Reviewerly/exHarmony huggingface dataset:
An Efficient Machine Learning-based Text Summarization in the Malayalam Language

Automatic text summarization is a procedure that packs enormous content into a more limited book that incorporates significant data.Malayalam is one of the toughest languages utilized in certain areas of India, most normally in Kerala and in Lakshadweep.Natural language processing in the Malayalam language is relatively low due to the complexity of the language as well as the scarcity of available resources.In this paper, a way is proposed to deal with the text summarization process in Malayalam documents by training a model based on the Support Vector Machine classification algorithm.Different features of the text are taken into account for training the machine so that the system can output the most important data from the input text.The classifier can classify the most important, important, average, and least significant sentences into separate classes and based on this, the machine will be able to create a summary of the input document.The user can select a compression ratio so that the system will output that much fraction of the summary.The model performance is measured by using different genres of Malayalam documents as well as documents from the same domain.The model is evaluated by considering content evaluation measures precision, recall, F score, and relative utility.Obtained precision and recall value shows that the model is trustable and found to be more relevant compared to the other summarizers.
On the Detection of Powerline Elements with Efficient Transformers

Powerline inspection operations involve capturing and inspecting visual (RBG/therma/LiDAR) footage of powerline elements from elevated positions above and around the powerline, which are currently performed with the help of helicopters and/or Unmanned Aerial Vehicles (UAV). Current technological advances in the areas of robotics and machine learning are towards enabling fully autonomous operations. To this end, one of the tasks to be addressed is the robust, precise and fast powerline object detection problem. End-to-end Object Detection with Transformers (DETR) was a recently introduced method that demonstrates time and accuracy advances with respect to other detectors. However, this architecture comes with some computational complexity issues, which can mainly be attributed to Transformer encoder/decoder components, limiting its applicability in fast processing high-resolution feature maps and long sequences in general. To address these issues, we propose incorporating low-complexity Transformer implementations and evaluate them in a recently captured powerline detection dataset.
Mitigating Cache Contention-Based Attacks by Logical Associativity

Many cache designs have been proposed to guard against last-level cache, contention-based, side-channel attacks. One of the most well-known implementations, CEASER-S, applies an encryption cypher with a periodically changing key as a cache indexing function. By increasing the re-keying frequency, CEASER-S can defeat an attack. However, this can lead to performance degradation. In this paper, we propose cache logical associativity. By combining this approach with CEASER-S, our cache, CEASER-SH, sacrifices less performance while maintaining the same security level against more advanced contention-based side-channel attacks. For example, compared with CEASER-S, CEASER-SH with a logical associativity of 3 can reduce the miss rate degradation by about 30&#x0025; and that of the CPI by 1&#x0025; while maintaining the same security level against a strong Prime&#x002B;Probe attack.
Non-Interactive Correlation Differential Privacy for Healthcare Data

The current differential privacy protection methods for healthcare datasets have achieved some results, but there are still some problems to be solved : (1) Most of the existing methods consider the privacy leakage caused by record correlation, and there are few studies on the privacy leakage caused by feature correlation. (2) Existing research also uses feature extraction methods when dealing with feature correlation, but the set extracted by this method is not the maximal feature set, which reduces the availability to a certain extent. In view of the above considerations, this thesis proposes a noninteractive correlation differential privacy protection method for healthcare data. First, In terms of feature correlation, an undirected graph is constructed with features as nodes, and the concept of feature sensitivity is proposed. Secondly, The Bron-Kerbosch algorithm is used to solve the maximal independent set, which solves the problem of information leakage caused by slow processing speed and feature correlation caused by multiple features. Finally, Experiments on two different datasets verify the effectiveness of the proposed differential privacy protection method.
Expression Trees

In the previous chapter, we presented red-black trees. These binary search trees provide faster insertion performance compared to AVL trees but slower search time.
Neural Networks and Machine Learning

The previous chapter presented an implementation of a generic algorithm for solving TSP.
Report from the MMM 2020 Special Session on Multimedia Datasets for Repeatable Experimentation (MDRE 2020)

Information retrieval and multimedia content access have a long history of comparative evaluation, and many of the advances in the area over the past decade can be attributed to the availability of open datasets that support comparative and repeatable experimentation. Hence, sharing data and code to allow other researchers to replicate research results is needed in the multimedia modeling field, as it helps to improve the performance of systems and the reproducibility of published papers. This report summarizes the special session on Multimedia Datasets for Repeatable Experimentation (MDRE 2020), which was organized at the 26th International Conference on MultiMedia Modeling (MMM 2020), held in January 2020 in Daejeon, South Korea. The intent of these special sessions is to be a venue for releasing datasets to the multimedia community and discussing dataset related issues. The presentation mode in 2020 was to have short presentations (approximately 8 minutes), followed by a panel discussion moderated by Aaron Duane. In the following we summarize the special session, including its talks, questions, and discussions.
Semantic Similarity of Inverse Morpheme Words Based on Word Embedding

Inverse morpheme words are compound words that have the same morphemes but are arranged in the opposite order. The majority of related works on the subject have focused on a narrow investigation of dictionary definitions, with few studies based on large-scale corpora. Based on the People’s Daily corpus (1946–2017), we add and delete words from a base list and then obtained a word list consisting of 668 pairs of inverse morpheme words. Furthermore, we also calculated cosine similarity by using word embedding based on the distributed representation and discovered that 76% of inverse morpheme words have a cosine similarity of 0.4 or higher, and that word formation, part-of-speech, and frequency all have an impact on semantic similarity.
A Configurable Serious Game for Inhibitory and Interference Control

Psychological disorders are often associated with a lack of inhibitory and interference control. In this paper, we present the software architecture design of a configurable serious game dedicated to inhibitory and interference control tests. The proposed architecture is based on generic components that are reusable across different game platforms and modes. Our design enables mental health practitioners to easily configure the serious game to adapt it to their test objectives and patients’ profile. The proposed game facilitates the tracking and analysis of inhibitory and interference control and their evolution over time, as data is logged over different gaming sessions. As a proof of concept, we implement a pilot application based on the proposed architecture, in both the 2D and VR modes. The developed application implements a gamified Stop Signal task (SST) augmented with interferences. We report the results of an empirical study assessing the perceived usability of the 2D and VR pilot games, using a standard usability test.
Multimodal Analysis of Interruptions

During an interaction, interactants exchange speaking turns. Exchanges can be done smoothly or through interruptions. Listeners can display backchannels, send signals to grab the speaking turn, wait for the speaker to yield the turn, or even interrupt and grab the speaking turn. Interruptions are very frequent in natural interactions. To create believable and engaging interaction between human interactants and embodied conversational agent ECA, it is important to endow virtual agent with the capability to manage interruptions, that is to have the ability to interrupt, but also to react to an interruption. As a first step, we focus on the later one where the agent is able to perceive and interpret the user’s multimodal behaviors as either an attempt or not to take the turn. To this aim, we annotate, analyse and characterize interruptions in human-human conversations. In this paper, we describe our annotation schema that embeds different types of interruptions. We then provide an analysis of multimodal features, focusing of prosodic features (F0 and loudness) and body (head and hand) activity, to characterize interruptions.
Strategies for the Cultivation of Innovative Talents (COIT) in Private Colleges and Universities Based on Big Data Analysis

<p style="text-align: justify;"><span style="font-family: "times new roman";">With the rapid development of computer technology, the era of information technology has come. So in the current era, many new technologies have been proposed. Among them, there are three popular technologies in recent years: big data (BD), artificial intelligence, cloud computing. Because we want to study the research on the innovative talent training strategy of private colleges and universities, so this paper finally chose to use BD analysis method to study the innovative talent training program of private colleges and universities. This paper, after reviewing the research measures on the COIT and other measures on the COIT in domestic private colleges and universities in recent years, and the measures of various kinds of talents training in major universities, the data results are derived from the mixed frog jumping algorithm to extract the relevant data and conduct a comprehensive analysis of linear coupling. Experimental results show that the method of BD analysis can better draw a research path on the COIT in private colleges and universities. </span>
Cross-Lingual Product Retrieval in E-Commerce Search

Cross-lingual product retrieval (CLPR) recalls semantically relevant products that match multilingual search queries. It plays a crucial role in E-commerce sites to serve cross-border customers. However, there exists no public large-scale dataset on CLPR, hindering the research on this topic. We present CLPR-9M ( https://tianchi.aliyun.com/dataset/dataDetail?dataId=121505 ), the first large-scale CLPR dataset containing 9 million query-product pairs, covering 10 major commodity categories and 3 language pairs, mined from real-world user logs. We also release a test dataset, annotated by bilingual experts with fine-grained labels. We build our baselines upon the widely used cross-lingual embedding retrieval framework and improve it from a range of aspects, including the pretrain-finetune paradigm, negative sampling, as well as optimization objective. Benchmarks are assessed and reported using multiple evaluation metrics, and will be beneficial for future research in this area.
City-based approach for GPU kernel execution trace visualisation

Detecting performance bottlenecks and bugs in GPU kernels is not an easy task. The multi-threaded nature of the GPU kernels makes the problem harder than other programs. Researchers used visualisation techniques to find performance bottlenecks and bugs in multi-thread programs. This work proposes a citybased visualisation framework for GPU kernels. The framework takes execution traces produced by instrumented GPU kernels and visualises them using a city-based metaphor. The framework is evaluated by a case study on the parallel reduce GPU kernel. The framework is shown to be effective in visualising performance bottlenecks and debugging the kernel.
PATRIOTIC EDUCATION OF STUDENTS OF PEDAGOGICAL SPECIALTIES BY MEANS OF A REGIONAL DISCIPLINE (based on the materials of the course «HISTORY AND CULTURE OF THE MOSCOW REGION»)

The article considers the pedagogical and educational potential of the regional studies course «History and culture of the Moscow region», read for students of pedagogical specialties. The significance of this training course in solving the tasks of educating young people in the spirit of citizenship and patriotism is substantiated. Examples of selection of historical course material and topics of lectures and seminars, as well as optimal forms of students’ work are given. The necessity of studying the cultural objects of the Moscow Region through excursions (near and far) is emphasized as an important component of educational activity.
Stretching Musicality to the Extreme

The exploration of the potential of noise in music history reflects composers’ relentless ambition to expand their sound palette, experiment with new organizational methods and create unprecedented sonic experiences. Noise music could not easily be accepted as music when it first appeared, even though the use of noisy sounds became increasingly common in musical practices of various genres from the second half of the twentieth century onwards. In noise music, noise does not serve to create a dynamic effect within a musical flow. The most fundamental component of music is, self-evidently, sound, which is a physical phenomenon. When excessive sonic information is vertically stacked, individual components can no longer be separately discerned but instead become a part of the resulting mass of sounds. A typical Merzbow sound, characterized by extreme densities and harshness, was not apparent in his work from the beginning.
CF-AMVRGO: Collaborative Filtering based Adaptive Moment Variance Reduction Gradient Optimizer for Movie Recommendations

In recent decades, recommendation systems are used in a variety of applications like social connections, movies, music, and venues. The existing algorithms has certain limitations like data sparsity, cold start problem, and poor scalability to degrade the movie recommendation performance. To address the aforementioned concerns, a new matrix factorization-based collaborative filtering algorithm is developed to enhance movie recommendation performance. In a neural collaborative filtering algorithm, an adaptive moment variance reduction gradient optimizer is applied in low-rank matrix factorization for representing the users and items in a low dimensional latent space to obtain effective movie recommendation performance. In this paper, the proposed algorithm performance is evaluated on three benchmark datasets; movielens 100K, 1M, and 10M datasets. Simulation results showed that the proposed algorithm obtained the difference of 0.08 and 0.056 roots mean square error value in movielens 1M and 10M datasets, which are better compared to the existing collaborative filtering-based deep learning algorithm.
Conceptual Framework of Digital Transformation in Higher Education an Emerging Trend

Higher education institutions contributed greatly toward socioeconomic development of every country through providing high quality education in the era of digitalization under Industry 4.0. The 4th Industrial Revolution is a fusion of advances in artificial intelligence, internet of things, big data, robotics, etc., and in this era of 4IR, the higher education system needs transformation in its environment, the roles of teachers and learners, and teaching methods according to the technological developments of Industry 4.0. The traditional system of education has greatly contributed to the industrial revolution and technological advancements. Today higher education all over the world faces formidable challenges in preparing the human resources for Industry 4.0. There is a need to renovate training programs and renew methods, apply technology to the teaching process, and renew the models of linkage between higher education and industry. The present study aims to identify needs and understand conceptual frameworks of digital transformation and to determine opportunities and challenges in the era of 4IR for adopting digital tools in higher education. The study is based on secondary information collected through various journals, books, and articles to synthesize 4IR and higher education. The findings shows that the 4IR has actively contributed to educate and train people to accept future challenges of Industry 4.0 and government to frame special policies and provide key planning for the development of higher education in the era of Industry 4.0.
Nondeterminism and Guarded Commands

chapter Share on Nondeterminism and Guarded Commands Authors: Krzysztof R. Apt View Profile , Ernst-Rüdiger Olderog View Profile Authors Info & Claims Edsger Wybe Dijkstra: His Life,Work, and LegacyJuly 2022 Pages 169–204https://doi.org/10.1145/3544585.3544595Published:13 July 2022Publication History 0citation1DownloadsMetricsTotal Citations0Total Downloads1Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Efficient and secure selective cipher scheme for MIoT compressed images

Recently, the pervasiveness of multimedia contents raised serious security and privacy concerns, especially with limited devices such as the ones used in the Multimedia Internet of Things (MIoT), which are constrained in terms of computations, memory capacity and power consumption. In this paper, we propose a lightweight format-compliant compression–selection cipher scheme, which could be adopted with different types of image compression algorithms (lossless or lossy). The two proposed cipher variants leverage the compressed data characteristics such as randomness and uniformity, as compared to uncompressed data; this distinguishes the proposed solution from the existing ones. Also, the proposed cipher supports configurable encryption parameters, such as the encryption data rate, to satisfy the requirements of the different MIoT applications, and to optimize the trade-off between security and efficiency. The two proposed variants, which do not require additional operations to produce a format-compliant code-stream, are based on the dynamic key approach, and they require a single round of simple operations. The first variant consists only of one cryptographic operation, permutation or substitution, while the second variant consists of two operations, permutation and substitution. The low number of rounds, combined with simple operations, result into low computational complexity and consequently, low energy consumption and latency. The process for updating the permutation and substitution tables is also lightweight, and it could be implemented in parallel for each sub-compressed part (tile or frequency level), which increases the security level at a minimal computational cost. The proposed substitution, permutation, and substitution–permutations variants exhibit a high throughput with an enhancement of at least 311%, 176%, and 125% compared to the optimized Advanced Encryption Standard (AES) implementation, respectively.
Disseminating Information Through Social Media During Covid-19 Pandemic Among University Students

The current rate of technological innovation makes it easier to convey data from one person to another. As technology progresses, it becomes easier for everyone to access social media, to the point where some news organisations seek to exploit it as a news source. Meanwhile, in order to avoid public panic, it is critical to educate society at a young age about the impact of fake news on social media. The purpose of this study is to explain the purpose of social media usage and to investigate whether the use of social media platforms to disseminate information has increased the spread of fake news in Malaysia, as well as the effect of information disseminated among university students during the COVID pandemic. Social media is well-known for its ease of use, particularly among young people, in sharing and expressing their opinions on domestic and international topics. Using social media has become second nature to us, particularly during the COVID-19 pandemic. Because of the strength of social media, the public's opinion of finding information during a lockdown is quite positive. The study was carried out in a qualitative manner. The researcher gathered all of the data by asking the participants general research questions. The method employed to obtain the results was an in-depth interview. The findings revealed that information disseminated via social media was successful in raising awareness during the pandemic, particularly among university students. According to the findings, certain scientific and technical advancements should benefit the global community rather than be manipulated in order to obtain accurate and true knowledge; society must be astute. False knowledge would have negative repercussions, and society would inevitably exist in a state of deception. Obtaining reliable information during the COVID-19 pandemic is critical to ensuring that individuals become social media literate in the future. Keywords: Social media, fake news, effectiveness of information, university students, COVID-19.