Reviewerly/exHarmony huggingface dataset:
Dear XAI Community, We Need to Talk!

Despite progress in the field, significant parts of current XAI research are still not on solid conceptual, ethical, or methodological grounds. Unfortunately, these unfounded parts are not on the decline but continue to grow. Many explanation techniques are still proposed without clarifying their purpose. Instead, they are advertised with ever more fancy-looking heatmaps or only seemingly relevant benchmarks. Moreover, explanation techniques are motivated with questionable goals, such as building trust, or rely on strong assumptions about the ’concepts’ that deep learning algorithms learn. In this paper, we highlight and discuss these and other misconceptions in current XAI research. We also suggest steps to make XAI a more substantive area of research.
Speeding Things Up. Can Explainability Improve Human Learning?

Active learning strategies have been employed when AI alone can not make an accurate prediction (e.g. due to concept drift, class evolution, etc.). In such circumstances, the algorithm requests a teacher, usually a human, to select or verify the system’s prediction on the most informative points. The most informative usually refers to the instances that are the hardest for the algorithm to label. However, it has been proven that humans are more likely to make an accurate judgment, but the decision will not be error-free. In this article, we quantify to what extent explainable artificial intelligence can teach a human about the nuances of the data and help them to be better teachers for the machine in such complex circumstances. This is done via a within-subject experimental setup where human annotators are tasked with classifying queries (i.e. unknown texts) in a multi-class setting. The queries were chosen in an active learning setting. For the treatment condition, we provide the human annotators with LIME-explanations of the queries. We also carried out robustness checks controlling for the annotators’ self-perceived level of expertise, in terms of AI and task knowledge. Our findings support the hypothesis that XAI can have lasting effects, even after the treatment is removed from the task. Moreover, we find that an increase in self-perceived AI or task knowledge does not change the outcomes qualitatively.
Feature Importance versus Feature Influence and What It Signifies for Explainable AI

When used in the context of decision theory, feature importance expresses how much changing the value of a feature can change the model outcome (or the utility of the outcome), compared to other features. Feature importance should not be confused with the feature influence used by most state-of-the-art post-hoc Explainable AI methods. Contrary to feature importance, feature influence is measured against a reference level or baseline. The Contextual Importance and Utility (CIU) method provides a unified definition of global and local feature importance that is applicable also for post-hoc explanations, where the value utility concept provides instance-level assessment of how favorable or not a feature value is for the outcome. The paper shows how CIU can be applied to both global and local explainability, assesses the fidelity and stability of different methods, and shows how explanations that use contextual importance and contextual utility can provide more expressive and flexible explanations than when using influence only.
An Interactive XAI Interface with Application in Healthcare for Non-experts

Explainable artificial intelligence (XAI) has gained increasing attention in the medical field, where understanding the reasons for predictions is crucial. In this paper we introduce an interactive and dynamic visual interface providing global, local and counterfactual explanations to end-users, with a use case in healthcare. The dataset used in the study is about predicting an individual’s coronary heart disease (CHD) within 10 years using the decision tree classification method. We evaluated our XAI system with 200 participants. Our results show that the participants reported an overall good assessment of the user interface, with non-expert users showing a higher satisfaction than users who have some degree of knoweldge of AI.
An Evaluation of Contextual Importance and Utility for Outcome Explanation of Black-Box Predictions for Medical Datasets

Contextual Importance and Utility (CIU) is a model-agnostic method for producing situation- or instance-specific explanations of the outcome of so-called black-box systems. A major difference between CIU and other outcome explanation methods (also called post-hoc methods) is that CIU produces explanations without producing any intermediate interpretable model. CIU’s notion of importance is similar as in Decision Theory but differs from how importance is defined for other outcome explanation methods. Utility is also a well-known concept from Decision Theory that is largely ignored in current Explainable AI research. CIU is here validated by providing explanations for the two popular medical data sets - heart disease and breast cancer in order to show the applicability of CIU explanations on medical predictions and with different black-box models. The explanations are compared with corresponding ones produced by the Local Interpretable Model-agnostic Explanations (LIME) method [17], which is currently one of the most used post-hoc explanation methods. The paper’s main contribution is to provide new CIU results and insights on several benchmark data sets and showing in what way CIU differs from LIME-based explanations.
Evaluating Feature Relevance XAI in Network Intrusion Detection

As machine learning models become increasingly complex, there is a growing need for explainability to understand and trust the decision-making processes. In the domain of network intrusion detection, post-hoc feature relevance explanations have been widely used to provide insight into the factors driving model decisions. However, recent research has highlighted challenges with these methods when applied to anomaly detection, which can vary in importance and impact depending on the application domain. In this paper, we investigate the challenges of post-hoc feature relevance explanations for network intrusion detection, a critical area for ensuring the security and integrity of computer networks. To gain a deeper understanding of these challenges for the application domain, we quantitatively and qualitatively investigate the popular feature relevance approach SHAP when explaining different network intrusion detection approaches. We conduct experiments to jointly evaluate detection quality and explainability, and explore the impact of replacement data, a commonly overlooked hyperparameter of post-hoc feature relevance approaches. We find that post-hoc XAI can provide high quality explanations, but requires a careful choice of its replacement data as default settings and common choices do not transfer across different detection models. Our study showcases the viability of post-hoc XAI for network intrusion detection systems, but highlights the need for rigorous evaluations of produced explanations.
Role of three-dimensional automated breast ultrasound as a breast screening tool in additional to mammography

Back ground The biggest cause of cancer-related deaths among women globally is breast cancer. Breast cancer outcomes are improved by early identification; hence, survival rates are generally favorable when these tumors are found early. Radiologists play a crucial role in breast imaging. The ‘gold standard’ for screening and early detection of breast cancer at the moment is radiograph mammography. The majority of ‘intermediate risk’ women − those with a 15–25% lifetime risk of breast cancer − are women with thick breast tissue. The ‘perfect storm’ of reduced mammographic sensitivity and elevated breast cancer risk affects them. Patients and methods This is a prospective diagnostic study done from May 1, 2021 to May 30, 2022. Patients were recruited from those who had undergone screening using ultrasound examination [handheld ultrasound and automated breast ultrasound (ABUS)], and some of them had undergone mammography. Results In our investigation, 60 female patients had 60 findings that were validated by histopathological biopsy or at least 6 months of follow-up; 16 of the 60 findings were benign abnormalities and 44 were malignant lesions. The gold standard for lesion categorization, biopsy or 6-month follow-up, was used to determine the diagnostic accuracy of the ABUS test. In comparison to the reference index, the diagnostic accuracy metrics for ABUS were 90% accurate but with 88% sensitivity and 91% specificity. Positive predictive value was 78%, whereas negative predictive value was 95%. Positive likelihood ratio was 9.63, whereas negative likelihood ratio was 0.14, with relative risk being 16.33. False-positive rate was 9% and false-negative rate was 13%, with prevalence of 27%. Conclusions When used as a supplemental scan to mammography in a screening program for breast cancer, the automated breast ultrasound performs diagnostically similarly to handheld ultrasound. More imaging and interpretation training can increase the specificity of ABUS.
Otokodlayıcı Tabanlı Boyut Azaltma ve Akıllı Saat Tabanlı Giyilebilir Hareket Algılayıcıları Kullanarak Yaşlılarda Düşme Tespiti

Falling is a serious health risk that can even result in death, especially for the elderly. For this reason, it &#x0D; is crucial to prevent falls and, in cases where prevention is not possible, to detect and intervene as soon &#x0D; as possible. Smartwatches are an ideal tool for fall detection due to their constant presence, rich sensor &#x0D; resources, and communication capabilities. The aim of this study is to detect falls in elderly people with &#x0D; high accuracy using motion sensor data obtained from smartwatches. To achieve this, a dataset was &#x0D; created consisting of falls and daily activities. Then, the feature vector was extracted which has &#x0D; provided successful results in signal processing studies. Afterward, the dimensionality of the dataset &#x0D; was reduced using an autoencoder-based approach in order to decrease the workload on smartwatches &#x0D; and ensure more accurate and faster classification. The dataset was classified using machine learning &#x0D; methods including naive Bayes, logistic regression, and C4.5 decision tree, and successful results were &#x0D; obtained. Their performances were then compared. It was observed that reducing the dimensionality &#x0D; had positive effects on both the classification accuracy and the computation time.
KAOTİK YILAN OPTİMİZE EDİCİ

Metaheuristic algorithms provide approximate or optimal solutions for optimization problems in a &#x0D; reasonable time. With this feature, metaheuristic algorithms have become an impressive research area &#x0D; for solving difficult optimization problems. Snake Optimizer is a population-based metaheuristic &#x0D; algorithm inspired by the mating behavior of snakes. In this study, different chaotic maps were &#x0D; integrated into the parameters of the algorithm instead of random number sequences to improve the &#x0D; performance of Snake Optimizer, and Snake Optimizer variants using four different chaotic mappings &#x0D; were proposed. The performances of these proposed variants for eight different chaotic maps were &#x0D; examined on classical and CEC2019 test functions. The results revealed that the proposed algorithms &#x0D; contribute to the improvement of Snake Optimizer performance. In the comparison with the literature, &#x0D; the proposed Chaotic Snake Optimizer algorithm found the best mean values in many functions and &#x0D; took second place among the algorithms. As a result of the tests, Chaotic Snake Optimizer has been &#x0D; shown to be a promising, successful, and preferable algorithm.
Consumer Health Information on Public Library Websites: Availability and Characteristics

Consumers increasingly search for health information online but can become frustrated in their efforts. Here, public libraries can play an important role as trusted sources. A random sample of 200 U.S. public libraries was used to identify the availability of online consumer health information (CHI) and related characteristics. We found that 110 libraries provided online CHI. The average site provided 28 sources and required two clicks to reach the information. About a third of libraries collaborated by sharing sources or linking to existing content. Collaboration may provide a way to expand the availability and quality of online CHI on public library websites.
Certainty Factor Method for an Expert System for Orthopedic Disease Diagnosis

Health is an important thing in human survival, including bone disease. Some people think that bones are passive, dead tissue, but they are not. Bones and skeleton are a very important part of orthopedics. Bones are not only a framework that strengthens the body but are also part of the structure of joints, as protection for the body, where the ends of muscles are attached. One of the diseases that is widely felt in society is bone disease. Bone disease is a condition that damages the skeleton and makes bones weak and susceptible to fractures. The high rate of diseases that attack the bones is caused by the conditions and behavior of society, such as stress, lack of exercise, wrong diet. Another cause is also ignorance and lack of knowledge about bone disease itself. By using an expert system, patients can save time going to the hospital, because the system replaces experts in their field, especially chiropractors. The method that will be used in this research is the Certainty Factor Method. The results obtained later are diagnostic results based on the symptoms entered and issued in the form of decisions and percentages. The diagnosis results identified Cervical Spondylosis (Nerve Pain) with a confidence level of 89.60%. It is hoped that this expert system will make it easier for patients to diagnose bone disease and carry out initial treatment in treating the disease.
Gastric Diagnosis Expert System using the Fuzzy Mamdani Method

The stomach is one of the important digestive organs in humans. Gastric disease is inflammation of the stomach lining caused by microorganisms, this disease is caused more by Helycobacterpylori bacteria, apart from being caused by bacteria, stomach disease can also be caused by irregular lifestyle and eating patterns. Diseases that attack the stomach are still considered trivial by the general public, so not many people know about stomach diseases and the symptoms that exist. This is what causes people to be reluctant to see a doctor when they suffer from pain that attacks the stomach. When a disease attacks the stomach, people only use experience or intuition to cure it, so it is not treated properly. By using an expert system, patients can save time going to the hospital and can improve service to patients. The results of the Mamdani fuzzy logic calculations require a report on the possibility of stomach disease suffered by the user/patient, examples of possibilities are small, somewhat large and large based on the highest value results. It is hoped that with this system, it will be easier for patients to diagnose gastric diseases to carry out prevention and early diagnosis and treatment.
Hybrid evolutionary intelligent network for sentiment analysis using Twitter data during COVID‐19 pandemic

Abstract COVID‐19 pandemic has impacted many nations, causing physical as well as mental health concerns globally. In most countries, governments enforced strict lockdowns and social distancing, thus affecting people's daily lives. People usually tweet their views on online platforms that is unstructured text with implicit meaning. With the evolution of artificial intelligence in the natural language processing domain, the prediction of sentiments accurately has become a challenge. To contribute as a solution to this, a hybrid approach is proposed for sentiment prediction with the use of an evolutionary‐based approach, transfer‐based learning and machine learning. The proposed approach uses bidirectional encoder representations from transformers (BERT) with genetic algorithm (GA) and support vector machine (SVM), namely, hybrid evolutionary intelligent model (GA‐BERT‐SVM). These approaches aid in extracting important features considering semantics and context present in the text. To avoid the limitations of the backpropagation approach, such as trapping in local minima and overfitting the data, the initial parameters (weights and biases) of the dense layers has been optimized using GA. Additionally, the pretrained BERT layers are utilized without any modification, following a standard transfer learning approach. The BERT embeddings are concatenated with the SVM for training and classification. GridSearchCV and GeneticSearchCV is used for obtaining optimal parameters of SVM. A multi‐classification problem is tackled using a benchmark COVID‐19 dataset, which comprises of Twitter data and is categorized into COVIDSENTI‐A, COVIDSENTI‐B, COVIDSENTI‐C and a combined dataset called COVIDSENTI. Experimental evaluation demonstrates promising results of the proposed model in terms of accuracy, F1‐score, precision and recall, surpassing state‐of‐the‐art approaches.
MODEL QUADRUPLE HELIX DALAM PENGELOLAAN PUSAT BACA MASYARAKAT SEBAGAI SARANA BELAJAR ANAK

Community Reading Center (TBM) as a means for children to learn is one of the programs formulated by the government to build a learning community by increasing children's interest in reading. However, it isn’t all TBM are able to support this purpose due to existence of conditiions facing it, as experienced by TBM Al Bayun in Bandasari Village. Therefore, in the Unpad’s PKM (Integrated KKN-PPM) activity, it is generally intended to assist the village government in carrying out one of its tasks, while specifically intended to help the management of Al Bayun TBM to become a child learning facilities. The Model chosen for the implementation of its activities is the quadruple helix collaboration. While the method of activity is in the form of assistance, counseling to the community related to literacy, and technical guidance in managing TBM. PKM can be held as planned and generally, the set of activities in TBM management can be carried out properly. This is inseparable from the collaboration between the government, universities, publishing business actors, and civil society. The four collaborating parties can play their respective roles so that the aims and objectives of PKM activities can be achieved. This success also shows that with collaboration, the problems faced are more likely to be handled and all limitations can be completed
An Analysis of Korea's Awareness of Prevention of Terrorism and Countermeasures in the Comprehensive Security Conditions

The article suggests measures to improve awareness and respond to preventive terrorism, by analyzing the current terrorist environment and threats based on the question of what purpose and intention South Korea should seek when it comes up with preventive countermeasures against terrorism under a comprehensive security situation. Through theoretical review of terrorism, it explores the causes of terrorism and the various types and characteristics of terrorism that are currently emerging, analyzes terrorist threats and trends that are rapidly on the rise under comprehensive security conditions, and derives implications through specific threat patterns and trends. Based on these analyses, it sought legal measures to improve awareness of preventive concepts, establish a foundation for prevention on terrorism and response, and suggested the applicability of new technologies implemented in the era of the 4th industrial revolution. In so doing, it proposes a comprehensive policy and a system to prevent conditions that allow for domestic terrorist threat in the future.
A Study on the Consumer Perception of Digital Healthcare using Unstructured Big Data: Focusing on Keyword Changes During and After COVID-19

This study collected various keywords by using TextStorm, a solution program that can effectively perform big data analysis using unstructured data to confirm consumer perception of digital healthcare during and after COVID-19, by utilizing cafes and blogs, news media within portal sites such as Naver, Daum, and Google. Based on these analysis results, the academic implications proposed are as follows. First, it is the application of medical innovation and artificial intelligence in the medical field. Second, it is the i ssue o f data security, social issues, and ethics. On the other hand, the practical implications for deepening social science research related to digital healthcare are as follows. First, education for medical institutions and experts should be strengthened and accessibility should be improved. Second, attention should be paid to ethical use and cost-effectiveness improvement of artificial intelligence technology. Based on these implications, we aim to contribute to the development of the digital healthcare field.
Kyber, Saber, and SK-MLWR Lattice-Based Key Encapsulation Mechanisms Model Checking with Maude

Facing the potential threat raised by quantum computing, a great deal of research from many groups and industrial giants has gone into building public-key post-quantum cryptographic primitives that are resistant to the quantum attackers. Among them, there is a large number of post-quantum key encapsulation mechanisms (KEMs), whose purpose is to provide a secure key exchange, which is a very crucial component in public-key cryptography. This paper presents a formal security analysis of three lattice-based KEMs including Kyber, Saber, and SK-MLWR. We use Maude, a specification language supporting equational and rewriting logic and a high-performance tool equipped with many advanced features, such as a reachability analyzer that can be used as a model checker for invariant properties, to model the three KEMs as state machines. Because they all belong to the class of lattice-based KEMs, they share many common parts in their designs, such as polynomials, vectors, and message exchange patterns. We first model these common parts and combine them into a specification, called base specification. After that, for each of the three KEMs, by extending the base specification, we just need to model some additional parts and the mechanism execution. Once completing the three specifications, we conduct invariant model checkings with the Maude search command, pointing out a similar man-in-the-middle attack. The occurrence of this attack is due to the fact that authentication is not part of the KEMs, and therefore an active attacker can modify all communication between two honest parties.