Reviewerly/exHarmony huggingface dataset:
Engineered Correlated Loss For an Integrated Source of Photon Pairs with ~100 dB Pump Self-Rejection

Dissipative quantum state engineering relies on designing the coupling of quantum systems to local environments. By tuning the coupling geometry and relative loss levels, it is possible to enact designed forms of loss capable to drive the system into a target final state [1] – [3] . Recently we have proposed a family of novel structures relying on dissipation into a common environment, which may be applied to generate entangled photon pairs [1] , photon-number squeezing [2] , act as robust multi-mode beam-splitters or equalizers [4] . These devices can be realized in the integrated form, and thus be a valuable resource for quantum information processing and optical quantum computation.
Spike propagation in a nanolaser-based optoelectronic neuron

With the recent growth of Artificial Intelligence and Neural Networks systems, alternatives to the Von Neumann architecture are on development to run these algorithms efficiently in terms of speed, size and power consumption [1] – [3] . Electrical transmission of information is highly dissipative, transistors are reaching their size limit since their functionalities are affected by quantum effects and digital computing is falling short to manage the vast number of continuous variables involved in AI algorithms at a fast pace.
Сети Петри для моделирования обучения ИНС

The article is discussed artificial neural networks, which are mathematical models of natural (biological) neural networks. It also describes some of the problems associated with their training, and one of the possible solutions using specially selected tests. The author describes the problems of modeling artificial intelligence, which is in demand today. Understanding the processes involving artificial intelligence (AI) in systems is an important condition for their effective use in practice. In this sense, the modeling process using Petri nets can reveal such features. To process the simulation results, a simulation method called Petri nets was used.
High-Frequency Cryptocurrency Trading Strategy using Tweet Sentiment Analysis

Sentiments are extracted from tweets with the hashtag of cryptocurrencies to predict the price and sentiment prediction model generates the parameters for optimization procedure to make decision and re-allocate the portfolio in the further step. Moreover, after the process of prediction, the evaluation, which is conducted with RMSE, MAE and R2, select the KNN and CART model for the prediction of Bitcoin and Ethereum respectively. During the process of portfolio optimization, this project is trying to use predictive prescription to robust the uncertainty and meanwhile take full advantages of auxiliary data such as sentiments. For the outcome of optimization, the portfolio allocation and returns fluctuate acutely as the illustration of figure.
A Reliable Blockchain-Based Image Encryption Scheme for IIoT Networks

IoT is used in industrial setup to increase security and provide ease to the user. The manual efforts decrease in this environment. The previous work concentrates on capturing images and transmitting the encrypted image. It uses the Merkle root and blockchain to make the transmission reliable. The suggestion increases reliability to the previous work. The system uses the Merkle root to endorse the key to the transmitting devices. The work increases reliability by 2.58% compared to the previous contribution.
Types And Causes Of Errorsin Essay Writing: Error Analysis Of Stu-dents’ Essays At Ssc And O-Level In Pakistan

This comparative study analyses the errors in writing essays in English by the students of Secondary School Certificate (SSC/Matriculation) and the General Certificate of Education-Ordinary level (GCE-O-Level) in the Province Khyber Pakhtunkhwa (KP) in Pakistan. The population of the study has included the students and English Language teachers of SSC and O-Level in KP through Random Sampling Technique. The study is descriptive in nature. The data has been collected in the form of written essays and through structured interviews from the students and teachers respectively. The data is analyzed descriptively by employing Corder model of Error Analysis (1967), and the frequency of various types of errors has also been discussed. The study has revealed that O-Level students are comparatively competent in essay writing than the SSC level students as SSC students committed more errors in writing essays as compared to O-Level students in Pakistan. The findings of the study reveals that ELT teachers in Pakistan need to adopt effective teaching methodologies for teaching essay writing skills and are suggested to provide proper feedback to their students in order to make them aware of the errors and the possible causes for committing such errors. The findings of the study will facilitate the teaching-learning process of essay writing in English in Pakistan.
Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval

We present Retrieve in Style (RIS), an unsupervised framework for fine-grained facial feature transfer and retrieval on real images. Recent work shows that it is possible to learn a catalog that allows local semantic transfers of facial features on generated images by capitalizing on the disentanglement property of the StyleGAN latent space. RIS improves existing art on: 1) feature disentanglement and allows for challenging transfers (i.e., hair and pose) that were not shown possible in SoTA methods. 2) eliminating the need for per-image hyperparameter tuning, and for computing a catalog over a large batch of images. 3) enabling face retrieval using the proposed facial features (e.g., eyes), and to our best knowledge, is the first work to retrieve face images at the fine-grained level. 4) robustness and natural application to real images. Our qualitative and quantitative analyses show RIS achieves both high-fidelity feature transfers and accurate fine-grained retrievals on real images. We discuss the responsible application of RIS.
Identify the essential factors affecting the adoption and application of cloud accounting

The purpose of this study was to identify the main factors affecting the acceptance of cloud accounting in Iran. According to the methodology, this research is descriptive-exploratory and applied. The statistical population of this research is all faculty members taught at the Ph.D. in Accounting and Ph.D. Students in Accounting who have passed the comprehensive test. For sampling, one-step cluster sampling method was used to calculate the sample size. The sample size was 229 people. To ensure the response, a questionnaire was sent to 250 individuals in person and in virtual office. A total of 233 questionnaires were collected. Analysis methodology The findings are exploratory factor analysis. The research findings show that thirteen factors of cost, workload, education, external pressure, pressure of institutions, management attitudes, pattern existence, individual skills, cost benefits, ease of access The ability to adapt, the sense of need, and personality factors are factors that are effective in accepting cloud accounting.
Houston College Sport Programsâ Hurricane Harvey Communication: A Twitter Content Analysis

This study provides a Twitter content analysis of tweets by Houston-based Division I college sport programs during Hurricane Harvey. A content analysis was performed on the tweets appearing on the main intercollegiate athletics Twitter pages of University of Houston, Houston Baptist University, Prairie View A&M University, Rice University, and Texas Southern University in response to Hurricane Harvey. The researchers based their study on grounded theory informed by a study conducted by Inoue and Havard (2015). While this study examined tweets rather than newspaper and magazine articles like Inoue and Havard (2015), this study confirmed the theme findings in Inoue and Havard (2015) applied well in a Twitter social media setting as well. New themes that were added by the researchers in the current study proved to be applicable.
Application of Cognitive Internet of Things in Well Test Data Stimulation Measures

In order to slow down the decline of crude oil production, it is necessary to continuously implement stimulation measures for oil wells when the production of oil and gas fields enters the middle and late stage of development. Choosing the right time to carry out measures is one of the important means to control costs and improve benefits. If the cost of measures is regarded as an investment, the investment recovery is the basis to judge whether the measures are feasible. This paper analyzes the cost and benefit of stimulation measures from the perspective of economics, and gives the best time to implement stimulation measures and the model of end point production. The results show that the best time of stimulation is when the income and cost of oil well are equal. The end point production model can be used to judge whether the stimulation has economic value, so as to provide a theoretical basis for single well stimulation decision.
Reverse Recommendation: without user details

The recommendation is the new and probably the only method to ensure any onlineapplication effectively caters to a global audience. An application that caters to a very widegroup of people from different backgrounds so people can find the data they could be lookingfor. We see the use of recommendations almost everywhere in our digital lives. We can lookat recommended apps, posts, dishes, songs, and this list extends till infinity. Personalizing anapplication for any specific user helps in achieving better conversion rates. This is achievedthrough recommending the user based on the data collected about the user. The more data iscollected, the more precise the recommendation gets, thus resulting in greater conversionrates. Various machine learning algorithms could be used for creating a recommendationsystem based on the type of recommendation required. The recommendation could get betterwith the collection of more data and then training the algorithm on the particular dataset.More precise recommendations could lead to the reverse effect of the recommendation. Itcould be used to model the user behavior according to what the developer wants. This couldlead to negative consumer patterns as user now does what the developer wants them to do.But this does not mean we forbid the idea of recommendation asa whole. A recommendationis a great tool for achieving more conversion rates. In this paper, the authors proposerecommending using groups. This not only ensures that reverse consumer behavior isprevented from being achieved, but is also computationally cheaper.
Development and Algorithm Research of Deep Learning Training Cloud Platform

In recent years, colleges and universities pay special attention to the cultivation of students’ practical ability, which requires efficient and practical cloud platform training system. Due to the rapid increase in the number of students, the shortage of teachers and the shortage of special venues, the centralized traditional experimental training mode can not meet the actual needs. This paper designs a training system based on cloud platform technology: the development board and experimental box used by students can be connected to the cloud platform anytime and anywhere, and the experimental process and results can be accurately recorded in the cloud database; Students don’t have to carry out experiments at fixed time and place, which solves the problem of shortage of experimental sites and stimulates students’ autonomous learning ability; Teachers can track the progress of students’ experiments online and guide students interactively, which makes full use of limited teachers’ resources; Managers can analyze equipment data and faults online, evaluate students’ experiments and teachers’ guidance accurately, and solve management problems.
The "Self-Assessment" Method within a Mature Third-Party Risk Management Process in the Context of Information Security

This article was written after examining the information security risk management capabilities of eight popular commercial software solutions marketed to facilitate third-party risk management process through "self-assessment" surveys. The author argues that none of these solution vendors incorporated the core components of a mature information security risk management process as part of their off-the-shelf offerings. For the most part, the solutions were simple surveying and response scoring software, which is insufficient for performing risk assessments, as defined in ISO 27005 or NIST Special Publication 800-30.In response to the identified deficiencies, this article proposes a practical third-party risk management process for improving the usefulness of the "self-assessment" method. The majority of the process items were tested in the real world. Both - organisations that perform third-party risk assessments and solution providers can benefit from understanding the examined deficiencies and adopting the process in the real world.
HighlightMe: Detecting Highlights From Human-Centric Videos

We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos. Our method works on the graph-based representation of multiple observable human-centric modalities in the videos, such as poses and faces. We use an autoencoder network equipped with spatial-temporal graph convolutions to detect human activities and interactions based on these modalities. We train our network to map the activity- and interaction-based latent structural representations of the different modalities to per-frame highlight scores based on the representativeness of the frames. We use these scores to compute which frames to highlight and stitch contiguous frames to produce the excerpts. We train our network on the large-scale AVA-Kinetics action dataset and evaluate it on four benchmark video highlight datasets: DSH, TVSum, PHD2, and SumMe. We observe a 4-12% improvement in the mean average precision of matching the human-annotated highlights over state-of-the-art methods in these datasets, without requiring any user-provided preferences or dataset-specific fine-tuning.
PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition

In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. To tackle the domain-shift which exists under the UDA setting, we first exploited a recent Domain Generalization (DG) technique, called Relative Norm Alignment (RNA). It consists in designing a model able to generalize well to any unseen domain, regardless of the possibility to access target data at training time. Then, in a second phase, we extended the approach to work on unlabelled target data, allowing the model to adapt to the target distribution in an unsupervised fashion. For this purpose, we included in our framework existing UDA algorithms, such as Temporal Attentive Adversarial Adaptation Network (TA3N), jointly with new multi-stream consistency losses, namely Temporal Hard Norm Alignment (T-HNA) and Min-Entropy Consistency (MEC). Our submission (entry 'plnet') is visible on the leaderboard and it achieved the 1st position for 'verb', and the 3rd position for both 'noun' and 'action'.
Elements of the Conception of an Independent Assessment of Pedagogy Graduates’ Qualification

The aim of the article is to describe the elements of the conception of an independent assessment of the qualifications of pedagogy graduates. The National Education Project sets the task of ensuring the global competitiveness of Russian education. The federal project “Teacher of the Future” aims to introduce a national system for teachers’ professional development. The author’s analysis of the literature on the problem showed that general problems of qualifications certification are being investigated. A number of publications describe the purpose, functions and procedures of qualifications assessment in assessment and certification centers. Scholars and practitioners point to the need to create an all-Russian authorized body in the qualifications assessment and certification system. The problems of organizing the activities of qualifications assessment centers as a tool for assessing the quality of education are considered. Publications considering the problem of qualifications assessment in various economy sectors, such as tourism and service, state and municipal service, construction, finance, petroleum products transportation and nanoindustry, are presented. Some works analyze the qualifications assessment of teachers and job training instructors of secondary vocational education institutions, and university staff. Several publications discuss the independent assessment of the qualifications of secondary pedagogical education institution graduates, but the problem of an independent assessment of the qualifications of higher pedagogical education program graduates remains little studied. Consequently, the development of a conception for an independent assessment of pedagogy graduates’ qualifications is relevant. The key idea of the conception the author presents is that the independent assessment of the qualifications of pedagogical training graduates is considered as a quality assurance tool. Quality assurance is achieved by comparing the results of mastering federal state higher educational standards and the requirements of professional standards. The author describes the elements of the conception: regulatory framework and innovative components. The regulatory framework in the conception structure is set at the federal level and contains legislation, requirements, methods, guidelines. The innovative conception components include the characteristics of the participants, conditions, motivational components and areas of activity, content lines and methods of assessment, a toolkit for a substantive description of professional advantages and disadvantages, reflexive and effective components, and a mechanism for implementing the conception. Specification and implementation of the conception are designated as a prospect of the research.
Heterogeneous Graph Embedding Based on Edge-aware Neighborhood Convolution

Graph neural networks have shown superior performance in representing graph-structured data. However, directly applying most of existing methods to heterogeneous graph learning tasks may be ill-suited or infeasible, since multiple different types of nodes, edges and attributes challenge the methods originally developed for homogeneous networks. Given this situation, heterogeneous graph embedding recently has gained much research attention. Existing heterogeneous graph embedding methods often adopt random walk-based sampling techniques to model both heterogeneity and rich attribute information. These methods strongly rely on customized sample generation strategies and usually consume a lot of memory. To avoid these restrictions, we present a novel model based on edge-aware neighborhood convolution which is designed to extract features from the neighborhood of different types of nodes. The first and second-order proximity-based objectives are formulated for learning node representations. On the task of link prediction and recommendation, experiments on five heterogeneous graph datasets show the effectiveness of our model.
Digital Transformation and Ethics in the Context of Responsible Artificial Intelligence

This new book by Peter Kirchslager discusses digitalization, robotization, and automation of the economy, and the use of artificial intelligence (AI) from an ethical perspective. The book can be read in the context of the responsible research and innovation (RRI) framework, where ethics is one of the key elements. The Horizon Europe programme of the European Union has several cross-disciplinary projects and interventions to research on ethical dimensions of the digital economy. Read More ...
Global Optimization Algorithm for Cloud Service Composition

Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.
The Development of Career Path Recommendation Expert System (CPRES) Model in Higher Education

: This article covers the development of a student career recommendation system, an educationalinnovation that assists students in planning career options or professional skills based on graduate profiles. Choosinga student's career path in higher education without the proper service information in the career recommendation route can lead to challenges in suggesting a profession by students based on their field of expertise and profiles of graduates of the study program they are interested in. In higher education, choosing a student's career path without the right service information in the career recommendation path can cause difficulties in recommending a career by students according to the field of expertise and profiles graduates of the study program they are interested in. This study aims to develop a student career path recommendation system using an expert system through an innovative Career Path Recommendation Expert System (CPRES) system that is already valid and its practicality is proven by the usability score approach, while the effectiveness of the student career model uses an expert system (CPRES) analyzed based on the level of rigor.
The case for certificate automation

IT departments spend too much time on repetitive, manual tasks that eat up resources that could be spent on strategic initiatives. 1 And companies that are applying automation are realising benefits beyond just the time saved. Automation is transforming businesses and positioning them for competitive advantage.