Reviewerly/exHarmony huggingface dataset:
Using, Analyzing and Evaluating Eye-tracking at Different Educational Levels: Towards an Evaluation Framework for Impact Assessment

This article presents general components of an evaluation framework for impact assessment based on former research and analysis of research publications. We collected data from three online databases (Google scholar, EBSCO host and Web of Science) using several combinations of search terms. It turned out that there is currently no evaluation framework for impact assessment that covers instructional design, competence development and educational policies. Thus, our long-term objective is the establishment of such an evaluation framework for impact assessment and its continuous validation and improvement. It should be applicable and beneficial for learning designers, providers, researchers and further stakeholders. In addition, it should be adaptable for different educational levels, such as school and higher education as well as lifelong learning. In this article we present first ideas, measures and indicators for the selected research example of eyetracking at different educational levels. They will build a first basis and structure of our planned evaluation framework for impact assessment with an outlook of our future research plans.
Adversarial Purification with Score-based Generative Models

While adversarial training is considered as a standard defense method against adversarial attacks for image classifiers, adversarial purification, which purifies attacked images into clean images with a standalone purification model, has shown promises as an alternative defense method. Recently, an Energy-Based Model (EBM) trained with Markov-Chain Monte-Carlo (MCMC) has been highlighted as a purification model, where an attacked image is purified by running a long Markov-chain using the gradients of the EBM. Yet, the practicality of the adversarial purification using an EBM remains questionable because the number of MCMC steps required for such purification is too large. In this paper, we propose a novel adversarial purification method based on an EBM trained with Denoising Score-Matching (DSM). We show that an EBM trained with DSM can quickly purify attacked images within a few steps. We further introduce a simple yet effective randomized purification scheme that injects random noises into images before purification. This process screens the adversarial perturbations imposed on images by the random noises and brings the images to the regime where the EBM can denoise well. We show that our purification method is robust against various attacks and demonstrate its state-of-the-art performances.
Leveraging Language to Learn Program Abstractions and Search Heuristics

Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, and generalizable machine learning systems. Effective program synthesis depends on two key ingredients: a strong library of functions from which to build programs, and an efficient search strategy for finding programs that solve a given task. We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization on three domains -- string editing, image composition, and abstract reasoning about scenes -- even when no natural language hints are available at test time.
Roundness measurement with deflectometry: principles and first results

Precision cylindrical surfaces are very important elements in mechanical engineering applications involving sealing and guiding for precise movements. It is always important to measure the geometry of these parts to ensure smooth operation. There are several devices on the market that use tactile methods to measure the roundness of cylindrical sections. Although the metrological performance is very good, there is always a risk of scratching the measured part. Non-contact optical methods are very attractive alternatives. Deflectometry is one of those methods with great potential to accomplish this task. It is based on the indirect measurement by analyzing the distortions observed in the image of a structured pattern reflected by the measured surface. However, the application of deflectometry in precision measurement is restricted to flat or near flat surfaces. This paper presents principles and configurations for extending deflectometry to measure external and internal high quality cylindrical surfaces. In both cases the central element is a 45° conical mirror, capable of optically transforming cylindrical surfaces into planar images. An on-axis configuration allows the observation of a sequence of phase-shifted images of appropriate structured patterns reflected on the measured cylindrical surface. The patterns are formed by radial lines with sinusoidal profiles, which are rotated to promote the phase shift. An integration algorithm, suitable for periodic signals, reconstructs the geometry of cylindrical sections. As a result, it is possible to reconstruct the profile of cylindrical sections with great precision, and to determine the roundness parameters. However, it is not possible to determine the absolute radius of the measured section. The paper presents calibration considerations and the first measurements results of sections of precision pin gages. Comparisons with tactile measurements showed deviations of up to 0.07 µm in high polished cylindrical surfaces. The paper also discusses the main limitations and needed improvements. There is much work to be done before the concepts developed can be the basis of a new commercial system. However, there is great potential for the emergence of a new family of optical measurement systems.
Asymptotic Behavior of Large Eigenvalues of the Two-Photon Rabi Model

We investigate the asymptotic behavior of large eigenvalue for the two-photon Rabi Hamiltonian, i.e., for the two-photon Jaynes–Cummings model without the rotating wave approximation. We prove that the spectrum of this Hamiltonian consists of two eigenvalue sequences \((E_n^+)_{n=0}^\infty \), \((E_n^-)_{n=0}^\infty \), satisfying the same two-term asymptotic formula with remainder \({{\,\mathrm{O}\,}}(n^{-1/3})\) when n tends to infinity. We also propose a conjecture on a three-term asymptotic formula modeled on the GRWA for the one-photon Rabi model.
Prior knowledge‐based statistical estimation of linear false tracks in uniform distributed clutter

A large number of initialised false tracks in clutter environments will cause a heavy burden on the detection and track-processing system. The quantity of initialised false tracks in clutter used to be estimated by experience or simulation, which lacks a fundamental explanation in theory. In order to estimate the amount of linear initialised false tracks with analytical expressions, statistical estimators are proposed based on prior knowledge of the clutter in a two-dimensional scenario. Meanwhile, the differential Hough transform is presented to verify the estimators. To be specific, firstly, a one-dimensional scenario is illustrated to present the statistical estimators of the number of initialised linear and equal-length false tracks, which is based on statistical estimation theory. Secondly, regarding the clutter map as a uniform distributed binary image, the estimators described at first are extended to a two-dimensional situation. Finally, to verify the validity of the proposed estimators, a differential Hough transform algorithm is utilised to obtain the number of linear and equal-length tracks in the simulation. The results show that the estimators proposed herein are reasonable in a certain clutter environment.
Interest in new sports as a factor in the formation of a sustainable desire for a healthy lifestyle among students

The article presents the results of research to inoculation a steady interest in physical education for students of the pedagogical university, using the example of currently fashionable sports. The observation results allowed the authors using natural factors and street workout platforms to form professional pedagogical abilities and skills for future teachers who can be trained background interest in new sports. In particular, the authors against the background of visual examples of physical exercises of the city’s population in the Park of culture and recreation, students were informed about the importance of practice run before training on the development of physical qualities. They gave a detailed description of the performance of general developmental exercises for the purpose of health improvement, the development of physical qualities. In the main part of the lesson, students received not only theoretical knowledge, but also learned how to perform and train the simplest exercises for the development of physical qualities. The results of the experiment allowed the authors to increase students’ interest in a healthy lifestyle and teach them basic principles and methods of training.
A Study of Intrinsic Leisure Motivation among Prospective Teachers in relations to their Academic Achievement

Teachers are the prime agents in bringing about social change.This huge responsibility is resting on teachers' shoulders, and as a result, it makes this profession a very demanding career.Even teachers require leisure-time.They need to be recreationally motivated, which includes fulfilling personal goals such as the need for affiliation, liberation from stress or the boredom of daily routines, and the pursuit for a challenge.For this study, the tool used was the Intrinsic Leisure Motivation scale (ILM) developed by Ellen Weissinger and Deborah L. Bandalos (1995).The sample of the study consisted of 92 prospective teachers from Aligarh Muslim University.The analysis revealed that the Intrinsic Leisure Motivation of prospective teachers had scores above the average.Intrinsic Leisure Motivation sub-dimensions like selfdetermination, challenge and competence had almost the same highest mean while commitment had the least mean.A significant difference was found in favour of prospective male teachers in the Intrinsic Leisure Motivation (on sub-dimensions like self-determination, commitment and challenge).Another finding of this study was that no significant difference was found in rural and urban prospective teachers' mean scores.Lastly, the sample under study did not share a meaningful relationship between Intrinsic Leisure Motivation (with its respective sub-dimensions)and academic achievement.The educational implications have been drawn and discussed, according to the findings, in the research paper.
A new analysis of differential privacy’s generalization guarantees (invited paper)

We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and gives structural insights that we expect will be useful elsewhere. We show: 1) that differential privacy ensures that the expectation of any query on the conditional distribution on datasets induced by the transcript of the interaction is close to its true value on the data distribution, and 2) sample accuracy on its own ensures that any query answer produced by the mechanism is close to its conditional expectation with high probability. This second claim follows from a thought experiment in which we imagine that the dataset is resampled from the conditional distribution after the mechanism has committed to its answers. The transfer theorem then follows by summing these two bounds. An upshot of our new proof technique is that the concrete bounds we obtain are substantially better than the best previously known bounds.
Efficient parallel edge-centric approach for relaxed graph pattern matching

Prior algorithms on graph simulation for distributed graphs are not scalable enough as they exhibit heavy message passing. Moreover, they are dependent on the graph partitioning quality that can be a bottleneck due to the natural skew present in real-world data. As a result, their degree of parallelism becomes limited. In this paper, we propose an efficient parallel edge-centric approach for distributed graph pattern matching. We design a novel distributed data structure called ST that allows a fine-grain parallelism, and hence guarantees linear scalability. Based on ST, we develop a parallel graph simulation algorithm called PGSim. Furthermore, we propose PDSim, an edge-centric algorithm that efficiently evaluates dual simulation in parallel. PDSim combines ST and PGSim in a Split-and-Combine approach to accelerate the computation stages. We prove the effectiveness and efficiency of these propositions through theoretical guarantees and extensive experiments on massive graphs. The achieved results confirm that our approach outperforms existing algorithms by more than an order of magnitude.
Work Environment and Job Satisfaction as Rational Tools For Employee Satisfaction - A Case of Quess Corp Ltd

Employee satisfaction is essential to ensure higher revenues for the organization. No amount of trainings or motivation would help, unless and until individuals develop a feeling of attachment and loyalty towards their organization. Employees waste half of their time fighting with their counter parts or sorting out issues with them. Satisfied employees are the happy employees who willingly help their fellow workers and cooperate with the organization even during emergency situations. Such employees do not think of leaving their jobs during crisis but work hard together as a single unit to overcome challenges and come out of the situation as soon as possible. For them, their organization comes first, everything else later. They do not come to office just for money but because they really feel for the organization and believe in its goals and objectives. Satisfied employees also spread positive word of mouth and always stand by each other. Instead of wasting their time in gossiping and loitering around they believe in doing productive work eventually benefitting the organization. They take pride in representing their respective organizations and work hard to ensure higher revenues for the organization.
On Randomized Classification Layers and Their Implications in Natural Language Generation

In natural language generation tasks, a neural language model is used for generating a sequence of words forming a sentence.The topmost weight matrix of the language model, known as the classification layer, can be viewed as a set of vectors, each representing a target word from the target dictionary.The target word vectors, along with the rest of the model parameters, are learned and updated during training.In this paper, we analyze the properties encoded in the target vectors and question the necessity of learning these vectors.We suggest to randomly draw the target vectors and set them as fixed so that no weights updates are being made during training.We show that by excluding the vectors from the optimization, the number of parameters drastically decreases with a marginal effect on the performance.We demonstrate the effectiveness of our method in image-captioning and machine-translation.
Predicting Most Influential Paper Award Using Citation Count

The early identification of the influential papers is of great significance for assessing the scientific achievements of researchers and institutions as it can help in addressing the processes in an academic and scientific field, such as promotions, recruitment decisions, and funding allocation. This work evaluates features for predicting the most influential paper award that is given by several renowned conferences, ten years subsequent to their publication. The data of five renowned conferences, i.e., ICSE, ICFP, POPL, PLDI, and OOPSLA is used to predict the long-term citations to identify the most influential paper of the respective conference. GD boost model is considered to be better performing among the five different machine learning algorithms. The results show that a three to five years of the time window is good enough to evaluate the most influential paper award. Additionally, the assessment of time window and the citation trajectory of awarded and non awarded papers shows that the citation trajectory of the awarded paper vary from the Citation gain patterns of non-awarded paper.
Contact app under scrutiny [NHSX Covid-19 contact-tracing app]

Shortcomings in the NHSX contact-tracing app could limit its effectiveness and scare away users. The article investigates concerns raised by computer engineers about timestamp and Google Analytics tracking. An essential element of the contact-tracing app is that the host phone can identify other nearby devices. Problems that have already received a lot of attention include the app failing when both devices are locked, and the way secret keys are generated. But the article has investigated further issues: the app's tracking of user timestamps and data points that record the exact moment when the app makes a connection with another phone. The implications for user security and data privacy issues are highlighted.
Use, Knowledge, and Perception of Education in Technological Resources of Teachers in Extremadura Prior to the Impact of COVID-19

The technological infrastructure in educational centres in Extremadura has grown considerably since the 1990s. Numerous technological resources have been implemented to help teaching and to improve students' digital competence. This study has aimed to observe the use of ICT resources by teachers in Extremadura and their perceptions of their application in the classroom before the impact of the COVID-19 pandemic on education. A total of 1953 teachers from Extremadura have participated and a cross-sectional descriptive design with a survey has been used. A questionnaire was designed for data collection and validated by carrying out a series of processes, such as the preparation of a first draft based on a previously applied questionnaire, adaptation and review by the research team, validation by experts and reliability checking. The results have shown differences according to gender, level of teaching performance and teaching experience, in the use of IT resources and training in technology. Likewise, it was found that there is a clear positive correlation between these parameters analysed in the teaching staff of Extremadura. The results obtained are of great value and open up new lines of research to compare the differences in the use, knowledge and perception of teachers' education in ICT resources.
Socio-Psychological Factors of User Trust in Information in Electronic Mass Communication

The relevance of the topic of trust is because, in the modern world, the media have a huge impact on public consciousness. Based on what people have heard in the news, read on the Internet, a definite opinion is formed on this or that issue. That is why a person is simultaneously exposed to different sides of such media like television, newspapers, broadcast media. It is not easy to navigate and understand the huge flow of information since a person does not always have the opportunity to check the material's reliability. Most often, after receiving information, a person perceives it as the only correct one, which contributes to the formation of false ideas that have nothing to do with the truth.The constant increase in the volume of information and its sources (in particular, the Internet) raises the question of trust in information. The focus of attention is the very existence of this phenomenon, its relationship with various factors, the relationship between trust in information and broader concepts.During the experiment, the authors revealed that the properties of the information that determine its reliability have a specific hierarchy of significance depending on the type of information. The more important the information is for the user, the more significant is its reliability for trusting it. Once the information's importance decreases, its credibility is determined by several properties of the information. The authors determined which sources users trust most and least and made a user's portrait with the most developed critical thinking.The results obtained can be used to develop a program to increase audience confidence in online media and to develop a program to form a more critical audience attitude to information. The obtained results on trust in information and its socio-psychological factors are based on the key methodological principles of psychological science and supplement the existing knowledge on the issues of trust and distrust concerning information in electronic mass communication.
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph

Spectral Method is a commonly used scheme to cluster data points lying close to Union of Subspaces by first constructing a Random Geometry Graph, called Subspace Clustering. This paper establishes a theory to analyze this method. Based on this theory, we demonstrate the efficiency of Subspace Clustering in fairly broad conditions. The insights and analysis techniques developed in this paper might also have implications for other random graph problems. Numerical experiments demonstrate the effectiveness of our theoretical study.
Put Chatbot into Its Interlocutor’s Shoes: New Framework to Learn Chatbot Responding with Intention

Most chatbot literature that focuses on improving the fluency and coherence of a chatbot, is dedicated to making chatbots more humanlike.However, very little work delves into what really separates humans from chatbots -humans intrinsically understand the effect their responses have on the interlocutor and often respond with an intention such as proposing an optimistic view to make the interlocutor feel better.This paper proposes an innovative framework to train chatbots to possess humanlike intentions.Our framework includes a guiding chatbot and an interlocutor model that plays the role of humans.The guiding chatbot is assigned an intention and learns to induce the interlocutor to reply with responses matching the intention, for example, long responses, joyful responses, responses with specific words, etc.We examined our framework using three experimental setups and evaluated the guiding chatbot with four different metrics to demonstrate flexibility and performance advantages.Additionally, we performed trials with human interlocutors to substantiate the guiding chatbot's effectiveness in influencing the responses of humans to a certain extent.Code will be made available to the public.