Reviewerly/exHarmony huggingface dataset:
Effect of Image Down-sampling on Detection of Adversarial Examples

Detecting adversarial examples and rejecting to input them into a CNN classifier is a crucial defense method to prevent the CNN being fooled by the adversarial examples. Considering that attackers usually utilize down-sampling to match the input size of CNN and the detection methods are commonly evaluated on down-sampled images, we study how the detectability of adversarial examples is affected by the interpolation algorithm if the legitimate image is down-sampled prior to be attacked. Since the down-sampling changes the relationships among neighboring pixels, the steganalysis-based detectors relying on the neighborhood dependencies are probably affected sharply. Experimental results on ImageNet verify that the detection accuracy varies among different interpolation kernels dramatically (the largest difference of accuracy is up to about 9%), and such novel phenomena appear valid universally across the tested CNN models and attack algorithms for the steganalysis-based detection method. Our work is of interest to both attackers and defenders for the purpose of benchmarking the attack algorithm and detection method respectively.
Countering the Anti-detection Adversarial Attacks

The anti-detection adversarial attack is an evolutionary attack. It can both fool a CNN model to give error classification outputs and evade some detection-based defenses. In this paper, we aim at detecting the adversarial images generated by a typical anti-detection attack which can evade some existing noising-based detectors. We find that this anti-detection attack makes shifting effects in the residual domain. Specially, zero residual elements shift to non-zero elements, and shifting also occurs among non-zero residual elements. Those shifting effects inevitably change the co-occurrence relationships among neighbor residual elements. Furthermore, the attacker considers the R, G, and B channels are isolated when adding the adversarial perturbation, which further disturbs their co-occurrence relationships. So, we propose the $$3^{rd}$$ -order co-occurrence probabilities of R, G, and B residuals as features and construct a binary ensemble classifier to detect the anti-detection adversarial images. Experimental results show that the proposed method achieves detection accuracy >99% and >96.9% on ImageNet and Cifar-10 respectively, outperforming state-of-the-arts. In addition, the proposed method has good generalization ability and is difficult to be attacked again.
A Joint Link-Retrieve Framework for Open Table-and-Text Question Answering

Open Table-and-Text Question Answering (OTTQA) task aims at answering questions that require retrieving and combining information from unstructured passages and semi-structured tables. Despite the great success achieved by the existing models, they still flaw in several drawbacks, including lacking valuable global or contextual information at the linking stage, ignoring potential error propagation and noise distraction from linking to retrieval, and neglecting the huge structural gap between table and passage. To address these problems, in this paper we propose a novel joint link-retrieve OTTQA framework, where the table block embedding and passage embedding are shared in the unified framework, and all modules are trained jointly. More specifically, we encode the table block and passage by taking full advantage of their global and contextual information and then leverage a contrastive learning approach to deeply interact with their embedding and link them together. Meanwhile, we establish a novel retrieval method to retrieve fusion blocks after linking, which does not require explicitly generating or encoding the fusion block but only focuses on the passage most relevant to the question. Furthermore, considering the structural gap between the table and text, we introduce the Table-to-Text as an auxiliary task to help the model better understand the structural difference and capture the inner semantic correlations between them. Our empirical study demonstrates that the proposed framework achieves new state-of-the-art performance.
Time-aware Quaternion Convolutional Network for Temporal Knowledge Graph Reasoning

Temporal knowledge graphs (TKGs) have been applied in many fields, reasoning over TKG which predicts future facts is an important task. Recent methods based on Graph Convolution Network (GCN) represent entities and relations in Euclidean space. However, Euclidean vectors cannot accurately distinguish entities in similar facts, it is necessary to further represent entities and relations in complex space. We propose Time-aware Quaternion Graph Convolution Network (T-QGCN) based on Quaternion vectors, which can more efficiently represent entities and relations in quaternion space to distinguish entities in similar facts. T-QGCN also adds a time-aware part to show the influence of the occurrence frequency of historical facts when reasoning. Specifically, T-QGCN uses QGCN with each historical fact frequency to aggregate graph structural information for each timestamp in TKGs and uses RNN to dynamically update entity representation and relation representation. To decode in quaternion space and better use historical representations, we design a new decoding module based on Convolution Neural Network (CNN) to help T-QGCN perform better. Extensive experiments show that T-QGCN has better performance than baselines for the entity prediction task on four datasets.
PRODUCTION OF HYDROGEN VIA AN ELECTROLYSER FED BY PV CELLS: SIMULATION MODEL

The optimisation of hydrogen production by an electrolyser supplied by renewable sources is considered. To reach the goal, the first step has been to compile a simulation program in LabVIEW, an icon programming language, that reproduces a photovoltaic (PV) plant connected to an alkaline electrolyser. The virtual instrument is developed on the basis of incident radiation, PV cells and electrolysers models. After establishing the PV cells type, the number of the PV cells forming the plant, the tilt of all panels, the number of strings, the latitude and also the main characteristics of the electrolyser, (e.g. the nominal power, the number of the electrolytic cells, the working temperature and pressure), the program computes the produced hydrogen, the electrolyser running hours and other less important data, during a chosen period of the year.<br> Different tilted photovoltaic panels have been considered directly coupled with the electrolyser and connected to a chopper between the two systems.<br> On the basis of this simulation program it is possible to study the optimisation of hydrogen production via an electrolyser fed by a photovoltaic plant working during the whole year.
Green Economy: Issues and Challenges in Arab Countries

Developing countries, like most Islamic countries, need economies that foster growth and development, protect natural capital, create jobs, improve human well-being, and reduce poverty and inequality.increase.Such an economy is called a green economy.The main problems complicating the implementation of a green economy in most Arab Countries are weak management, increasing poverty, numerous regional and border conflicts, and a growing economic dependence on natural resources.height, food insecurity.Governments as policy makers can introduce policies, regulations and incentives to promote a green economy.Therefore, this paper seeks to discuss how a green economy can improve livelihood security and poverty reduction in Islamic countries.We are also trying to understand what are the key actions needed to achieve a green economy in Arab Countries.
Using Deep Ensemble Learning In Digital Mammogram for Automated Classification Breast Mass

The most common illness affecting women is breast cancer.To raise survival rates and decrease the abovementioned dangers, which have gradually grown in recent years on account of more advanced computer-aideddiagnosis (CAD) systems, early detection and treatment are essential.CAD systems are crucial for lowering subjectivity and enhancing expert assessments through detection and identification.Both steady feature extraction and then the classification rate are attained by this technique.Many of the procedures for detecting and classifying breast cancer are currently being worked on.This procedure's primary goals are to identify the tumour location and boost classification and segmentation precision.
An Automatic Long Answer Evaluation system based on n-gram and Euclidian Distance

Assessing the value of one's answers constitutes one of the most essential aspects of learning.In modern day and age of digital technology, there have been produced a great deal of systems that are able to automatically handle evaluation.Long answers and brief answers are the two primary varieties of this type of response.Some of the existing scoring systems that are used on lengthy answers have demonstrated outcomes that are around average when it comes to putting a score on the student's answer.The information retrieval approach is employed in such systems to determine the degree of similarity between the answers provided by the students and the answers provided by the references; nevertheless, such scoring methods do not yet produce the best possible results.Each answer in a short answer just contains a select few keywords.The evaluation of such brief responses, which only contain a small number of keywords, calls for a unique approach, particularly when it comes to the weighing procedure.Cosine, dice, Euclidian distance, and overlap are the four metrics that have been analysed in this particular piece of research.To begin, the texts need to be transformed into some form of numerical expression.In order to accomplish this goal, the text was first broken up into n-grams at the word level, and then a bag containing all of these n-grams was constructed.After the frequencies of the n-grams have been computed, the frequency matrix of the dataset can be constructed.In order to produce a bag of n-grams, numerous filters, such as stemming algorithms, stop word removers, number and comma removers, etc., are utilised.The entire experimental inquiry was carried out with a dataset consisting of plagiarised long replies from a corpus.
Steganography a Technique to Hide Information within an Image

Steganography is the science and art of secret communication between two parties that seek to conceal the content of their messages. It is a technology that embeds information into cover photos without losing information after embedding. Steganography is the technique and method of hiding a message so that no one but the sender and receiver will suspect how the message survived. This article compares two different techniques. The first technique uses the least significant bit (LSB) without encryption or compression. Now in the second method, the encrypted message is first encrypted and then the least significant bit is used. The image is then transformed into the frequency domain using a Discrete Cosine Transform (DCT). The LSB algorithm is applied in the local domain where the image parts corresponding to the payload are embedded in the least significant parts of the overlay image and the image is embedded in the least significant parts of the overlay image to generate a steganographic image. The DCT algorithm is appliedin the frequency domain and transforms from the steganographic image domain to the spatial domain where the image title is interpolated to the least significant parts of the image. This white paper mainly provides an overview of steganography requirements benefits and related technologies. This white paper shows which steganographic methods are most effective for which requirements are met and which applications are most compatible with which steganographic methods.
GATEKEEPING KEBIJAKAN REDAKSI PEMBERITAAN “TRAGEDI KANJURUHAN” PROGRAM SEA MORNING SHOW

The tragedy at Kanjuruhan Stadium in Malang has become a black record in domestic football matches. This tragedy claimed hundreds of lives, including women and children. This dark incident became the spotlight of various media, both media in Indonesia and foreign media. The editorial team of SEA Today News is concerned about the coverage of this tragedy, considering that football is a very popular sport in Indonesia. The purpose of this study was to find out the editorial policy gatekeeping that SEA Today News did in broadcasting the Kanjuruhan Tragedy. This researcher uses a case study method with a qualitative approach. The data collection technique used by the author is moderate participatory observation, where the writer has a balance between being an insider and an outsider, participating in some activities but not all. Besides that, structured interviews are used because the writer knows for sure what information will be obtained. In analyzing the editorial policy of SEA Today regarding the reporting of the Kanjuruhan Tragedy, the author uses the stages of the gatekeeping process by Shoemaker and Reese. The results of this study indicate that the gatekeeping process for the publication of the Kanjuruhan by the editorial staff of SEA Today begins with the selection of angles, script writing, selection of footage and sources determined by the program producers.
Alice Through The Crypto Glass: Part 4

Bringing this series to a close, the author considers potential applications of blockchain technology.
APPLIED STATISTICS AND MACHINE LEARNING IN EARTH SCIENCES: CHOOSING THE RIGHT APPROACH FOR MODERN SCIENTIFIC RESEARCH

In the rapidly evolving domain of Earth Sciences, the application of statistics and machine learning (ML) has become indispensable for advancing research and understanding complex phenomena. With the increasing availability of large, high-quality datasets, researchers are harnessing the power of these methodologies to analyze, interpret, and model diverse Earth Science problems. However, the choice of which technique to apply depends on the specific problem, data type, and desired outcome. This article aims to provide a comprehensive guide to the foundations of applied statistics and ML in Earth Sciences, illustrating the most effective techniques for various research problems and helping researchers make informed decisions on where to use what.
Research on The Application of Blockchain-Based Data Security in Healthcare

This study focuses on the use of blockchain data security technology and its application in pharmaceutical companies, providing a theoretical foundation for achieving digitalization, efficient traceability, and ensuring the quality and safety of drugs in the rapidly advancing technological landscape. The research is based on a thorough review of the literature, supplemented by case studies, and provides insights into the data security scheme required for pharmaceutical companies' traceability processes. It also highlights the various steps involved in the traceability procedures, which have the potential to produce data and involve multiple participants. Effective technical security measures should be employed to manage and use the data to ensure its security and provide tools for maintaining drug quality. Also, the study highlights the technical foundation required to implement data security management, including blockchain and data security technology in the various data states analyzed, provides valuable management insights for medical enterprises or institutions, and explores the realization principles of various data security technologies in conjunction with blockchain technology. The research provides a comprehensive application of literature and cases and serves as a guide for enterprises' actual data security management, providing resources for pharma companies undergoing digital transformation to make strategic decisions about information management. Overall, the study offers valuable insights for learning lessons and generating new ideas in pharmaceutical traceability.
DESIGNING SUPPLEMENTARY MATERIALS FOR TEACHING-LEARNING OF WRITING EXPLANATORY TEXT BASED ON A GENRE-BASED APPROACH

Writing an explanatory text following the advanced contents in the textbook may be arduous for rural students. Territory differences remarkably denote that students' needs for topics in the explanatory text are also distinct. Therefore, the exposure topics must be by the students' conditions. This study aimed to design supplementary materials as an e-book based on the genre-based approach for students' learning of writing explanatory text with closely-related topics. The students were the eleventh graders of SMAN 01 Seponti at Kayong Utara regency. This study employed research and development (R&amp;D) with the procedure of the ADDIE concept (Analyze, Design, Develop, Implement, Evaluate). The data collection employed instruments, field notes, interview protocol, documenting notes, and questionnaires. We collected the data through observation, interview, document analysis, and questionnaire distribution. The data were analyzed using fieldnote reflection, interview transcription, textbook analysis results, and the rating scale interpretation. The need analysis results show that the textbook used by the teacher to teach writing the explanatory text was not in line with the student's condition. After the e-book was entirely developed, the internal evaluation was inquired. It resulted in the 'very good' category for its general appearance, layout and design, methodology, activities, language skills, language content, topic content, teachability and flexibility, and assessment, with percentages of 93,33%, 93,33%, 93,33%, 86,67%, 93,33%, 93,33%, 93,33%, 86,67%, 80%, respectively. The study implies that the product is feasible, and this study may add insights for future research.
DESIGNING STUDENTS' BOOK FOR WRITING IN EFL CLASSROOM: A PROJECT BASED LEARNING ACTIVITIES

The aims of this research are as follows: first, to know the students' need for the teaching material for the writing process; second, to develop the students' book for writing with project-based learning. This research and development are based on the Borg and Gall model development. The subjects of this research are Grade IX students at the Junior High School of Muhammadiyah Pringsewu. The results of this research are as follows: first, the steps of developing a student's book for writing using PjBL at ninth grade JHS of Muhammadiyah Pringsewu research and information are collecting, the planning of the product, and development of students' book. Second, the results of Expert Judgment, English teachers, and student responses showed that overall, students' book obtained an average score of 3.5 with "good" categorized and was declared "feasible" to be used in the writing process. Third, the student book is effective in supporting students' autonomous learning. This research implies that students and teachers can use this book to support the teaching and learning process, especially in teaching writing skills.
Robust facial landmark detection by probability‐guided hourglass network

Abstract The absence of local features and global shape constraints severely limits the performance of the hourglass network for facial landmark detection in unconstrained environments. Moreover, diverse feature types and scales may result in low accuracy. This paper proposes a probability‐guided hourglass network to enhance the shape constraints for robust facial landmark detection. Firstly, a multi‐scale pre‐processing module is designed to extract features at different scales. Secondly, based on the heatmaps generated by the stacked hourglass network, the coarse localizations are obtained, while the probability maps are generated with local features. Finally, a probability‐based boundary regression method is proposed and the hausdorff distance is modified as the loss function to constrain the feature shape. Adaptive weights are also added to the loss function, which can help relieve the data imbalance problem. Subjective and objective experimental results on the challenging datasets show that this method outperforms the state‐of‐the‐arts on unconstrained conditions.
Health Risk Assessment of Heavy Metals in Soil around Metal Scrap Recycling Areas during Wet and Dry Seasons in Ogijo, Ogun State, SW Nigeria

Aim: The aim of the study is to investigate the levels and risks associated with heavy metals in the soil samples collected during the dry and wet seasons around metal scrap recycling areas in Ogijo, Ogun State.&#x0D; Study Design: The soil samples were collected randomly within the industries and communities in the vicinity of the scrap metal recycling industries (SMRI) and analyzed with XRF.&#x0D; Place and Duration of Study: The study was carried out in Ogijo in Sagamu Local Government Area, Ogun State, located within Southwestern Nigeria. The soil samples were collected in dry and wet seasons (5th to 7th March and 24th to 25th September 2020 respectively.&#x0D; Methodology: Soil samples within the industries and communities in the vicinity of the SMR were collected randomly, dried, and then sieved through a 2mm mesh stainless sieve. The sieved sample was analyzed for heavy metal concentration by using S2 Ranger, and Bruker UKX-Ray Fluorescence Spectroscopy techniques (XRF).&#x0D; Results: The results of the analyzed soil showed that the range of concentrations of heavy metals in the soil samples collected during dry season were: Fe (40100 - 87300 mg/kg); Mn (400 - 3500 mg/kg); Zn (600 -1300 mg/kg); Pb (400 - 1000 mg/kg) and during wet season: Fe (26100 -82700); Mn (500- 4200) mg/kg. Zn (600 – 1300) mg/kg; and Pb were only detected in S3 sample (600mg/kg). The results of the heavy metals were found to exceed the WHO permissible limits of heavy metals. Contamination index, degree of contamination, and pollution load indices indicated slight contamination to excessive pollution, very high risk of the degree of contamination, and extremely polluted soils respectively. The geo-accumulation index was uncontaminated to moderate contamination (0&lt;Igeo≤1) with different metals at different locations that were not detected and with all the detected Zn concentrations (except at S1) to extreme contamination (Igeo≥5).Heavy metal enrichment during both dry and wet seasons ranged from background rank (EF ≤ 1) to moderate enrichment. Eri values ranged from low (Eri) &lt;40) to moderate ecological risk (Eri &gt; 80) with all the risk indices of the locations low (RI &lt;150). The cumulative non-carcinogenic, health index (HI) for ingestion and dermal for soil during the dry season were all above 1 indicating non - carcinogenic health concerns for the residents of the study areas as they recorded higher values. The CRI was far above the acceptable limit indicating that residents in S3 during the wet season and S2 and S9 during the dry season have the probability of contracting cancer through ingestion within the life time of 70 years.&#x0D; Conclusion: The results of pollution indices show that the quality of soil in the study entirely deteriorated with respect to the DPR background. This indicates that the soil could not be used for any agricultural or domestic purposes.
THE EFFECT OF ECONOMIC GROWTH DETERMINANTS ON ECONOMIC GROWTH: A STUDY ON DIFFERENT FINANCIAL SYSTEMS

One of the main goals of a country is to achieve economic growth. This has led many researchers to understand and determine the factors that boost economic growth. The relationship between these two still remains to be unanswered. It has been discussed in many kinds of research that the variability could result from the different types of financial systems being observed by different countries. By taking 15 years of data from 32 OECD countries, this study attempted to identify if there is any difference in the effects of the factors of economic growth across two distinct financial systems (bank-based and market-based). The results of the study suggest that, overall, financial system differences influence the economic growth of a country. These insights give further clarification on how the economic growth determinants were acting differently in different countries.