Reviewerly/exHarmony huggingface dataset:
Prediction of Mobile-App Network-Video-Traffic Aggregates using Multi-task Deep Learning

Traffic prediction has proven to be useful for several network management domains and represents one of the main enablers for instilling intelligence within future networks. Recent solutions have focused on predicting the behavior of traffic aggregates. Nonetheless, minimal attempts have tackled the prediction of mobile network traffic generated by different video application categories. To this end, in this work we apply Multi-task Deep Learning to predict network traffic aggregates generated by mobile video applications over short-term time scales. We investigate our approach leveraging state-of-art prediction models such as Convolutional Neural Networks, Gated Recurrent Unit, and Random Forest Regressor, showing some surprising results (e.g. NRMSE < 0.075 for upstream packet count prediction while NRMSE < 0.15 for the downstream counterpart), including some variability in prediction performance among the examined video application categories. Furthermore, we show that using smaller time intervals when predicting traffic aggregates may achieve better performances for specific traffic profiles.
Suppressing quantum errors by scaling a surface code logical qubit

Practical quantum computing will require error rates that are well below what is achievable with physical qubits. Quantum error correction offers a path to algorithmically-relevant error rates by encoding logical qubits within many physical qubits, where increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low in order for logical performance to improve with increasing code size. Here, we report the measurement of logical qubit performance scaling across multiple code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, both in terms of logical error probability over 25 cycles and logical error per cycle ($2.914\%\pm 0.016\%$ compared to $3.028\%\pm 0.023\%$). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a $1.7\times10^{-6}$ logical error per round floor set by a single high-energy event ($1.6\times10^{-7}$ when excluding this event). We are able to accurately model our experiment, and from this model we can extract error budgets that highlight the biggest challenges for future systems. These results mark the first experimental demonstration where quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.
Inaccessibility of Services as a Threat to Forensic Analysis

Computer and Internet-based crimes are widespread problems that affect a vast number of individuals. Combating such criminals has become a difficult task for competent authorities, as it has enabled a new wave of criminal activity. This phenomenon has drew the attention of security and justice systems all over the world, and these institutions must now deal with the task of reforming and redefining the laws and methodologies used to investigate computer-related criminal activity (Garfinkel, 2010). On the other hand cybercriminals are also making use of existing technologies or systems and new means or techniques to make cybercrime detection, investigation and prevention very difficult. The paper will look at anti-forensics strategies such as data concealment, artefact erasure, trail obfuscation, and attacks on the forensic instruments themselves. Investigators must deal with anti-forensics approaches on a regular basis as digital forensics becomes more significant in current investigations. This article will explore the challenges that investigators and forensic practitioners face when conducting investigations. Keywords: Digital Forensics, Cloud Computing, Cloud Service Provider, Full Disk Encryption
Design and Implementation of Personalized Push Service Based on Feature Extraction and Pattern Recognition

Personalized push service is one of the more popular research and application fields, which has received more and more attention. Its application prospects are also more and more extensive. This research mainly designs and implements personalized push services through feature extraction and pattern recognition. In this study, the Chinese texts of user-visited pages are classified according to keywords, so as to obtain the user’s interest characteristic data. Then, according to the frequency of each feature category, the weight of the user’s interest feature is calculated, and the user’s interest field is predicted and identified. After that, resources that match the user’s interest field are pushed to it. In order to verify the effectiveness of the improved model, this study carried out experiments and comparisons on the precision rate, recall rate, and comprehensive classification rate of the original model and the improved model on the implemented personalized push service system. In the research, the error between the interest results under each interest topic in the test set and the results obtained by the statistical analysis of the training set is within a reasonable range, the maximum of which is about 5%. The accuracy of interest degree prediction in different scenarios can reach more than 90%, which directly confirms the good applicability and effectiveness of the analysis and calculation method and the constructed model for user interest in this study. The personalized push service framework proposed in this study has good application value in the field of time-sensitive information services.
Retraction Note to: An Efficient Channel Attention CNN for Facial Expression Recognition

The authors have retracted this conference paper because, during the pre-processing of the dataset in this study, the training set, test set and validation set were normalised together. As a result, the authors no longer have confidence in the veracity of the results and conclusions presented.
“Problems” Are the Problem: Multiple Perspectives on Problem Formation and Framing

Problem-solving—which includes problem framing, problem formulation, and solution generation—is perhaps one of the most important activities for managers, entrepreneurs, and innovators. Still, existing research on problem-solving largely focuses on solution generation and overlooks the roles of problem framing and formulation, even though problem frames and formulations fundamentally determine the course of problem-solving. This symposium tries to illustrate and specify the roles of problem frames and formulations in problem- solving. The five papers in the symposium proposal build models on how problem frames and formulations influence solution generation and outcomes such as innovation in online communities, effective strategies, and entrepreneurial success. The papers also rely on a wide range of methods, from inductive research, simulations to experiments that provide valuable guidance for future empirical research on problem framing and formulation. Translating, decontextualizing and recontextualizing problems for cross boundary innovation Presenter: Hila Lifshitz-Assaf; New York U. “The subtle art of asking”: What shapes effective Help-Seeking in Online Communities? Presenter: Vaibhav Krishna; Scientist, ETH ZURICH Problem framing in strategic decision making Presenter: Arnaldo Camuffo; Bocconi U. The Way You See the Problem, Is the Problem: Experimental Evidence from Hackathons Presenter: Chiara Spina; INSEAD How business leaders define and solve problems with multiple goals Presenter: Daniella Laureiro Martinez; ETH Zürich Presenter: Ann Xavier; Scientist, ETH ZURICH Presenter: Zorica Zagorac-Uremovic; Swiss Federal Institute of Technology Zurich, ETH Presenter: Axel Zeijen; ETH Zürich
Equity and Equality Perspectives to Promoting Digital inclusion in Higher Education

Given the continuing pandemic context, examining the issue of digital inclusion is both timely and significant. Using equity and equality perspectives, we examine the extent to which digital inclusion policies have improved students’ access to digital technologies and promoted digital inclusion in an Australian university. Grounded in Adams’ Equity Theory and Espinoza’s, Equality and Equity Conceptual Model, we share our findings from a mixed methods study of a survey of 284 undergraduate students and interviews with 15 staff members and 9 students. While students shared their experiences of undertaking an undergraduate university degree, teaching and support staff shared their views on the design, development and support experience of undergraduate students’ digital literacy. The findings suggest the critical need to promote a better understanding of equity and equality perspectives across key stakeholders in higher education institutions during policymaking, particularly on resource allocation to promote digital inclusion.
Terahertz optical logic calculation based on diffraction neural network

Based on the diffraction neural network, a small-volume diffractive optical element for optical logic calculation in the terahertz band is designed. The optical logic calculation ability and calculation accuracy of the diffraction neural network are calculated. The results show that after training, the diffraction neural network can accurately calculate eight kinds of optical logic and has 100% recognition accuracy when the number of network layers is greater than 5. This article mainly introduces the design principle and calculation process of the diffraction neural network, and compares the recognition accuracy of different network layers.
Development of Intelligent Drone Remote Control System Based on Internet of Things

Drones have been under development for many years.As a result of market development and the large amount of research investment, the development of drones has been very rapid in recent years.The application of drones in the IoT is becoming increasingly extensive.Drones are convenient for performing a wide range of monitoring tasks.They can be mounted with sensors and used in various fields, and tasks that were difficult to achieve in the past can now be completed.However, to successfully complete a task, the accurate control of drones is necessary.In this paper, we propose a new drone control method in which image recognition technology is used to combine real-time images and distance-sensing feedback based on an IoT environment.The drone can also be intuitively controlled by moving the head and making gestures with the hands.The drone can identify obstacles along a route by viewing the surrounding environment via its supersonic sensor and its flight can be precisely controlled.The proposed drone control system based on the IoT environment has the advantages of providing users with intuitive control, a better experience, and low-cost, high-stability image recognition for drone control.
Bayesian Nonparametric Learning and Knowledge Transfer for Object Tracking Under Unknown Time-Varying Conditions

We consider the problem of a primary source tracking a moving object under time-varying and unknown noise conditions. We propose two methods that integrate sequential Bayesian filtering with transfer learning to improve tracking performance. Within the transfer learning framework, multiple sources are assumed to perform the same tracking task as the primary source but under different noise conditions. The first method uses Gaussian mixtures to model the measurement distribution, assuming that the measurement noise intensity at the learning sources is fixed and known a priori and the learning and primary sources are simultaneously tracking the same source. The second tracking method uses Dirichlet process mixtures to model noise parameters, assuming that the learning source measurement noise intensity is unknown. As we demonstrate, the use of Bayesian nonparametric learning does not require all sources to track the same object. The learned information can be stored and transferred to the primary source when needed. Using simulations for both high- and low-signal-to-noise ratio conditions, we demonstrate the improved primary tracking performance as the number of learning sources increases.
The effectiveness of the gradual change strategy in the achievement of the second intermediate grade students in the subject of design elements and principles

The effectiveness of the gradual change strategy in the achievement of the The effectiveness of the gradual change strategy in the achievement of the second intermediate grade students in the subject of design elements and second intermediate grade students in the subject of design elements and principles principles
An Improvement Using Global Positioning System (GPS) and Cloud Firestore for Integration of Information System in Surabaya Public Transportation

Surabaya is a developing city which has a significant growth in trades and services, thus requiring facilities and access in the field of transportation infrastructure. Surabaya has been fulfilled by adequate urban infrastructure. Therefore, not need to worry to reach all corners of Surabaya. Public transportation consists of share taxis, city buses, bus rapid transit, and commuter trains. There are many transportation modes in Surabaya, but there is no integrated information system of it. This study is to help passengers feel ease to access and choose any public transportation in Surabaya. The system provides recommendation best route, cheap ticket price, monitoring vehicle position, visualization on the map and arrival time while using public transportation in Surabaya. The practice has proved that the system has good usability and strong real-time. A positioning device is installed on every public transportation, where it will send the vehicle’s position to the server. The integrated information system is based on Android with minimum Jelly Bean version. This research has two android mobile applications, they are naik.in for passenger which runs in user side and naik.in for driver which runs in vehicle side. The database used is cloud firestore.
Formation of Students' Research Skills in Solving Olympiad Tasks in Mathematics

The article is devoted to the problem of the formation of students' research skills when learning to solve Olympiad problems in mathematics. The peculiarity of the Olympiad task is that it is non-standard, outwardly similar to the academic task on the subject and requires logic and research. In this regard, the solution of the Olympiad problem in mathematics requires research skills in constructing a graphical and physical model, drawing up equations to describe the state of interacting objects, mastering the mathematical apparatus in atypical conditions. In addition, to determine the relevance of the research topic, a questionnaire was conducted through the Google platform, the participants of which were received from students of the educational program «training of a mathematics teacher» at a university and young professionals working in high school. As a result, situational tasks and critical situations were identified, which instantly contribute to the formation of research skills to meet modern requirements. Accordingly, it was analyzed that Olympiad tasks will help to break away from the usual system of thinking and look for new things. Based on historical data, it is known that students participating in the Olympiad have a high IQ. And, judging by the PISA results, the formation of research skills is important for the intellectual development of schoolchildren. For the formation of such skills and, as a consequence, cognitive independence of the individual when studying each topic of mathematics, it is recommended to include students in solving Olympiad problems. An example of a task system developed taking them into account in the preparation of mathematical Olympiad tasks is given.
Synthetic Pulsar (Speculative Sonification)

Synthetic Pulsar (Speculative Sonification) looks at the pulsar as a dazzling multi-dimensional object of scientific and creative focus. While the pulsar tra- verses a wide range of disciplines – astrophysics, radio technique, and sound technology – the workings and nature of such an object can never be fully cap- tured, hence remaining incompletely understood. Proposed work probes this complexity through a process of speculative data sonification: amplifying, mod- ulating and augmenting astrophysical data - sourced from the European Pulsar Network - within an original implementation of pulsar synthesis program (nuPG) designed by the author.
Single Access for the Use of Information Technology in University Education During the SARS-CoV-2 Pandemic

The proper use of information technology (IT) in Universities during the Covid-19 pandemic has been of vital importance, so it is necessary to generate a methodological-technological proposal that serves as a guide to meet the needs of educational programs and users (students, teachers, and administrative staff) who work from home. The present research presents an exploratory, descriptive, correlational, and cross-sectional study composed of (1) review of the literature, (2) application of a survey, (3) study of information reliability (Cronbach's alpha), (4) study of correlations (Pearson’s bivariate) and (5) proposed solution. Generating an analysis of different Universities in Mexico and identifying how they are attending the educational processes and the technological tools they use. Based on the above, a methodological-technological proposal is presented based on resilience. It provides access to information and digitization of the educational processes of any university, which helps users adapt to the new hybrid environment achieving an acceptable level of digital maturity at all levels. In addition, it provides a set of technological options that the user can implement and select the most appropriate based on the needs of their academic processes, integrating academics, students, administrators, and those responsible for the computer centers through a single digital platform and with a single username and password.
Research on Integration of Emotion Analysis in English Modular Teaching Based on Natural Language Processing

Now, the application of intelligent technologies such as machine learning and deep learning in natural language processing has achieved good results. This article studies the integration of emotion analysis in English module teaching of natural language processing. Vocabulary is a very important part in English teaching. Learning vocabulary well can improve students’ reading ability. However, in the process of students’ learning, vocabulary is the most basic and difficult to learn. Poor vocabulary learning and insufficient accumulation will restrict students’ reading ability. Improving vocabulary teaching mode and learning methods can stimulate students’ interest in learning and effectively improve their reading ability. In the third part of the article, the neural network language model and statistical model are used to analyze the key technologies of natural language processing, and then the Naive Bayes algorithm and support vector machine model algorithm are used to optimize the data. Finally, two classes are selected for comparative experiment, then, by integrating emotional teaching into students’ classroom and analyzing students’ interest, the conclusion is that integrating emotional teaching in teaching can effectively improve students’ academic achievements, and at the same time, integrating emotional teaching in teaching can also stimulate students’ enthusiasm for learning English and effectively change students’ learning attitude.
TEACHING SIMPLE PAST TENSE USING VIDEO CLIPS WITH ENGLISH SUBTITLES AT X MIPA GRADE OF SMAN 1 TEMBILAHAN

This study aims to improve students' ability to understand the Simple Past Tense. This research is a Classroom Action Research (CAR). This research was carried out in one cycle in one cycle there were four meetings and in this study included the stages of planning, observation, and reflection. research participants are students of class X MIPA 3 in the academic year 2021/2022. The number of participants is half of the students in the class due to the current condition of the corona virus outbreak so that only 16 students were used as students in this research class, there were 7 boys and 6 girls. In this study, learning is not only explained through the material but students are given several video clips as a treatment which contains simple past tense sentences so that learning is more fun and not boring besides that students can also understand the simple past tense material easily so that students' understanding of the simple past tense is increasing. The results showed that using video clips with English subtitles could improve students' understanding of the simple past tense. This can be proven from the results of the students' average scores. The average score of students before the action was 44 with a sufficient category. Then at the second meeting, 74 in the good category then increased again at the third meeting, namely 87 in the very good category and at the last meeting it decreased slightly, namely 84 but still in the very good range. The students really liked the learning process using video clips. Based on the previous statement, it can be concluded that teaching Simple Past tense using video clips with English subtitles can improve students' understanding of the simple past tense in class X MIPA SMAN 1 Tembilahan.
STUDENTS’ LEARNING STRATEGIES IN DEVELOPING SPEAKING SKILL AT ENGLISH STUDY PROGRAM ISLAMIC UNIVERSITY OF INDRAGIRI

English is a language that is used as a medium of international communication. Unfortunately, the ability of English students at the Islamic University of Indragiri in speaking skills is still considered low. They find it challenging to develop their speaking ability. Therefore they need some strategies to develop their speaking skills. Therefore, this study investigates the learning strategies used by English students to develop their speaking skills. The subjects of this study were 20 English students of semester four at the Islamic University of Indragiri. The research design was descriptive. To collect the data, the researchers administered a questionnaire consisting of 18 statements using the Likert scale. The results from the analysis showed that the students used almost all of the strategies proposed by Oxford (1990). They are cognitive strategies, metacognitive strategies, compensatory strategies, memory-related strategies, effective strategies, and social strategies. In order, the most frequent strategies employed by them were Cognitive Strategies (19%), compensatory strategies (17.75%), and social strategies (16.80%).
STUDENTS’ SELF EFFICACY IN SPEAKING ENGLISH

The purpose of this research is to determine how students' self-efficacy in speaking English at the second grade of computer and network engineering in SMKN 01 Kempas. This research is categorized as descriptive research. It was done at second grade of computer and network engineering, consisting of 30 students, 17 male, and 13 female. The instrument used was a questionnaire. After the data collected, the results obtained are the percentage of students' self-efficacy in speaking English from 37.50% to 67.85% of students studied online due to the corona pandemic, only 21 students participated in answering the questionnaire. one student has a low category,13 student have a medium category, and three student have a good category.