Reviewerly/exHarmony huggingface dataset:
A Qualitative Approach to Help Adjust the Design of Management Subjects in ICT Engineering Undergraduate Programs through User Experience in a Smart Classroom Context

Qualitative research activities, including first-day of class surveys and user experience interviews on completion of a subject were carried out to obtain students’ feedback in order to improve the design of the subject ‘Information Systems’ as a part of a general initiative to enhance ICT (Information and Communication Technologies) engineering programs. Due to the COVID-19 (corona virus disease 2019) pandemic, La Salle URL adopted an Emergency Remote Teaching tactical solution in the second semester of the 2019–2020 academic year, just before implementing a strategic learning approach based on a new Smart Classroom (SC) system deployed in the campus facilities. The latter solution was developed to ensure that both on-campus and off-campus students could effectively follow the course syllabus through the use of new technological devices introduced in classrooms and laboratories, reducing the inherent difficulties of online learning. The results of our findings show: (1) No major concerns about the subject were identified by students; (2) Interaction and class dynamics were the main issues identified by students, while saving time on commuting when learning from home and access to recorded class sessions were the aspects that students considered the most advantageous about the SC.
Quantum Computing with Superconducting Circuits in the Picosecond Regime

We discuss the realization of a universal set of ultrafast single- and two-qubit operations with superconducting quantum circuits and investigate the most relevant physical and technical limitations that arise when pushing for faster and faster gates. With the help of numerical optimization techniques, we establish a fundamental bound on the minimal gate time, which is determined independently of the qubit design solely by its nonlinearity. In addition, important practical restrictions arise from the finite qubit transition frequency and the limited bandwidth of the control pulses. We show that for highly anharmonic flux qubits and commercially available control electronics, elementary single- and two-qubit operations can be implemented in about 100 picoseconds with residual gate errors below $10^{-4}$. Under the same conditions, we simulate the complete execution of a compressed version of Shor's algorithm for factoring the number 15 in about one nanosecond. These results demonstrate that compared to state-of-the-art implementations with transmon qubits, a hundredfold increase in the speed of gate operations with superconducting circuits is still feasible.
A Lightweight Flexible Wireless Electrooculogram Monitoring System With Printed Gold Electrodes

Electroocugraphy (EOG) is a simple and non-invasive method in which biopotentials developed across the eyes are measured during various eye activities such as eye blinking, winking, and horizontal/vertical eyeball movements. The measured biopotential is called the electroculogram (EOG) signal. This paper presents a single channel EOG measurement system which is implemented on a four layer flexible polymide substrate. The EOG measurement system with its signal conditioning stages is implemented on the top layer of the flexible board whereas, the EOG measurement electrodes are printed on the bottom layer of the flexible board using gold. This eliminates the requirement of external long wires during EOG monitoring. The middle two layers of the flexible substrate are used for implementing the circuit ground plane and active shielding. The entire circuit is powered by a rechargeable Li-ion coin battery. It also uses a Bluetooth 5.0 transceiver module to send the EOG data wirelessly. The system is designed for an effective EOG signal bandwidth of 1.6 Hz to 47 Hz with an effective signal gain above 68.5 dB over the signal bandwidth. The system also has an excellent common-mode rejection ratio (CMRR) response above 70 dB. The system is validated with eight healthy subjects for the detection of different eye activities with an accuracy of 77.08 %. The mass of the entire flexible board along with its battery is only 7.7 g. Such light mass, flexible substrate, and integrated printed electrodes make this EOG monitoring prototype an ideal unit for long term monitoring of biopotentials, without causing any discomfort to the wearer.
Source Selection in Transfer Learning for Improved Service Performance Predictions

Learning performance models for network and cloud services is challenging due to the dynamics of the operational environment stemming from network changes, and scaling and migration decisions in the cloud. This requires exchange or adaptation of the models in order to maintain prediction accuracy over time. Approaches that incorporate previously acquired knowledge using transfer learning is a viable technique for timely and robust model adaptation, especially when the training data is limited. In this paper, we study the challenge of source selection in transfer learning for improved service performance prediction. We quantify the impact of different source domains on the accuracy of a target model in another domain. The evaluation is performed using data traces obtained from a testbed that runs a Video-on-Demand service and a Key-Value Store under various load conditions. We find that the choice of source domain can yield a transfer gain, and sometimes a substantial transfer penalty. To mitigate this, we propose and evaluate two source-selection approaches with the aim of selecting a source domain with relevant knowledge for the target domain. A key result is that such source selection should encourage source-domain diversity rather than domain similarity in scenarios with few samples in the target domain.
The Global Cyber Security Model: Counteracting Cyber Attacks through a Resilient Partnership Arrangement

In this paper, insights are provided into how senior managers can establish a global cyber security model that raises cyber security awareness among staff in a partnership arrangement and ensures that cyber attacks are anticipated and dealt with in real time. We deployed a qualitative research strategy that involved a group interview involving cyber security and intelligence experts. The coding approach was used to identify the themes in the data and, in addition, a number of categories and subcategories were identified. The mind map approach was utilized to identify the thought processes of senior managers in relation to ensuring that the cyber security management process is effective. The global cyber security model can be used by senior managers to establish a framework for dealing with a range of cyber security attacks, as well as to upgrade the cyber security skill and knowledge base of individuals. In order for a cyber security mentality to be established, senior managers need to ensure that staff are focused on organizational vulnerability and resilience, there is an open and transparent communication process in place, and staff are committed to sharing cyber security knowledge. By placing cyber security within the context of a partnership arrangement, senior managers can adopt a collectivist approach to cyber security and benefit from the knowledge of external experts.
Semi-supervised recommendation attack detection based on Co-Forest

In recommendation attack, malicious users attempt to bias the recommendation results by injecting fake profiles into the rating database. To detect such attack, three types of methods, i.e., unsupervised, supervised and semi-supervised, have been proposed. Among these works, the advantage of semi-supervised methods is that they can use the unlabeled user profiles to improve the detection performance. However, the existing semi-supervised methods suffer from low precision. Aiming at this problem, in this paper, we propose a semi-supervised detection approach named SSADR-CoF based on the Co-Forest algorithm. Being different from the existing semi-supervised methods which only use a few of features to train a single classifier for the detection, the proposed approach uses a series of features to train an ensemble of classifiers to detect the recommendation attack. We first use the window dividing and rating behavior statistical methods to extract a series of user rating behavior mode features for training the detection model. Then, we use a small number of labeled user profiles to initialize an ensemble of classifiers, and use the ensemble of classifiers to assign labels to the unlabeled user profiles. Finally, we use the labeled and the newly labeled user profiles to iteratively update the classifiers for the detection. Experiments conducted on three benchmark datasets MovieLens 10M, MovieLens 25M, and Amazon show that the proposed approach can effectively improve the precision of the semi-supervised methods under the condition of maintaining high recall and AUC.
From feeling like rape to a minor inconvenience: Victims’ accounts of the impact of computer misuse crime in the United Kingdom

This paper provides a unique insight into the impact of computer misuse crime, such as hacking, denial of service attacks, ransomware and computer viruses/malware on individual and small and medium sized enterprises/organisation (SME/O) victims in the UK. Based upon in depth interviews with 52 (38 individual and 14 SME/O) victims it shows that these victims experience many of the impacts associated with other volume crimes. Building upon research on comparable offences the paper proposes eight categories of impact, which include:little or no impact, financial, disruption, psychological (which was broken down further), feelings of violation, loss of digital possessions, health and secondary impacts. The research highlights that although for some the impact is very minor, many experience much more extensive impacts, even when there is no financial loss and for some the impact was so severe it was compared to rape.
Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach

We address rotation averaging (RA) and its application to real-world 3D reconstruction. Local optimisation based approaches are the de facto choice, though they only guarantee a local optimum. Global optimisers ensure global optimality in low noise conditions, but they are inefficient and may easily deviate under the influence of outliers or elevated noise levels. We push the envelope of rotation averaging by leveraging the advantages of a global RA method and a local RA method. Combined with a fast view graph filtering as preprocessing, the proposed hybrid approach is robust to outliers. We further apply the proposed hybrid rotation averaging approach to incremental Structure from Motion (SfM), the accuracy and robustness of SfM are both improved by adding the resulting global rotations as regularisers to bundle adjustment. Overall, we demonstrate high practicality of the proposed method as bad camera poses are effectively corrected and drift is reduced.
Post2Vec: Learning Distributed Representations of Stack Overflow Posts

Past studies have proposed solutions that analyze Stack Overflow content to help users find desired information or aid various downstream software engineering tasks. A common step performed by those solutions is to extract suitable representations of posts; typically, in the form of meaningful vectors. These vectors are then used for different tasks, for example, tag recommendation, relatedness prediction, post classification, and API recommendation. Intuitively, the quality of the vector representations of posts determines the effectiveness of the solutions in performing the respective tasks. In this work, to aid existing studies that analyze Stack Overflow posts, we propose a specialized deep learning architecture Post2Vec which extracts distributed representations of Stack Overflow posts. Post2Vec is aware of different types of content present in Stack Overflow posts, i.e., title, description, and code snippets, and integrates them seamlessly to learn post representations. Tags provided by Stack Overflow users that serve as a common vocabulary that captures the semantics of posts are used to guide Post2Vec in its task. To evaluate the quality of Post2Vec's deep learning architecture, we first investigate its end-to-end effectiveness in tag recommendation task. The results are compared to those of state-of-the-art tag recommendation approaches that also employ deep neural networks. We observe that Post2Vec achieves 15-25 percent improvement in terms of F1-score@5 at a lower computational cost. Moreover, to evaluate the value of representations learned by Post2Vec, we use them for three other tasks, i.e., relatedness prediction, post classification, and API recommendation. We demonstrate that the representations can be used to boost the effectiveness of state-of-the-art solutions for the three tasks by substantial margins (by 10, 7, and 10 percent in terms of F1-score, F1-score, and correctness, respectively). We release our replication package at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/maxxbw/Post2Vec</uri> .
Porn2Vec: A robust framework for detecting pornographic websites based on contrastive learning

Pornographic websites have become one of the largest origins spreading vulgar contents, which seriously threaten the mental and physical health of juveniles. Unfortunately, the existing pornography detection approaches are ineffective against the pornographic websites, which are armed with adversarial attack examples. In this paper, we propose Porn2Vec, a robust end-to-end framework for detecting pornographic websites using contrastive learning. Particularly, we first model pornographic websites with a heterogeneous graph consisting of websites, webpages, images, texts, and their interactive relationships, and formalize pornographic website detection into node classification task on the graph. Subsequently, we present a novel contrastive learning based heterogeneous graph embedding method to learn the high-level representation of websites by jointly aggregating image-based, text-based, and structure-based features. Finally, the learned website features are fed into a neural network to train an automatic model for pornographic website detection. Experimental results show that Porn2Vec outperforms the existing state-of-the-art methods, demonstrating a more promising and robust performance for detecting well-disguised pornographic websites equipped with adversarial attack examples.
Perceived barriers for distance teaching in higher education during the COVID-19 crisis: “I never did a video before”

The purpose of this paper is to analyse barriers that a selected group of professors at the University of Barcelona faced due to the COVID-19 restrictions, their perceptions of the current situation, and the potential for long-term adoption of new teaching methods that emerged from this situation. It remains unclear whether these professors will return to traditional teaching methods as soon as COVID-19 restrictions are lifted, or whether they will adopt digital teaching elements in the future. The group of professors sought technical help during the first weeks of online teaching. This research uses a qualitative approach through a self-administered qualitative survey. We analyse data using open-ended questions about barriers they faced in creating educational content via video. Answers were coded and analysed using thematic analysis with an inductive approach. We identified two overarching themes: educational material delivery and professors and distance teaching. Findings indicate that professors lacked digital skills as well as a lack of knowledge of emergency remote teaching. There were differences in their perceptions of teaching methodologies. The results are discussed in relation to research from other countries about the use of videos for teaching before and during the COVID-19 crisis.
iContractBot: A Chatbot for Smart Contracts’ Specification and Code Generation

Recently, Blockchain technology adoption has expanded to many application areas due to the evolution of smart contracts. However, developing smart contracts is non-trivial and challenging due to the lack of tools and expertise in this field. A promising solution to overcome this issue is to use Model-Driven Engineering (MDE), however, using models still involves a learning curve and might not be suitable for non-technical users. To tackle this challenge, chatbot or conversational interfaces can be used to assess the non-technical users to specify a smart contract in gradual and interactive manner. In this paper, we propose iContractBot, a chatbot for modeling and developing smart contracts. Moreover, we investigate how to integrate iContractBot with iContractML, a domainspecific modeling language for developing smart contracts, and instantiate intention models from the chatbot. The iContractBot framework provides a domain-specific language (DSL) based on the user intention and performs model-to-text transformation to generate the smart contract code. A smart contract use case is presented to demonstrate how iContractBot can be utilized for creating models and generating the deployment artifacts for smart contracts based on a simple conversation.
Specifying QoS Requirements and Capabilities for Component-Based Robot Software

Assembling robotic multi-agent systems is becoming increasingly attractive due to the emergence of affordable robots. For coordinated missions such fleets usually have to communicate over unreliable channels and still achieve adequate performance. To support system designers in quantifying adequateness, in this paper we present a domain-specific language (DSL) that allows domain-experts to specify (i) quality of service (QoS) requirements of the communication channels; and (ii) QoS capabilities of the involved software components. Such QoS specifications complement the QoS management that has recently been introduced into ROS 2. To fully utilize this approach we have also developed an associated ROS 2 DSL which enables us to verify QoS specifications and provide feedback to the users already at design time. We have evaluated the developed language workbench following the Goal-Question-Metric (GQM) approach which demonstrates that the QoS DSL is complete with respect to ROS 2 and can be easily extended. Additionally, we generate a proof-of-concept implementation for a QoS monitor that can be seamlessly integrated into existing ROS 2 projects.
Training Rare Object Detection in Satellite Imagery with Synthetic GAN Images

When creating a new labeled dataset, human analysts or data reductionists must review and annotate large numbers of images. This process is time consuming and a barrier to the deployment of new computer vision solutions, particularly for rarely occurring objects. To reduce the number of images requiring human attention, we evaluate the utility of images created from 3D models refined with a generative adversarial network to select confidence thresholds that significantly reduce false alarms rates. The resulting approach has been demonstrated to cut the number of images needing to be reviewed by 50% while preserving a 95% recall rate, with only 6 labeled examples of the target.
Research on Book Recommendation Algorithm Based on Collaborative Filtering and Interest Degree

With the increase of library collections, it is difficult for readers to quickly find the books they want when choosing books. Book recommendation system is becoming more and more important. Based on the previous research, this paper proposes a book recommendation algorithm based on collaborative filtering and interest. Take the interest of the book itself as an important measurement index, including the number of searches, borrowing time, borrowing times, borrowing interval, and renewal times. Through the analysis of MAE and RMSE experiments, the results show that the method proposed in this paper converges faster than the traditional method.
A Temperature Compensation Approach for Dual-Mass MEMS Gyroscope Based on PE-LCD and ANFIS

Because the dual-mass MEMS gyroscope's output is greatly influenced by temperature, which can lead to errors that cannot be ignored.To solve this problem, a novel compensation method is proposed: a parallel processing algorithm, which integrates the Permutation entropy (PE), Local Characteristic-scale Decomposition (LCD) and Adaptive network-based fuzzy inference system (ANFIS).Firstly, LCD is used to decompose the output which contains temperature noises and drifts into a trend component and several intrinsic scale components (ISC), according to autocorrelation and complexity, three different categories will be obtained by PE: pure noise output, mixed output, and drift output.The different processes are as follows, the noise output is discarded, the mixed output is filtered by SG (Savitzky-Golay filter), then dual ANFIS is applied.Since the drift output completely reflects the temperature characteristics, the degree of non-linearity is high, the ANFIS with complex rules is used for processing.And the mixed output is composed of intermediate layer modes, containing a relatively small amount of temperature characteristics, simple rule ANFIS is adopted for processing.Finally, the signal is reconstructed.After that, the temperature error experiment is carried out, the result shows the method can effectively eliminate the error and compensate for the drift, it has a fast convergence speed and good effect, and has the advantage of good compensation efficiency.
A Generative Adversarial Network for Data Augmentation: The Case of Arabic Regional Dialects

Abstract Text Generation using Generative Adversarial Networks (GANs) has been successful in domains such as sentiment analysis using Sentimental GAN (SentiGAN) model. We adopt a similar model to generate sentences for five regional Arabic dialects (Egypt, Gulf, Maghreb, Levant, and Iraq). The objective is to overcome the scarcity of richly annotated Dialectal Arabic (DA) datasets by automatic generation of such corpora. The DA generation process for a specific dialect, relies on a generator to create new text, and a discriminator to evaluate that text, with a dynamic update that will allow the process to run automatically without supervision. Novelty and diversity are the two metrics used to verify the consistency and quality of the generated DA text before enriching the sought datasets. Experimental results confirm the reliability and value of the generated datasets when tested by different classifiers.
A simple and fast method for Named Entity context extraction from patents

The process of extracting relevant technical information from patents or technical literature is as valuable as it is challenging. It deals with highly relevant information extraction from a corpus of documents with particular structure, and a mix of technical and legal jargon. Patents are the wider free source of technical information where homogeneous entities can be found. From a technical perspective the approaches refer to Named Entity Recognition (NER) and make use of Machine Learning techniques for Natural Language Processing (NLP). However, due to the large amount of data, to the complexity of the lexicon, the peculiarity of the structure and the scarcity of the examples to be used to feed the machine learning system, new approaches should be studied. NER methods are increasing their performances in many contexts, but a gap still exists when dealing with technical documentation. The aim of this work is to create an automatic training sets for NER systems by exploiting the nature and structure of patents, an open and massive source of technical documentation. In particular, we focus on collecting the context where users of the invention appear within patents. We then measure to which extent we achieve our goal and discuss how much our method is generalizable to other entities and documents.