Reviewerly/exHarmony huggingface dataset:
How are you getting on with these?

This contribution explores how a counselor uses requests to encourage clients to actively participate in email counseling. Applying positioning theory and relational work from interpersonal pragmatics, I answer the following research questions: How does the counselor use requests to position clients as active participants? How do affordances of the medium influence the counselor’s efforts in such requests? I analyze five naturally occurring email counseling exchanges through content and discourse analysis, adding insight from a practitioner interview. The examined requests aim to improve joint understanding, foster reflection, elicit solutions, and elicit feedback on coping techniques. Results show that the counselor uses intricate relational work that constructs clients as active participants. The counselor employs medium affordances such as asynchronicity or emoticons.
A Multi-task Conformer for Spoofing Aware Speaker Verification

Spoofing attacks conducted via logical methods such as voice synthesis and voice conversion could significantly degrade the performance of a speaker verification system. Many spoofing countermeasures have been proposed, but they are only trained separately from speaker verification systems. There are only a few efforts to design a single model capable of rejecting both utterances spoken by different speakers as well as spoofing utterances. Meanwhile, Conformer, a combination of Transformer and Convolution Neural Network, has shown remarkable success for automatic speech recognition, but its application in speaker verification and anti-spoofing systems has not yet been explored. In this work, we proposed a multitask Conformer with statistical pooling for both speaker verification and voice spoofing detection. Our system achieved a 70% relative SASV-EER improvement over baselines on the ASVspoof 2019 LA evaluation set.
Phishing Detection Using Machine Learning Algorithm

The rapid increase of phishing attacks has led individuals and organizations losing billions of dollars as well as worried about the confidentiality and privacy of their data. This tremendous annual increase of phishing attacks shows that the current detection methods available are not sufficient, therefore more effective phishing detection methods should be developed. This paper proposed a novel phishing detection model using machine learning, to improve efficacy and accuracy in phishing detection. This paper explores the current state-of-the-art in phishing detection along with their drawbacks and proposes a new novel method based on image visualisation of website code and features extraction from malicious URLs which is under development.
A Design Model of Music Teaching and Auxiliary System Based on Artificial Neural Networks

The Internet has now ingrained itself into every aspect of people's lives and has grown to be a necessity. Machine learning technology is applied to the design of music teaching and teaching assistant systems with the aim of bringing convenience to students and teachers in teaching and learning and improving teaching efficiency and quality through the design and implementation of a friendly and interactive intelligent music assistant teaching system. The B/S mode framework aids in the realisation of the hierarchy and module structure of the system design during the system implementation process. Last but not least, this paper looks at the system modules, confirms all of the system's operations, and makes sure each functional module is working properly. The algorithm is straightforward, effective, simple to use, and user-friendly, and the system is expandable, portable, and transparent. It is an excellent teaching tool for music.
Perfect edge state transfer on abelian Cayley graphs

Recently, perfect (quantum) edge state transfer (PEST, in short) was proposed as a potential model for quantum information processing. In a previous paper (X. Cao (2021) [12]), a characterization on cubelike graphs having PEST is presented. In this paper, a general characterization of abelian Cayley graphs having PEST is established by generalizing the main idea of the prior paper. Some concrete constructions of abelian Cayley graphs having PEST are provided. Additionally, some existence results are confirmed as well. Notably, we show that for every even number n, there is an abelian group G with order n, and a subset S in G such that Cay(G,S) has PEST at time π/2. Particularly, we show that PEST may occur in some abelian Cayley graphs which don't exhibit perfect state transfer (PST).
Development of A Model For the in-Service Training of Secondary School Science Teachers

The present study aimed for developing a model for in-service training for science teachers of secondary schools keeping in view deficiencies and drawbacks found in the existing model. The population consisted of 2191 secondary school science teachers of Southern Punjab and the sample consisted of 724 trainees who were imparted training under Science Education Project (SEP) in the years 2002 and 2003 from four districts Multan, Layyah, Khanewal and Lodhran that were selected by random sampling technique. The questionnaire was developed that comprised 38 items including one open-ended question regarding suggestions for improving the INSET programme. The items of the questionnaire were developed regarding various indicators like reason of participation, use of teaching method, use of A.V. Aids and information regarding the commencement of INSET. The major findings and conclusion of the study were: (i) trainees participated for their professional growth (ii) the lecture method was frequently used and (iii) trainees were informed before the commencement of the training course. They also suggested that INSET should be conducted by using modern methods of teaching at the local or tehsil level. Relevant subject teachers and highly qualified master trainers should be nominated for INSET. Descriptive and inferential statistics were applied to analyze the data. The proposed Model of INSET for secondary school science teachers was developed on the basis of findings, conclusions and recommendations.
An architecture smell knowledge base for managing architecture technical debt

Many software projects suffer from architecture erosion and architecture technical debt. One challenge is to identify affected parts and prioritize them for refactoring. Architecture smells are indicators of potential architecture technical debt, but architecture smells are ambiguous and their impact is not always clear. To address this, we have built a knowledge base that improves understanding of architecture smells and identifies violated software design principles and affected quality attributes. The design principles help our understanding of what causes architecture smells, while the impaired quality attributes represent the consequences. We conducted a systematic literature review to identify these relations and built an architecture smell ontology. This ontology provides a knowledge base that architects can use to prioritize the smells according to the project's individual quality goals.
Hybrid Activation Function in Deep Learning for Gait Analysis

Various values are carried out in Neural Network Connections, to check whether the neurons are activated equally or not and the activation layer is called to conduct the process statistically balanced. Mathematical functions like ReLU, softmax and sparsemax are used in Activation Layer. In this paper the investigator combined softmax and sparsemax in the last activation layer of the Deep Learning Convolutional Neural Network for gait analysis using silhouettes. Human identification process is done without the conscious of the person. The researcher used gait features to predict the person. Python Language that runs over tensorflow is used to implement the functions in activation layer. Benchmarking dataset CASIA Band C is used for better performance. The combined hybrid formula is implemented in the last activation layer along with probability calculation. The results reveals that the hybrid usage of both softmax and sparsemax function in the activation layer helps in better performance and provides high accuracy while comparing the individual usage of functions separately.
Gesture Control Integrated Wheelchair

In a world of developing science and technology, humans fulfill their daily needs and other small desires with our own. There are some or particularly many people who are physically challenged, feel difficult and seek other people&#x0027;s help to even move from one place to another. Taking care of them and thinking about all their needs is a solution that is proposed: &#x201C;Gesture controlled smart wheelchair&#x201D;. A gesture manipulation wheelchair is believed to be able to do responsibilities like navigation, impediment detection, etc. the usage of sensors and intelligence. The preliminary part of the work was improvement of a movement-controlled wheelchair to test and verify the gesture operation. Following that, a real-time operating wheelchair with a mode switching choice between joystick manage mode and head gesture manipulate mode became designed to satisfy the needs of the consumer. There are lots of wheelchairs available in the existing market, but the features may vary and are not completely meeting up their expectations. Physically challenged people account for 27.5 percent of the population. In kids under 18 years, the prevalence of disability is envisioned at 6&#x0025; and for the age organization above 18 years, it&#x0027;s 14&#x0025;. It is predicted that there are three.4 million disabled children and 10.2 million disabled adults inside the U.S. In addition, roughly 4.6 percent of people experience disability as a result of a spinal cord injury. Many assistive technology and devices are available to assist physically challenged people. The framework is made of a transmitter and a receiver that join wirelessly with each other. For wi-fi transmission, 433MHz RF Transmitter and Receiver Unit has been used because it transmits information through an antenna at the rate of lKbps &#x2013; 10Kbps and the range may be adjusted as required. The MPU 6050 sensor detects rotation on both the X and Y axes. based totally on that, the RF transmitter sends messages to the RF receiver. If the sensed rotation is at positive X-axis, the transmitter sends specific radio waves as information. That means the motor driving force will power the wheels on the left. If the sensed rotation is at a bad X-axis, the transmitter sends any other specific information as radio frequencies. which means the motor driving force will drive the wheels at right. The transmitter provides a unique value if the located rotation is on the terrible Y-axis. meaning the motor motive force will force the wheels ahead. RF receiver is interfaced with Arduino N ano which accepts the messages from the transmitter and sends them to Arduino Nano. Those communications are commonly converted into American preferred Coding for information Interchange (ASCII) code, which is sooner or later decoded via Arduino. DC cars are pushed through an L293D motor driving force.
Analysis of Student Sentiment Level using Perceptual Neural Boltzmann Machine Learning Approach for E-learning Applications

Gathering and analyzing feedback has long been an important topic. E-learning has become one of the most effective training methods. Specifically, e-learning is considered as a supportive and a useful way to understand students and their learning problems. The sentiment analysis systems provide recommendations for related student feedback details and play an important role in using information about disseminating e-learning projects. Sentiment analysis-based machine learning technique plays an important role in building a recommender system. It has been very difficult to identify the student interest score with the previous system and does not understand the learning problems in their automatic sentiment analysis. This proposed framework for the recommender system is based on machine learning models using sentiment analysis. Perceptual Neural Boltzmann Machine (PNBM) is used to classify sentiment score and the web data-based student feedback based automatic sentiment analysis. This study aims to develop a PNBM system-based analysis of student feedback and satisfaction by assigning proper sentiment score through student interest score based and communicating under the social media e-learning chat-based. The proposed system gives better simulation results of analysis of classification accuracy, time complexity, sensitivity and specificity, and analysis of Sysnet based sentence level.
(LISACMT) Language Identification and Sentiment analysis of English-Urdu ‘code-mixed’ text using LSTM

Sentiment analysis is the method of identifying and extracting the opinions, attitudes, and polarity indicated in a text. Individuals, organizations and companies make decisions based on the sentiments expressed by users toward products, services, social or cultural issues, and government policies. Advancements in Natural Language Processing and Artificial Intelligence makes text analysis easier. However, text classification is more focused on the English language. The increase in social-media content available on a number of platforms is predominantly informal and noisy. Social-media content influenced by the regional languages makes the text analysis more challenging. A new phenomenon called code-mixing which involves mixing of linguistic units of one language into the utterances of another language is nowadays exhibited on social-media textual content. To deduce useful information from this type of text is a challenging task. This paper presents a language identification and sentiment analysis method for English-Urdu code-mixed text using deep learning approaches. For language identification Artificial Neural network along with character based embedding has been used. A baseline Long Short-Term Memory approach using word based embeddings has been employed for sentiment classification. Both language identification and sentiment classification approaches showed promising results.
An Efficient VLSI Design of 4-bit ALU

Energy dissipation has emerged as one of the highest important factors in the formation of VLSI. Now a days each and every electronic gadget has been designed in a smart way with lot of applications, hence these may dissipate higher power. Reversible logic has got higher interest in current trends because, its characteristics are less heat dissipation capability. This logic plays crucial role for the construction of low power structures.
Speaker Diarization For Vietnamese Conversations Using Deep Neural Network Embeddings

Speaker diarization, known as finding “who spoke when” is the method of dividing a conversation into segments spoken by the same speaker. While speaker diarization has numerous applications, there are little to no reports on its application in Vietnamese speech processing system. In addition, the key to accurately do such task is to learn discriminative speaker representations, or speaker embeddings. Recently X-Vectors and ECAPA-TDNN, based on deep neural networks, has emerged as state-of-the-art speaker embeddings networks for English corpora. In this work, we build a speaker diarization system for Vietnamese telephone conversations, and explore the capabilities of X-Vectors and ECAPA-TDNN in the system. We also evaluate the discriminative characteristics of these speaker embeddings networks on a bare-bones speaker verification system. Used data include proprietary datasets (IPCC-110000, IPCC-2000, VTR-1350) and a public dataset (ZALO-400). While these datasets can be used directly for training and testing for speaker verification task, for speaker diarization task we have to simulate multi-way conversations. Our conducted experiments show that ECAPA-TDNN system out-perform the X-Vectors system on both speaker verification and speaker diarization tasks.
A Local Structure-aware 3D Hand Pose Estimation Method for Egocentric Videos

In this paper, we propose a method to estimate 3D hand pose from first-person videos. First, in order to remove the effect of background, we build a hand detection module based on YOLO network then apply this module in RGB images. Then, depth information of detected hand regions is employed to construct point clouds. Finally, a local structure aware model named SplitPointnet which consists of six PointNet++ models is proposed to simultaneously estimate joints in five fingers and the thumb region. Experimental results obtained on a large dataset of egocentric vision FPHAB have shown that the proposed method results better hand pose estimations than the state-of-the-art methods with the average error is 66.26 mm.
A Blockchain-based Trustworthy Cloud Services Digital Ecosystem

The exponential growth of the domain of cloud services globally due to ease of use, cost benefits and flexibility in usage requires increasingly strong and convincing levels of trust on multiple levels. Several risks and issues exist that affect the reliability of the market such as the rogue or phantom clouds, loss of control over data, performance or audit capability. Further-more, frequent variations in exposed interfaces and lack of proper descriptions hinder reliability, interoperability and security of the cloud based applications. The present work aims at entrusting the current Cloud Service domain designing a semantic aware-Market place relying on the Blockchain technology to establish reliable business interactions among the actors.
Comparative study on super resolution techniques for upper gastrointestinal endoscopic images

Endoscopy is considered the gold standard for diagnosis of gastrointestinal diseases. Image quality is an important creteria for a better accurate prediction of the diseases. Actually, in many current health facilities in developing countries as Vietnam, due to the endoscope limitation and environmental impacts, endoscopic images are of very low resolution. As a result, some textures and colors in lesion regions of the image could be ignored. This paper investigates different techniques for enchancement of image resolution. Spefically, we implement fundamental interpolation methods such as Nearest Neighbor Interpolation (NNI), Bilinear Interpolation (BLI) and Bicubic Interpolation (BCI) and advanced methods using deep learning such as Efficient Supixel Convolution Neuron Network (ESPCN), Residual Dense Network (RDN) and Super Resolution Dense Network All (SRDenseNet All). We then compare the performance of these techninques according to SSIM, PSNR and framerate metrics. The experimental results on dataset of upper gastrointestinal endoscopic images show that deep learning super-resolution method (RDN) provides the highest efficiency. This method produces sharper images, some of them look more intuitive and provide more information to doctors that can improve their diagnosis and treatment.
Chatbot at University, a Communication Tool to Increase Work Productivity

The implementation of intelligent services at university level is another step towards the transformation of education. The main goal of any innovation in education is to improve the quality of education, this is a complicated concept, because there are many variables that need to be considered in the success or failure of these innovations. These innovations include the implementation of chatbots. The use of chatbots in a common space has confirmed its viability and it is therefore quite logical that these solutions can be applied in a university environment. Although it has had many applications in several sectors over the last decade, their implementation in higher education is still in its infancy. The chatbot market is expected to grow tremendously between 2020 and 2024, so it is high time for universities to implement chatbots. This paper will look at how companies have improved their services and processes by implementing chatbot services and how this can translate into a more efficient and effective university environment.
Enhancing Consumer Usage of AI-Chatbots: The Role of Perceived Humanness, Social Presence, and Social Interactivity

AI-Chatbots have been widely used in e-commerce activities and they can replace human customer service in the future. However, an industrial report shows that many consumers are reluctant to use AI-Chatbots. The purpose of this paper is to verify the effect of social elements on consumers&#x0027; intention to utilize and intention to explore AI-chatbots. Based on the SOR model, this paper proposes that social elements of AI-Chatbots affect perceived intimacy, which is moderated by the consumers&#x0027; need for human interaction. Perceived intimacy further facilitates consumers&#x0027; trust in AI-Chatbots. Perceived intimacy and trust in AI-Chatbots reflect the inner states of the consumers and influence consumers&#x0027; intention to use and explore AI-Chatbots. In this study, 302 valid data were collected by a survey to test the significant role of social elements. Contributions and limitations of this study were discussed.
SqueezeVGGNet: A Methodology for designing low complexity VGG Architecture for Resource Constraint Edge Applications

Convolutional Neural Networks also known as ConvNets are now extensively used in various machine learning tasks for solving various problems in computer vision, biomedical, defence industry, entertainment etc. These neural networks for most of the applications are focused towards increasing the accuracy. However, besides maintaining the accuracy within a tolerable range, reduction in the network model size can have a lot of advantages from its mobility, easy deployment, remote upgradation and energy efficiency point of view. To attain these advantages, we propose a universal strategy to realize the convolution operation of a n x n filter kernel with fewer parameters, which also reduces the number of channels. We have proposed a compressed VGGNet model based on VGGNet neural network which resulted in 20x lesser parameters compared to its classical counterpart with an improved inference time by 3 times whilst maintaining similar accuracy. A complete hardware design of the compressed VGG architecture has also been implemented. A quantitative and qualitative analysis for various variants of VGGNet and other models reveal the reduction in the number of parameters in the range of 18-20x and the number of network operations contributing to the model complexity has shown a reduction of 2.5x with respect to its vanilla counterpart making it easier to deploy onto FPGAs and edge devices
SecSoC: A Secure System on Chip Architecture for IoT Devices

IoT technology is finding new applications every day and everywhere in our daily lives. With that, come new use cases with new challenges in terms of device and data security. One of such challenges arises from the fact that many IoT devices/nodes are no longer being deployed on owners' premises, but rather on public or private property other than the owner's. With potential physical access to the IoT node, adversaries can launch many attacks that circumvent conventional protection methods. In this paper, we propose Secure SoC (SecSoC), a secure system-on-chip architecture that mitigates such attacks. This include logical memory dump attacks, bus snooping attacks, and compromised operating systems. SecSoC relies on two main mechanisms, (1) providing security extensions to the compute engine that runs the user application without changing its instruction set, (2) adding a security management unit (SMU) that provide HW security primitives for encryption, hashing, random number generators, and secrets store (keys, certificates, etc.). SecSoC ensures that no secret or sensitive data can leave the SoC IC in plaintext. SecSoC is being implemented in Bluespec System V erilog. The experimental results will reveal the area, power, and cycle time overhead of these security extensions. Overall performance (total execution time) will also be evaluated using IoT benchmarks.