Miracle-dz/UniFAR-FTUs huggingface dataset:
Who Wants to Use an Augmented Reality Shopping Assistant Application?

Brick-and-mortar retailers need to stay competitive to the convenience provided by online channels. Technologies, such as personalized shopping assistants on smartphones can empower customers in-store towards a similar experience as in an online scenario. For instance, an augmented reality shopping assistance application with explainable recommendations (XARSAA) can mimic the behavior of recommender systems in personalizing offers to consumers in physical shops. However, before deploying such technologies, it is essential that retailers get to know the demographics of their customer base. Existing literature rarely addresses the influence of customers demographics towards XARSAA technologies. Therefore, we follow a design science approach, and develop an instantiation of a XARSAA artifact, which is artificially evaluated through a controlled online user experiment with 315 participants. Results illustrate multiple demographics which influence customers attitude towards an augmented reality shopping assistant application in brick-and-mortar stores. Additionally, we provide insights into the design of such technology to guide researchers in its implementation.
Batch Contrastive Regularization for Deep Neural Network

: As neural network becomes deeper, it becomes more capable of generating more powerful representation for a wide variety of tasks. However, deep neural network has a large number of parameters and easy to overfit the training samples. In this paper, we present a new regularization technique, called batch contrastive regularization. Regularization is performed by comparing samples collectively via contrastive loss which encourages intra-class compactness and inter-class separability in an embedded Euclidean space. To facilitate learning of embedding features for contrastive loss, a two-headed neural network architecture is used to decouple regularization classification. During inference, the regularization head is discarded and the network operates like any conventional classification network. We also introduce bag sampling to ensure sufficient positive samples for the classes in each batch. The performance of the proposed architecture is evaluated on CIFAR-10 and CIFAR-100 databases. Our experiments show that features regularized by contrastive loss has strong generalization performance, yielding over 8% improvement on ResNet50 for CIFAR-100 when trained from scratch.
Mining M-Grams by a Granular Computing Approach for Text Classification

Text mining and text classification are gaining more and more importance in AI related research fields. Researchers are particularly focused on classification systems, based on structured data (such as sequences or graphs), facing the challenge of synthesizing interpretable models, exploiting gray-box approaches. In this paper, a novel gray-box text classifier is presented. Documents to be classified are split into their constituent words, or tokens. Groups of frequent m tokens (or m-grams) are suitably mined adopting the Granular Computing framework. By fastText algorithm, each token is encoded in a real-valued vector and a custom-based dissimilarity measure, grounded on the Edit family, is designed specifically to deal with m-grams. Through a clustering procedure the most representative m-grams, pertaining the corpus of documents, are extrapolated and arranged into a Symbolic Histogram representation. The latter allows embedding documents in a wellsuited real-valued space in which a standard classifier, such as SVM, can safety operate. Along with the classification procedure, an Evolutionary Algorithm is in charge of performing features selection, which is able to select most relevant symbols – m-grams – for each class. This study shows how symbols can be fruitfully interpreted, allowing an interesting knowledge discovery procedure, in lights with the new requirements of modern explainable AI systems. The effectiveness of the proposed algorithm has been proved through a set of experiments on paper abstracts classification and SMS spam detection.
Social Media as an Auxiliary News Source

: Obtaining a balanced view of an issue can be a time consuming and arduous task. A reader using only one source of information is in danger of being exposed to an author’s particular slant on a given issue. For many events, social media provides a range of expressions and views on a topic. In this paper we explore the feasibility of mining alternative data and information-sources to better inform users on the issues associated with a topic. For the purpose of gauging the feasibility of augmenting available content with related information, a text similarity metric is adopted to measure relevance of the auxiliary text. The developed system extracts related content from two distinct social media sources, Reddit and Twitter. The results are evaluated through conducting a user survey on the relevance of the returned results. A two tailed Wilcoxon test is applied to evaluate the relevance of addition information snippets. Our results show that by partaking the experiment a users’ level of awareness is augmented, second, that it is possible to better inform the user with information extract from a online microblogging sites.
Towards a Taxonomy for Buy Online Pick up in Store Service

: Following the best practices in retail, along with home delivery, many companies also offer customers the option to buy online and pick up in store (BOPIS). The paper provides insights into the essential components of the BOPIS service conceptualized in the form of a taxonomy together with literature review on omnichannel retail and channel integration quality dimensions. The data were collected through semi-structured interviews and focus group discussion in the scope of case study with an Irish retailer. The study represents a valuable contribution into the existing body of knowledge on omnichannel retail.
Airborne Visual Tracking of UAVs with a Pan-Tilt-Zoom Camera

: The visual detection and tracking of UAVs using a Pan-Tilt-Zoom (PTZ) camera attached to another aerial platform is the scope of this article. The long-term tracker is performing image background subtraction using visual homography and is used to initialize a short term tracker that works in parallel to enhance the tracking for various motions. The moving UAV is detected using optical ﬂow concepts and its bounding box encapsulates its detected features. A Kalman predictor provides a robust smooth tracking of the bounding box in the temporary absence of a detected UAV. The camera pans and tilts so as the tracked UAV is centered within its Field-of-View and zooms in order to expand the UAV’s view. Experimental results are offered using an evader-tracker UAV-group to validate the presented tracking algorithm.
Web Accessibility of Palestinian Universities: Can We Access Higher Education Information during COVID-19?

University web portals are considered one of the main access gateways for universities. Accessibility of university online services is a major issue for undergraduate and graduate students with disabilities. Online registration makes people with disabilities more independent to register courses, add, drop courses, or attend courses independently. Yet, many people with disabilities in Palestine face major challenges when using university websites. In order to understand the issues that face people with disabilities when they use websites of Palestinian universities, this study evaluates the accessibility of the home pages of these universities during COVID-19. In order to evaluate partially the accessibility of Palestinian universities during COVID-19, we apply automatic evaluation on all homepages of eighteen Palestinian universities. The most violated guideline is empty link which is related to success criterion 2.4. 4 Link Purpose. The second highest violated error is linked image missing alternative text which is related to the success criterion 1.1. 1 Non-text Content. The obtained results show that all the universities websites are not conforming to Web Content Accessibility Guidelines (WCAG) 2.0 level A. Copyright © 2020 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.
Saudi Stock Market Sentiment Analysis using Twitter Data

Sentiment analysis in the finance domain is widely applied by investors and researchers, but most of the work is conducted for English text. In this work, we present a framework to analyze and visualize the sentiments of Arabic tweets related to the Saudi stock market using machine learning methods. For the purpose of training and prediction, Twitter API was used for collecting off-line data, and Apache Kafka was used for real-time streaming tweets. Experiments were conducted using five machine learning classifiers with different feature extraction methods, including word embedding (word2vec) and the traditional BoW methods. The highest accuracy for the sentiment classification of Arabic tweets was 79.08%. This result was achieved with the SVM classifier combined with the TF-IDF feature extraction method. At the end, the predicted sentiments of the tweets using the outperforming classifier were visualized by several techniques. We developed a website to visualize the off-line and streaming tweets in various ways: by sentiments, by stock sectors, and by frequent
The Estimation of Occupants' Emotions in Connected and Automated Vehicles

One critical factor of success and user acceptance in connected automated vehicles (CAVs) is trust in technology, being the main obstacle that remains from a customer’s perspective. Trust in automated systems is based on feelings of safety and acceptance, being the emotional process the most influential aspect. One of the main ambitions of SUaaVE project (SUpporting acceptance of automated VEhicle) is to develop an emotional model to understand the passenger’s state during the trip (in Real-Time), based on body biometrics, allowing to adapt the vehicle features to enhance the in-vehicle user experience, while increasing trust, and therefore acceptance. This research addressed a initial experiments to identify changes in the emotional state of the occupants in different driving experiences (in a driving simulator and in real conditions) by measuring and analysing the physiological signals of the participants, serving as a basis for the generation of the emotional model. The results showed that it is possible to estimate the level of Arousal and Valence of the participants during the journey from the analysis of ECG, EMG and GSR signals. These results have positive implications for the automobile industry facilitating a better integration of human factor in the deployment of
Interaction Design Issues in the Development and Assessment of Stress Management Apps: A Scoping Literature Review and Analysis

A number of smartphone apps have been developed in recent years to help people cope with stress and promote mental well-being. Such apps have attracted significant attention in current research. However, interaction design issues, such as usability and user experience, have so far been relatively unexplored. This paper presents a meta-analysis of studies of mobile apps for stress management and mental well-being, specifically focusing on interaction design issues. Through a scoping literature search we selected the total of 46 articles, published in the last decade, for qualitative in-depth analysis. The analysis reveals that the main interaction design issues addressed in the papers are ease of use, user engagement, and privacy. Key opportunities and challenges for future work are discussed.
Single-phase Five-level Boost Inverter for Stand-alone PV Applications

This paper proposes a single-phase five-level boost inverter (SFBI) with twelve switches and a flying capacitor (FC) to boost the output voltage. SFBI can produce peak output voltage up to double the DC-input voltage. Hence, a dc-dc boost converter may not require in a stand-alone PV system. SFBI has advantages such as low FC requirement, equal voltage stress and sensorless operation. Also, it does not require any of the bulky inductors/capacitors, multiple voltage sources/transformers. A sophisticated capacitor voltage balancing algorithm is not required to produce balanced five-level boost voltage due to self-balancing of FC voltage in every switching cycle. SFBI is explained in detail using various operating modes and compared with other multilevel inverters to elevate its advantages. The SFBI is implemented in PSCAD/EMTDC and steady-state results are presented to verify its operation.
A Curious Case of Meme Detection: An Investigative Study

: In recent times internet ”memes” have led the social media-based communications from the front. Speciﬁ-cally, the more viral memes tend to be, higher is the likelihood of them leading to a social movement, that has signiﬁcant polarizing potential. Online hate-speeches are typically studied from a textual perspective, whereas memes being a combination of images and texts have been a very recent challenge that is beginning to be acknowledged. Our paper primarily focuses on the meme vs. non-meme classiﬁcation, to address the crucial primary step towards studying memes. To characterize a meme, metric based empirical analysis is performed, and a system is built for classifying images as meme/non-meme using visual and textual features. An exhaustive set of experimentation to evaluate conventional image processing techniques towards extracting low-level descriptors from an image is performed, which suggests the effectiveness of Haar wavelet transform based feature extraction. Further study establishes the importance of both graphic and linguistic content within a meme, towards their characterization and detection. Along-with the deduction of an optimal F-1 score for meme/non-meme classiﬁcation, we also highlight the efﬁciency induced by our proposed approach, in comparison with other popular techniques. The insights gained in understanding the nature of memes through our systematic approach, could possibly help detect memes and ﬂag the ones that are potentially disruptive in nature.
Can I Just Pass by? Testing Design Principles for Industrial Transport Robots

Currently, two types of industrial collaborative robots are emerging: collaborative robot arms and transport robots. For such robots to cooperate with humans, intuitive interaction is required. They have to display behaviour that is predictable and legible and elicits positive emotions. In this paper we examine the application of two general design principles to the design of transport robots: (1) use analogies from nature, and (2) adhere to social rules. Both are expected to result in better user-experience and understanding of the behaviour and intentions of a transport robot. The current study tests the effects of using 1a) a curved path and 1b) average walking speed in combination with deceleration upon nearing the human, and 2a) swerving to the right and 2b) respecting personal space. The principles tested in this study show positive effects for user experience and legibility. However, predictability is not improved. Options for additional adjustments, such as the use of communicative lights, are discussed.
Automated Tag Enrichment by Semantically Related Trends

The technological evolution of modern content sharing applications led to unbridled increase of video content creation and with it multimedia streaming, content sharing and video advertising. Managing huge volumes of video data becomes critical for various applications such as video browsing, retrieval, and recommendation. In such a context, video tagging, the task of assigning meaningful human-friendly words (i.e., tags) to a video, has become an important pillar for both academia and companies alike. Indeed, tags may be able to effectively summarize the content of the video, and, in turn, attract users and advertisers interests. As manual tags are usually noisy, biased and incomplete, many efforts have been recently made in devising automated video tagging approaches. However, video search engines handle a massive amount of natural language queries every second. Therefore, a key aspect in video tagging consists of proposing tags not only related to video contents, but also popular amongst users searches. In this paper, we propose a novel video tagging approach, in which the proposed tags are generated by identifying semantically related popular search queries (i.e., trends). Experiments demonstrate the viability of our proposal.
Value of Collaboration: The Opportunities and Limits of Shared Value Creation in the Collaborative Practices of Mining

This study examines the value of collaboration in the branch of mining and asks what kind of value can be expected from the collaborative practices of mining, and what are the opportunities and limitations of shared value creation. The research was conducted as an interview study (n=17) in the Finnish municipality of Sodankylä that has been a forerunner in Finland in developing collaborative practices with the mining industry. The study constructs a value typology, which illustrates the expected value of the collaborative practices in mining, and a framework to evaluate the value of collaboration. The results show that the value of collaboration consists of different dimensions, and that the value may be process, outputs and outcomes related, or relate to the productivity of value creation. In addition, the results reveal that there are two different perceptions of the potential value of collaboration: win-win and trade-off. In conclusion, it appears that the collaboration process itself can create value for all, but the collaboration cannot meet everyone’s value expectations related to outputs and outcomes. Therefore, there are opportunities for shared value creation in the collaborative practices of mining, but it is not possible meet everyone's value expectations.
Developing Music Harmony Awareness in Young Students through an Augmented Reality Approach

: This paper presents AREmbody , an augmented-reality mobile application for the development of tonal harmony awareness. Continuing from previous prototypes based on full body and desktop interaction, AREmbody beneﬁts from a very simple portable setup which allows physical interaction and supports the activity of one or more users. The application combines in a single mobile device a video processor, a media player and a movement tracker, opening the way to the design of harmonic games with challenges and recordable scores. Thus the application not only fosters music education activities in the classroom, but also extends them outside the school times and places, promoting educational practices in informal and private contexts.
Estimating Personalization using Topical User Profile

: Exploring the effect of personalization on different queries can improve the ranking result. There is a need for a mechanism to estimate the potential for personalization for queries. Previous methods to estimate the potential for personalization such as click entropy and topic entropy are based on the prior clicked document for query or query history. They have limitations like unavailability of the prior clicked data for new/unseen queries or queries without history. To alleviate the problem, we provide a solution for the queries regardless of query history. In this paper, we present a new metric using the topic distribution of user documents in the topical user proﬁle, to estimate the potential for personalization for all queries. Using the proposed metric, we can achieve more performance for queries with history and solve the cold start problem of queries without history. To improve personalized search, we provide a personalization ranking model by combining personalized and non-personalized topic models where the proposed metric is used to estimate personalization. The result reveals that the personalization ranking model using the proposed metric improves the Mean Reciprocal Rank and the Normalized Discounted Cumulative Gain by 5% and 4% respectively.
Conversation Management in Task-oriented Turkish Dialogue Agents with Dialogue Act Classification

We study the problem of dialogue act classification to be used in conversation management of goal-oriented dialogue systems. Online chat behavior in human-machine dialogue systems differs from human-human spoken conversations. To this end, we develop 9 dialogue act classes by observing real-life human conversations from a banking domain Turkish dialogue agent. We then propose a dialogue policy based on these classes to correctly direct the users to their goals in a chatbot-human support hybrid dialogue system. To train a dialogue act classifier, we annotate a corpus of human-machine dialogues consisting of 426 conversations and 5020 sentences. Using the annotated corpus, we train a self-attentive bi-directional LSTM dialogue act classifier, which achieves 0.90 weighted F1-score on a sentence level classification performance. We deploy the trained model in the conversation manager to maintain the designed dialogue policy.
Grammar-based Fuzzy Pattern Trees for Classification Problems

: This paper introduces a novel approach to induce Fuzzy Pattern Trees (FPT) using Grammatical Evolution (GE), FGE, and applies to a set of benchmark classiﬁcation problems. While conventionally a set of FPTs are needed for classiﬁers, one for each class, FGE needs just a single tree. This is the case for both binary and multi-classiﬁcation problems. Experimental results show that FGE achieves competitive and frequently better results against state of the art FPT related methods, such as FPTs evolved using Cartesian Genetic Programming (FCGP), on a set of benchmark problems. While FCGP produces smaller trees, FGE reaches a better classiﬁcation performance. FGE also beneﬁts from a reduction in the number of necessary user-selectable parameters. Furthermore, in order to tackle bloat or solutions growing too large, another version of FGE using parsimony pressure was tested. The experimental results show that FGE with this addition is able to produce smaller trees than those using FCGP, frequently without compromising the classiﬁcation performance.
Systematic Crowd Mobility Management to Avoid Catastrophic Disasters of Human Stampedes: The Case of Mina Hajj

Every year, the Holy city of Makkah hosts more than two million Muslims for the pilgrimage ’Hajj’. During this event, a set of activities should be done by pilgrims. One of the rituals performed is throwing stones in al-Jamarat area of Mina at pillars target. The throwing Jamarat is a paradigm of stoning the devil, as done by the prophet Abraham. All pilgrims must reach the site at different times in specific hours of particular days. However, this stoning ritual represents the most bottleneck ceremony where the area becomes packed at routes and around pillars structure. The problem is that, unfortunately, humans cannot be controlled while they are in a crowd. That takes this place a high safety hazard by increasing accidents’ probably which cause death of many Muslims last few years. Inspired from this event, this paper presents a new human mobility models which simulates stoning in Hajj by adopting a new design to solve the safety problems. This suggests control huge crowds by conducting the relationship between the Jamarat basin, pilgrims, stoning performance, and the impact of organizing the pilgrims in three rows around the Jamarat basin. This experiment estimate the throwing time at each pillar in order to manage the temporal and spatial human movement inside the Jamarat area. The simulation outcomes encourage adopting the new design to prevent crowd panic and disasters.