Reviewerly/exHarmony huggingface dataset:
Bidirectional Contrastive Split Learning for Visual Question Answering

Visual Question Answering (VQA) based on multi-modal data facilitates real-life applications such as home robots and medical diagnoses. One significant challenge is to devise a robust decentralized learning framework for various client models where centralized data collection is refrained due to confidentiality concerns. This work aims to tackle privacy-preserving VQA by decoupling a multi-modal model into representation modules and a contrastive module and leveraging inter-module gradients sharing and inter-client weight sharing. To this end, we propose Bidirectional Contrastive Split Learning (BiCSL) to train a global multi-modal model on the entire data distribution of decentralized clients. We employ the contrastive loss that enables a more efficient self-supervised learning of decentralized modules. Comprehensive experiments are conducted on the VQA-v2 dataset based on five SOTA VQA models, demonstrating the effectiveness of the proposed method. Furthermore, we inspect BiCSL's robustness against a dual-key backdoor attack on VQA. Consequently, BiCSL shows much better robustness to the multi-modal adversarial attack compared to the centralized learning method, which provides a promising approach to decentralized multi-modal learning.
Spatial Attention Adapted to a LSTM Architecture with Frame Selection for Human Action Recognition in Videos

Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this work we propose an attention mechanism adapted to a CNN–LSTM base architecture. To carry out the training and testing phases, we used the HMDB-51 and UCF-101 datasets. We evaluate the performance of our system using accuracy as the evaluation metric, obtaining 57.3% and 90.4% for HMDB-51 an UCF-101 respectively
PENGEMBANGAN SISTEM BERBASIS WEB MARKETPLACE PAKAN TERNAK DAN PRODUK PETERNAKAN (EPAKAN) MENGGUNAKAN FRAMEWORK LARAVEL

Efforts to develop livestock production that bear a stable and quality feed load, because feed is a determining factor for business continuity and continuity. The creation of the android-based ePakan application makes it very easy for breeders and non-breeders to buy and sell livestock products, because access is via smartphones. A good marketplace application and needs continuous updates to the appropriate application based on the needs of the user (end user). The web-based ePakan application is expected to make it easier for users to access the ePakan marketplace. The development of the web-based ePakan marketplace system was built using the PHP programming language with the laravel framework. The method used for system development uses Extreme Programming. The features available in this web-based ePakan marketplace include purchasing products, adding products, adding baskets, requesting partners, canceling orders, requesting to withdraw balances. From the results of Blackbox testing with the Equivalence Partitioning method, the web-based ePakan Marketplace functions as needed.
SISTEM INFORMASI MONITORING SALES DALAM MENJUAL PRODUK DENGAN FITUR SMS KODE VERIFIKASI BERBASIS WEB (STUDI KASUS TUNAS ARTA MANDIRI)

CV Tunas Arta Mandiri is a company that sells agricultural tools and fertilizers as well as being a distributor for small shops, in marketing the company has sales as the spearhead of the company, there are often problems in the marketing of products through sales such as lack of monitoring during seller transactions When this happens this triggers undisciplined for certain parties to deposit sales results in a timely manner, causing various losses for the company. In addition, the company also wants to improve good relations between the company and consumers by providing information related to the latest products and discounted prices. Based on these problems, Cv. Tunas Arta Mandiri needs a monitoring information system that can monitor buying and selling transactions directly and the system is also able to improve good relations between business actors and consumers. Therefore, a product sales monitoring information system was created that has 2 users, namely admin and sales, where sales are in charge of inputting transaction data directly and the admin is in charge of supervising transactions by confirming the data inputted by sales Programming is done with the PHP programming language, database MySQL, Apache2 web server, and Gammu as an SMS server. The hardware used to send SMS uses the Huawei E3278 modem and the method used in this study is waterfall. It is concluded that the monitoring information system has succeeded in monitoring the sale and purchase transactions directly by involving consumers and the system is also able to provide information via SMS to consumers about product information etc.
SISTEM PAKAR IDENTIFIKASI PANEN BUAH NANAS MENGGUNAKAN METODE BACKWARD CHAINING BERBASIS ANDROID

This study aims to create an expert system based on android mobile device platform that can analyze the harvest period of pineapple plants based on the source of expert knowledge. This study uses 4 main variables namely Seed Origin, Planting Age, Etherel (Carbit) and Percentage of Maturity. In addition this identification system provides features for sources of knowledge in the cultivation of pineapple plants including ways of cultivation and obstacles that can interfere with the growth of these plants. The identification system uses the Backward Chaining inference method that determines the main conclusions in the rules and then determines supporting hypotheses that refer to these conclusions. System functional testing uses the Equivalence Partitioning method in the functional testing scenario. Testing accuracy using 10 random test cases then validated by an expert. From the results of testing the accuracy of using 10 test-cases obtained 80% accuracy results so that the results of the identification of the system are categorized well in determining the harvest period of pineapple.
A New Methodology: The Quadruple-Evidence Method

The field of traditional Chinese studies is an academic tradition with its roots in the Confucian classics and the humanities. Its research paradigm is rooted in the long-term practice of the interpretation of ancient classics but has little to do with natural sciences. The Chinese classics have been at the centre of much scholarship and learning in China for thousands of years. The official education system in the Shang and Zhou Dynasties was based upon them, as were numerous schools of thought throughout the Warring States Period. Later, they were central to the studies of scholars in the Han and Song periods, and formed the basis of textual criticism in the Ming and Qing Dynasties.
Evaluation of electronic evidence in civil proceedings (on the examples of certain categories of court cases)

In this article, the author reveals the concept, essence and peculiarity of evaluating electronic evidence to establish the circumstances of a civil case. There are reasons that affect the difficulty of developing established approaches to the evaluation of electronic evidence in civil proceedings: the relative novelty of the use of electronic evidence by courts; a radical change in the legal framework, as a result of which electronic evidence began to be considered as an independent means of proof; multiplicity of forms of expression of electronic evidence in practice. Attention is also drawn to the fact that the list of electronic evidence and its carriers proposed by the law is not exclusive, which is also an additional factor that complicates the development of a uniform judicial practice of evaluating electronic evidence in civil proceedings.&#x0D; The characterization of the criteria for evaluating electronic evidence in civil proceedings is carried out: the criterion of propriety, the criterion of admissibility, the criterion of sufficiency, the criterion of reliability, the criterion of the interconnection of evidence. It is noted that the most questions arise when assessing the admissibility and reliability of electronic evidence, although sometimes the criterion of appropriateness of electronic evidence can be the subject of disputes in a civil case. Yes, verification of the appropriateness of electronic evidence should be carried out by identifying in their content information about the circumstances of the case that must be established. But such verification can be complicated if the evidentiary information is not clearly recorded.&#x0D; The use of various criteria for evaluating electronic evidence is clearly demonstrated with examples from the judicial practice of domestic courts and international judicial institutions. More attention is paid to and analysis of the legal positions of the Supreme Court regarding the evaluation of electronic evidence in civil proceedings. The importance of using electronic evidence to confirm the commission of legal actions regarding intangible goods, especially those that function in the electronic environment (in particular, cryptocurrency) is highlighted.
Self-Supervised Endoscopic Image Key-Points Matching

Feature matching and finding correspondences between endoscopic images is a key step in many clinical applications such as patient follow-up and generation of panoramic image from clinical sequences for fast anomalies localization. Nonetheless, due to the high texture variability present in endoscopic images, the development of robust and accurate feature matching becomes a challenging task. Recently, deep learning techniques which deliver learned features extracted via convolutional neural networks (CNNs) have gained traction in a wide range of computer vision tasks. However, they all follow a supervised learning scheme where a large amount of annotated data is required to reach good performances, which is generally not always available for medical data databases. To overcome this limitation related to labeled data scarcity, the self-supervised learning paradigm has recently shown great success in a number of applications. This paper proposes a novel self-supervised approach for endoscopic image matching based on deep learning techniques. When compared to standard hand-crafted local feature descriptors, our method outperformed them in terms of precision and recall. Furthermore, our self-supervised descriptor provides a competitive performance in comparison to a selection of state-of-the-art deep learning based supervised methods in terms of precision and matching score.
Graph Algorithms and Graph Theory-Symmetry Special Issue

Understanding the structure of graphs and the interplay between their parameters is a fundamental question that has been actively researched over the last century [...]
Educating epistemological principles, virtues and research skills. Review on Marina Klimenko’s textbook “Research Methods in the Social Sciences”

proving university students’ research skills, research integrity and best standards for scientific excellence is crucial for all disciplines. Marina Klimenko, a senior lecturer at the University of Florida, developed an innovative digital textbook entitled Research Methods in the Social Sciences (2020) with the focus on investigative psychology. The edition was powered by the e-learning portal and published by Sentia Publishing. The author’s own epistemological and research expertise is combined here with her competence in higher education didactics. Klimenko’s textbook seems to be perfectly tailored for prospective researchers – and useful for various disciplines representing the social sciences and humanities.
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations

Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with "black-box" models that are unable to present the detected patterns in a human-readable way. Technical developments recently led to eXplainable Artificial Intelligence (XAI) techniques that aim to open such black-boxes and enable humans to gain new insights from detected patterns. We investigated the application of XAI in an area where specific insights can have a significant effect on consumer behaviour, namely electricity use. Knowing that specific feedback on individuals' electricity consumption triggers resource conservation, we created five visualizations with ML and XAI methods from electricity consumption time series for highly personalized feedback, considering existing domain-specific design knowledge. Our experimental evaluation with 152 participants showed that humans can assimilate the pattern displayed by XAI visualizations, but such visualizations should follow known visualization patterns to be well-understood by users.
Appraisal of a Random Bit Generator Utilizing Smartphone Sensors as Entropy Source

We aim to access entropy sources available within smartphones in order to construct and evaluate a random number generator which is competitive in comparison with existing and proven random number generators. A prototype utilizing the herein proposed algorithm shall generate data that can be tested against the Statistical Test Suit provided by NIST. Although our initial intention of using cosmic radiation failed, we were able to extract randomness from incoming video and audio sources. We found that it is possible to access these sources of entropy utilizing sensors from smartphones resulting in 15 out of 15 successful passed tests within the Statistical Test Suit. We also found that wrong methods of sensor data collection using our prototype eventually generates weak random numbers and fails NIST's Statistical Test Suit. Finally, we suggest that in order to reach the initial goal of providing a smartphone-based true nondeterministic random number generator the detection of muons shall be researched.
Combining AI and AM - Improving Approximate Matching through Transformer Networks

Approximate matching (AM) is a concept in digital forensics to determine the similarity between digital artifacts. An important use case of AM is the reliable and efficient detection of case-relevant data structures on a blacklist, if only fragments of the original are available. For instance, if only a cluster of indexed malware is still present during the digital forensic investigation, the AM algorithm shall be able to assign the fragment to the blacklisted malware. However, traditional AM functions like TLSH and ssdeep fail to detect files based on their fragments if the presented piece is relatively small compared to the overall file size. A second well-known issue with traditional AM algorithms is the lack of scaling due to the ever-increasing lookup databases. We propose an improved matching algorithm based on transformer models from the field of natural language processing. We call our approach Deep Learning Approximate Matching (DLAM). As a concept from artificial intelligence (AI), DLAM gets knowledge of characteristic blacklisted patterns during its training phase. Then DLAM is able to detect the patterns in a typically much larger file, that is DLAM focuses on the use case of fragment detection. We reveal that DLAM has three key advantages compared to the prominent conventional approaches TLSH and ssdeep. First, it makes the tedious extraction of known to be bad parts obsolete, which is necessary until now before any search for them with AM algorithms. This allows efficient classification of files on a much larger scale, which is important due to exponentially increasing data to be investigated. Second, depending on the use case, DLAM achieves a similar or even significantly higher accuracy in recovering fragments of blacklisted files. Third, we show that DLAM enables the detection of file correlations in the output of TLSH and ssdeep even for small fragment sizes.
Entrepreneur Innovativeness and Firm Performance

Purpose: The purpose of the study is to investigate the influence of ınnovativeness of the entrepreneur and firm performance.&#x0D; Design/ Methodology/Approach: This study adopted a descriptive design. This study’s target population is 3rd party suppliers in Nairobi, Kenya. Primary data collection involved the development and administration of self-administered questionnaires. The analysis of the quantitative information was aided by SPSS software.&#x0D; Findings: The findings indicated that innovativeness (β=0.358, p=0.000) has a positive and significant influence on firm performance.&#x0D; Contribution to policy and practice: The study recommends that 3rd party suppliers need to adopt1 new1 technologies1 for1 cultivation1 of1 organizational1capabilities. They1 also1 need1 to1 be1 proactive1 in1 their1 research1 and1 development1 departments1 by1 incorporating1 technological1 advancements1 in1 order1 to1 maximize1 the1 potential1 that1 is1 in1 innovation1strategies.&#x0D; Originality/Value: The adoption of better decisions to improve the implementation of other projects to help save on time and money. The results of this research are of value to the Kenyan government through the relevant Ministries to invest heavily in business education to enhance the populace’s awareness of the part of innovativeness in fostering economic growth. Additionally, the outcomes will assist organizations that want to restructure their innovativeness in order to increase economic productivity.
Discovering Transferable Forensic Features for CNN-generated Images Detection

Visual counterfeits are increasingly causing an existential conundrum in mainstream media with rapid evolution in neural image synthesis methods. Though detection of such counterfeits has been a taxing problem in the image forensics community, a recent class of forensic detectors -- universal detectors -- are able to surprisingly spot counterfeit images regardless of generator architectures, loss functions, training datasets, and resolutions. This intriguing property suggests the possible existence of transferable forensic features (T-FF) in universal detectors. In this work, we conduct the first analytical study to discover and understand T-FF in universal detectors. Our contributions are 2-fold: 1) We propose a novel forensic feature relevance statistic (FF-RS) to quantify and discover T-FF in universal detectors and, 2) Our qualitative and quantitative investigations uncover an unexpected finding: color is a critical T-FF in universal detectors. Code and models are available at https://keshik6.github.io/transferable-forensic-features/
Applying Eigencontours to PolarMask-Based Instance Segmentation

Eigencontours are the first data-driven contour descriptors based on singular value decomposition. Based on the implementation of ESE-Seg, eigencontours were applied to the instance segmentation task successfully. In this report, we incorporate eigencontours into the PolarMask network for instance segmentation. Experimental results demonstrate that the proposed algorithm yields better results than PolarMask on two instance segmentation datasets of COCO2017 and SBD. Also, we analyze the characteristics of eigencontours qualitatively. Our codes are available at https://github.com/dnjs3594/Eigencontours.
The Impact of Remittance on Economic Growth in Bangladesh

This research examines the impact of remittances on the economic progress of developing countries such as Bangladesh, which receives significant amount of remittance. To investigate this, we used a time series analysis to undertake an empirical study for Bangladesh from 1995 to 2016. Several time series techniques are used in this work, including the Johansen–Juseliues test and the Granger causality test. We discovered that in the long run, overseas remittances have a greater impact on economic growth (per capita GDP). There also exists a unidirectional causality among the variables such as foreign remittances and domestic investment. In the coming days, we recommend that the Bangladesh government be cautious and smart in preserving bilateral and multilateral relationships with donor organizations and countries, with a particular focus on achieving the Sustainable Development Goals (SDGs).
Reconstruction of the distribution of sensitive data under free-will privacy

The local privacy mechanisms, such as k-RR, RAPPOR, and the geo-indistinguishability ones, have become quite popular thanks to the fact that the obfuscation can be effectuated at the users end, thus avoiding the need of a trusted third party. Another important advantage is that each data point is sanitized independently from the others, and therefore different users may use different levels of obfuscation depending on their privacy requirements, or they may even use entirely different mechanisms depending on the services they are trading their data for. A challenging requirement in this setting is to construct the original distribution on the users sensitive data from their noisy versions. Existing techniques can only estimate that distribution separately on each obfuscation schema and corresponding noisy data subset. But the smaller are the subsets, the more imprecise the estimations are. In this paper we study how to avoid the subsets-fractioning problem when combining local privacy mechanisms, thus recovering an optimal utility. We focus on the estimation of the original distribution, and on the two main methods to estimate it: the matrix-inversion method and the iterative Bayes update. We consider various cases of combination of local privacy mechanisms, and compare the flexibility and the performance of the two methods.
Public libraries of Ukraine under nazi occupation in 1941—1944: civil administration area

The purpose of the study is to look into the daily reading leisure of an average urban dweller under Nazi occupation of Ukraine by examining activity of public libraries (which were called "mass libraries" in the Soviet tradition). The substance of the study. The study developed understanding of the "normal" town library network in the area of civil administration under occupation (per town: one public library for adults and (possibly) a library / branch for children) through characterizing operating conditions and working schedule of the facilities and specific features of library service; the library staff lists were ascertained. The commercial element of the activity of public libraries was examined in detail as it was the main means of their subsistence. Conclusions. Summarizing population's life with regard to activity of town libraries during the occupation period, it is worth to note: 1) despite the Nazis' view of the local population only as labor force of the Reich, the occupation authorities allowed library services in public libraries but only in urban areas; 2) it was the local authorities who determined the scale (the number of facilities, the amount of their staff) and the operating conditions (working schedule, cost of library services) of public services; 3) there was a certain idea of the library service "standards": the number of facilities, the staff, working hours, paid services, rules of service. Some of them stem from the Soviet experience, and some were the cost of survival under conditions of occupation; 4) public libraries were in popular demand despite the fact that readers had to pay for services; 5) apparently, reading played the role of a psychological compensatory mechanism under conditions of occupation. Thus, public libraries were an active functional part of the cultural environment during the Nazi occupation of Ukraine.