Reviewerly/exHarmony huggingface dataset:
X-RECOSA: Multi-Scale Context Aggregation for Multi-Turn Dialogue Generation

In multi-turn dialogue generation, responses are not only related to the topic and background of the context but also related to words and phrases in the sentences of the context. However, currently widely used hierarchical dialog models solely rely on context representations from the utterance-level encoder, ignoring the sentence representations output by the word-level encoder. This inevitably results in a loss of information while decoding and generating. In this paper, we propose a new dialog model X-ReCoSa totackle this problem which aggregates multi-scale context information for hierarchical dialog models. Specifically, we divide the generation decoder into upper and lower parts, namely the intention part andthe generation part. Firstly, the intention part takes context representations as input to generate the intention of the response. Then the generation part generates words depending on sentence representations. Therefore, the hierarchical information has been fused into response generation. we conduct experiments on the English dataset DailyDialog. Experimental results exhibit that our methodoutperforms baseline models on both automatic metric-based and human-based evaluations.
Design of Information Technology Governance in Educational Institutions Using COBIT 2019 Framework

In the contemporary era, the role of information technology in the performance of organizations, institutions, and companies is of utmost importance. The implementation of information technology can effectively enhance decision-making efficiency and effectiveness. Educational institutions have been utilizing information technology to support their academic activities and business processes; however, the governance of this technology has never been audited before. This study aims to conduct an audit and analysis of information technology governance that employs the COBIT 2019 framework. The study's results suggest that educational institutions must improve risk and security management in the use of information technology and evaluate their business processes' implementation in accordance with the established regulations and requirements.
Chosen-Key Secure Even-Mansour Cipher from a Single Permutation

At EUROCRYPT 2015, Cogliati and Seurin proved that the 4-round Iterated Even-Mansour (IEM) cipher with Independent random Permutations and no key schedule EMIP4(k, u) = k⊕p4 ( k⊕p3 ( k⊕p2 (k⊕p1 (k⊕u)))) is sequentially indifferentiable from an ideal cipher, which implies chosen-key security in the sense of correlation intractability. In practice, however, blockciphers such as the AES typically employ the same permutation at each round. To bridge the gap, we prove that the 4-round IEM cipher EMSP[φ]p4 (k, u) = k4⊕p (k3⊕p (k2⊕p(k1⊕p(k0⊕u)))), whose round keys ki = φi(k) are derived using an affine permutation φ : {0, 1}n → {0, 1}n with certain properties, is sequentially indifferentiable from an ideal cipher. The function φ can be a linear orthomorphism, or φ(k) := k≫a for some fixed integer a using cyclic shift. To our knowledge, this is the first indifferentiability-type result for blockciphers using identical round functions.
Comparative Analysis of ETL Tools in Big Data Analytics

Massive amounts of data are being generated these days all around the world. The majority of social networking platforms, ecommerce sites, the public and private sectors, healthcare institutions, cloud networks, and various servers are all generating immense amount of data. Collected data from various sites could be in a structured or unstructured format. Extract Transform Load (ETL) is a crucial component of the growing demand for quicker business decisions aimed at many contemporary applications. Due to the volume and speed of data, real-time ETL is built on the foundation of multi-source, unstructured data stream extraction and transformation employing disc data in dispersed environments. The entire procedure is pipelined while processing so that the final resultant data can provide some essential and useful conclusions to work on. Some analytical findings are once again helpful in making decisions. However, the produced results may differ in some numbers, graphs, and figures in many circumstances. This occurs as a result of the usage of some unrealistic tools for big data processing. This paper proposed several methods and appropriate ETL (extract, transform, and load) tools for the big data processing, which may result in appropriate analytics and conclusions from the data.
Coherent VCSEL homodyne neural networks

We demonstrated a large-scale space-time-multiplexed homodyne optical neural network (ONN) using arrays of high-speed (GHz) vertical-cavity surface-emitting lasers (VCSELs). Injection locking enables precise phase control over tens of VCSEL devices simultaneously, facilitating photoelectric-multiplication-based matrix operations and all-optical nonlinearity, operating at the quantum-noise limit. Our VCSEL transmitters exhibit ultra-high electro-optic conversion efficiency (V<sub>&pi;</sub>=4 mV), allowing neural encoding at 5 attojoule/symbol. Three-dimensional neural connectivity allows parallel computing. The full-system energy efficiency reaches 7 fJ/operation, which is >100-fold better than the state-of-the-art digital microprocessors and other ONN demonstrations. Digit classification is achieved with an accuracy of 98% of the group truth.
Bibliometric-Enhanced Information Retrieval: 13th International BIR Workshop (BIR 2023)

The 13th iteration of the Bibliometric-enhanced Information Retrieval (BIR) workshop series will take place at ECIR 2023 as a full-day workshop. BIR tackles issues related to, for instance, academic search and recommendation, at the intersection of Information Retrieval, Natural Language Processing, and Bibliometrics. As an interdisciplinary scientific event, BIR brings together researchers and practitioners from the Scientometrics/Bibliometrics community on the one hand, and the Information Retrieval community on the other hand. BIR is an ever-growing topic investigated by both academia and the industry.
Project Recommendation for Open Source Communities

The open source community is a loosely organized, ad hoc group of contributors from different parts of the world who share a passion for addressing a need, from small projects to significant developments. Open source communities like Github, provide the developers with the largest platform for performing coding, sharing and storing of codes or projects. Open source projects are free to start, maintain and contribute. Open Source Software is free to use and explore, contributions in open source software helps to make the technology better for everyone including non-developers. Finding a relevant project is a difficult task in open source communities A recommendation system for an open source community would help solving the difficulty and save time and energy of developers. An open source recommendation system enables the users find a relevant project, which is the prime feature of recommendation system in open source communities in an easy way. To ensure high quality recommendation the proposed system will be integrating the dataset description and also the source code comments present in the project repository read-me documents. In this model, KNN algorithm is used to recommend the projects
Attention-based Radar and Camera Fusion for Object Detection in Severe Conditions

Object detection based on LiDAR and camera fusion has been successfully proved in the field of deep learning in recent years. However, LiDAR is affected by sensor noise in severe weather conditions such as rain, snow and mist, and the detection quality of the camera is limited in sparse lighting conditions. Considering radar is relatively more reliable in most harsh environments, we propose an attention-based deep learning framework for camera and radar fusion, which significantly improves object detection accuracy in severe weather conditions. Specifically, our framework is composed of three parts: 1) a radar branch for predicting the location of objects in the radar plane, 2) a projected attention module, and 3) a camera branch trained based on the attention fusion feature from the projected attention module. In contrast to previous approaches, our approach focuses the learning attention to object locations based on radar data projections, reducing the effect of image blurring in rainy and foggy weather conditions. We evaluate our model on the RADIATE dataset [1], focusing specifically on rainy days when the camera image is obscured by raindrops. The experimental results demonstrate that our proposed method shows significantly improved performance in rainy weather compared to other methods.
Decomposition and Preconditioning of Deep Convolutional Neural Networks for Training Acceleration

Deep convolutional neural networks (DCNNs) [7] have brought significant improvements to the field of computer vision for a wide range of problems [3, 7]. Larger models and larger datasets have led to breakthroughs in accuracy; however, it results in much longer training time and memory intensity, which negatively impact the development of CNNs [1, 9]. There are two ways to parallelize training: model parallelism and data parallelism [1].
Regulatory issues related to autonomous vehicles

Self-driving cars mean revolution and evolution. The revolution: Humans will leave the traditional rules of the road behind as they turn over the driving to a machine. The evolution: States will not be defining who a driver is but what a driver is, as the term will gradually change from a person steering the vehicle to a product doing so. Along the way, changes are inevitable to the traditional model of auto insurance. The National Highway Traffic Safety Administration (NHTSA) is working to create a national policy on automated vehicles, and the NHTSA is encouraging states to develop best practices while focusing on consistent regulatory objectives. This article looks at where the states are now in the regulatory process and presents the issues surrounding the expected shift in liability.
MAIN CHARACTERISTICS AND STEREOTYPES OF GENERATION Z: ANALYSIS OF FOREIGN EXPERIENCE

The article considers and analyzes a new generation of students who have specific abilities and skills to work with modern digital devices. The term «digital natives», determined by the American researcher M. Prensky, has its synonyms – generation Z or network generation. Each generation is distinguished by certain values inherent in its representatives, has its own objectively determined, spontaneously formed social program, which depicts the state of material and spiritual culture, type of social relations, specific material and ideal relations through which the generation is formed. Generation Z also has its values and features. The aim of the study is to analyze the views of foreign researchers on the main characteristics and stereotypes of Generation Z as a new generation of students of higher education. Therefore, it is not surprising that many scientists, both practitioners and theorists (including psychologists, educators, and methodologists) pay close attention to the development of digital technologies and their impact on worldviews, relationships with other people, life in general. A number of characteristic features of Generation Z has been highlighted and characterized: freedom, personalization, information control, honesty, cooperation, entertainment and satisfaction, speed, desire for innovation. Two approaches to assessing the capabilities of modern students – Generation Z have been presented, and both positive and negative stereotypes of the generation (distraction syndrome, Internet addiction, health problems, social immaturity, low motivation, selfishness, narcissism, etc.) have been analyzed. Each subsequent generation (generation Z, Alpha) will always be different from the previous generation, because there are many different factors (external or internal) that affect the formation of the generation. The task of modern education is to study and analyze the changes taking place in ICT, the educational process, society and to predict the impact of these changes on the educational process and on students.
360° Image Saliency Prediction by Embedding Self-Supervised Proxy Task

The development of Metaverse industry produces many 360° images and videos. Transmitting these images or videos efficiently is the key to success of Metaverse. Since the subject’s field of view is limited in Metaverse, from the perception perspective, bit rates can be saved by focusing video encoding on salient regions. On different ways of handling 360° image projections, the existing works either consider combining local and global projections or just use only global projection for saliency prediction, which results in slow detection speed or low accuracy. In this work, we address this problem by Embedding a self-supervised Proxy task in the Saliency prediction Network, dubbed as EPSNet. The main architecture follows an autoencoder with an encoder for feature extraction and a decoder for saliency prediction. The proxy task is combined with the encoder to enforce it to learn local and global information. It is designed to find the location of a certain local projection in the global projection via self-supervised learning. A cross-attention fusion mechanism is used to fuse the global and local features for location prediction. Then, the decoder is trained based on the sole global projection. In this way, the time-consuming local-global feature fusion is placed in the training stage only. Experiments on public dataset show that our method has achieved satisfactory results in terms of inference speed and accuracy. The dataset and code are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zzz0326/EPSNet</uri> .
Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer

Abstract Breast cancer is the second dangerous cancer in the world. Breast cancer data often contains more redundant information. Redundant information makes the breast cancer auxiliary diagnosis less accurate and time consuming. Dimension reduction algorithm combined with machine learning can solve these problems well. This paper proposes the single parameter decision theoretic rough set (SPDTRS) combined with the probability neural network (PNN) model for breast cancer diagnosis. We find that when the parameter value of SPDTRS is 2.5 and the SPREAD value is 0.75, the number of 30 attributes of the original breast cancer data dropped to 12, the accuracy of the SPDTRS-PNN model training set is 99.25%, the accuracy of the test set is 97.04%, and the test time is 0.093 s. The experimental results show that the SPDTRS-PNN model can improve the ac-curacy of breast cancer recognition, reduce the time required for diagnosis.
New efficient fractal models for MapReduce in OpenMP parallel environment

Parallel data processing is one of the specific infrastructure applications categorized as a service provided by cloud computing. In cloud computing environments, data-intensive applications increasingly use the parallel processing paradigm known as MapReduce. MapReduce is based on a strategy called "divide and conquer," which uses ordinary computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used in the MapReduce application using proposed fractal network models. Two fractal network models are offered, and their work is compared with a well-known network model, the hypercube. The first fractal network model achieved an average speedup of 3.239 times while an efficiency ranged from 73-95%. In the second model of the network, the speedup got to 3.236 times while keeping an efficiency of 70-92%. Furthermore, the path-finding algorithm employed in the recommended fractal network models remarkably identified all paths and calculated the shortest and longest routes.
Towards playing Risk with a hybrid Monte Carlo based agent

Over the last few decades, games have proven to be great test environments in the artificial intelligence field due to their well-defined rules and clear evaluation methods. Therefore, aiming at the advance in the artificial intelligence field, this paper proposes the development and analysis of a hybrid Monte Carlo based agent for the game Risk, a famous strategy board game. To do so, the proposed agent is going to face a heuristic agent based on an already tested agent. The expectation is to identify the pros, cons, and efficiency of using Monte Carlo in games like Risk.
Towards a time-aware HMM for the Truco game

Similar to Poker, the game of Truco has challenges for Artificial intelligence. Considering a large number of game states, a scenario characterized by partial visibility, stochastic behavior, and score susceptible to bluff; this game offers a good set of rules to test and improve AI techniques. In this article, we describe the creation of a Hidden Markov Model (HMM) agent using temporal control. The model has an embedded vector that adjusts its probabilities for further game actions, consequently, improving the model playing performance. The evaluation is given with over 210,000 matches, serving as empirical proof of the idea.
PELATIHAN KONTEN MARKETING PADA INDUSTRI OTOMOTIF DENGAN MEDIA VIDEO MARKETING DI PT WAHANA INVESTASINDO SALATIGA

In industries, whether large, small or medium, need video marketing as a marketing strategy in this 5.0 era. The video marketing strategy plays a very big role in efforts to reach potential customers and campaigns for a brand or brand that will have an impact on increasing product sales. PT Wahana Investasindo Salatiga is an industry engaged in the automotive sector with the Nissan Mobil brand. The sales agents who are members of the industry so far only carry out promotional activities and product sales using direct sales, canvassing and word of mouth methods. Therefore, this community service activity is intended to provide solutions in the form of training to create video marketing content to improve marketing so that it has an impact on increasing product sales. There are 10 training participants. The evaluation of this training is based on the understanding of the training participants in the given sessions and the video content that the participants have successfully created. In addition, a questionnaire was also given to be filled out by participants to assess the quality of the training at the end of the session.
UNDERWATER IMAGE ENHANCEMENT

Underwater image processing has been always a very challenging problem.Under the effect of environmental factors, underwater images are exposed to some problems, such as colour cast, low visibility, and few edge details.Image Improvement includes numerous operations similar as discrepancy stretching, noise trimming, mock coloring, noise filtering etc.To improve the view of images.Underwater images bears mediocre because of nature of luminosity.When light enters into the water it got replicated , Noticed and scattered as water is denser medium then air, so the amount of luminosity sink when it enters from breeze to water and got dispersed in different tracks.Scattering causes the blurring and reduces the color contrast.So a novel approach is proposed to enhance underwater images by gamma correction to improve the brightness of dimmed images.The dark channel prior clear haze and noise effect, providing better observable quality.At the final stage, the contrast limited adaptive histogram equalization contrast and brightness of dehazed image.The enhanced fuzzy-free, natural appealing output can be used for display and analysis purpose.
Agricultural financing and economic performance in the Obudu local government area of cross River State, Nigeria

One of the challenges facing agricultural sector development in Nigeria is inadequate financing by the government and financial institutions. This poor agricultural funding has an impact on economic activities. Thus, this study investigated the connection between agricultural funding and economic performance in Cross Rivers State's Obudu Local Government Area (LGA). The survey study methodology was employed, and a questionnaire was used as its primary data source, combined with a stratified sample technique. The targeted population was restricted to farmers and agriculturalists in the study area. Descriptive statistics, such as frequency and percentages tables, were calculated, and the data gathered from the questionnaire were compiled, computed, and analysed. The results of hypothesis testing showed a relationship between agricultural finance and economic performance, as well as a connection between agricultural credit schemes and agricultural sector expansion, using analysis of variance (ANOVA) as the statistical technique. They also showed that credit programmes for agricultural finance encourage the expansion of the agricultural sector, which improves economic performance, as manifested in economic growth and development. The study recommends, firstly, that the government release grants to farmers at regular intervals to support their activities. Secondly, it is necessary to raise the level and size of agricultural loans through the reduction of interest rates to allow for more economic development in the country. The government should implement an effective and efficient supervisory framework to monitor interest rate regulation within the agricultural loan policy.
The Effect of Information Sharing, Knowledge Sharing, and Social Capital on Business Performance Mediated by Innovation (Study on Small Food Industry in Pekanbaru City

The increase in the number of SMEs every year in the city of Pekanbaru creates high, dynamic, and turbulence competition that demands business actors to continue to improve supply chain management, knowledge sharing, social capital and innovation so that their business performance increases to provide added value and excel in competition. This study aims to determine the effect of Information Sharing, Knowledge Sharing and Social Capital on Business Performance mediated by innovation. This study uses data analysis tools and structural equation modelling (SEM) using Smart-PLS program ver 3.0. The sampling technique chosen is the Cluster Sampling method (236 small food industry players in Pekanbaru City). The study results show that: Information Sharing has a positive and not significant effect on innovation. Knowledge sharing and social capital have a positive and significant impact on innovation. Information sharing and social capital positively and significantly affect business performance. Knowledge sharing has a positive and not significant effect on the performance of the business. Innovation has a positive and significant effect on business performance. Information sharing and social capital through innovation on performance business have a positive but insignificant effect. Knowledge sharing through innovation on business performance has a positive and significant effect.