Reviewerly/exHarmony huggingface dataset:
The effect of consumer review on the perceived trustworthiness of online retailers: Item response theory perspective

Word of Mouth also works as a primary determinant of people's positive attitude towards online shopping and retailers. Notably, people are more likely to spend online shopping if they trust online retailers. This study also focuses on the online retail industry in the United Arab Emirates. The Item Response Theory primarily supports the conceptual model of the current research. We employed a cross-sectional design and selected a sample of n= 304 online consumers. Results revealed that the relationships between Positive Word of Mouth, Brand Image, and Causal Attributions are strongly validated (p&gt; 0.000). Besides, the relationships between. Product Quality, Brand Image, and Causal Attributions are also affirmed with the path value at 1.014 and significance value at p&gt; 0.000. Moreover, we also found a potentially significant relationship between Positive Word of Mouth and Product Quality p&gt; 0.000). We also affirmed that the relationship between Brand Image and Causal Attribution is also validated with the significance values at p&gt; 0.000. Lastly, the proposed relationships between we proposed a significant relationship are also validated (p&gt; 0.000). Thus, we conclude that, today, when competition is increasing day by day, it is crucial to examine the consumer psychology that may highlight our several factors as done by the Item Response Theory. Further, we have discussed the study limitations and contributions accordingly.
The ongoing political crises and their impact on micro and small enterprises: a case study of Ethiopia

This study aimed to highlight the challenges and difficulties that face small business enterprises in inhibiting Etiopia.The author wants to reveal the impact of consecutive crises and conflicts on small businesses.To achieve this goal, a case study research in combination with a detailed review of the literature was done; the study population was 50 small business owners selected randomly from many small business owners.From the findings it was possible to summarize that the political environment has a significant and positive impact on small business success and the study also revealed that the war caused colossal material damage in the region, which has a total birr value of 288 billion.In addition, the most critical problems facing small businesses in Ethiopia's Amhara region are the difficulty of getting funds, poor infrastructure, corruption, difficulties in securing raw materials, lack of skilled employees, peace and security issues, challenges in getting licensing, and the absence of specific small business authority.Even though the political crisis is country wide and affects the business sector in the whole nation, the study is limited to Amhara region only due to certain constraints such as financial problems and time.In this regard, the findings of the study can be used to find a way to support small businesses to revive again.This study presents the effect of the current continuous war on small businesses in Ethiopia and will provide the basis for future researchers in this regard.
When mobile advertising is interesting: interaction of minors with ads and influencers’ sponsored content on social media

Social ads, along with apps, display and geolocation are the most used actions in the context of mobile marketing. Adolescents are also identified as the main target audience for mobile marketing because they are very active users, they have a sound command of technology and their role as prescribers, especially in social networks. For this reason, the interaction between advertising on social networks among minors (10-14 years) was studied, differentiating between those standard format ads and those commercial content published by influencers. To this avail, more than 2,400 ads were analyzed after monitoring for a week the commonly used device of a sample of minors from the Metropolitan Area of Santiago de Chile. Standard formats, despite being the most used, they hardly generated interaction, unlike commercial content by influencers, which achieved a higher level of interaction (visual and click interaction), although they were less employed. These results open the door to question the advertising management of a digital space which was created for dialogue such as social networks, in which advertisers prioritize selling products and services.
Application of Artificial Intelligence in Education. Problems and Opportunities for Sustainable Development

The article is devoted to the application of artificial intelligence in education and highlighting opportunities and problems in the context of sustainable development. The current state of introduction of artificial intelligence technologies in the educational process is analyzed. The technologies of artificial intelligence, which are most often used in the educational process are generalized into the following categories: cognitive services; virtual, mixed and augmented reality; Internet of things and peripheral computing; metacognitive scaffolding. The advantages of using the artificial intelligence in educational processes depending on the impact on its beneficiaries are generalized: students, teachers, students’ parents, heads of educational institutions, local governments. It is proved that the learning process can be significantly effective through the use of artificial intelligence technologies and the creation of individual training programs based on the use of analytical data obtained from the application of these technologies. It is found that the main advantage of using artificial intelligence in education is the individualization and personalization of the educational process. The benefits are pointed out which heads of educational institutions and local governments receive from the use of artificial intelligence technologies in the educational process in order to ensure sustainable development, including transparency and accountability of decisions, rational and energy efficient use of resources. Discussion issues of confidentiality and depersonalization of students’ data who are processed by artificial intelligence during the educational process are revealed; it is recommended to differentiate the responsibility for access, storage and use of personal and confidential data by educational institutions. Prospects for further scientific research are outlined - further personalization and individualization of the educational process.
Learning the travelling salesperson problem requires rethinking generalization

Abstract End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes. While state-of-the-art learning-driven approaches for TSP perform closely to classical solvers when trained on trivially small sizes, they are unable to generalize the learnt policy to larger instances at practical scales. This work presents an end-to-end neural combinatorial optimization pipeline that unifies several recent papers in order to identify the inductive biases, model architectures and learning algorithms that promote generalization to instances larger than those seen in training. Our controlled experiments provide the first principled investigation into such zero-shot generalization, revealing that extrapolating beyond training data requires rethinking the neural combinatorial optimization pipeline, from network layers and learning paradigms to evaluation protocols. Additionally, we analyze recent advances in deep learning for routing problems through the lens of our pipeline and provide new directions to stimulate future research.
Explaining Anomalies in Industrial Multivariate Time-series Data with the help of eXplainable AI

The large amount of data generated by industrial plants provides an excellent opportunity to use Machine Learning (ML) for a better understanding of plant behaviour. Thus, supporting plants operators in running their plants efficiently. An example of an anomaly detection method is to help plant operators detect if their plant is still running normally or if curative actions are needed. Users of ML-based solutions often complain of a challenge called lack of interpretability, i.e., the degree to which one can understand the model&#x0027;s outcome. To address the need for better interpretability of ML models, the re-search field eXplainable Artificial Intelligence (XAI) has recently received increasing attention in the industrial domain. This paper performs a survey and investigates different XAI techniques that can be applied for detecting anomalies in industrial plant assets. The paper focuses on multivariate time-series data because a) it is the most predominant type of data in industrial systems and b) all the available XAI techniques have applications for various data types such as images, tabular, or textual data. However, these techniques are generally not well suited to explain anomalies in multivariate time-series. To solve this problem, we build an anomaly detection method for the multivariate time-series data generated by the industrial simulators using auto-encoders. Based on feature attribution, examples, and trees, seven different XAI techniques are ideated, developed, and discussed. Out of the seven XAI techniques, a SHAP based explainer, called DTFS, correctly identified the root cause of the anomaly with an accuracy of 86&#x0025; and took 1.53 seconds to explain our benchmark system.
BOFRF: A Novel Boosting-Based Federated Random Forest Algorithm on Horizontally Partitioned Data

The application of federated learning on ensemble methods is a common practice with the goal of increasing the predictive power of local models. However, although existing federated solutions utilizing ensemble methods can achieve this when the datasets of sites are balanced and of good quality, i.e., the local models are already above a certain accuracy threshold, they usually fail to provide the same level of improvement to the models of sites that have an unsuccessful classifier because of their poor quality or imbalanced data. To address this challenge, we propose a novel federated ensemble classification algorithm for horizontally partitioned data, namely Boosting-based Federated Random Forest (BOFRF), which not only increases the predictive power of all participating sites, but also provides significantly high improvement on the predictive power of sites having unsuccessful local models. We implement a federated version of random forest, which is a well-known bagging algorithm, by adapting the idea of boosting to it. We introduce a novel aggregation and weight calculation methodology that assigns weights to local classifiers based on their classification performance at each site without increasing the communication or computation cost. We evaluate the performance of our proposed algorithm in different federated environments that we set up by using four healthcare datasets. The empirical results show that BOFRF improves the predictive power of local random forest models in all cases. The advantage of BOFRF is that the level of improvement it provides for sites having unsuccessful local models is significantly high unlike existing solutions.
Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series

This work exposes an innovative clustering method of solar radiation stations, using static and dynamic parameters, based on multi-criteria analysis for future objectives to make the forecasting of the solar resource easier. The innovation relies on a characterization of solar irradiation from both a quantitative point of view and a qualitative one (variability of the intermittent sources). Each of the 76 Spanish stations studied is firstly characterized by static parameters of solar radiation distributions (mean, standard deviation, skewness, and kurtosis) and then by dynamic ones (Hurst exponent and forecastability coefficient, which is a new concept to characterize the “difficulty” to predict the solar radiation intermittence) that are rarely used, or even never used previously, in such a study. A redundancy analysis shows that, among all the explanatory variables used, three are essential and sufficient to characterize the solar irradiation behavior of each site; thus, in accordance with the principle of parsimony, only the mean and the two dynamic parameters are used. Four clustering methods were applied to identify geographical areas with similar solar irradiation characteristics at a half-an-hour time step: hierarchical, k-means, k-medoids, and spectral cluster. The achieved clusters are compared with each other and with an updated Köppen–Geiger climate classification. The relationship between clusters is analyzed according to the Rand and Jaccard Indexes. For both cases (five and three classes), the hierarchical clustering algorithm is the closest to the Köppen classification. An evaluation of the clustering algorithms’ performance shows no interest in implementing k-means and spectral clustering simultaneously since the results are similar by more than 90% for three and five classes. The recommendations for operating a solar radiation clustering are to use k-means or hierarchical clustering based on mean, Hurst exponent, and forecastability parameters.
A relation-centric algorithm for solving text-diagram function problems

A tutorable algorithm for solving text-diagram function (TDF) problems is an essential technology for building the humanoid tutorial service since ”function” is a core portion of mathematics. Solving TDF problems encounters challenges in understanding diagrams, representing the functions of being compound objects, and solving the mixture group of functions and universal relations. To address these challenges, this paper proposes a relation-centric algorithm, leveraging on making breakthrough in handling function, understanding diagram, and generating tutorable solution. The proposed algorithm comprises two phases: problem understanding and symbolic solver. In the problem understanding, it proposes a S2P (Syntax Semantics with Period) model method of acquiring relations from text and a L2 (Line Segment with Labels Pattern) model method of acquiring relations from diagrams. To get the problem fully understood, this phase acquires both universal relations and period relations. In the symbolic solver, the function is first built from the acquired relations. Then an equation-function interaction method is created to solve a mixture system of relations and functions. The developed algorithm is the first one for solving text-diagram function problems. Experimental results show that the proposed algorithm not only has high accuracies of 74.5% on Math23KtoF and 80.8% on TnD1K datasets, but also can produce the tutorable solutions of TDF problems.
A GMM-DTW-Based Locomotion Mode Recognition Method in Lower Limb Exoskeleton

Exoskeleton robots can provide support and protection to the human body as well as be able to perform a series of tasks under control. Locomotion mode recognition is an essential prerequisite for assisted control of lower limb exoskeleton robots. A system is designed to be applied to a variety of individuals with quick and accurate identification. In this article, we propose a novel real-time locomotion mode recognition algorithm based on Gaussian mixture model (GMM) features aligned by dynamic time warping (DTW). The spatial distribution of the sensor data is represented by the GMM features, which emphasize the coupling between joints. This peculiarity promotes the distinction of similarity judgment and shows superior generalization performance. Furthermore, we expand the study of the warping path. We further validate the reasonableness of the DTW algorithm for feature matching to improve the recognition rate. The data of several inertial measurement units (IMUs) and pressure sensors were collected by 17 subjects in three terrains, including level ground, stairs, and ramp. We define the five locomotion modes as walking on level ground, stair ascent (SA), stair descent, ramp ascent, and ramp descent. Compared with the algorithms in previous papers, the GMM-DTW algorithm can achieve substantial recognition rates and good generalization performance with low latency. This work establishes the groundwork for the soft control of exoskeleton robots in the future.
Analysis of Evolutionary Diversity Optimization for Permutation Problems

Generating diverse populations of high quality solutions has gained interest as a promising extension to the traditional optimization tasks. This work contributes to this line of research with an investigation on evolutionary diversity optimization for three of the most well-studied permutation problems, namely the Traveling Salesperson Problem (TSP), both symmetric and asymmetric variants, and Quadratic Assignment Problem (QAP). It includes an analysis of the worst-case performance of a simple mutation-only evolutionary algorithm with different mutation operators, using an established diversity measure. Theoretical results show many mutation operators for these problems guarantee convergence to maximally diverse populations of sufficiently small size within cubic to quartic expected run-time. On the other hand, the result on QAP suggests that strong mutations give poor worst-case performance, as mutation strength contributes exponentially to the expected run-time. Additionally, experiments are carried out on QAPLIB and synthetic instances in unconstrained and constrained settings, and reveal much more optimistic practical performances, while corroborating the theoretical finding regarding mutation strength. These results should serve as a baseline for future studies.
Supervised Machine Learning Algorithms to Predict Provenance of Archaeological Pottery Fragments

Code and data sharing are crucial practices to advance toward sustainable archaeology. This article explores the performance of supervised machine learning classification methods for provenancing archaeological pottery through the use of freeware R code in the form of R Markdown files. An illustrative example was used to show all the steps of the new methodology, starting from the requirements to its implementation, the verification of its classification capability and finally, the production of cluster predictions. The example confirms that supervised methods are able to distinguish classes with similar features, and provenancing is achievable. The provided code contains self-explanatory notes to guide the users through the classification algorithms. Archaeometrists without previous knowledge of R should be able to apply the novel methodology to similar well-constrained classification problems. Experienced users could fully exploit the code to set up different combinations of parameters, and they could further develop it by adding other classification algorithms to suit the requirements of diverse classification strategies.
Authenticated Wireless Links between a Drone and Sensors Using a Blockchain: Case of Smart Farming

Agriculture is confronted with several significant difficulties, such as rising air temperatures and population growth, causing the implementation of smart farming operations as an optimum solution. This research aims to contribute to the growing knowledge of the potential role of blockchain technology in promoting the concept of smart farming by enhancing the efficiency of farming operations by boosting agricultural production, lowering environmental impact, and automating the work of farmers. It proposes a secure blockchain-based framework to establish trust among smart farming users. The framework utilizes asymmetric key exchange mechanism using an ECC authentication algorithm and SHA-256 hash function cryptography to secure communication between sensors and drones in the farm field. The SHA-256 hashing function ensures data integrity as attempts to tamper with data result in a different hash value, breaking the chain of blocks. To demonstrate the feasibility of the proposed framework, a proof-of-concept implementation was developed on the Ethereum blockchain, in which smart contracts were used to model the framework operations. The proof of concept’s performance was examined using Hyperledger Caliper for latency, throughput, and transaction success rate. The findings clearly indicate that blockchain technology can provide an efficient and scalable mechanism to advance smart farming and address some of the barriers that inhibit smart farming, particularly regarding to data integrity and availability.
Better Decision Heuristics in CDCL through Local Search and Target Phases

On practical applications, state-of-the-art SAT solvers dominantly use the conflict-driven clause learning (CDCL) paradigm. An alternative for satisfiable instances is local search solvers, which is more successful on random and hard combinatorial instances. Although there have been attempts to combine these methods in one framework, a tight integration which improves the state of the art on a broad set of application instances has been missing. We present a combination of techniques that achieves such an improvement. Our first contribution is to maximize in a local search fashion the assignment trail in CDCL, by sticking to and extending promising assignments via a technique called target phases. Second, we relax the CDCL framework by again extending promising branches to complete assignments while ignoring conflicts. These assignments are then used as starting point of local search which tries to find improved assignments with fewer unsatisfied clauses. Third, these improved assignments are imported back to the CDCL loop where they are used to determine the value assigned to decision variables. Finally, the conflict frequency of variables in local search can be exploited during variable selection in branching heuristics of CDCL. We implemented these techniques to improve three representative CDCL solvers (Glucose, MapleLcm DistChronoBT, and Kissat). Experiments on benchmarks from the main tracks of the last three SAT Competitions from 2019 to 2021 and an additional benchmark set from spectrum allocation show that the techniques bring significant improvements, particularly and not surprisingly, on satisfiable real-world application instances. We claim that these techniques were essential to the large increase in performance witnessed in the SAT Competition 2020 where Kissat and Relaxed LcmdCbDl NewTech were leading the field followed by CryptoMiniSAT-Ccnr, which also incorporated similar ideas.