Reviewerly/exHarmony huggingface dataset:
Supporting Group Decision-Making Processes based on Group Dynamics

Organizations, friends, family members, etc., construct groups and conduct discussions to make decisions about the problems. Group decision-making is a complicated and time-consuming process since the group members involved in a discussion may have different personalities and opinions about the potential options. In other words, ending up with a choice that satisfies all the group member is challenging. Hence, group recommender systems have emerged and attracted attention in the past two decades to support groups dealing with this difficulty. In fact, the main aims of group recommender systems are to help groups to end up with a good with limited costs, appropriate justifiability, and more fair choice. In this research, we aim to address three main questions regarding the group decision-making process and with respect to the main aims of group recommender systems: (i) how to predict groups’ choices, (ii) how to design a conversational system for groups, and (iii) how to support groups in a dynamic environment. Conversational group recommender systems can use the predicted group choice to interact more effectively with groups. For example, conversational group recommender systems can use this information by narrowing down the candidate set to the items that are not very different from the predicted choice and recommending them in each turn. Thereby, the system can help groups to reduce decision-making costs (time). Conversational GRSs are also able to capture the dynamics of groups or how the group preferences evolve during the discussion and interaction with the system since group members express their feedback to the system during their interaction with the system. The system can exploit this feedback to infer the evolution of the group. The supporting functionalities can use this information to support the groups in different group dynamic states. This research attempts to address these questions.
Deep Learning Approach for Sentimental Analysis of Hotel Review on Bengali text

We live in an age of technology. Technology is advancing day by day. People in India (Kolkata), Bangladesh, who use the Bengali language as a communication language, use the internet daily. Internet users are increasing day by day. Moreover, people like to visit many tourist places. So, they have to stay in a hotel or resort. They give various reviews on websites and many social media. But not everybody gives thoughts in the English language. People in Bangladesh, India (Kolkata) give reviews in Bengali. So, this is hard to detect for the hotel management system. So, in this paper, we are planning to build a model that can analyze the Bengali text and detect whether the reviews are bad or good since there is no work on Bengali text in this particular area. The datas are collected by surveys, various social media reviews, ratings, etc., and then label those data. After cleaning and extracting multiple features, the datas were trained into DL(Deep Learning) and ML(Machine Learning) models. At the end, it is found that the Long Term Short Term (LSTM) comparatively gives better results than other models. This work will create a significant effect on the hotel and tourism industries.
Deep Learning Based Underwater Image Enhancement Using Deep Convolution Neural Network

Underwater Image Enhancement (UIE) has received a lot of attention due to increased civilian and military uses, though there has been substantial progress in this area. Underwater photography, on the other hand, has low contrast and unclear features due to light absorption and scattering. Deep learning has become extremely prevalent in underwater image enhancement and restoration in recent times because of its extensive feature learning abilities, yet precise enhancement still has problems. To address this issue, we have proposed a UIE approach using Deep Learning (DL) techniques. A Deep Convolution Neural Network (CNN) framework for underwater IE and restoration by channelling the damaged underwater image and extracting multi-contextual information. The experiments were performed on the EUVP (Enhancing Underwater Visual Perception) dataset and the results outline that the recommended approach outperforms the other most recent methodologies and gives efficient outcomes.
Issues in Credit Card Transactional Data Stream: A Rational Review

Online transactions are trending worldwide now and in future developments. A big amount of transactional data is generated like networking, stock market, telecommunications, and weather forecasting. This data can be classified for the knowledge extraction and learning. Credit card nowadays is very easy methods for physical and online transactions. Transactions using a credit/debit card having some advantages and flaws. Some of the problems with the credit card transactions are also highlighted here, further in the paper focused on the extensive studies of the various learning methods used by various authors on the imbalanced data stream of the credit card transactions.
Incremental sequential patterns for multivariate temporal association rules mining

Most temporal association rule mining algorithms can mine the temporal relationship between items, but the sequential relations between successive items remain unknown, while sequential pattern mining algorithms can discover the sequential relationship between successive items but the quantitative time interval of sequential patterns remains unknown. Furthermore, they only focus on mining a historical temporal database, which ignores the fact that temporal databases are continually appended or updated. To address these problems, by integrating the advantages of these two algorithms, we extend the sequential pattern mining algorithm—PrefixSpan by introducing the concept of fuzzy temporal pattern and incremental learning, a new algorithm called Incremental sequential patterns for multivariate temporal association rules mining (ISPTAR) is proposed. First, the temporal transaction dataset obtained by fuzzy discretization of multivariate time series is converted into a temporal sequence dataset. Second, based on the PrefixSpan algorithm, the fuzzy temporal sequential patterns are mined by taking the valid time interval of the sequential pattern and the temporal relationship between items in the sequential pattern into account. Third, fuzzy temporal association rules can be constructed to mine the association between different attributes based on the mined fuzzy temporal sequential patterns. In addition, when new data are added, the proposed algorithm can update the sequential patterns without rescanning the historical database. We compare the ISPTAR algorithm with other sequential patterns mining and association rules mining algorithms on four real datasets from the UCI machine learning data repository. Experimental results demonstrate that ISPTAR, in most cases, outperformed the other algorithms in execution time and the number of generated rules. In addition, the rules generated by ISPTAR contain more temporal information, which is helpful to assist important decision-making. • PrefixSpan is extended to mine fuzzy sequential pattern from multivariate time series. • Time interval of sequential pattern can be mined from temporal sequence dataset. • Original mined information can be inherited to mine Incremental sequential patterns. • Fuzzy temporal association rules can be mined based on the mined sequential patterns.
What do Students’ Interactions with Online Lecture Videos Reveal about their Learning?

Video viewing is an important component of online learning, yet little is known about what information about learning outcomes can be derived from students' video control actions. We investigate the extent to which information on student learning is contained in their video-watching clickstreams (e.g. pausing, playing) immediately after watching a video. We analyzed data from 10,492 students who used an online learning platform for their Algebra 1 course. Our experiments encode students' video-control clickstreams into sequences in several ways (e.g. aggregate actions, shuffle actions, and merge action types), and train Long Short-term Memory (LSTM) neural network models to predict after-video quiz scores (N = 32,482) from the sequences in a student-independent fashion. The results suggest that the action sequences contain a limited amount of information about student learning (r = 0.108 between model-predicted- and actual- quiz scores), with most of the information in simple counts of actions (r = 0.081) rather than the temporal ordering of actions. Combining information from video action sequences and traditional knowledge estimates from item-response theory (IRT) outperformed (r = 0.224) either approach independently. Implications for student modeling and adaptive learning support for viewing lecture videos are discussed.
A High-Level Representation of the Navigation Behavior of Website Visitors

Knowing how visitors navigate a website can lead to different applications. For example, providing a personalized navigation experience or identifying website failures. In this paper, we present a method for representing the navigation behavior of an entire class of website visitors in a moderately small graph, aiming to ease the task of web analysis, especially in marketing areas. Current solutions are mainly oriented to a detailed page-by-page analysis. Thus, obtaining a high-level abstraction of an entire class of visitors may involve the analysis of large amounts of data and become an overwhelming task. Our approach extracts the navigation behavior that is common among a certain class of visitors to create a graph that summarizes class navigation behavior and enables a contrast of classes. The method works by representing website sessions as the sequence of visited pages. Sub-sequences of visited pages of common occurrence are identified as “rules”. Then, we replace those rules with a symbol that is given a representative name and use it to obtain a shrinked representation of a session. Finally, this shrinked representation is used to create a graph of the navigation behavior of a visitor class (group of visitors relevant to the desired analysis). Our results show that a few rules are enough to capture a visitor class. Since each class is associated with a conversion, a marketing expert can easily find out what makes classes different.
Global and local attentional feature alignment for domain adaptive nuclei detection in histopathology images

Automated nuclei detection is crucial prerequisites for a number of histopathology related image analysis such as cancer diagnosis. Although existing deep learning based nuclei detection methods have achieved promising results, they cannot effectively deal with domain shift problem caused by different staining procedures and organ specific nuclear morphology. To handle this problem, in this paper a novel adversarial feature alignment method is proposed for domain adaptive nuclei detection, which includes both global alignment and local attentional alignment components to transfer the knowledge from source domain to target domain. Specifically, in local attentional alignment component, by using nuclei locations as guidance we extract local features and perform adversarial alignment. Furthermore, to address the issue that these local features from nuclei regions often contain insufficient information because of the small size of nuclei, we introduce an efficient location-aware self-attention (LocSA) module to refine local features by utilizing cues from all nuclei for obtaining discriminative features to perform successful feature alignment. Extensive experimental results are provided on two adaptation scenarios and our method demonstrates favorable performance against existing domain adaptation methods, which highlights the effectiveness of the proposed method for domain adaptive nuclei detection.
The Impacts of Voltage Disturbances Due to Faults In the Power Supply System of A Data Center

The internal power condition system (IPCS) in data centers is prone to have cable faults that cause voltage dips and swells. The voltage dips and swells impact the power supply units (PSUs) with the servers. The servers connected with the PUSs restart or turn-off when the input voltage comes out of the voltage-tolerance range. This paper analyses the impact of such voltage disturbances on server outages due to a single-phase fault in the IPCS. The voltage-tolerance range of the PSUs is considered according to the guideline of the Information Technology Industry Council (ITIC). The voltage dip propagates to the healthy load sections from the fault location, while voltage swells are also observed due to sudden load reduction. Moreover, the current limitation mode of the inverter in the uninterrupted power supply (UPS) is identified as a cause of voltage dip to almost zero experienced by the PSUs. The reliability of the data center considering the outage probability of the servers are finally quantified to show the impacts of the voltage dips and swells in the IPCS.
The challenges of providing explanations of AI systems when they do not behave like users expect

Explanations in artificial intelligence (AI) ensure that users of complex AI systems understand why the system behaves as it does. Expectations that users may have about the system behaviour play a role since they co-determine appropriate content of the explanations. In this paper, we investigate user-desired content of explanations when the system behaves in unexpected ways. Specifically, we presented participants with various scenarios involving an automated text classifier and then asked them to indicate their preferred explanation in each scenario. One group of participants chose the type of explanation from a multiple-choice questionnaire, the other had to answer using free text.
Sentiment Analysis for Amazon Product Reviews

Sentiment analysis is a classicfication process whereby machine learning techniques are applied on text-driven datasets in order to analyse the emotion / opinion expressed in a text, e.g. a message being positive or negative about a certain topic. The problem is to conduct a sentiment analysis (positive and negative sentiment) on online product reviews of Products (unlocked mobile phones) sold on Amazon.com. The trained model can be used to predict users’ sentiment based on their online reviews. In this project, different machine learning algorithms are compared, trained and tested on a dataset containing 400000 reviews. The performance of three different algorithms were compared: Multinomial Naive Bayes (MNB), Logistic Regression and Long short-term memory network (LSTM). The Logistic Regression model resulted in the highest performance with Accuracy of 0.95 and AUC of 0.94. The dataset consists of 400 thousand reviews of products (unlocked mobile phones) sold on Amazon.com which is publicly available on Kaggle. Solution to the problem would be useful for a brand to gain a broad sense of user’s’ sentiment towards a product through online reviews Further study is needed to investigate if the classfication remains accurate when including more than two classes (e.g. Introducing a neutral class).
Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing

In recent decades, haze has become an environmental issue due to its effects on human health. It also reduces visibility and degrades the performance of computer vision algorithms in autonomous driving applications, which may jeopardize car driving safety. Therefore, it is extremely important to instantly remove the haze effect on an image. The purpose of this study is to leverage useful modules to achieve a lightweight and real-time image-dehazing model. Based on the U-Net architecture, this study integrates four modules, including an image pre-processing block, inception-like blocks, spatial pyramid pooling blocks, and attention gates. The original attention gate was revised to fit the field of image dehazing and consider different color spaces to retain the advantages of each color space. Furthermore, using an ablation study and a quantitative evaluation, the advantages of using these modules were illustrated. Through existing indoor and outdoor test datasets, the proposed method shows outstanding dehazing quality and an efficient execution time compared to other state-of-the-art methods. This study demonstrates that the proposed model can improve dehazing quality, keep the model lightweight, and obtain pleasing dehazing results. A comparison to existing methods using the RESIDE SOTS dataset revealed that the proposed model improves the SSIM and PSNR metrics by at least 5–10%.
Student Education Management Strategy Based on Artificial Intelligence Information Model under the Support of 5G Wireless Network

With the popularity of the Internet and the advancement of information technology, more and more people are accepting the teaching and sharing of knowledge through the digitalization of information. The widespread adoption of 5G technology has pushed online learning even further into the mainstream. However, because online teaching does not have the drawback of being intuitive like classroom teaching, teachers' assessments of students' learning situations are less accurate. As a result, how to effectively evaluate students' academic performance in the context of 5G wireless network technology is a pressing issue that must be investigated. By processing these heterogeneous large-scale learning records and integrating multiple perspectives to analyze this learning record information to identify students' learning behaviors, this study proposes an integrated analysis algorithm based on artificial intelligence information technology. The possible learning outcomes of students are predicted based on their current learning situation, so teachers can provide auxiliary teaching strategies to students who may have learning difficulties based on the predicted information. The method proposed in this article uses information technology to predict students' grades, and the analysis shows that the method is very effective. In this article, different grades of classification methods are used to analyze and predict the whole students. All grade classification methods are effective in describing decision rules. No matter what grades classification method is used, the error rate of students' grades distribution is predicted to be below 40%.
Robust distributed fusion with trajectory random finite sets

This paper concentrates on the distributed Multi-sensor Multi-target Tracking (DMMT) in the trajectory random finite set (TRFS) filtering framework. The trajectory probability hypothesis density (TPHD) filter and the trajectory cardinality PHD (TCPHD) are performed locally in different sensors for DMMT. Our analysis shows that standard generalized covariance intersection (GCI) and weighted arithmetic average (WAA) fusion with the posterior multitrajectory densities of the TPHD and TCPHD filters rely heavily on the proper association of trajectories estimated from various sensor nodes. Then, inspired by the analysis, we develop two novel distributed fusion algorithms for the Gaussian mixture TPHD and TCPHD filters. First, we obtain the associated and unassociated trajectories at different sensors via clustering and Linear programming (LP). Second, the associated trajectories are fused via covariance intersection (CI) parallelly. Third, two principle cardinality consensus algorithms are proposed to fuse unassociated and CI fused trajectories. Moreover, two analytical expressions, which are crucial for fusion weight in CI fusion, are provided. Finally, numerical experiments demonstrate the efficacy of our proposed approaches in computational efficiency and accuracy, compared with the state-of-the-art solutions in challenging scenarios.
Calculating the acoustic input impedance of a simplified brass instrument as an educational laboratory activity

The concept of acoustic impedance is often difficult for students in introductory acoustics courses to make sense of, especially students without advanced mathematics backgrounds. This work summarizes a laboratory activity for students in a general education musical acoustics class where a simplified brass musical instrument is examined, focusing on how the geometry of the air column affects the input impedance of the instrument. Students are guided through making bore profile measurements for use in a computation of the input impedance. Options for making experimental measurements of the simplified instrument are explained. The laboratory activity was successfully used with students who reported their increased understanding of the acoustics of brass musical instruments.
Why we Should Rethink Our Approach to Embodiment and Presence

When interacting with objects in the environment, it feels natural to have a body which moves in accordance to our intentions. Virtual reality (VR) provides a tool to present users with an alternative virtual body and environment. In VR, humans embody the presented virtual body and feel present in the virtual environment. Thus, embodiment and presence frequently co-occur and share some communalities. Nevertheless, both processes have been hardly considered together. Here, we review the current literature on embodiment and presence and present a new conceptual framework, the Implied Body Framework (IBF), which unifies both processes into one single construct. The IBF can be used to generate new hypotheses to further improve the theoretical conceptualisation of embodiment and presence and thus, facilitate its transfer into application.
Unsupervised Hierarchical Classification Approach for Imprecise Data in the Breast Cancer Detection

(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical classification technique for multivariate interval-valued data is suggested for diagnosis of the breast cancer; (2) Methods: an unsupervised hierarchical classification method for imprecise multivariate data (called HC-ID) is performed for diagnosis of breast cancer (i.e., to discriminate between benign or malignant masses) and the results have been compared with the conventional (unsupervised) hierarchical classification approach (HC); (3) Results: the application on real data shows that the HC-ID procedure performs better HC procedure in terms of accuracy (HC-ID = 0.80, HC = 0.66) and sensitivity (HC-ID = 0.61, HC = 0.08). In the results obtained by the usual procedure, there is a high degree of false-negative (i.e., benign cancer diagnosis in malignant status) affected by the high degree of variability (i.e., uncertainty) characterizing the worst data.