Reviewerly/exHarmony huggingface dataset:
AMS-Net: Modeling Adaptive Multi-Granularity Spatio-Temporal Cues for Video Action Recognition

Effective spatio-temporal modeling as a core of video representation learning is challenged by complex scale variations in spatio-temporal cues in videos, especially different visual tempos of actions and varying spatial sizes of moving objects. Most of the existing works handle complex spatio-temporal scale variations based on input-level or feature-level pyramid mechanisms, which, however, rely on expensive multistream architectures or explore multiscale spatio-temporal features in a fixed manner. To effectively capture complex scale dynamics of spatio-temporal cues in an efficient way, this article proposes a single-stream architecture (SS-Arch.) with single-input namely, adaptive multi-granularity spatio-temporal network (AMS-Net) to model adaptive multi-granularity (Multi-Gran.) Spatio-temporal cues for video action recognition. To this end, our AMS-Net proposes two core components, namely, competitive progressive temporal modeling (CPTM) block and collaborative spatio-temporal pyramid (CSTP) module. They, respectively, capture fine-grained temporal cues and fuse coarse-level spatio-temporal features in an adaptive manner. It admits that AMS-Net can handle subtle variations in visual tempos and fair-sized spatio-temporal dynamics in a unified architecture. Note that our AMS-Net can be flexibly instantiated based on existing deep convolutional neural networks (CNNs) with the proposed CPTM block and CSTP module. The experiments are conducted on eight video benchmarks, and the results show our AMS-Net establishes state-of-the-art (SOTA) performance on fine-grained action recognition (i.e., Diving48 and FineGym), while performing very competitively on widely used Something-Something and Kinetics.
High-performance chaotic secure communication based on VCSELs systems with phase modulation and dispersion compensation

Based on two Response Vertical-Cavity Surface-Emitting Lasers (R-VCSELs) subject to identical chaos optical injection with phase modulation and dispersion compensation, we propose and numerically investigate a high-speed bidirectional chaotic secure communication system. The driving VCSEL (D-VCSEL) is used to generate the injected chaotic signals to two R-VCSELs. After introducing the phase modulation and dispersion compensation into injection path, the bandwidth of chaotic carrier from the linear polarization components of R-VCSEL1 and R-VCSEL2 can be extended to 45GHz, and corresponding Time Delay Signatures (TDSs) can be suppressed to about 0.15. Moreover, high-quality chaos synchronization between corresponding polarization components of two R-VCSELs but very low correlation between the D-VCSEL and any R-VCSEL can be achieved. On this basis, the bidirectional dual-channel information transmission of 30 Gbit/s over 100 km fiber link is successfully realized.
UPAYA MENINGKATKAN MOTIVASI DAN HASIL BELAJAR PENCATATAN KAS KECIL MELALUI METODE DISCOVERY BAGI SISWA SMK NEGERI I SRAGEN

This research aims to increase Motivation and Financial Accounting Learning Outcomes regarding Petty Cash Recording material through the application of the Discovery method. This type of research is classroom action research (PTK), which is carried out in two cycles. Each cycle consists of planning, implementing actions, observing, analyzing and reflecting. The research subjects were students of class XII-AKL.1 Semester I of SMK Negeri 1 Sragen for the 2022/2023 academic year, totaling 36 students. The data analysis technique used is qualitative data analysis. Test result data is analyzed using quantitative descriptive techniques and non-test data is analyzed using qualitative descriptive. The conclusion of the research results shows through the application of the Discovery method. can increase student motivation and learning achievement, namely 1) The average percentage of student learning motivation increased by 11.67%, namely in cycle I it reached 72.22% and cycle II increased to 83.89%. 2) The average value of students' skill competency increased by 10.09 points, namely the first cycle reached 74.49 and the second cycle increased to 84.58. 3) The average class score increased by 5.96 points, namely in the first cycle it reached 70.57 and in the second cycle it increased to 76.53. 4) Classical learning completeness increased by 8.33%, namely in the first cycle it reached 77.78% and in the second cycle it increased to 86.11%. ABSTRAKPenelitian ini bertujuan untuk meningkatkan Motivasi dan Hasil Belajar Akuntansi Keuangan materi Pencatatan Kas Kecil melalui penerapan metode Discovery. Jenis penelitian ini adalah penelitian tindakan kelas (PTK), yang dilaksanakan dalam dua siklus. Setiap siklus terdiri dari perencanaan, pelaksanaan tindakan, observasi, analisis dan refleksi. Subyek penelitian adalah siswa kelas XII-AKL.1 Semester I SMK Negeri 1 Sragen Tahun pelajaran 2022/2023 yang berjumlah 36 siswa. Teknik analisis data yang digunakan adalah dengan analisis data kualititatif data hasil tes dianalisis dengan teknik deskriptif kuantitatif dan data nontes dianalisis dengan deskriptif kualitatif Kesimpulan hasil penelitian menunjukkan melalui penerapan metode Discovery. dapat meningkatkan motivasi dan prestasi belajar siswa, yaitu 1) Rata-rata persentase motivasi belajar siswa meningkat sebesar 11,67%, yaitu pada siklus I sebesar mencapai 72,22% dan siklus II meningkat menjadi 83,89%. 2) Nilai rata-rata kompetensi keterampilan siswa meningkat sebesar 10,09 poin yaitu siklus I mencapai 74,49 dan siklus II meningkat menjadi 84,58. 3) Nilai rata-rata kelas meningkat sebesar 5,96 poin, yaitu pada siklus I mencapai 70,57 dan siklus II meningkat menjadi 76,53. 4) Ketuntasan belajar secara klasikal meningkat sebesar 8,33% yaitu pada siklus I mencapai 77,78% dan siklus II meningkat menjadi 86,11%.
Manage Data Better or Die

Genetic Engineering & Biotechnology NewsVol. 43, No. 10 Artificial IntelligenceManage Data Better or DieBiotechnology and pharmaceutical companies can survive and grow only by collecting and making use of gigantic amounts of informationMike MayMike MaySearch for more papers by this authorPublished Online:13 Oct 2023https://doi.org/10.1089/gen.43.10.16AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 43Issue 10Oct 2023 Information© 2023 by GEN PublishingTo cite this article:Mike May.Manage Data Better or Die.Genetic Engineering & Biotechnology News.Oct 2023.40-42, 44.http://doi.org/10.1089/gen.43.10.16Published in Volume: 43 Issue 10: October 13, 2023PDF download
A Toolbox of Feminist Wonder: Theories and methods that can make a difference

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to; 1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists; 2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy.
Parola Saklama Tekniklerinin Evrimi ve Güncel En İyi Uygulamaları

Passwords have historically been pivotal for access control and authentication, yet their security remains a recurring concern in today’s digital world. As evidenced by high-profile data breaches, secure password storage has always been paramount, but often not achieved. While users grapple with the creation of strong, memorable passwords, the burden also falls on service providers to store these passwords securely. Even though alternative authentication mechanisms have emerged, password-based authentication remains pervasive. Surprisingly, studies highlight that developers frequently exhibit misconceptions or negligence towards password storage security. This paper traces the progression of password storage methods by explaining four password hashing methods. By informing of four modern password storage systems, this work seeks to bridge the knowledge gap, advocating for better practices and illuminating the significance of prioritizing security alongside functionality.
Exploring the user experience of hands-free VR interaction methods during a Fitts’ task

Despite advancements in interaction with immersive Virtual Reality (VR) systems, using hand gestures for all interactions still imposes some challenges, especially in interactions with graphical user interfaces that are usually performed with point-and-click interfaces. Therefore, exploring the use of alternative hands-free methods for selection is essential to overcome usability problems and provide natural interaction for users. The results and insights gained from this exploration can lead to enhanced user experiences in VR applications. This study aims to contribute to the literature with the evaluation of the usability of the most commonly used hands-free methods for selection and system control tasks in immersive VR and their impact on standard and validated experience and usability metrics, namely the sense of presence, cybersickness, system usability, workload, and user satisfaction. A Fitts’ selection task was performed using a within-subjects design by nine participants experienced in VR. The methods evaluated were the handheld controllers, the head gaze, eye gaze, and voice commands for pointing at the targets, and dwell time and voice commands to confirm the selections. Results show that the methods provide similar levels of sense of presence and low cybersickness while showing low workload values and high user satisfaction, matching the experience of traditional handheld controllers for non-multimodal approaches. The assisted eye gaze with dwell was the preferred hands-free method and the one with the highest values of usability. Still, developers should minimize the number of gaze movements to reduce fatigue. The evaluation also showed that using a multimodal approach for selections, especially using the voice, decreases user satisfaction and increases users’ frustration.
DIGITAL TRANSFORMATION OF EDUCATION AS A TREND IN THE DEVELOPMENT OF EDUCATIONAL REFORMS AND A PROCESS OF SOCIAL AND CULTURAL CHANGE

The relevance of the research topic is that the digitalisation of education is an imminent change in the form of education in the digital age, a requirement for innovative development of education, the only way to change the new ecology of education and the internal driving force of social progress. The digital transformation of education as a trend of educational reforms and innovations is based on informatisation and digitalisation, both of which will not happen automatically but require constant research, transition from technology application to integrated innovations, formation of digital consciousness, culture, more effective methods and management of educational organisations. Promoting the digitalisation of education is an inevitable requirement of innovative development and opportunities for the development of digital transformation of modern education, which is of great theoretical importance and practical value for guiding the digital transformation of education in Ukraine. The purpose of the study is to develop the concept of digital transformation of education as a trend in the development of educational reforms and innovations. Research methodology. For the analysis, certain general scientific methods were used, including induction and deduction, analysis and synthesis, comparison and analogy, generalisation and abstraction, as well as special scientific methods such as modelling, content analysis and deconstruction. Research results. The article considers the transformation of education driven by digital technologies as a social, cultural, organisational phenomenon and process. The concept of digitalisation of education as a process of innovation and major social and cultural change is formed. Technological innovations as a source of development of digitalisation of education and building a new ecology of smart society are analysed. The strategies for digital transformation of educational institutions as a basis for promoting innovation and change are identified. It can be noted that the digital readiness of education means appropriate preparation before starting the process of digital transformation of education, fulfilling the basic conditions of transformational changes. The level of digital competence of an educational organisation affects its ability to use digital technologies.
Protecting Inference Privacy With Accuracy Improvement in Mobile-Cloud Deep Learning

With the wide spread of data-driven deep learning applications, a growing number of users outsource compute-intensive inference processes to the cloud. To protect inference privacy, Liu (INFOCOM 2022) proposed two steganography-based solutions, named GHOST and GHOST <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , relying on the mobile-cloud collaborative framework, where the mobile device hides sensitive images into public cover images before feature extraction, while launching adversarial attacks on the cloud-side deep neural network (DNN) to obtain desired results. Although both solutions demonstrate significant advantages in private deep learning, they suffer from limited practicality; since the inference accuracy decreases sharply as the hiding ratio increases. To address this, we propose two improved solutions, IGHO and IGHO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , which ensure high inference accuracy even when abundant sensitive images need to be hidden. Specifically, IGHO as the improved version of GHOST proposes two feature fusion methods, feature synthesis and pixel synthesis, to preprocess cover images, making the poisoned DNN learn hidden sensitive features better, while IGHO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> as the improved version of GHOST <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> designs a novel feature mining generative adversarial network (FMGAN) to craft adversarial perturbations highly robust against variable sensitive types. Experimental results show that the proposed solutions highly improve the practicality of GHOST and GHOST <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> .
Neural Abstractive Summarization for Long Text and Multiple Tables

Abstractive summarization aims to generate a concise summary covering the input document's salient information. Within a report document, the salient information can be scattered in the textual and non-textual content. However, existing document summarization datasets and methods usually focus on the text and filter out the non-textual content. Missing tabular data can limit produced summaries' informativeness, especially when summaries require covering quantitative descriptions of critical metrics in tables. Existing datasets and methods cannot meet the requirements of summarizing long text and dozens of tables in each report document. To deal with the scarcity of available datasets, we propose FINDSum, the first large-scale dataset for long text and multi-table summarization. Built on 21,125 annual reports from 3,794 companies, FINDSum has two subsets for summarizing each company's results of operations and liquidity. Besides, we present four types of summarization methods to jointly consider text and table content when summarizing reports. Additionally, we propose a set of evaluation metrics to assess the usage of numerical information in produced summaries. Our summarization methods significantly outperform advanced baselines, which verifies the necessity of incorporating textual and tabular data when summarizing report documents. We also conduct extensive comparative experiments to identify vital model components and configurations that can improve summarization results.
A DNA Based Image Data Hiding Using 3D Logistic Chaotic Map

Since steganography and cryptography are information security concerns, they gain wide importance in theresearcher's studies. DNA based steganography is based on exploiting DNA nucleotides as confidential informationcarriers which enhance the data hiding process massively. A special kind of hiding mechanism is adopted in this work byusing the DNA sequences that depend on iterating a 3D logistic chaotic map to generate three pseudo-randomsequences. The proposed approach encodes the plain message bits together with two of the random keys into DNAsequences. It ensures extra DNA encryption, by carrying out some DNA sequence operations. Besides, it creates a fakesequence by embedding the generated sequence into another public reference sequence. The fake sequence is then hidden back into the cover image to achieve a double layer of data hiding. Experiments verifies that our approach satisfies the robustness of data hiding under reasonable average embedding capacity of 0.83 ,blindness, zero-payload, low modification rate of 5.16% (on average), zero-expansion rate, and high probability cracking.it is also considered as a double layer of security , dual hiding , not pure data hiding system, with adequate visual imperceptibility
Trustworthy Artificial Intelligence and Automatic Morse Code Based Communication Recognition with Eye Tracking

Morse code is one of the oldest communication techniques and used in telecommunication systems. Morse code can be transmitted as a visual signal by using reflections or with the help of flashlights, but it can also be used as a non-detectable form of communication by using the tapping of fingers or even blinking of eyes. In this paper, we develop a computer vision based approach that automatically characterizes the characters conveyed wherein a person can communicate to system or another person through Morse code with eye gestures. We can decode this visual eye tracking based language with the help of our automatic computer vision driven method. Our approach uses a normal webcam to detect the gestures made by the eyes and are interpreted as dots and dashes. These dots and dashes are used to represent the Morse code-based words. Image processing techniques-based blink and pupil detectors are employed. Blink detector helps us to detect a blink and the time that took for each blink. A blink that takes 2 to 4 seconds is acknowledged as a dot whereas a blink that takes more than 4 seconds is represented as a dash. The pupil detector helps us to detect the movement of the pupils, and if pupils move towards right with respect to a person then it is acknowledged as next letter and if the pupils are moved towards left with respect to a person then it is acknowledged as next word. In this way, we decode the Morse code which will be communicated using eyes and establish a non-detectable communication between a person and an automatic system. Our experimental results on an unconstrained visual scene with preliminary greeting words indicate the promise of an automatic eye tracking based system with success rate of 98.25% that can be of use in non-verbal communications.
Quantitative Experimental Evaluation of RFID Propagation Loss with Wooden and Metal Bookshelves

Radio frequency identification (RFID) is wireless multimedia applications for bookshelves experimental system to replace the barcodes media technology. The RFID propagation channel characteristics and environment effect should be known. In this study to evaluate the RFID propagation loss with wooden and metal bookshelves based on data of measurement. Experimental study evaluation of RFID multimedia system with bookshelves is using vector network analyzer (VNA) and the microstrip patch antennas of transmitter (Tx) and receiver (Rx) antennas at a frequency range from 2.4 GHz to 2.5 GHz. The results of experiment are considering the path loss, received signal strength (RSS), and comparison the path loss differences with cumulative distribution function (CDF) to evaluated, respectively. In this research work are necessary for RFID antenna design and evaluate the RFID multimedia systems.
Implementing the Algebra of Logic Approach to Analyse the Stochastic Behaviour of an Industrial Ice Cream Plant

This article presents a study focused on the application of the algebra of logic approach to analyzing the stochastic behavior of an industrial ice cream plant.The goal is to investigate the plant's complex interactions and stochastic nature, which can impact its efficiency and performance.This research presents calculations for various reliability parameters aiming to analyze the performance of an Industrial Ice-Cream Plant.A comprehensive ten-component model is developed to demonstrate the plant's operational functionality.By adopting the algebra of logic framework, which combines logical reasoning and probabilistic modelling, the study aims to model and simulate the plant's operations, capturing the inherent uncertainties and randomness involved.The overall system reliability is evaluated considering the Weibull and Exponential distributions as failure rates models.Furthermore, numerical examples are employed to compute the Mean Time to Failure (MTTF), a crucial reliability metric.The results will provide valuable insights into the plant's stochastic behavior, enabling decision-makers to optimize processes, identify bottlenecks, and enhance overall productivity in the industrial ice cream production domain.
PERAN ORANG TUA DALAM MENINGKATKAN MOTIVASI BELAJAR PESERTA DIDIK DI MI MUHAMMADIYAH 1 PANTENAN

The purpose of this research is to investigate how parents can help increase their children's motivation to learn. This research was conducted at MI Muhammadiyah 1 Pantenan. The type of research used is descriptive qualitative where the research aims to understand the subject in more detail, examine certain objective conditions and the researcher himself as an instrument. The subjects of this study were the parents of students, students and homeroom teachers. The data collection techniques used are observation, interviews and documentation. The results of the study show that parents play an important role in arousing student learning motivation, giving motivation is important for parents with the aim that children are able to study properly when at home or at school, because parents are motivators for children when arousing children's learning motivation before children are in school. guided by the teacher when at school.