Reviewerly/exHarmony huggingface dataset:
A Systematic Review on the Motivations of Cyber-Criminals and Their Attacking Policies

Cyber-crime is a very hideous crime, which in this digital era of information technology has become a very critical, from one person’s problem to a whole nation’s problem. The very purpose of this review paper is to discuss about motivational values of cyber criminals, it was found that while they might have similarity, that they targeted grey areas of lives, but at the same time, with different cyber criminals there are different motivations behind their acts. In order to It also very important to have to have a better underestimating of physiology of the adversary, as it helps the law enforcers and others, that what might occur from what and what it may lead to. It also helps law enforcers to file a case against the adversary. It is understood that an illegal activity occurs from different people, on different scales, and on different targets, due to weak guardian ship and a strong motivated criminal, and with the mounting number of improvements in information security systems the attacks are increasing and becoming more and more common, and it is getting harder to pin-point the real culprit behind the curtains of vast internet.
Cognitive-inspired Post-processing of optical character recognition for Swedish addresses

Optical character recognition (OCR) has many ap-plications, such as digitizing historical documents, automating processes, and helping visually impaired people read. However, extracting text from images into a digital format is not an easy problem to solve, and the outputs from the OCR frameworks often include errors. The complexity comes from the many variations in (digital) fonts, handwriting, lighting, etc. To tackle this problem, this thesis investigates two different methods for correcting the errors in OCR output. The used dataset consists of Swedish addresses. The methods are therefore applied to postal automation to investigate the usage of these methods for further automating postal work by automatically reading addresses on parcels using OCR. The first method, the lexical implementation, uses a dataset of Swedish addresses so that any valid address should be in this dataset (hence there is a known and limited vocabulary), and misspelled addresses are corrected to the address in the lexicon with the smallest Levenshtein distance. The second approach uses the same dataset, but with artificial errors, or artificial noise, added. The addresses with this artificial noise are then used together with their correct spelling to train a machine learning model based on Neural machine translation (NMT) to automatically correct errors in OCR read addresses. The results from this study could contribute by defining in what direction future work connected to OCR and postal addresses should go. The results were that the lexical implementation outperformed the NMT model. However, more experiments including real data would be required to draw definitive conclusions as to how the methods would work in real-life applications.
Sentiment of EmojiSets: How Emoji Sequences Improve Sentiment Cognition

The capability to infer emotional insights from emojis found in social media has projected emoji analysis into the spotlight of current emoji-based research. Previous studies mainly used text-surrounding emojis to estimate sentimentality scores. However, trying to conclude the same score based solely on emojis is challenging. In this paper this challenge was welcomed, and with it we created a new concept. This revolutionary scoring method, named the EmojiSets Sentiment Score Rank, proposes using sets of emojis taken from tweets along with information from previous studies [1] to find a sentiment score. This bottom-up scoring approach gives each emoji a sentiment score. It then calculates the context-level sentiment score of a tweet solely dependent on the emojis found within it. To the best of the authors' knowledge, no such approach has been researched in the Emojis Sentiment Analysis area. We tested our model against over 1.2 million tweets concerning Covid-19 and compared it to the VADER model [7] to validate our assumption. Our model corrected around 72% of the tweets that the other model scored as neutral. To succor these findings, 32 human annotators were given the task of annotating 8040 randomly chosen tweets. When calculating similarity using the Jaccard Index, their results were consistent with our approach in over 70% of cases
Blockchain-based Privacy Preservation Framework for IoT-Based Information Systems

IoT-based systems have become an important part of our lives. We interact with these types of systems many times in a single day like smart watches, smart home systems, smart vehicle systems, and more. Mostly they perform their functionality by utilizing our data. This data is stored on a central repository mostly a cloud-based repository. This central storage mechanism has many critical data privacy issues with it. To resolve these privacy-related issues, a blockchain-based framework is proposed that stores data on distributed ledger instead of any central location. The proposed framework categorizes participants into trusted participants that are mostly data owners and the other is untrusted. It is based on two separate blockchain networks. One is the main chain that is utilized for the transactions related to data storage and the other is a side chain that is utilized for the transaction related to access policy. In this way, transactions take place on the network without any miscellaneous data or privacy threats.
Time-Based Moving Target Defense Using Bayesian Attack Graph Analysis

The moving target defense (MTD) is a proactive cybersecurity defense technique that constantly changes potentially vulnerable points to be attacked, to confuse the attackers, making it difficult for attackers to infer the system configuration and nullify reconnaissance activities to a victim system. We consider an MTD strategy for software-defined networking (SDN) environment where every SDN switch is controlled by a central SDN controller. As the MTD may incur excessive usage of the network/system resources for cybersecurity purposes, we propose to perform the MTD operations adaptively according to the security risk assessment based on a Bayesian attack graph (BAG) analysis. For accurate BAG analysis, we model random and weakest-first attack behaviors and incorporate the derived analytical models into the BAG analysis. Using the BAG analysis result, we formulate a knapsack problem to determine the optimal set of vulnerabilities to be reconfigured under a constraint of SDN reconfiguration overhead. The experiment results prove that the proposed MTD strategy outperforms the full MTD and random MTD counterparts in terms of the maximum/average of attack success probabilities and the number of SDN reconfiguration updates.
SkillDB - An Evaluation on the stability of XAI algorithms for a HR decision support system and the legal context

Explainability of artificial intelligence is an essential factor, which is required especially in critical domains e.g. legal requirements. The following research project focuses on an HR process, in which the quality and stability of several local XAI algorithms was tested. By increasing the number of repetitions of the algorithms, the results revealed that a more stable output with respect to the length of the confidence interval from feature importances could be achieved. In terms of the feature importance, by comparing the different algorithms, features importances could be strengthened/weakened and thus the respective relevances could be obtained.
Textionnaire: An NLP-Based Questionnaire Analysis Method for Complex and Ambiguous Task Decision Support

Questionnaires are one of the most popular means of gathering information in many domains. Traditional health surveys, particularly those focusing on specific populations of patients or certain conditions tend to have sample sizes that are too small to apply advanced artificial intelligence methods. In this study, we introduce a method that enables researchers to apply deep learning algorithms on small questionnaire datasets. We introduce a method to represent the information of fixed response questions of questionnaires in text format analogous to a word presentation for a number stored in expanded form. Then we use a pre-trained, domain-relevant deep learning model for questionnaire classification. We applied this method to two databases: i) a hospital-maintained database of mental health questionnaires linked with hospital administrative resource uti-lization data, and ii) a similarly structured publicly available dataset, the National Survey on Drug Usage and Health. In all experiments, the deep pre-trained model had a better Area Under Receiver Operating Characteristic Curve (AUROC) than a 3-layer artificial neural network (ANN). The proposed method enhanced classification performance by 22.95 % on Mental Health private data and 16.679% on small sub-sample of NSDUH data compared to a baseline ANN machine learning algorithm. Also, our technique improved the performance by up to 15.38% when tested for generalizability of classification.
Bidirectional Cross-scale Feature Fusion for Long Video Micro-Expression 3D Spotting Network

Psycho-cognitive computing is an important part of intelligent human-computer interaction technology, which has received extensive attention in recent years. The research of micro-expressions can reflect the depth and breadth of mental cognitive computing. Micro-expression(ME) is a spontaneous, short-lived, and inadvertent facial expression. The research on ME is of great significance in sentiment analysis, criminal investigation, and psychology research. ME spotting refers to locating sequences of micro-expressions in a long video. ME detection is an extremely important step in the field of ME analysis. Based on the I3D backbone network of long video optical flow extraction and original video feature extraction, this paper extracts effective feature layers, performs downsampling, and uses the BiFPN module with a spatial attention mechanism to selectively fuse the extracted multi-scale feature layers. The final classification-regression network discriminates the detected expressions and locates the temporal boundaries where the expressions occur. The experimental results show that the proposed method effectively improves the F1 score index. Compared with other deep learning methods, this method performs better on both CAS(ME) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and SAMM. The proposed method provides a reliable foundation for the downstream tasks of ME research, such as ME recognition, and also provides an effective method for collecting natural macro-or micro-expression datasets from a large amount of video data in the future.
Design, Livability, and Sustainability

What is the relation of design to society? To what extent design is significant and contributive to the life word and humanity? And to what level and degree design is strategic for the sustainability and livability of the lifeworld and environment? The question leads this study to explore the issues, possibilities, and constraints of design in the context of humanities and the environment. Despite design has been explored, practiced, and developed as a creative skill, there is still space to question its purpose, scope, boundary, method, and practice concerning cultural production and services. This essay is a theoretical inquiry into the potentialities of design as the products and services of the human mind and comportment.
“Explain What a Treemap is”: Exploratory Investigation of Strategies for Explaining Unfamiliar Chart to Blind and Low Vision Users

Visualization designers increasingly use diverse types of visualizations, but assistive technologies and education for blind and low vision people often focus on elementary chart types. We explore textual explanation as a more generalizable solution. We define three dimensions of explanation strategies based on education theories: comparing to a familiar chart type, describing how to draw one, and using a concrete example. We develop a prototype system that automatically generates text explanations from a given chart specification. We conduct an exploratory study with 24 legally blind people to observe both the effectiveness and the perceived effectiveness of the strategies. The findings include: description of visual appearance is overall more effective than instructions for drawing, effective strategies differ by each chart type and by each participant, and the user's perceived effectiveness does not always lead to better performance. We demonstrate the feasibility of an explanation generation system and compile design considerations.
Kaleidoscope: Semantically-grounded, context-specific ML model evaluation

Desired model behavior often differs across contexts (e.g., different geographies, communities, or institutions), but there is little infrastructure to facilitate context-specific evaluations key to deployment decisions and building trust. Here, we present Kaleidoscope, a system for evaluating models in terms of user-driven, domain-relevant concepts. Kaleidoscope's iterative workflow enables generalizing from a few examples into a larger, diverse set representing an important concept. These example sets can be used to test model outputs or shifts in model behavior in semantically-meaningful ways. For instance, we might construct a "xenophobic comments" set and test that its examples are more likely to be flagged by a content moderation model than a "civil discussion" set. To evaluate Kaleidoscope, we compare it against template- and DSL-based grouping methods, and conduct a usability study with 13 Reddit users testing a content moderation model. We find that Kaleidoscope facilitates iterative, exploratory hypothesis testing across diverse, conceptually-meaningful example sets.
Asymptotic Time Complexity of Identification of Basic-level

Abstract Introduction This paper focuses on cognitive computing approaches to identifying the basic-level in a hierarchical structure. In particular, it provides asymptotic time complexities of the identification of the basic-level for five basic-levelness measures, i.e., feature-possession, category utility, category attentional slip, category’s cue validity with global threshold, category’s cue validity with feature-possession. Methods Asymptotic time complexities were analytically determined for each basic-levelness measure separately. Results First, the time complexity of auxiliary measures (i.e., utilized by basic-levelness measures) was determined. Second, the time complexity of the identification of the basic-level was determined. Finally, an optimization of the identification was proposed. Conclusions The identification of the basic-level requires polynomial time. In particular, category attentional slip and category’s cue validity with feature-possession require an additional iteration through all objects, which increase the time complexity.
A novel shape classification method using 1‐D convolutional neural networks

Abstract Most of the shape classification methods are based on a single closed contour. However, practical shapes always have complex contours, for example, a combination of multiple open contours. How to accurately identify complex shapes is an unsolved problem. In this research, a novel method is proposed to classify complex shapes. The proposed method firstly encodes a complex shape to an angle code and a sparsity code, then input these codes to a 1‐D CNN for extracting features and classification. Experiments on two datasets show this novel method is superior in terms of classification accuracy. These two datasets are practical shape dataset collected by this paper on internet and MPEG‐7 CE‐1 Part B. The proposed method achieves higher classification accuracy than compared methods. In order to show the performance of the proposed method on each class, the accuracy on each class is analyzed. Ablation experiment is conducted to show the contribution of each module in the network. The result shows that each module is meaningful in the network, because without any module the accuracy drops.
Semantic Attack on Disassociated Transaction Data

Abstract Accessing and sharing information, including personal data, has become easier and faster than ever because of the Internet. Therefore, businesses have started to take advantage of the availability of data by gathering, analysing, and utilising individuals’ data for various purposes, such as developing data-driven products and services that can help improve customer satisfaction and retention, and lead to better healthcare and well-being provisions. However, analysing these data freely may violate individuals’ privacy. This has prompted the development of protection methods that can deter potential privacy threats by anonymising data. Disassociation is one anonymisation approach used to protect transaction data. It works by dividing data into chunks to conceal sensitive links between the items in a transaction, but it does not account for semantic relationships that may exist among the items, which adversaries can exploit to reveal protected links. We show that our proposed de-anonymisation approach could break the privacy protection offered by the disassociation method by exploiting such semantic relationships. Our findings indicate that the disassociation method may not provide adequate protection for transactions: up to 60% of the disassociated items can be reassociated, thereby breaking the privacy of nearly 70% of the protected items. In this paper [an extension to our work reported in AlShuhail and Shao (Semantic attack on disassociated transactions. In: Proceedings of the 8th International Conference on information systems security and privacy-ICISSP, INSTICC. SciTePress, pp. 60–72, 2022)], we develop additional techniques to reconstruct transactions, with additional experiments to illustrate the impact of our attacking method.
A tool for declarative Trace Alignment via automated planning

We present a tool, called TraceAligner, for solving Trace Alignment by first compiling into Planning and then solving it with any available cost-optimal planner. TraceAligner can produce different variants of the output Planning instance, each offering different degrees of readability and solution efficiency. The Planning instance is expressed in PDDL, the Planning Domain Definition Language. The tool can be easily extended and coupled with any planner taking PDDL as input language. A thorough experimental analysis has shown that the approach dramatically outperforms existing ad-hoc tools, thus making TraceAligner the best-performing tool for Trace Alignment with declarative specifications.
quEHRy: a question answering system to query electronic health records

We propose a system, quEHRy, to retrieve precise, interpretable answers to natural language questions from structured data in electronic health records (EHRs).We develop/synthesize the main components of quEHRy: concept normalization (MetaMap), time frame classification (new), semantic parsing (existing), visualization with question understanding (new), and query module for FHIR mapping/processing (new). We evaluate quEHRy on 2 clinical question answering (QA) datasets. We evaluate each component separately as well as holistically to gain deeper insights. We also conduct a thorough error analysis for a crucial subcomponent, medical concept normalization.Using gold concepts, the precision of quEHRy is 98.33% and 90.91% for the 2 datasets, while the overall accuracy was 97.41% and 87.75%. Precision was 94.03% and 87.79% even after employing an automated medical concept extraction system (MetaMap). Most incorrectly predicted medical concepts were broader in nature than gold-annotated concepts (representative of the ones present in EHRs), eg, Diabetes versus Diabetes Mellitus, Non-Insulin-Dependent.The primary performance barrier to deployment of the system is due to errors in medical concept extraction (a component not studied in this article), which affects the downstream generation of correct logical structures. This indicates the need to build QA-specific clinical concept normalizers that understand EHR context to extract the "relevant" medical concepts from questions.We present an end-to-end QA system that allows information access from EHRs using natural language and returns an exact, verifiable answer. Our proposed system is high-precision and interpretable, checking off the requirements for clinical use.
Real-time and robust visual tracking with scene-perceptual memory

Unmanned aerial vehicle (UAV) based aerial visual tracking is one of the research hotspots in computer vision. However, the mainstream trackers for UAV still have two shortcomings: (1) the accuracy of correlation filter tracker is greatly improved with more complex model, it impedes accuracy-speed trade-off. (2) object occlusion and camera motion in the aerial tracking scene also seriously restrict the application of aerial tracking. To address these problems, and inspired by AutoTrack tracker, we propose an aerial correlation filtering tracker based on scene-perceptual memory, Fast-AutoTrack. Firstly, to perceive and judge tracking anomalies, such as object occlusion and camera motion, inspired by the peak sidelobe ratio and AutoTrack, a confidence score is designed by perceiving and remembering the changing trend of the confidence and the local historical confidence. Secondly, after tracking anomaly occurring, several search regions are predicted based on the local object motion trend and the Spatio-temporal context information for object re-detection. Finally, to accelerate the model updating, the perceptual hashing algorithm (PHA) is used to obtain the similarity of the search regions between two adjacent frames. On typical aerial tracking datasets UAVDT, UAV123@10fps, and DTB70, Fast-AutoTrack run 71.4% faster than AutoTrack with almost equal accuracy and show favorable accuracy-speed trade-off.
Accurate and robust visual tracking using bounding box refinement and online sample filtering

Discriminative correlation trackers have currently achieved excellent performance in terms of tracking robustness. However, these trackers still suffer from limited precision of bounding box estimation due to the challenging factors of occlusion, deformation and rotation. In this paper, in order to address these issues, we propose a three-stage tracking framework called BROST. The proposed tracker is mainly composed of DCF module, segmentation module and box refinement module. Firstly, the proposed tracker roughly locates the center position of the object through the DCF module, then utilizes the segmentation module to estimate the scale of the object and finally employs the box refinement module to improve the accuracy of target box estimation. In order to achieve high tracking robustness, we develop a confidence function of correlation response map to filter out the corrupted or occluded training samples of DCF module. Besides, we introduce a new mask initialization network into the segmentation module to make it more suitable for tracking task. The comprehensive experimental results on six challenging visual tracking benchmarks show that the proposed BROST tracker outperforms most of the state-of-the-art trackers and achieves favorable tracking performance on VOT benchmarks.