Reviewerly/exHarmony huggingface dataset:
Believe the HiPe: Hierarchical perturbation for fast, robust, and model-agnostic saliency mapping

Understanding the predictions made by Artificial Intelligence (AI) systems is becoming more and more important as deep learning models are used for increasingly complex and high-stakes tasks. Saliency mapping – a popular visual attribution method – is one important tool for this, but existing formulations are limited by either computational cost or architectural constraints. We therefore propose Hierarchical Perturbation, a very fast and completely model-agnostic method for interpreting model predictions with robust saliency maps. Using standard benchmarks and datasets, we show that our saliency maps are of competitive or superior quality to those generated by existing model-agnostic methods – and are over 20× faster to compute.
The Impact of Cyber Attacks on E-Governance During the COVID-19 Pandemic

A cyber-attack can damage data, computer programs, and network one or more computers through applying different methods and cybercriminal's activities to steal information. The increasing of new technologies among the users facilities them. The cyber-attacks are growing tremendously. E-governance is an application of IT and giving online services. These days the world is completely focused on creating social distance among the people, and billions of peoples around the world are working from home (online activities) and shops, and businesses are closed in the COVID-19 pandemic, which the WHO recommended. A remarkable cyber-crime has been recorded by the researcher's study in this environment, affecting society and businesses. This research's primary objective is to find cyber-attacks that steal information in the COVID-19 pandemic and assess the user loss. The results of five years have been compared on the machine learning techniques.
Deep stochastic configuration networks with optimised model and hyper-parameters

The selection of hyper-parameters in building neural networks is often left to end-users. However, this may lead to poor performance regardless of the amount of experience of the user. With deep implementation, this becomes even more challenging due to a large number of hyper-parameters. Deep stochastic configuration networks (DeepSCNs) have become increasingly popular over the past years because of their universal approximation property, fast learning and easy implementation. So far, understanding and setting the hyper-parameters for this type of network is still unexplored. This paper defines a suitable search space for DeepSCN, a performance estimation strategy for finding suitable hyper-parameters with search strategies Monte-Carlo tree search (MCTS) and random search. Simulations are performed using both searches over four benchmark datasets, and results indicate some significant improvements in modelling performance. Furthermore, a case study is presented to demonstrate that an optimised model and hyper-parameters can be found using MCTS.
Summary on the ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Grand Challenge

The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand Challenge (M2MeT) focuses on one of the most valuable and the most challenging scenarios of speech technologies.The M2MeT challenge has particularly set up two tracks, speaker diarization (track 1) and multi-speaker automatic speech recognition (ASR) (track 2).Along with the challenge, we released 120 hours of real-recorded Mandarin meeting speech data with manual annotation, including far-field data collected by 8-channel microphone array as well as near-field data collected by each participants' headset microphone.We briefly describe the released dataset, track setups, baselines and summarize the challenge results and major techniques used in the submissions.
Automatically Building Service-Based Systems With Function Relaxation

Building a quality service-based system (SBS) is one of the most important research topics in software engineering. Many studies investigate intelligent methods to simplify the process of building SBSs. In particular, some keyword-based SBS building methods allow service users to automatically build an SBS by only providing a few of keywords. This type of work usually constructs a directed weighted graph of a service repository. A set of minimum-weight group Steiner trees (MSTs) is extracted from the graph to represent the service functions and their relations. However, to the best of our knowledge, none of the existing keyword-based SBS building methods allow the relaxation of the function requirements for a user. A relaxed SBS may achieve a comparable functionality versus a complete SBS containing all the query functions. To fill in the above gap, we define a new problem: a bounded skyline SBS building problem, whose solution is more adaptive and less limited than the traditional keyword-based SBS building methods. To solve this problem, we propose two algorithms based on skyline query, dynamic programming, and lower bound pruning. In the experiments, we collect real-world datasets and label the nodes with keywords. We conduct a comprehensive study to demonstrate the time efficiency of our algorithms on automatically finding SBSs. We make the annotated real-world datasets and our source code open to peer researchers.
Will AI Console Me when I Lose my Pet? Understanding Perceptions of AI-Mediated Email Writing

Large language models are increasingly mediating, modifying, and even generating messages for users, but the receivers of these messages may not be aware of the involvement of AI. To examine this emerging direction of AI-Mediated Communication (AI-MC), we investigate people’s perceptions of AI written messages. We analyze how such perceptions change in accordance with the interpersonal emphasis of a given message. We conducted both large-scale surveys and in-depth interviews to investigate how a diverse set of factors influence people’s perceived trust in AI-mediated writing of emails. We found that people’s trust in email writers decreased when they were told that AI was involved in the writing process. Surprisingly trust increased when AI was used for writing more interpersonal emails (as opposed to more transactional ones). Our study provides insights regarding how people perceive AI-MC and has practical design implications on building AI-based products to aid human interlocutors in communication1.
Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior

Saliency methods — techniques to identify the importance of input features on a model's output — are a common step in understanding neural network behavior. However, interpreting saliency requires tedious manual inspection to identify and aggregate patterns in model behavior, resulting in ad hoc or cherry-picked analysis. To address these concerns, we present Shared Interest: metrics for comparing model reasoning (via saliency) to human reasoning (via ground truth annotations). By providing quantitative descriptors, Shared Interest enables ranking, sorting, and aggregating inputs, thereby facilitating large-scale systematic analysis of model behavior. We use Shared Interest to identify eight recurring patterns in model behavior, such as cases where contextual features or a subset of ground truth features are most important to the model. Working with representative real-world users, we show how Shared Interest can be used to decide if a model is trustworthy, uncover issues missed in manual analyses, and enable interactive probing.
Effects of Field of View on Egocentric Distance Perception in Virtual Reality

We performed a mixed-design study with 56 participants to compare the effect of horizontal FOV (hfov) and vertical FOV (vfov) on egocentric distance perception in four different realistic virtual environments (VEs). We also compared VE attributes of indoor/outdoor and cluttered/uncluttered. The participants blind-walked towards four different targets at 3m, 4m, 5m, and 6m distance while wearing a backpack computer and a wide FOV head-mounted display (HMD). The combinations of 165°, 110° and 45° hfovs, and 110° and 35° vfovs was simulated in the same HMD. The results indicated more accurate distance judgement with larger hfov with no significant effect of vfov. More accurate distance judgement in indoor VEs compared to outdoor VEs was observed. Also, participants judged distances more accurately in cluttered environments versus uncluttered environments. These results highlight that the environment is important in distance-critical VR applications and wider hfov should be considered for an improved distance judgment.
Reinventing reverse logistics through blockchain technology: a comprehensive review and future research propositions

Blockchain Technology (BCT) has effectively evolved in reverse logistics (RL) to speed up its operation by decentralising, tracing, and monitoring the goods delivered to the end consumers. This study outlines the current research fashion of BCT applicability in RL from 2015 to 22. A wide range of 226 research papers is selected from the SCOPUS database to conduct the bibliometric and network analysis for offering a comprehensive literature review on research clusters and fashions of BCT in RL. Some primary research clusters such as infrastructure development, sustainable manufacturing, logistics, circular supply chain, and waste management are identified. The network analysis has helped identify pioneer authors, journals, and countries actively involved in BCT research in RL. The content analysis findings indicate the evolution of BCT in various themes of RL. The articles also develop a result systematisation framework to concisely offer the outcomes of this research. Further, the current study provides recommendations for future research work for academic and industry practitioners based on the existing literature.
End-to-End diagnosis of breast biopsy images with transformers

Diagnostic disagreements among pathologists occur throughout the spectrum of benign to malignant lesions. A computer-aided diagnostic system capable of reducing uncertainties would have important clinical impact. To develop a computer-aided diagnosis method for classifying breast biopsy images into a range of diagnostic categories (benign, atypia, ductal carcinoma in situ, and invasive breast cancer), we introduce a transformer-based hollistic attention network called HATNet. Unlike state-of-the-art histopathological image classification systems that use a two pronged approach, i.e., they first learn local representations using a multi-instance learning framework and then combine these local representations to produce image-level decisions, HATNet streamlines the histopathological image classification pipeline and shows how to learn representations from gigapixel size images end-to-end. HATNet extends the bag-of-words approach and uses self-attention to encode global information, allowing it to learn representations from clinically relevant tissue structures without any explicit supervision. It outperforms the previous best network Y-Net, which uses supervision in the form of tissue-level segmentation masks, by 8%. Importantly, our analysis reveals that HATNet learns representations from clinically relevant structures, and it matches the classification accuracy of 87 U.S. pathologists for this challenging test set.
The Geothermal Artificial Intelligence for geothermal exploration

Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). We processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92–95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal/non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72–76% accuracy.
An improved artificial bee colony algorithm based on Bayesian estimation

Abstract Artificial bee colony (ABC) algorithm was proposed by mimicking the cooperative foraging behaviors of bees. As a member of swarm intelligence algorithms, ABC has some advantages in handling optimization problems. However, it has the exploration capacity over the exploitation capacity, which may lead to slow convergence speed and lower solution accuracy. Hence, to enhance the performance of the algorithm, a novel ABC based on Bayesian estimation (BEABC) is presented in this paper. First, instead of using the fitness ratio, the selection probability in ABC is replaced with a new probability calculated by Bayesian estimation. Second, to help the bees adopt more useful information during updating new food sources, a directional guidance mechanism is designed for onlooker bees and scout bees. Finally, the comprehensive performance of BEABC is evaluated by 24 single-objective test functions. The numerical experiment results indicate that BEABC dominates its peers over most test functions, and the significant statistics show that the significant excellence rate of BEABC is $$76\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>76</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> in the overall comparison. In addition, to further test the performance of BEABC, seven multi-objective problems and two real-word optimization problems are solved. The comparison results show that BEABC can achieve better results than other EA competitors.
Interval type-2 outlier-robust picture fuzzy clustering and its application in medical image segmentation

Based on picture fuzzy set theory, picture fuzzy clustering has achieved good results on some data as more information is involved in the clustering process. However, current picture fuzzy clustering methods still suffer from two common weaknesses, i.e., the sensitivity to outliers and the neglect of the uncertainty caused by different fuzzy degrees, which influence their performance in practical applications like medical image segmentation. To solve these issues, we present two new picture fuzzy clustering methods in this paper. First, to improve immunity to outliers, we propose an outlier-robust picture fuzzy clustering method named ORPFC by using a robust distance measurement, which treats the data objects far away from cluster prototypes as outliers and limits their effects on the prototype update. Second, to handle the uncertainty caused by fuzzy degrees, we further present an interval type-2 enhanced method called IT2ORPFC by incorporating the interval type-2 fuzzy set theory into ORPFC. In each iteration, IT2ORPFC estimates positive memberships, neutral memberships, and refusal memberships according to different fuzzification coefficients and then conducts type reduction for reliable type-1 clustering results. In the experiments, the proposed methods obtain robust and reliable results on eleven datasets. Specifically, ORPFC and IT2ORPFC achieve rewarding performance on segmenting medical images with noise.
A new coverage path planning algorithm for unmanned surface mapping vehicle based on A-star based searching

This paper investigates the coverage path planning issues and mapping platform construction for unmanned surface mapping vehicle (USMV). An improved BA∗ (IBA∗) algorithm is designed from the unit decomposition method and map update method to solve the problem of insufficient continuity and the high-precision environmental modeling. By means of task decomposition and map dynamic updating, the IBA∗ algorithm overcomes the shortcoming of local optimization of BA∗ algorithm effectively, and has a significant decrease more than 10% in the path length, 15% in the number of turns, 85% in the unit number and 2% in the coverage. Moreover, the IBA∗ algorithm has a greater advantage than the Boustrophedon algorithm and the PPCPP algorithm. Results of simulations and the mapping experiments verify the excellent performance of our IBA∗.
Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support

In the era of bathing in big data, it is common to see enormous amounts of data generated daily. As for the medical industry, not only could we collect a large amount of data, but also see each data set with a great number of features. When the number of features is ramping up, a common dilemma is adding computational cost during inferring. To address this concern, the data rotational method by PCA in tree-based methods shows a path. This work tries to enhance this path by proposing an ensemble classification method with an AdaBoost mechanism in random, automatically generating rotation subsets termed Random RotBoost. The random rotation process has replaced the manual pre-defined number of subset features (free pre-defined process). Therefore, with the ensemble of the multiple AdaBoost-based classifier, overfitting problems can be avoided, thus reinforcing the robustness. In our experiments with real-world medical data sets, Random RotBoost reaches better classification performance when compared with existing methods. Thus, with the help from our proposed method, the quality of clinical decisions can potentially be enhanced and supported in medical tasks.
HydroMod : Constructive Modules for Prototyping Hydraulic Physical Interfaces

In recent years, actuators that handle fluids such as gases and liquids have been attracting attention for their applications in soft robots and shape-changing interfaces. In the field of HCI, there have been various inflatable prototyping tools that utilize air control, however, very few tools for liquid control have been developed. In this study, we propose HydroMod, new constructive modules that can easily generate liquid flow and programmatically control liquid flow, with the aim of lowering the barrier to entry for prototyping with liquids. HydroMod consists of palm-sized small modules, which can generate liquid flow with the electrohydrodynamics (EHD) phenomenon by simply connecting the modules. Moreover, users can configure and control the flow path by simply recombining the modules. In this paper, we propose the design of the modules, evaluate the performance of HydroMod as a fluid system, and also show the possible application scenarios of fluid prototyping using this system.
UBP-Miner: An efficient bit based high utility itemset mining algorithm

HUIM (High utility itemset mining) is a key problem in data mining. The goal is to find itemsets having a high importance or profit in a database, to identify useful knowledge that can support decision-making. In recent years, many HUIM algorithms have been put forward. Among them, utility-list-based algorithms have become very popular as they are easily extendable and efficient. Although several improvements were made, efficiency remains a critical issue. To address this problem, this paper proposes to improve the utility-list construction process, a key operation that has not been much studied in prior work. A novel set of bitwise operations is proposed called BEO (Bit mErge cOnstruction) to speed up the construction process. Besides, a novel data structure called UBP (Utility Bit Partition) is designed to support BEO. This structure is integrated into a novel UBP-Miner algorithm, which also applies several search space reduction strategies. Experimental results show that UBP-Miner is faster than several state-of-the-art algorithms such as HUI-Miner* and ULB-Miner on common benchmark datasets.
Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

With the increasing number of online social posts, review comments, and digital documentations, the Arabic text classification (ATC) task has been hugely required for many spontaneous natural language processing (NLP) applications, especially within the coronavirus pandemics. The variations in the meaning of the same Arabic words could directly affect the performance of any AI-based framework. This work aims to identify the effectiveness of machine learning (ML) algorithms through preprocessing and representation techniques. This effectiveness is measured via different AI-based classification techniques. Basically, the ATC process is influenced by several factors such as stemming in preprocessing, method of feature extraction and selection, nature of datasets, and classification algorithm. To improve the overall classification performance, preprocessing techniques are mainly used to convert each Arabic word into its root and decrease the representation dimension among the datasets. Feature extraction and selection always play crucial roles to represent the Arabic text in a meaningful way and improve the classification accuracy rate. The selected classifiers in this study are performed based on various feature selection algorithms. The overall classification evaluation results are compared using different classifiers such as multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), and Linear SVC. All of these AI classifiers are evaluated using five balanced and unbalanced benchmark datasets: BBC Arabic corpus, CNN Arabic corpus, Open-Source Arabic corpus (OSAc), ArCovidVac, and AlKhaleej. The evaluation results show that the classification performance strongly depends on the preprocessing technique, representation methods and classification technique, and the nature of datasets used. For the considered benchmark datasets, the linear SVC has outperformed other classifiers overall when prominent features are selected.
No Negative Effects of Reading on Screen on Comprehension of Narrative Texts Compared to Print: A Meta-analysis

While some argue digital reading media may impair text comprehension, the empirical literature is ambiguous, in particular when it comes to the reading of narrative texts. Therefore, a comprehensive and systematic meta-analysis of studies exploring the effect of screen reading media on reading comprehension of narrative texts was conducted (k = 32, N = 2239). Multimedia and interactive functions in general, the type of multimedia and interactive functions, the change in effect over time, and the type of digital reading device (computer, e-reader, and tablet) were explored as moderating variables. In general, the analyses did not reveal a significant impact of the reading medium (screen vs. paper) on the reading comprehension of a narrative text. Moreover, there does not seem to be a difference over time and between different types of digital reading devices. Also, the analysis of the subsample of studies using plain digital text without any additional functions in comparison to print showed no significant differences. In contrast, multimedia and interactive functions of digital texts affect reading comprehension positively, regardless of the type of additional function. In conclusion, the results do not suggest a negative effect of digital reading media on reading comprehension when reading narrative texts.