Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Supervised Attention for Text Generation

The attention-based encoder-decoder framework is widely used in many natural language generation tasks. The attention mechanism builds alignments between target words and source items that facilitate text generation. Previous work proposes supervised attention that uses human knowledge to guide the attention mechanism to learn better alignments. However, well-designed supervision built from ideal alignments can be costly or even infeasible. In this paper, we build a Generalized Supervised Attention method (GSA) based on quasi alignments, which specify candidate sets of alignments and are much easier to obtain than ideal alignments. We design a Summation Cross-Entropy (SCE) loss and a Supervised Multiple Attention (SMA) structure to accommodate quasi alignments. Experiments on three text generation tasks demonstrate that GSA improves generation performance and is robust against errors in attention supervision.
Comparative Analysis of Short-range Wireless Technologies for m-Health: Newborn Monitoring Case Study

The Healthcare and the Internet of Things (IoT) are being integrated to improve the people life quality in many aspects, as for example, through the increasing of the patients wellness and also optimizing Hospitals activities. One of the main actors in this integration is the communication link that connects the people to the systems, which is made in most of the cases through wireless devices. There are many available wireless technologies to be applied in a great diversity of healthcare scenarios, in which would stands out specific technologies advantages for each one. Therefore, among the great number of information about it, in this work it is detailed the physical layer characteristics of 9 most used technologies in short-range wireless applications for healthcare. Also, it is made a technology selection for one specific application scenario of newborn babies
Proof-of-Useful-Randomness: Mitigating the Energy Waste in Blockchain Proof-of-Work

Proof-of-Work (PoW) is one of the fundamental and widely-used consensus algorithms in blockchains. In PoW, nodes compete to receive the mining reward by trying to be the first to solve a puzzle. Despite its fairness and wide-availability, traditional PoW incurs extreme computational and energy waste over the blockchain. This waste is considered to be one of the biggest problems in PoW-based blockchains and cryptocurrencies. In this work, we propose a new useful PoW called Proof-of-Useful-Randomness (PoUR) that mitigates the energy waste by incorporating pre-computed (disclosable) randomness into the PoW. The key idea is to inject special randomness into puzzles via algebraic commitments that can be stored and later disclosed. Unlike the traditional wasteful PoWs, our approach enables pre-computed commitments to be utilized by a vast array of public-key cryptography methods that require offline-online processing (e.g., digital signature, key exchange, zero-knowledge protocol). Moreover, our PoW preserves the desirable properties of the traditional PoW and therefore does not require a substantial alteration in the underlying protocol. We showed the security of our PoW, and then fully implemented it to validate its significant energy-saving capabilities.
Similarity of Software Libraries: A Tag-based Classification Approach

The number of software libraries has increased over time, so grouping them into classes according to their functionality simplifies repository management and analyses. With the large number of software libraries, the task of categorization requires automation. Using a crawled dataset based on Java software libraries from Apache Maven repositories as well as tags and categories from the indexing platform MvnRepository.com, we show how the data in this set is structured and point out an imbalance of classes. We introduce a class mapping relevant for the procedure, which maps the libraries from very specific, technical classes into more generic classes. Using this mapping, we investigate supervised machine learning techniques that classify software libraries from the dataset based on their available tags. We show that a tag-based approach to classify libraries with an accuracy of 97.46% can be achieved by using neural networks. Overall, we found techniques such as neural networks and naı̈ve Bayes more suitable in this use case than a logistic regression or a random forest.
A Framework for Security and Risk Analysis of Enrollment Procedures: Application to Fully-remote Solutions based on eDocuments

More and more online services are characterised by the need for strongly verifying the real-world identity of end users, especially when sensitive operations have to be carried out: just imagine a fully-remote signature of a contract, and what could happen whether someone managed to perform it by using another person’s name. For this reason, the identity management lifecycle contains specific procedures – called enrollment or onboarding – providing a certain level of assurance on digital users’ real identities. These procedures must be as secure as possible to prevent frauds and identity thefts. In this paper, we present a framework composed of a specification language, a security analysis methodology and a risk analysis methodology for enrollment solutions. For concreteness, we apply our framework to a real use case (i.e., fully-remote solutions relying on electronic documents as identity evidence) in the context of a collaboration with an Italian FinTech startup. Beyond validating the framework, we analyse and highlight the essential role of mitigations on the overall security of enrollment procedures.
Multi-document Arabic Text Summarization based on Thematic Annotation

Reduce document(s) by keeping keys and significant sentences from a set of data is called text summarization. It has been around for a long time in natural language processing research, it is improving over the years due to a considerable number of methods and research in this area. The paper suggests Arabic multi-document text summarization. The originality of the approach is that the summary based on thematic annotation such as input documents are analyzed and segmented using LDA. Then segments of each topic are represented by a separate graph because of the redundancy problem in multi-document summarization. In the last step, the proposed approach applies a modified pagerank algorithm that utilizes cosine similarity measure as a weight between edges. Vertices that have high scores are essential. Therefore, they construct the final summary. To evaluate summary systems, researchers develop serval metrics divided into three categories, namely: automatic, semiautomatic and manual. This study research chooses automatic evaluation methods for text summarization, mainly Rouge measure (Rouge-1, Rouge-2, Rouge-L, and Rouge-SU4).
Comparing Support Vector Machine and Neural Network Classifiers of CVE Vulnerabilities

The Common Vulnerabilities and Exposures (CVE) database is the largest publicly available source of structured data on software and hardware vulnerability. In this work, we analyze the CVE database in the context of IoT device and system vulnerabilities. We employ and compare support vector machine (SVM) and neural network (NN) algorithms on a selected subset of the CVE database to classify vulnerability records in this framework. Our scope of interest consists of records that describe vulnerabilities of potential IoT devices of different types, such as home appliances, SCADA (industry) devices, mobile controllers, networking equipment and others. The purpose of this work is to develop and test an automated system of recognition of IoT vulnerabilities to test two different methods of classification (SVM and NN) and to find an optimal timeframe for training (historical) data.
Multi-Party Private Set Intersection Protocols for Practical Applications

: Multi-party Private Set Intersection (MPSI) is an attractive topic in research since a practical MPSI protocol can be deployed in several real-world scenarios, including but not limited to ﬁnding the common list of customers among several companies or privacy-preserving analyses of data from different stakeholders. Several solutions have been proposed in the literature however, the existing solutions still suffer from performance re-lated challenges such as long run-time and high bandwidth demand, particularly when the number of involved parties grows. In this paper, we propose a new approach based on threshold additively homomorphic encryption scheme, e.g. , Paillier, which enables us to process the bit-set representation of sets under encryption. By doing so, it is feasible to securely compute the intersection of several data sets in an efﬁcient manner. To prove our claims on performance, we compare the communication complexity of our approach with the existing solutions and show performance test results. We also show how the proposed protocol can be extended to securely compute other set operations on multi-party data sets.
Formation of Hexagonal BN by Thermal Decomposition of Melamine Diborate

The formation of h-BN by thermal decomposition of melamine diborate was examined by XRD, IR and thermal analysis. The melamine diborate, which was precipitated as white powders by cooling a hot solution of melamine and boric acid, was presumed to have a monoclinic structure with a=2.0259nm, b=1.4180nm, c=0.3504nm, β=92.40°, and its solubility in 100cm3 water was about 0.6g at room temperature. On dehydration, the crystalline diborate was converted to an amorphous solid at the temperature ranges from 100 to 200°C. The melamine component decomposed at about 300°C, and the formation of BN skeletons occurred above 600°C. Turbostatic BN with 60% purity, which was obtained at 900°C, was well crystallized by further heating at 2000°C, accompanied by grain growth and purification.
Dimensions of systems and transformations: Towards an integrated framework for system transformations

The forward looking analysis of pathways of transformation of sociotechnical systems to sustainability has become a key concern of innovation studies (Geels 2002; Grin, Rotmans, and Schot 2010; Köhler et al. 2019; Rogge, Pfluger, and Geels 2020). This is driven by broad concerns about the major challenges we face as societies and the need for thorough and fast change of systems as opposed to diffusion of individual innovations to fix small-scale problems. The normative claim is that through intelligent, coordinated activity, mankind can cope with existing challenges. To do so, however, it takes an ambition to voluntarily shape and alter the direction of change of socio-technical systems that provide energy, transportation, health and so on.
Multilabel Remote Sensing Image Annotation With Multiscale Attention and Label Correlation

Deep-learning-based multilabel image annotation is receiving increasing attention in the field of remote sensing due to the great success of deep networks in single-label remote sensing image classification. Compared with those low-level features, the features extracted by the convolutional neural network (CNN) are more informative and can alleviate the problem of semantic gap. However, the CNN model tends to ignore the smaller objects when objects of different sizes exist in an image. In addition, how to efficiently leverage the correlation among multiple labels to enhance annotation performance remains an open issue. In this article, we propose an end-to-end deep learning framework for multilabel remote sensing image annotation. The framework is composed of a multiscale feature fusion module, a channel-spatial attention learning module, and a label correlation extraction module. The multiscale features from different layers of a CNN model are first fused and refined by using a channel-spatial attention mechanism. Then, the label correlation information is extracted from a label co-occurrence matrix and embedded into the multiscale attentive features to increase the discriminative ability of the resulting image features. The experiments on two benchmark datasets demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.
Modeling Academic Research Collaborator Selection Using an Integrated Model

Expert finding systems try to alleviate the information overload problem and recommend experts who can satisfy users’ needs. They support researchers to find research collaborators automatically. The main challenge of current expert finding systems is that they retrieve experts based on the content of their documents but ignore the human interaction perspective. The human interaction perspective comprises the factors that influence collaborator selection decisions in real life. This study aimed to develop a collaborator selection model for expert finding systems in research universities. This model includes human capital, social capital, and cultural capital factors that influence collaborator selection. The researchers integrated the Scientific and Technical Human Capital (STHC) model and Social Capital Theory to determine these factors. The authors conducted a survey comprising 349 researchers from Malaysian research universities to validate the research hypotheses. A partial least squares structural equation model (PLS-SEM) was employed to analyze all the survey data. The empirical results revealed that the significant factors that influence collaborator selection in the research universities context were cognitive accessibility, reliability, relevance, commitment, physical accessibility, cultural experiences, complementary skills, and research experience. Surprisingly, the results revealed that network ties, relational accessibility, and reputation were insignificant factors for collaborator selection. This study proposed a research model for collaborator selection in the research universities context and provided several recommendations for the policymakers and practitioners. The model will help to provide sufficient criteria to select academic research collaborator in universities and can be used by expert finding systems designers, researchers, collaborators, and universities.
AMT: Acoustic Multi-target Tracking with Smartphone MIMO System

Acoustic target tracking has shown great advantages for device-free human-machine interaction over vision/RF based mechanisms. However, existing approaches for portable devices solely track single target, incapable for the ubiquitous and highly challenging multi-target situation such as double-hand multimedia controlling and multi-player gaming. In this paper, we propose AMT, a pioneering smartphone MIMO system to achieve centimeter-level multi-target tracking. Targets’ absolute distance are simultaneously ranged by performing multi-lateration locating with multiple speaker-microphone pairs. The unique challenge raised by MIMO is the superposition of multisource signals due to the cross-correlation among speakers. We tackle this challenge by applying Zadoff-Chu(ZC) sequences with strong auto-correlation and weak cross-correlation. The most distinguishing advantage of AMT lies in the elimination of target raised multipath effect, which is commonly ignored in previous work by hastily assuming targets as particles. Concerning the multipath echoes reflected by each non-particle target, we define the novel concept of primary echo to best represent target movement. AMT then improves tracking accuracy by detecting primary echo and filtering out minor echoes. Implemented on commercial smartphones, AMT achieves on average 1.13 cm and 2.46 cm error for single and double target tracking respectively and on average 97% accuracy for 6 controlling gestures recognition.
EfficientNet Based Iris Biometric Recognition Methods with Pupil Positioning by U-Net

In this work, the deep-learning pupil location based iris recognition methods are studied for biometric authentication. First, by using U-Net, the developed design utilizes the semantic segmentation scheme to locate and extract the region of interest (ROI) of the pupil zone. Based on the located ROI of the pupil zone in the eye image, the iris region can be extracted effectively, and the entered eye image is cut to the small eye image with the ROI of the iris that has just been adjusted. Then the iris features of the cropped eye image are optionally enhanced by adaptive histogram equalization or the Gabor filter process. Finally, the cropped eye image with important iris region is classified by EfficientNet. By using the CASIA v3 database, the proposed deep learning based iris recognition scheme achieves recognition accuracies of up to 98.2%, and the Equal Error Rate (EER) of the proposed design can be close to near 0%.
Yggdrasil — Early Detection of Cybernetic Vulnerabilities from Twitter

The use of computational systems for enhancing collaborative learning is widely spread. While interest in the application of Computer-Supported Collaborative Learning (CSCL) systems in cybersecurity exists, endeavors in this sense are difficult to compare due to different choices of methods and tools. In this sense, we advance a typology of CSCL systems in cybersecurity and introduce an automated system, Yggdrasil, which identifies emerging vulnerability threats from tweets containing references to explicit news articles. Such a system can be used to enhance collaborative learning processes of experts and non-experts in cybersecurity regarding the early detection of vulnerabilities, by automatically extracting information from online communities. For this purpose, we built a corpus of 650 annotated tweets that contain links to cybersecurity related articles, posted by key profiles in the cybersecurity landscape. Using insights from state-of-the-art approaches in early detection of vulnerabilities from tweets and sentiment analysis from Twitter, we conducted two experiments for the development of Yggdrasil. Our results argue that the use of the BERT language model is an adequate approach for identifying relevant posts or news articles, having an accuracy over 91% in various configurations. Furthermore, we also illustrate the use of transfer learning as a viable alternative for the early detection of cybernetic vulnerabilities.
Solid Phase Microextraction Followed by GC-MS Analysis of Volatile Flavor Compounds in Fresh Pumpkin and Pumpkin Juice

A comparative study was conducted for analyzing the difference in volatile flavor compound profile in fresh pumpkin and pumpkin juice using solid phase microextraction (SPME) followed by GC-MS method. Results indicated that 44 and 53 kinds of volatile compounds were identified in fresh pumpkin and pumpkin juice, respectively, and the main components were alcohol, aldehyde and ketone. Processed pumpkin juice from fresh pumpkin retained its characteristic aroma. However, cooking aromas such as furfural, pyridine and furan in pumpkin juice, and carotenoids degradation products, such as aldehyde, ketone and ionone, were also found in pumpkin and pumpkin juice due to heat treatment.
Profile-Based Cluster Evolution Analysis: Identification of Migration Patterns for Understanding Student Learning Behavior

Educational process mining is one of the research domains that utilizes students’ learning behavior to match students’ actual courses taken and the designed curriculum. While most works attempt to deal with the case perspective (i.e., traces of the cases), the temporal case perspective has not been discussed. The temporal case perspective aims to understand the temporal patterns of cases (e.g., students’ learning behavior in a semester). This study proposes modified cluster evolution analysis, called profile-based cluster evolution analysis, for students’ learning behavior based on profiles. The results show three salient features: (1) cluster generation; (2) within-cluster generation; and (3) time-based between-cluster generation. The cluster evolution phase modifies the existing cluster evolution analysis with a dynamic profiler. The model was tested on actual educational data of the Information System Department in Indonesia. The results showed the learning behavior of students who graduated on time, the learning behavior of students who graduated late, and the learning behavior of students who dropped out. Students changed their learning behavior by observing the migration of students from cluster to cluster for each semester. Furthermore, there were distinct learning behavior migration patterns for each category of students based on their performance. The migration pattern can suggest to academic stakeholders to understand about students who are likely to drop out, graduate on time or graduate late. These results can be used as recommendations to academic stakeholders for curriculum assessment and development and dropout prevention.
Asynchronous Time-Sensitive Networking for Industrial Networks

Time-Sensitive Networking (TSN) is expected to be a cornerstone in tomorrow's industrial networks. That is because of its ability to provide deterministic quality-of-service in terms of delay, jitter, and scalability. Moreover, it enables more scalable, more affordable, and easier to manage and operate networks compared to current industrial networks, which are based on Industrial Ethernet. In this article, we evaluate the maximum capacity of the asynchronous TSN networks to accommodate industrial traffic flows. To that end, we formally formulate the Flow Allocation Problem (FAP) in the mentioned networks as a convex mixed-integer non-linear program. To the best of the authors' knowledge, neither the maximum utilization of the asynchronous TSN networks nor the formulation of the FAP in those networks have been previously addressed in the literature. The results show that the network topology and the traffic matrix highly impact on the link utilization.
Consensus of Incommensurate-order Fractional Multiagent Systems with a Fixed-length Memory

This paper is devoted to considering the consensus problem of fractional-order multiagent systems (FMASs) with incommensurate orders and a fixed-length memory. For this purpose, a method is proposed wherein a fractional protocol with a time-varying lower terminal is designed for the FMASs to reach consensus. It is developed based on approximating the system's monodromy matrix that determines the system's stability if its spectral radius locates on the unit circle. The major advantage of the proposed method is its ability to solve FMASs with incommensurate orders and shortened memories. Therefore, it is possible to design fractional protocols that do not suffer from the buffer overflow, which is a common issue for the current ones. The proposed method is examined and verified by means of a test example.