of web documents. From the four different phases of the web mining, web document pre-processing is a very important phase.
Application of Blockchain Technology to facilitate security in Smart Grid

The Blockchain technology has a wide application in the field of Smart Grid as far as the distribution and transmission of power sector is concerned. This is due to the involvement of interconnected power network, advanced communication control technologies and advanced metering infrastructure, which is generally employed to enhance the use of green energy resources and for mitigating the energy scarcity. It is considered to be a new vision of traditional power grid which works on bi-directional energy and energy data transfer so that we can manage, deliver and integrate green and renewable energy technologies. Now, deploying smart grid in decentralized infrastructure rather than in centralized one is the main goal of today's smart grid. Therefore, blockchain technology is being incorporated in smart grid paradigm, considering it has exceptional features which may help us to obtain the same. In this paper, a detailed application of Blockchain technology and its influence in security of smart grid has been discussed.
Compression of YOLOX object detection network and deployment on FPGA

Object detection is an important task in computer vision. There are many practical applications using object detection based on deep learning nowadays. For deployment on FPGA with limited resource and operator support, object detection faces problems such as how to improve speed and reduce power consumption. YOLOX is a high-performance anchor-free YOLO version. To deploy YOLOX network on FPGA, we first replace the Focus layer of YOLOX, adjust the structure of the SPP layer, and change the activation function to meet the operator support constraints. Then we perform sparse training and use scaling factors of BN layer to select out the insignificant channels. The convolutional layer channels are pruned according to the degree of sparseness and pruning ratios. Finally, the network is quantified, compiled via the Vitis AI tool, and deployed on the Xilinx FPGA development board. Comparing the performance with different pruning ratios, the experiments demonstrate that the network runs significantly faster on the FPGA after pruning, and the power consumption is also reduced.
Geology and mineralogy of the Haft Sandogh alunite deposit (NW Takestan): implication for alumina and potassium extraction

Geology and mineralogy of the Haft Sandogh alunite deposit (NW Takestan): implication for alumina and potassium extraction
Analysis of Filtering Window Impacts on Estimation Accuracy of Information Leakage from Exposed Power Delivery Network of Cryptographic Devices

In this paper, an analysis of filtering window impacts on estimation accuracy of electromagnetic (EM) information leakage from exposed areas in power delivery network (PDN) of cryptographic devices is presented. There are exposed areas in the PDN such as decoupling capacitors, through via plating, and small power/ground interconnections where full-byte secret key information can be leaked in the form of EM fields. Various waveforms are measured using EM field probes on the PDN of the target cryptographic device. Correlation electromagnetic analysis (CEMA), field distribution analysis, and secret key extraction are conducted based on the measured waveforms via EM field radiation from the exposed PDN. It is verified that adopting a proper filtering window to remove impacts of noise irrelevant to secret key processing is important for an accurate and efficient estimation of the information leakage from the PDN. Based on the experiment and analysis results, an efficient leakage location detection method using the filtered waveforms is discussed.
An enhanced Skin-tone Block-map Image Steganography using Integer Wavelet Transforms

Steganography is the technique of hiding a confidential message in an ordinary message where the extraction of embedded information is done at its destination. Among the different carrier files formats; digital images are the most popular. This paper presents a Wavelet-based method for hiding secret information in digital images where skin areas are identified and used as a region of interest. The work presented here is an extension of a method published earlier by the authors that utilized a rule-based approach to detect skin regions. The proposed method, proposed embedding the secret data into the integer Wavelet coefficients of the approximation sub-band of the cover image. When compared to the original technique, experimental results showed a lower error percentage between skin maps detected before the embedding and during the extraction processes. This eventually increased the similarity between the original and the retrieved secret image.
CAFE and SOUP: Toward Adaptive VDI Workload Prediction

For Virtual Desktop Infrastructure (VDI) system, effective resource management is rather important where turning off spare virtual machines would help save running cost while maintaining sufficient virtual machines is essential to secure satisfactory user experience. Current VDI resource management strategy works in a passive manner by either reactively driving available capacity based on user demands or following manually configured schedules, which may lead to unnecessary running costs or unsatisfactory user experience. In this article, we propose a first attempt toward proactive VDI resource management, where two adaptive learning approaches for VDI workload prediction are proposed by learning from multi-grained historical features. For non-persistent desktop pool, based on the aggregation session count of pool-sharing users, the CAFE approach induces a pool-level workload predictive model by utilizing coarse-to-fine historical features extracted from aggregation workload data. For persistent desktop pool, based on the session connection status of individual users within the same pool, the SOUP approach induces user-level workload predictive model by incorporating encoded multi-grained features extracted from the logon behavior of individual users into an aggregation pool-level model. Extensive experiments on datasets of real VDI customers and electricity load evidently verify the effectiveness of the proposed adaptive approaches for VDI workload prediction as well as other workload prediction tasks.
Towards a Privacy-Aware Electric Vehicle Architecture

Connected vehicles need to generate, store, process, and exchange a multitude of information with their environment. Much of this information is privacy-critical and thus regulated by privacy laws like the GDPR for Europe. In this paper, we analyze and rate exemplary data (flows) of the electric driving domain with regard to their criticality based on a reference architecture. We classify the corresponding ECUs based on their processed privacy-critical data and propose technical mitigation measures and technologies in form of generic privacy-enhancing building blocks according to the classification and requirements derived from the GDPR.
Predicting Blendshapes of Virtual Humans for Low-Delay Remote Rendering using LSTM

This paper proposes a novel framework to reduce the perceptual delay in rendering the facial expressions of virtual humans. The proposed method reduces the delay by predicting the future blend coefficients that represent facial expressions. The prediction accuracy of the proposed method is improved by 27% on average over a conventional LSTM. We also subjectively confirmed that the proposed method achieves natural facial expressions.
FPGA Implementation of Modular Multiplication for Cryptographic Applications

One of the most computationally expensive arithmetic operation's is modular multiplication. To create a realistic public-key cryptosystem, efficient algorithms and implementations are needed. Based on different integer lengths many algorithms of modular multiplication are implemented for public-key cryptosystems. Each implementation is efficient in parameters like power, area delay and high throughput. With the recent works on Verifiable Delay Functions (VDF's) the need for modular multiplication implementations to have the lowest possible latency has increased, as before low latency was not important as of high throughput in these multiplications. VDF's are designed to realise the underlying calculation in a predetermined amount of time that can be publicly validated. Inherently sequential arithmetic operations are used in VDF constructions. Recently, efficient VDF constructions based on Rivest, Shamir, and Wagner's time-lock puzzles have been suggested. For these VDF constructions, an RSA group exponentiation operation must be realised. Low-latency circuit implementation is proposed in this paper, along with an optimized FPGA version of the algorithm.