Reviewerly/exHarmony huggingface dataset:
Exploring the Employment Quality Evaluation Model of Application-Oriented University Graduates by Deep Learning

In view of the employment difficulties of college graduates, this paper analyzes the overflow of graduates in a particular period caused by the expansion of enrollment in various colleges and universities and the social phenomenon of social positions in short supply. First, the employment status of application-oriented college students and the deficiencies of employment guidance courses are summarized. Then, deep learning technology is combined with the relevant employment concept to construct an employment training model to guide college students in employment. Besides, a questionnaire on learning effect and employment quality is designed from four perspectives: learning motivation, concentration, teaching process, and final results. The information collected through the questionnaire demonstrates that the employment quality and learning effect of male and female students are not significantly affected by gender differences. In addition, the P values of learning motivation, concentration, and teaching process are all less than 0.01, and the unstandardized coefficient of the teaching process is 0.349, which has the most significant impact on the learning effect. In short, the three factors positively affect the learning effect. Therefore, it comes to the conclusion of improving the ability and strategy of classroom employment guidance. If one wants to be successful in job hunting and career selection, it is not enough just to be competitive but also to be good at it. Being good at the competition is reflected in having good psychological quality, strength, and a good competitive state. In the job hunting and career selection competition, attention should be paid to whether the expected value is appropriate. College students should have sufficient self-awareness before preparing to submit resumes. During the interview, they should overcome emotional anxiety. If a person can treat study, work, and life in a good mood from beginning to end, he will win the competition. The research reported here can provide some reference suggestions for the employment quality of application-oriented college graduates.
Splash! Identifying the Grand Challenges for WaterHCI

Bodies of water can be a hostile environment for both humans and technology, yet they are increasingly becoming sources, sites and media of interaction across a range of academic and practical disciplines. Despite the increasing number of interactive systems that can be used in-, on-, and underwater, there does not seem to be a coherent approach or understanding of how HCI can or should engage with water. This workshop will explicitly address the challenges of designing interactive aquatic systems with the aim of articulating the grand challenges faced by WaterHCI. We will first map user experiences around water based on participants’ personal experiences with water and interactive technology. Building on those experiences, we then discuss specific challenges when designing interactive aquatic experiences. This includes considerations such as safety, accessibility, the environment and well-being. In doing so, participants will help shape future work in WaterHCI.
A Methodology for Controlling Bias and Fairness in Synthetic Data Generation

The development of algorithms, based on machine learning techniques, supporting (or even replacing) human judgment must take into account concepts such as data bias and fairness. Though scientific literature proposes numerous techniques to detect and evaluate these problems, less attention has been dedicated to methods generating intentionally biased datasets, which could be used by data scientists to develop and validate unbiased and fair decision-making algorithms. To this end, this paper presents a novel method to generate a synthetic dataset, where bias can be modeled by using a probabilistic network exploiting structural equation modeling. The proposed methodology has been validated on a simple dataset to highlight the impact of tuning parameters on bias and fairness, as well as on a more realistic example based on a loan approval status dataset. In particular, this methodology requires a limited number of parameters compared to other techniques for generating datasets with a controlled amount of bias and fairness.
Communication-efficient federated learning for multi-institutional medical image classification

Federated learning (FL) has emerged with increasing popularity in the medical image analysis field. In collaborative model training, it provides a privacy-preserving scheme by keeping data localized. In FL frameworks, instead of collecting data from clients, the server learns a global model by aggregating local training models from clients and broadcasts the updated model. However, in the situation where data is not identically and independently distributed (non-i.i.d), the model aggregation requires frequent message passing, which may face the communication bottleneck. In this paper, we propose a communication-efficient FL framework based on the adaptive server-client model transmission. The local model in the client will only be uploaded to the server under the conditions of (1) a probability threshold and (2) an informative model updating threshold. Our framework also tackles the data heterogeneity in federated networks by involving a proximal term. We evaluate our approach on a simulated multi-site medical image dataset for diabetic retinopathy (DR) rating. We demonstrate that our framework not only maintains the accuracy on non-i.i.d dataset but also provides a significant reduction in communication cost compared to other FL algorithms.
Generating repairs for inconsistent models

Abstract There are many repair alternatives for resolving model inconsistencies, each involving one or more model changes. Enumerating them all could overwhelm the developer because the number of possible repairs can grow exponentially. To address this problem, this paper focuses on the immediate cause of an inconsistency. By focusing on the cause, we can generate a repair tree with a subset of repair actions focusing on fixing this cause. This strategy identifies model elements that must be repaired, as opposed to additional model elements that may or may not have to be repaired later. Furthermore, our approach can provide an ownership-based filter for filtering repairs that modify model elements not owned by a developer. This filtering can further reduce the repair possibilities, aiding the developer when choosing repairs to be performed. We evaluated our approach on 24 UML models and four Java systems, using 17 UML consistency rules and 14 Java consistency rules. The evaluation data contained 39,683 inconsistencies, showing our approach’s usability as the repair trees sizes ranged from five to nine on average per model. Also, these repair trees were generated in 0.3 seconds on average, showing our approach’s scalability. Based on the results, we discuss the correctness and minimalism with regard to the cause of the inconsistency. Lastly, we evaluated the filtering mechanism, showing that it is possible to further reduce the number of repairs generated by focusing on ownership.
The role of the AI availability and perceived risks on AI adoption and organizational values

Artificial intelligence (AI) is the simulation of human intelligence in various machines which are programmed to think like humans. AI could have many forms, such as Natural Language processing (NLP), expert systems, unsuper-vised learning, content extraction, question answering, image recognition, machine vision, speech to text and machine translation, and Robotics. De-spite the potential of AI technologies, little is known about its adoption by organizations worldwide, and especially in the Arab World and the Middle East. Thus, this research intends to investigate the relationships and the im-pact of the availability of AI technologies and the associated perceived risks on AI adoption in organizations and its perceived organizational values, and also considering two mediating variables: AI replacing humans and AI creat-ing jobs, in one Arab and Gulf country, the UAE. By reviewing the literature and describing the AI applications and services available in UAE, a research model was proposed and tested quantitatively on a sample of 150 employees who are familiar with organizational AI technologies. The results revealed that the availability of AI technologies significantly impacts organizations’ decisions to adopt those technologies in the UAE. In addition, the more the AI applications are available, the more the AI will be replacing humans in job positions and opportunities. On the other hand, perceived security, priva-cy, and technical risks can significantly influence organizations’ intentions to adopt AI technologies, and can, surprisingly, contribute to creating new job opportunities which serve the human side positively. The research concludes in some implications, limitation, and future directions.
How Do We Answer Complex Questions: Discourse Structure of Long-form Answers

Long-form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions.To better understand this complex and understudied task, we study the functional structure of long-form answers collected from three datasets, ELI5 (Fan et al., 2019), We-bGPT (Nakano et al., 2021) and Natural Questions (Kwiatkowski et al., 2019).Our main goal is to understand how humans organize information to craft complex answers.We develop an ontology of six sentence-level functional roles for long-form answers, and annotate 3.9k sentences in 640 answer paragraphs.Different answer collection methods manifest in different discourse structures.We further analyze model-generated answers -finding that annotators agree less with each other when annotating model-generated answers compared to annotating human-written answers.Our annotated data enables training a strong classifier that can be used for automatic analysis.We hope our work can inspire future research on discourselevel modeling and evaluation of long-form QA systems. 1
Understanding ‘passivity’ in digital health through imaginaries and experiences of coronavirus disease 2019 contact tracing apps

Growing interest is being directed to the health applications of so-called ‘passive data’ collected through wearables and sensors without active input by users. High promises are attached to passive data and their potential to unlock new insights into health and illness, but as researchers and commentators have noted, this mode of data gathering also raises fundamental questions regarding the subject's agency, autonomy and privacy. To explore how these tensions are negotiated in practice, we present and discuss findings from an interview study with 30 members of the public in the UK and Italy, which examined their views and experiences of the coronavirus disease 2019 contact tracing apps as a large-scale, high-impact example of digital health technology using passive data. We argue that, contrary to what the phrasing ‘passive data’ suggests, passivity is not a quality of specific modes of data collection but is contingent on the very practices that the technology is supposed to unobtrusively capture.
Video Super-Resolution Using Multi-Scale and Non-Local Feature Fusion

Video super-resolution can generate corresponding to high-resolution video frames from a plurality of low-resolution video frames which have rich details and temporally consistency. Most current methods use two-level structure to reconstruct video frames by combining optical flow network and super-resolution network, but this process does not deeply mine the effective information contained in video frames. Therefore, we propose a video super-resolution method that combines non-local features and multi-scale features to extract more in-depth effective information contained in video frames. Our method obtains long-distance effective information by calculating the similarity between any two pixels in the video frame through the non-local module, extracts the local information covered by different scale convolution cores through the multi-scale feature fusion module, and fully fuses feature information using different connection modes of convolution cores. Experiments on different data sets show that the proposed method is superior to the existing methods in quality and quantity.
Adaptive Youla–Kučera parametric control of unknown tip-tilt disturbance rejection in image stabilization systems

We propose a new, to the best of our knowledge, adaptive control method to attenuate beam jitter in image stabilization systems, which breaks the restrictions of existing adaptive control methods in terms of time consumption and control stability, and then provides an innovative way for tip-tilt disturbance rejection. In this approach, disturbance rejection could be converted to optimize error attenuation through the parameters design of the Youla-Kučera (YK) parametric controller, which are determined by a new on-line power spectrum density (PSD) estimation of tip-tilt errors to identify the frequency and amplitude of unknown disturbances. Experimental results demonstrate that the proposed method working in an adaptive correction process can attenuate the beam jitter to approximately 1/3 less than the original control error.
Image-based human sperm counting method

Modern people have a great interest in health, but most people are less interested in their sperm health than other body conditions. In this paper, we propose a method to measure the average number of sperms through a sperm image sequence captured by a Smartphone with a small microscope lens so that the image acquisition could be easily performed. In order to distinguish between noise and sperm in the photographed sperm image, sperms are searched by using difference image based on the assumption that sperms are always moving. In addition, the number of detected sperms is measured and tested based on the WHO criteria. Experimental results show the effectiveness of the proposed method.
The Effect of Blended Learning Model with Moodle on the Students’ Writing Achievement

This study is an experimental research that aims to investigate the effect of the Blended Learning Model with Moodle on the students’ writing achievement. This research was conducted at SMK Tritech Informatika on Jalan Bhayangkara No. 484 Kecamatan Medan Tembung. The samples of the study were the eleventh-grade students of the first semester in the 2019/2020 academic year. There were two parallel classes, which each class consisted of 18 students. The researcher administered a pre-test and post-test about writing news item text to the students to obtain the data. Then, the researcher analyzed the data using a t-test to determine the effect of the model used. The result of the analysis shows that there was a difference in writing achievement between the students who were taught using Blended Learning Model and those who were taught using the conventional method. The value of the t-test proved it. the t-score was 3.12 and t-cv at the level of significance of 0.05 (1.690), which means that t-test (3.12) &gt;t-cv (1.690). Based on the research finding, this study concludes that Blended Learning Model has a significant effect on the students' writing achievement.
Autocorrelations of Vectorial Boolean Functions

Recently, Bar-On et al. introduced at Eurocrypt’19 a new tool, called the differential-linear connectivity table (DLCT), which allows for taking into account the dependency between the two subciphers \(E_0\) and \(E_1\) involved in differential-linear attacks.This paper presents a theoretical characterization of the DLCT, which corresponds to an autocorrelation table (ACT) of a vectorial Boolean function. We further provide some new theoretical results on ACTs of vectorial Boolean functions.
Cloud based Secure Storage of Files using Hybrid Cryptography and Image Steganography

Cloud Computing has made it possible to provide individuals as well as organizations with a utility that is cost-effective. It empowers businesses by delivering these services using the internet. Files can be shared through the cloud. These files may contain sensitive information that needs to be kept hidden from anonymous users. This is done using cryptographic algorithms. High level of security can be provided using hybrid cryptography to encrypt the data. Advanced Encryption Standard (AES) and Triple Data Encryption Standard (3DES) are the symmetric key encryption algorithms used to secure. An asymmetric key encryption algorithm, Rivest-Shamir-Adleman (RSA) helps in providing a hybrid cryptography model. The security of the key generated can be further enhanced using image steganography method Least Significant Bit (LSB). These issues regarding the security and its challenges will be addressed in this paper and also analyse the measures to handle it.
Newspaper Article Classification using Machine Learning Techniques

Newspaper articles offer us insights on several news. They can be one of many categories like sports, politics, Science and Technology etc. Text classification is a need of the day as large uncategorized data is the problem everywhere. Through this study, We intend to compare several algorithms along with data preprocessing approaches to classify the newspaper articles into their respective categories. Convolutional Neural Networks(CNN) is a deep learning approach which is currently a strong competitor to other classification algorithms like SVM, Naive Bayes and KNN. We hence intend to implement Convolutional Neural Networks - a deep learning approach to classify our newspaper articles, develop an understanding of all the algorithms implemented and compare their results. We also attempt to compare the training time, prediction time and accuracies of all the algorithms.
Motivation Research and Development of Educational and Cognitive Activity of Technical University Students

The article is devoted to the problem of the motivation development of educational and cognitive activity of technical high school students. The article presents the results of the research on the motivation development of educational and cognitive activity of students of the direction 21.03.01 “Petroleum Engineering” of the Surgut Institute of oil and gas (a branch of the Industrial University of Tyumen in Surgut). As a result of the research, a method of stimulating the development of motivation of educational and cognitive activity of students was proposed, based on the combined use of interactive teaching methods with various pedagogical techniques. The article also discusses the characteristics of the students’ motives of their educational activities, as they directly affect the quality of professional training, the formation of the professional personality.
US authorities recover most of Colonial Pipeline ransom

Shortly after the creation of a new Ransomware and Digital Extortion Task Force, the US Department of Justice (DOJ) announced that it had recovered most of the ransom payment made by Colonial Pipeline – although questions remain over how it managed to do that.
Major Developments in Visual Cryptography

Visual cryptography is a cryptographic mechanism that gets applied in visual information protection for secured transfer of data. The encryption makes the decryption a mechanical operation.The secret image gets converted into several share images. The share images are distorted/ noisy images which will give the correct image only when placed in the right combination. This paper reviews various visual encryption and decryption mechanism with the safe transmission of information from the literature. This paper about visual cryptography discusses, general access structure, halftone, colour, progressive, region incrementing, extended visual cryptography, colour extended visual cryptography, XOR and OR visual cryptography and the applications of visual cryptography. The applications discussed here are banking security, steganography, crime records, intelligence communication, multilayered Id cards and fingerprint records. The paper provides an overview of advances in VCS where each technique overcomes the problem(s) faced in the other manner.
An Efficient Hybrid Optimization Algorithm for Image Compression

In this work, a novel image compression approach is developed that is processed in several series of technologies.Here, the first process is the image segmentation and it is done using Adaptive ACM that partitions or segments the image into two regions such as ROI as well as non-ROI.Here, the adaptiveness of this ACM is determined with the idea of optimization algorithm.To handle the ROI regions, the JPEG-LS technique is exploited and to handle the non-ROI region the wavelet-based lossy compression technique is utilized.The outcome of both the JPEG-LS technique, as well as a wavelet-based compression approach is integrated with respect to the bit-stream amalgamation in order to produce the compressed image.Then, the compressed image is exploited to the image decompression that will be the overturn process of compression.It will comprise the bitstream separation that is subsequently individually process in both the wavelet-based decomposition and JPEG-LS decoding for obtaining the non-ROI regions and ROI.At last, the original image is obtained accurately.Moreover, the main objective of this paper falls in the adaptiveness under optimization.The maximum iteration and weighting factor in ACM are optimally chosen and for this a novel hybrid optimization technique is proposed, which hybridizes the concept of Differential Evolution method with Monarch Butterfly Optimization Algorithm.Here, the proposed method is compared with the conventional methods in order to shows its effectiveness for image compression.
Hyperledger

In previous chapters, I covered blockchain technologies that are focused on cryptocurrency, and in fact, each project I have covered so far has included its own currency. Hyperledger is different; it does not have a currency attached, although you can create a coin if needed. Instead, Hyperledger was created with the aim of being an open source platform targeted at utilizing blockchain to fit business needs.
Research on teaching quality evaluation method of network course based on intelligent learning

In view of the problems of poor evaluation effect and high evaluation delay in the existing evaluation methods for network course teaching quality, this paper designs an evaluation method based on intelligent learning. The design idea is as follows: with the help of public services, different forms of teaching tasks are summarised and data reduction is processed. On this basis, the restricted Boltzmann machine method based on intelligent learning is adopted to design the evaluation algorithm, and the teaching quality is evaluated with weighting parameters and preference parameters. Through the application part, different application functions such as information query, scene construction, data management and view viewing during the network course teaching are realised. The experimental results show that this method has high evaluation efficiency, short evaluation delay, and significantly reduced actual use of class time, which guarantees the teaching quality of teachers and the overall learning effect of students, and has high application advantages.