Reviewerly/exHarmony huggingface dataset:
Performance evaluation for the design of a hybrid cloud based distance synchronous and asynchronous learning architecture

The COVID-19 emergency suddenly obliged schools and universities around the world to deliver on-line lectures and services. While the urgency of response resulted in a fast and massive adoption of standard, public on-line platforms, generally owned by big players in the digital services market, this does not sufficiently take into account privacy-related and security-related issues and potential legal problems about the legitimate exploitation of the intellectual rights about contents. However, the experience brought to attention a vast set of issues, which have been addressed by implementing these services by means of private platforms. This work presents a modeling and evaluation framework, defined on a set of high-level, management-oriented parameters and based on a Vectorial Auto Regressive Fractional (Integrated) Moving Average based approach, to support the design of distance learning architectures. The purpose of this framework is to help decision makers to evaluate the requirements and the costs of hybrid cloud technology solutions. Furthermore, it aims at providing a coarse grain reference organization integrating low-cost, long-term storage management services to implement a viable and accessible history feature for all materials. The proposed solution has been designed bearing in mind the ecosystem of Italian universities. A realistic case study has been shaped on the needs of an important, generalist, polycentric Italian university, where some of the authors of this paper work.
RETRACTED ARTICLE: Research on intelligent classroom attendance management based on feature recognition

The regular attendance method is affected by many factors in the classroom intelligent attendance, which leads to lower attendance efficiency. In order to improve the efficiency of intelligent classroom attendance management, this study uses face recognition technology as a technology to study the attendance mode of face feature recognition. In the study, we used the multi-task cascade convolutional neural network to identify the face features and used the MTCNN face detection algorithm frame by frame for the sampled frame images and normalized the single face region image cropping to the face recognition model input size. Thereafter, the normalized single face region image is taken for feature extraction. In addition, this paper analyzes the effectiveness of the algorithm by design comparison experiments. The research shows that the algorithm proposed in this study has certain practical effects and can provide theoretical reference for subsequent related research.
BIM-Aided Scanning Path Planning for Autonomous Surveillance UAVs with LiDAR

An Unmanned Aerial Vehicle (UAV), equipped with a Light Detection And Ranging (LiDAR) scanner, can collect high-accuracy point cloud data of facilities in cluttered indoor environment.Recent developments in aerial robotics have demonstrated navigation through designated waypoints, yet little has been investigated on the trajectory to complete a full scan of the environment.This study develops an automated approach to integrate scan planning and trajectory generation of a LiDAR-carrying UAV.The proposed approach converts an as-designed Building Information Model (BIM) into an occupancy map, where a set of waypoints are generated with a greedy algorithm.The shortest collision-free path to traverse all the waypoints is computed with the A* algorithm and Genetic Algorithm (GA).After that, the straight-line segments are transformed into a minimum snap trajectory formed of piecewise polynomials.The planned trajectory is validated with both a MATLAB numerical solver and a Hardware-In-the-Loop (HIL) simulation in the Unreal Engine 4.
Relational reasoning and generalization using nonsymbolic neural networks.

The notion of equality (identity) is simple and ubiquitous, making it a key case study for broader questions about the representations supporting abstract relational reasoning. Previous work suggested that neural networks were not suitable models of human relational reasoning because they could not represent mathematically identity, the most basic form of equality. We revisit this question. In our experiments, we assess out-of-sample generalization of equality using both arbitrary representations and representations that have been pretrained on separate tasks to imbue them with structure. We find neural networks are able to learn (a) basic equality (mathematical identity), (b) sequential equality problems (learning ABA-patterned sequences) with only positive training instances, and (c) a complex, hierarchical equality problem with only basic equality training instances ("zero-shot" generalization). In the two latter cases, our models perform tasks proposed in previous work to demarcate human-unique symbolic abilities. These results suggest that essential aspects of symbolic reasoning can emerge from data-driven, nonsymbolic learning processes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Research on Multimodal Emotion Analysis Algorithm Based on Deep Learning

Abstract In recent years, more and more people are keen to express their feelings and opinions in the form of pictures and texts on social media at the same time, which makes the multimodal data with pictures and texts as the main content growing. Compared with monomodal data, multimodal data contains more information and can reveal the real feelings of users. The analysis of the emotion of these massive multimodal data is helpful to better understand people’s attitudes and viewpoints and has a wide range of application scenarios. In order to solve the problem of information redundancy in multimodal emotion classification tasks, based on the tensor fusion scheme, a multimodal emotion analysis method based on attention neural network is proposed. This method constructs a text feature extraction model and an image feature extraction model based on attention neural network, which highlights the key areas of image emotional information and the words containing emotional information, which makes the expression of single-modal features more concise and accurate. The tensor product of each mode is regarded as the joint feature expression of multimodal data, and the redundant information of joint features is eliminated by principal component analysis, and then the emotion category of multimodal data is obtained by support vector machine. The proposed model is evaluated on two real Twitter image data sets. The experimental results show that, compared with other emotion classification models, this method has a great improvement in classification accuracy, recall rate, F1 index and accuracy.
List-wise learning-to-rank with convolutional neural networks for person re-identification

In this paper, we present a novel machine learning-based image ranking approach using Convolutional Neural Networks (CNN). Our proposed method relies on a similarity metric learning algorithm operating on lists of image examples and a loss function taking into account the ranking in these lists with respect to different query images. This comprises two major contributions: (1) Rank lists instead of image pairs or triplets are used for training, thus integrating more explicitly the order of similarity and relations between sets of images. (2) A weighting is introduced in the loss function based on two evaluation measures: the mean average precision and the rank 1 score. We evaluated our approach on two different computer vision applications that are commonly formulated as ranking problems: person re-identification and image retrieval with several public benchmarks and showed that our new loss function outperforms other common functions and that our method achieves state-of-the-art performance compared to existing approaches from the literature.
National And Global Issues In Critical Thinking Development At Regional Studies

The article presents the authors" experience of effective critical thinking skills development among students specializing in regional studies.The authors explain why it is extremely important to equip university students with such skills and discuss different tasks and activities that can be used in class with bachelor and master students.Methods of theoretical (analysis of scientific and methodological literature) and practical analysis of critical thinking development as well as methods of observation and analytical systematization of education process are used in the article.The authors come to the conclusion that critical thinking development is an obligatory condition for the effective training of specialists in regional studies.This is also due to the fact that the list of competences, such specialists should acquire, includes the ability to analyze and interpret facts, events and statements.It is necessary to note that studying the ways of critical thinking development is a burning issue in various fields of humanities.High school lecturers possess a wide range of opportunities of effective critical thinking skills development while teaching foreign languages and other disciplines connected with regional studies.However, the usage of the tasks that are designed to improve critical thinking skills requires a profound theoretical background, great erudition and an ability to keep up discussions with students with different levels of competence.
The Impact of Social CRM Capabilities and Customer Engagement on the Firm Performance: Mediating Role of Social Media Usage

This research aims to analyze the impact of social customer relationship management (CRM) capabilities and customer engagement on the firm performance in the textile industry of Pakistan. Another purpose of the article is to examine the mediating role of social media usage on the nexus of social CRM capabilities, customer relationship management and firm performance. The customer relation officers of textile industry situated in the Punjab province of Pakistan are the respondents and data were gather by using questionnaires and PLS-SEM was employed for hypotheses testing. The findings revealed that social CRM capabilities and customer engagement have positive nexus with the firm performance while social media usage positively mediates the relationship among the social CRM capabilities, customer relationship management and firm performance. These findings provide the guidance to the regulations making authorities that they should enhance their emphasis on the CRM capabilities, employee engagement and social media usage that enhance the firm performance.
Practical Application and Evaluation of Atomic Swaps for Blockchain-based Recommender Systems

Recommender Systems are very popular tools within the online community, suggesting to their users a big variety of items like products, videos, music and locations to visit. Moreover, users actively populate these systems sending and reading opinions under the form of reviews, and potentially obtaining a reward for their activities. However, such systems typically rely on a central authority that acts as a trusted party having total control over the system. Decentralized Recommender Systems have been proposed to solve such issue distributing the control and responsibility on the hands of their users, but leading to risks in case of disputes or misbehaviour. Based on a general architecture of a Decentralized Recommender System, in this paper we identify the potential unfair exchanges that may rise during the activity between two users, and we propose a solution based on the concept of atomic swaps inherited from the blockchain technology. Finally, we provide an attack model to show that the proposed solution creates fair processes.
Speaking to machines: motivating speaking through oral interaction with intelligent assistants

What is it? Daring to voice new sounds, words, and phrases is an essential part of learning to speak a language. However, getting students, particularly in mono-lingual classes, to try to speak a foreign language can be a significant challenge. Voice interaction assistants, such as Siri, Alexa, or Google Assistant, offer new opportunities to create meaningful, fun tasks for language learning that require accurate spoken production. Designing good tasks requires an understanding of the learning context and needs as well as the interactional opportunities, constraints, and risks associated with any particular technology.
Differential fault location identification by machine learning

As the fault-based attacks are becoming a more pertinent threat in today's era of edge computing/internet-of-things, there is a need to streamline the existing tools for better accuracy and ease of use, so that we can gauge the attacker's power and a proper countermeasure can be devised in the long run. In this regard, we propose a machine learning (ML) assisted tool that can be used in the context of a differential fault attack. In particular, finding the exact fault location by analysing the output difference (typically the XOR of the nonfaulty and the faulty ciphertexts) is somewhat nontrivial. During the literature survey, we notice that the Pearson's correlation coefficient dominantly is used for this purpose, and has almost become the defacto standard. While this method can yield good accuracy for certain cases, we argue that an ML-based method is more powerful in all the situations we experiment with. We substantiate our claim by showing the relative performances (we choose the commonly used multilayer perceptron as our ML tool) with two variants of Grain-128a (a stream cipher, and a stream cipher with authentication), the lightweight stream cipher LIZARD and the lightweight block cipher SIMON-32 (where the faults are injected at the fifth last rounds). Our results demonstrate that a common ML tool can outperform the correlation with the same training/testing data. We believe that our work extends the state-of-the-art by showing how traditional cryptographic methods can be replaced by a more powerful ML tool.
P2PCF: A collaborative filtering based recommender system for peer to peer social networks

The recent privacy incidents reported in major media about global social networks raised real public concerns about centralized architectures. P2P social networks constitute an interesting paradigm to give back users control over their data and relations. While basic social network functionalities such as commenting, following, sharing, and publishing content are widely available, more advanced features related to information retrieval and recommendation are still challenging. This is due to the absence of a central server that has a complete view of the network. In this paper, we propose a new recommender system called P2PCF. We use collaborative filtering approach to recommend content in P2P social networks. P2PCF enables privacy preserving and tackles the cold start problem for both users and content. Our proposed approach assumes that the rating matrix is distributed within peers, in such a way that each peer only sees interactions made by her friends on her timeline. Recommendations are then computed locally within each peer before they are sent back to the requester. Our evaluations prove the effectiveness of our proposal compared to a centralized scheme in terms of recall and coverage.
On the Detection of Structural Aesthetic Defects of Android Mobile User Interfaces with a Metrics-based Tool

Smartphone users are striving for easy-to-learn and use mobile apps user interfaces. Accomplishing these qualities demands an iterative evaluation of the Mobile User Interface (MUI). Several studies stress the value of providing a MUI with a pleasing look and feel to engaging end-users. The MUI, therefore, needs to be free from all kinds of structural aesthetic defects. Such defects are indicators of poor design decisions interfering with the consistency of a MUI and making it more difficult to use. To this end, we are proposing a tool (Aesthetic Defects DEtection Tool (ADDET)) to determine the structural aesthetic dimension of MUIs. Automating this process is useful to designers in evaluating the quality of their designs. Our approach is composed of two modules. (1) Metrics assessment is based on the static analysis of a tree-structured layout of the MUI. We used 15 geometric metrics (also known as structural or aesthetic metrics) to check various structural properties before a defect is triggered. (2) Defects detection: The manual combination of metrics and defects are time-consuming and user-dependent when determining a detection rule. Thus, we perceive the process of identification of defects as an optimization problem. We aim to automatically combine the metrics related to a particular defect and optimize the accuracy of the rules created by assigning a weight, representing the metric importance in detecting a defect. We conducted a quantitative and qualitative analysis to evaluate the accuracy of the proposed tool in computing metrics and detecting defects. The findings affirm the tool’s reliability when assessing a MUI’s structural design problems with 71% accuracy.
Screen Gleaning: A Screen Reading TEMPEST Attack on Mobile Devices Exploiting an Electromagnetic Side Channel

We introduce screen gleaning, a TEMPEST attack in which the screen of a mobile device is read without a visual line of sight, revealing sensitive information displayed on the phone screen.The screen gleaning attack uses an antenna and a software-defined radio (SDR) to pick up the electromagnetic signal that the device sends to the screen to display, e.g., a message with a security code.This special equipment makes it possible to recreate the signal as a gray-scale image, which we refer to as an emage.Here, we show that it can be used to read a security code.The screen gleaning attack is challenging because it is often impossible for a human viewer to interpret the emage directly.We show that this challenge can be addressed with machine learning, specifically, a deep learning classifier.Screen gleaning will become increasingly serious as SDRs and deep learning continue to rapidly advance.In this paper, we demonstrate the security code attack and we propose a testbed that provides a standard setup in which screen gleaning could be tested with different attacker models.Finally, we analyze the dimensions of screen gleaning attacker models and discuss possible countermeasures with the potential to address them.
ABKS-PBM: Attribute-Based Keyword Search With Partial Bilinear Map

The way services offered by cloud computing gets its unprecedented and undisputed popularity, so its security concerns. Among them the storage as service model (SaaS) is of the forefront of these concerns. SaaS liberates individuals and enterprises from management of IT infrastructure and data centers to concentrate on their core business. Because of untrusted and out-of-premise architecture users are reluctant to outsource their personal and important data. Encryption before outsourcing addresses some of these issues but at the same time strips the data of its useful operation such as sharing and searching. Now to address this issue, the combination of keyword based searchable encryption (KSE) and attribute-based encryption (ABE) leads to an attribute-based keyword searching (ABKS). The resultant combined concept is capable of fine-grained search operation in the multi-owner/multi-user (M/M) setting. However, the underlying costly pairing operation and complex secret sharing mechanism of ABE makes it unsuitable in practical application for resource-limited devices. On top of it, in most of the existing ABKS schemes the size of the secret key and its associated pairing operation linearly expands to the number of attributes. This paper aims at presenting a novel ABKS scheme with pairing-free access verification and constant size secret key based on AND gate access structure and ciphertext-policy (CP) framework. The security of the proposed work is reduced to the standard Decisional Diffie-Hellmen (DDH) assumption, and also collision free and error tolerant. Finally, the performance evaluation and experimental results shows that the proposed scheme improved the overall efficiency and communication overhead.
Engage in Exploration: Pathology Gross Laboratory in the COVID-Era

The outbreak of Covid-19 has changed education, including the mechanism of delivery of gross pathology laboratories. Herein, we describe how we revised our preclinical gross pathology lab to a flipped model to fit with COVID-19 regulations. A series of short, session objective-driven videos are made available online. Students are expected to watch the videos before coming to the hands-on lab. Groups of 2 students enter the gross lab on a timed basis and rotate through a series of stations. At each station, students examine gross pathology specimens while answering questions designed to apply the clinical correlation of pathophysiology and heighten observational skills. One or 2 pathologists are available throughout the lab session to address the questions from the students. The design of this laboratory exercise maintains appropriate distancing and hygiene in the time of COVID-19. The laboratory rooms are mapped to set up an appropriate number of timed stations. Flow-through of the rooms is unidirectional. Comparing with the traditional show-and-tell of teaching gross pathology, the renovated flipped model is genuinely student-centered and focuses on active learning. Holding the specimen in their hands, students learn from discovery as they are completely engaged by exploring the specimen and deriving answers themselves. The flipped learning gross pathology method has been very well received and evaluated highly by both faculty and students.
Adversarial Synthesis of Human Pose from Text

This work focuses on synthesizing human poses from human-level text descriptions. We propose a model that is based on a conditional generative adversarial network. It is designed to generate 2D human poses conditioned on human-written text descriptions. The model is trained and evaluated using the COCO dataset, which consists of images capturing complex everyday scenes with various human poses. We show through qualitative and quantitative results that the model is capable of synthesizing plausible poses matching the given text, indicating that it is possible to generate poses that are consistent with the given semantic features, especially for actions with distinctive poses.
Automated Metric Analysis of Spanish Poetry: Two Complementary Approaches

The automatic metric analysis (commonly referred to as scansion) of Spanish poetry is not a trivial problem since it combines the nuances of the language, the different poetic traditions related to melodic patterns, and the personal stylistic preferences and intentions of the author. In this paper, we explore two alternative algorithmic approaches tailored to different applications scenarios. The first approach, Rantanplan, is a rule-based method that consists of four Natural Language Processing modules that work together to perform scansion and other related analysis: Part of Speech tagging, syllabification, stress assignment, and metrical adjustment. The second approach, Jumper, explores the possibility of performing scansion without syllabification, with a twofold purpose: to minimize the errors propagated in different parts of the linguistic processing pipeline (including the syllabification step), and to improve the efficiency of the process. Both systems outperform the state of the art and provide either a more informative solution (suitable, for instance, for teaching purposes) or a more efficient processing (when a correct scansion is all the linguistic knowledge required, as in scholar philological studies). The combined use of both systems turns out to provide a practical tool to clean-up manual annotation errors in corpora.