Reviewerly/exHarmony huggingface dataset:
Enhancing Security and Privacy Preservation of Sensitive Information in e-Health Datasets Using FCA Approach

Advances in data collection, storage, and processing in e-Health systems have recently increased the importance and popularity of data mining in the health care field.However, the high sensitivity of the handled and shared data, brings a high risk of information disclosure and exposure.It is therefore important to hide sensitive relationships by modifying the shared data.This major information security threat has, therefore, mandated the requirement of hiding/securing sensitive relationships of shared data.As a large number of data mining activities that attempt to identify interesting patterns from databases depend on locating frequent item sets, further investigation of frequent item sets requires privacy-preserving techniques.To solve many difficult combinatorial problems, such as data distribution problem, exact and heuristic algorithms have been used.Exact algorithms are studied and considered optimal for such problems, however they suffer scalability bottleneck, as they are limited to medium-sized instances only.Heuristic algorithms, on the other hand, are scalable, however, they perform poor on security and privacy preservation.This paper proposes a novel heuristic approach based on Formal Concept Analysis (FCA) for enhancing security and privacy preservation of sensitive e-Health information using itemset hiding techniques.Our approach, named FACHS (FCA Hiding Sensitive-itemsets) uses constraints to minimise side effects and asymmetry between the original database and the clean database (minimal distortion on the database).Moreover, our approach does not require frequent itemset extraction before the masking process.This gives the proposed approach an advantage in terms of total availability.We tested our FCAHS heuristic on various reference datasets.Extensive experimental results showed the effectiveness of the proposed masking approach and the time efficiency of itemset extraction, making it very promising for e-Health sensitive data security and privacy.
KRL_MLCCL: Multi-label classification based on contrastive learning for knowledge representation learning under open world

Knowledge representation learning(KRL) transforms knowledge graph(KG) from symbol space to vector space. However, KRL under open world assumption(OWA) is deeply trapped in the dilemma of lack of labels due to difficulty or high cost in labeling. To address this problem, we propose KRL_MLCCL:Multi-Label Classification based on Contrastive Learning(CL) Knowledge Representation Learning method. Specifically, (1)we formalize a problem of solving true knowledge graph objects(KGOs) matchings(KGOMs) under the OWA in the original KGOM sample space(KGOMSS)(multi-label classification with one known true matching(positive-example)). (2)we solve the problem in the new KGOMSS, generated through augmenting the true matching according to CL’s idea(multi-label classification with multiple known true matching). (3)we score the true matchings based on hermitian inner product and softmax and minimize a negative logarithm likelihood loss to establish KRL_MLCCL model preliminarily. (4)we migrate the learned model back to the original KGOMSS to solve the true matching problem. We creatively design and apply a positive-example augmentation way of CL enabling KRL_MLCCL with back migration ability: “pulling KGOs in true matching close and pushing KGOs in false matching away”, which helps KRL out of the labels shortage dilemma faced in modeling. We also propose a negative-example noise filtering algorithm to enhance this ability. The open world entity prediction(OWEP) experiment on dataset FB15K-237-OWE shows that the performance of KRL_MLCCL is increased by 3% in Hits@10 and 1.32% in MRR compared with the state-of-the-art in the baselines. The experiments of OWEP in KG also show that KRL_MLCCL has a better back migration ability.
Balancing exploration and exploitation in episodic reinforcement learning

One of the major challenges in reinforcement learning (RL) is its applications in episodic tasks, such as chess game, molecular structure design, healthcare, among others, where the rewards in such scenarios are usually sparse and can only be obtained at the end of an episode. The challenges posed by such episodic RL tasks place stringent demands on the exploration and credit assignment capabilities of the agent. In the current literature, many techniques have been presented to address these two issues, for example, various exploration methods have been proposed to increase the exploration ability of the agents to obtain diverse experience samples, and for the delayed reward problem, reward redistribution methods have provided dense task-oriented guidance to the agents by reshaping the sparse and delayed environmental rewards with the assistance of the episodic feedback. Although some successes have been achieved, with current existing techniques, the agents are usually unable to quickly assign credits to the explored key transitions or the related methods are prone to be misled by behavioral policies that fall into local optima and lead to sluggish learning efficiency. To alleviate inefficient learning due to sparse and delayed rewards, we propose a guided reward approach, namely Exploratory Intrinsic with Mission Guidance Reward (EMR), which organically combines intrinsic rewards of exploration mechanisms with reward redistribution in RL to balance exploration and exploitation of RL agents in such tasks. By using entropy-based intrinsic incentives and a simple uniform reward redistribution method, EMR will enable an agent with both the strong exploration and exploitation capability to efficiently overcome challenging tasks with such sparse and delayed rewards. We evaluated and analyzed EMR on several tasks in the Deep Mind Control Suite benchmark, experimental results show that the EMR-equipped agent has faster learning efficiency and even better performance than those using the exploration bonus or the reward redistribution method alone.
Blockchain-Enabled Communication Framework for Secure and Trustworthy Internet of Vehicles

The emerging field of the Internet of Vehicles (IoV) has garnered significant attention due to its potential to revolutionize transportation and mobility. IoV enables the development of innovative services and applications that can enhance the efficiency, safety, and sustainability of transportation systems. However, ensuring secure and reliable communication among different components of an IoV system poses a critical challenge. This study proposes a blockchain-based communication framework for secure and trustworthy IoV applications. The framework utilizes blockchain technology’s decentralization and security features to create secure communication channels between IoV system components, including vehicles, infrastructure, and service providers. An identity management system is also integrated into the framework to authenticate and authorize users and devices, thereby preventing unauthorized access and data breaches. To assess the proposed framework’s effectiveness, real-world IoV scenarios were used to conduct experiments, and the results demonstrate that the framework can provide secure and trustworthy communication for IoV applications. The proposed blockchain-enabled communication framework provides a promising solution for addressing security and trust challenges in IoV communication systems.
An AR Application for the Efficient Construction of Water Pipes Buried Underground

Unlike other civil engineering works, water pipe works require digging out before construction because the construction site is buried. The AR application is a system that displays buried objects in the ground in three dimensions when users hold a device such as a smartphone over the ground, using images from the smartphone. The system also registers new buried objects when they are updated. The target of this project is water pipes, which are the most familiar of all buried structures. The system has the following functions: “registration and display of new water pipe information” and “acquisition and display of current location coordinate information.” By applying the plane detection function to data acquired from a camera mounted on a smartphone, the system can easily register and display a water pipe model horizontally to the ground. The system does not require a reference marker because it uses GPS and the plane detection function. In the future, the system will support the visualization and registration of not only water pipes but also other underground infrastructures and will play an active role in the rapid restoration of infrastructure after a large-scale disaster through the realization of a buried-object 3D MAP platform.
Evaluation of the Use of Minimax Search in Connect-4&amp;lt;/br&amp;gt;—How Does the Minimax Search Algorithm Perform in Connect-4 with Increasing Grid Sizes?

As computers have become faster at performing computations over the decades, algorithms to play games have also become more efficient.This research paper seeks to see how the performance of the Minimax search evolves on increasing Connect-4 grid sizes.The objective of this study is to evaluate the effectiveness of the Minimax search algorithm in making optimal moves under different circumstances and to understand how well the algorithm scales.To answer this question we tested and analyzed the algorithm several times on different grid sizes with a time limit to see its performance as the complexity increases, we also looked for the average search depth for each grid size.The obtained results show that despite larger grid sizes, the Minimax search algorithm stays relatively consistent in terms of performance.
Increasing Interest and Learning Outcomes of Elementary School Students in Style Material Through Blended Learning

This study aims to increase student interest and learning outcomes in style material through a blended learning model for fourth grade students at SDIP Tunas Bangsa Banjarnegara. The research subjects were 24 grade IV students at SDIP Tunas Bangsa. This research method is a type of research that is collaborative classroom action research. This research was conducted in two cycles. Each cycle has 4 stages, namely planning, implementing, observing, and reflecting on actions. The indicator of success in this classroom action research is if the class average is ≥70 with a classical completeness percentage of 85%. The results of the study showed that using the blended learning model in science lessons on Gaya could increase the interest and learning outcomes of fourth grade students at SDIP Tunas Bangsa Banjarnegara. It can be seen that the average interest score in cycle I was 64.9, increasing in cycle II to 81.22. While the score of student learning outcomes in cycle I was 63% complete KKM increased to 87% completed in cycle II. After implementing Blended Learning, the data on increasing interest in learning can significantly increase the interest and learning achievement of grade IV students at SDIP Tunas Bangsa, Banjarnegara district.
The Spiffy – A Discord Chatbot

A chatbot is a software used to help to increase user interaction during online conversations between a human and machines. A Discord bot is an AI driven tool in which tasks are automated in your discord server. Using discord bot helps us to grow a community, increase interaction and performance of the discord server. This paper focuses on to creation of a discord chatbot named spiffy which is used in discord server and developed using python, json database structure and discordAPI. The chatbot provides user basic interaction with normal human commands, allowing user to type commands as well as receive the texts. The bot will able to answer the query that is generated by the user.
Analysis of Dengkeng river flood disaster preparedness in Cawas District

Abstract Cawas District is an area that has a high potential for flood risk. One of the causes of flooding in Cawas District is the collapse of the Dengkeng river embankment. Other factors that cause flooding include the narrowness of the river flow in the east, the amount of garbage blocking the flow of the Dengkeng river, flooding from the Bayat area, and the delay in opening the dam’s floodgates. To find out the preparedness of the community in Cawas District in the face of flooding, a study was carried out. The research was conducted using qualitative methods. The data collection uses interview, observation, and documentation techniques. While the data analysis using componential analysis techniques. To support the research results are attached: Dengkeng sub-watershed map and flood event map in Cawas District. Based on the research, it can be concluded that the level of preparedness of the community on the banks of the Dengkeng River in Cawas District in dealing with flooding is not ready.
Cancer Diagnosis Using Artificial Intelligence (AI) and Internet of Things (IoT)

Cancer is an ailment that affects people from all walks of life. It is not age-specific, nor is it gender or race-specific. Affecting the cell cycle of various body parts like the brain, breast, etc., it increases the mortality rate, especially with the barriers in its early stage of detection. The advancement in technology has generated big datasets with high-resolution images. The oncologist's and clinician's diagnosis lacks accuracy, long time intervals, and limited information for advanced clinical care, influencing the survival rate. In the digital era, domain experts are reaping the importance of artificial intelligence (AI) techniques. As technology advances, AI and the internet of things (IoT) continue to escalate in the healthcare area, especially in cancer diagnosis. Researchers are looking for novel ways to diagnose cancer without the human-errors and false positives. Hence, the chapter focuses on all these imperative aspects of improved patient outcomes.
Visuals for Emancipatory Technology: A Case Study in Co-Designing a Visual Language to Counter Online Gender-Based Violence on Indian Twitter

This essay is an account of the visual design for Uli, a user-facing browser plugin to detect and moderate online gender-based violence on Twitter. The authors of this essay, who were involved as visual designers in the team that developed Uli, discuss the co-design process behind creating the visual narrative of such a tool to represent the collective labour in its creation by journalists, activists, community influencers, writers, technologists, and researchers engaged in the struggle against the interwoven caste, religion, gender and sexuality-based violence both online and offline. The essay finally sheds light on how such a visual identity and narrative can promote an alternate visual culture that challenges the dominant visual language of social media that's complacent in the propagation of online gender-based violence.
An Adversarial Attack Considering Effectiveness and Concealment on Faster R-CNN

A well-designed adversarial attack method can expose the security vulnerabilities of the deep neural network models, thus providing support examples for defense strategies such as adversarial training. This paper investigates the adversarial attack against the object detection model Faster R-CNN. First, this work takes Faster R-CNN as a target model and formulates the adversarial attack as a multi-objective optimization problem. Second, a constrain considering perturbation magnitude, class label scores and bounding boxes coordinates is introduced to guarantee effectiveness and concealment of the attack. Finally, the proposed method is verified on two benchmark datasets for object detection. The experimental results show that the generated adversarial examples can reduce the @[.5,.95] of Faster R-CNN from 39.9% to 0.8% and 35.0% to 0.1% on MSCOCO2017 and TT100K, respectively. In addition, the generated perturbation achieves considerable concealment, where the average of perturbation magnitude in L1 norm only reaches 13.99 and 0.71 on the two benchmark datasets.
White Box Watermarking for Convolution Layers in Fine-Tuning Model Using the Constant Weight Code

Deep neural network (DNN) watermarking is a potential approach for protecting the intellectual property rights of DNN models. Similar to classical watermarking techniques for multimedia content, the requirements for DNN watermarking include capacity, robustness, transparency, and other factors. Studies have focused on robustness against retraining and fine-tuning. However, less important neurons in the DNN model may be pruned. Moreover, although the encoding approach renders DNN watermarking robust against pruning attacks, the watermark is assumed to be embedded only into the fully connected layer in the fine-tuning model. In this study, we extended the method such that the model can be applied to any convolution layer of the DNN model and designed a watermark detector based on a statistical analysis of the extracted weight parameters to evaluate whether the model is watermarked. Using a nonfungible token mitigates the overwriting of the watermark and enables checking when the DNN model with the watermark was created.
Shapley Values as a Strategy for Ensemble Weights Estimation

This study introduces a novel performance-based weighting scheme for ensemble learning using the Shapley value. The weighting uses the reciprocal of binary cross-entropy as a base learner’s performance metric and estimates its Shapley value to measure the overall contribution of a learner to an equally weighted ensemble of various sizes. Two variants of this strategy were empirically compared with a single monolith model and other static weighting strategies using two large banking-related datasets. A variant that discards learners with a negative Shapley value was ranked as first or at least second when constructing homogeneous ensembles, whereas for heterogeneous ensembles this strategy resulted in a better or at least similar detection performance to other weighting strategies tested. The main limitation being the computational complexity of Shapley calculations, the explored weighting strategy could be considered as a generalization of performance-based weighting.
Fundamental Sentiment Analysis by Natural Language Processing and Machine Learning for Email Classification

Due to its ease of use, speed, adaptability, and ability to keep a complete record of correspondence, email is a commonly used and trusted communication medium. The vulnerability of these emails to cyberattacks has increased. This study utilized the hybrid-based sentiment analysis approach for email fraud detection. The lexicon-based using the Word2Vec feature and Machine Learning (ML) classification approach using both the Bag of Words and TF-IDF feature extraction techniques are adopted to optimize accuracy. The ML classifiers adopted are Random Forest, k-nearest neighbors (k-NN), Support Vector Machine, and Logistic Regression. The predictive and classification comparison of the ML models suggests that Random Forest slightly outperformed the other models with 87% accuracy and 78% F1-score and k-NN performed worst.
Uncovering the Usability Test Methods for Human–Computer Interaction

Software has become one of the most important facets of human life. It is a set of programs or instructions that instructs a system about what to do. Software development represents a group of activities, rules, and guidelines that are represented in the procedure of analysis, requirements gathering and planning, designing and developing, system testing, deploying, maintaining, and supporting software products. HCI has come into sight in the last decade. For our research purpose, we want to understand both the fields and their relationship. We also want to get the knowledge of the HCI design process, its steps, and HCI testing techniques, so that we can further decide on our area of research. The paper is focused on a study of Software Engineering and Human–Computer Interface along with Human-Centered System Development Life Cycle (HCSDLC) model. We also reviewed that various researchers discussed Software Engineering with HCI as now user is considered the most important part of any development. User satisfaction is a major concern; therefore, Agile Development and Agile HCI are very importantly considered. In addition to the HCI development process study, we have discussed so many types of usability testing techniques and their comparisons also. Project managers and other team members including the testing team always find it challenging to select the most affordable and acceptable HCI usability testing method for a software project. When choosing a testing method, a variety of desired components has to be considered. A Systematic and Efficient Literature Review is required to be agreed upon to assist software project managers to make this difficult and significant option so that the most considerable HCI usability testing method or combination of testing methods can be chosen.
Heuristic search of optimal machine teaching curricula

Abstract In curriculum learning the order of concepts is determined by the teacher but not the examples for each concept, while in machine teaching it is the examples that are chosen by the teacher to minimise the learning effort, though the concepts are taught in isolation. Curriculum teaching is the natural combination of both, where both concept order and the set of examples can be chosen to minimise the size of the whole teaching session. Yet, this simultaneous minimisation of teaching sets and concept order is computationally challenging, facing issues such as the “interposition” phenomenon: previous knowledge may be counter-productive. We build on a machine-teaching framework based on simplicity priors that can achieve short teaching sizes for large classes of languages. Given a set of concepts, we identify an inequality relating the sizes of example sets and concept descriptions. This leverages the definition of admissible heuristics for A* search to spot the optimal curricula by avoiding interposition, being able to find the shortest teaching sessions in a more efficient way than an exhaustive search and with the guarantees we do not have with a greedy algorithm. We illustrate these theoretical findings through case studies in a drawing domain, polygonal strokes on a grid described by a simple language implementing compositionality and recursion.
Exposure to Marginally Abusive Content on Twitter

Social media platforms can help people find connection and entertainment, but they can also show potentially abusive content such as insults and targeted cursing. While platforms do remove some abusive content for rule violation, some is considered "margin content" that does not violate any rules and thus stays on the platform. This paper presents a focused analysis of exposure to such content on Twitter, asking (RQ1) how exposure to marginally abusive content varies across Twitter users, and (RQ2) how algorithmically-ranked timelines impact exposure to marginally abusive content. Based on one month of impression data from November 2021, descriptive analyses (RQ1) show significant variation in exposure, with more active users experiencing higher rates and higher volumes of marginal impressions. Experimental analyses (RQ2) show that users with algorithmically-ranked timelines experience slightly lower rates of marginal impressions. However, they tend to register more total impression activity and thus experience a higher cumulative volume of marginal impressions. The paper concludes by discussing implications of the observed concentration, the multifaceted impact of algorithmically-ranked timelines, and potential directions for future work.