Reviewerly/exHarmony huggingface dataset:
Participatory Data Design: Managing Data Sovereignty in IoT Solutions

Abstract Within the software engineering community, deciding how to collect, store and use personal data has become about more than just understanding our users. This paper considers ethical data use that includes cultural considerations and data ownership rights. We discuss indigenous data sovereignty as a concept and how it potentially impacts technological solutions that gather personal data from users. We propose an extension to typical user-centred design processes, which we call participatory data design. This incorporates the use of frameworks and tools that specifically focus on managing data within the cultural context it is gathered from. We also present a specific example of how we have used this approach in the context of a data collection project from Māori workers in New Zealand forestry. We conclude with a discussion of the wider implications of this approach.
Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning

In this work, we study leveraging extra text data to improve low- resource end-to-end ASR under cross-lingual transfer learning setting. To this end, we extend the prior work [1], and propose a hybrid Transformer-LSTM based architecture. This architecture not only takes advantage of the highly effective encoding capacity of the Transformer network but also benefits from extra text data due to the LSTM-based independent language model network. We conduct experiments on our in-house Malay corpus which contains limited labeled data and a large amount of extra text. Results show that the proposed architecture outperforms the previous LSTM-based architecture [1] by 24.2% relative word error rate (WER) when both are trained using limited labeled data. Starting from this, we obtain further 25.4% relative WER reduction by transfer learning from another resource-rich language. Moreover, we obtain additional 13.6% relative WER reduction by boosting the LSTM decoder of the transferred model with the extra text data. Overall, our best model outperforms the vanilla Transformer ASR by11.9% relative WER. Last but not least, the proposed hybrid architecture offers much faster inference compared to both LSTM and Transformer architectures.
Requirements specification of a software-intensive system in the health domain: An experience report

A great variety of methods, techniques, or approaches have been proposed to systematize and facilitate activities of software systems’ requirements elicitation and specification. Each of them has its specific purposes and contributes in its own way for obtaining a good understanding of software requirements. However, in practice, using an unique method is not enough to cover all tasks required during software-intensive system’s requirement engineering. This work does not aim to propose a new or a hybrid method, since each software project has its own characteristics, and methods must be selected and sometimes adapted to fit in each project specificities. This work reports the authors’ experience on how different methods were used together in a coordinated way for requirements specification. Also, possible benefits and drawbacks found by combining methods are also presented.
Process-Oriented Definition of Evaluation Indicators, Learning Behavior Collection and Analysis: A Case Study

The burgeon of online education platforms and online training platforms represented by MOOC has brought new development opportunities for educational innovation. In so many educational and training scenarios, there is a large amount of distributed users' learning data, which leads to the non-standardization of learning data collection and the non-uniformity of storage. In this paper, we design a process-oriented model for evaluating learning indicators. Then, we take KFCoding, which is an online training platform, as a case study to gather data for the learning process. Finally, we model the collected data and analyze the relationship between learning process data and learning outcomes.
Quicker Sort Algorithm: Upgrading time complexity of Quick Sort to Linear Logarithmic

Quick Sort is a famous algorithm. It was the fastest algorithm at one point in time. However, sometimes it can give polynomial time complexity. The only thing that is important in this algorithm is the selection of Pivot Element. In this paper, we proposed a new algorithm, which is based on Quick Sort, and through our testing, we conclude that this algorithm is giving good results for small as well as large data sets.
Movie Genre Prediction and Recommendation Using Deep Visual Features from Movie Trailers

Most of the existing video recommendation models use only the textual contents for recommending interesting videos to users. The textual contents are often missing for videos created by users. Humans are attracted to visual data than any other type of data. Plenty of visual feature information offers people useful inferences. The video classification and recommendation using deep learning is a challenging task. This work proposes a method for movie genre prediction and recommendation using visual features from trailers. This approach segments, each trailer into visual shots of 1 second and extracts the deep visual features of the video frames using the Resnet-152 model. To capture the temporal sequences, it uses the LSTM (Long Short Term Memory) architecture. The output obtained from the Resnet-LSTM model is a vector of the probability distribution of the video segment over the genres (action, comedy, horror, romance). For the recommendation task, this method uses the MovieLens 25M dataset along with the extracted visual features. Finally, the recommendation system will recommend movies based on the genre and rating. This method outperforms current state-of art-approaches.
Geometric Form Algorithm for Data Hiding

Abstract Hiding digital data is one of the arts of hiding information in the internet. This technique has an important role in the field of information security. Transmission of information over communication networks exposes it to theft from attackers and hackers. One solution of avoiding this problem is to hide the information in image so that the targeted message will not be known to repel the attention of thieves. In the proposed algorithm, the hiding of data is based on rearranging the target data and distributing it in a frame called Geometric Frame (GF) that have dimensions with the same dimensions of the cover image. The proposed algorithm is able to achieve the basic metrics used in steganography. In addition, the secret key improves the security objectives. The image quality for stage image will be improved due to ability to choosing the suitable frame for message bits. The proposed Geometric Form algorithm shows a significant result compared with the other related works.
Contemporaneous financial intermediation

Abstract Digital innovations in banking and payments recently have garnered a great deal of attention. Specifically, distributed ledger technology (DLT) has the potential to fundamentally change the roles and responsibilities of stakeholders in the financial sector. DLT is a novel and fast-evolving approach to record and share data, e.g., payment transactions, among members of a decentralized network. Using transaction cost theory, the paper examines how DLT will change the cross-border payment infrastructure. DLT can reduce the overall transaction costs potentially resulting in the disappearance of correspondent banks.
Review On Approaches for Theme Extraction and Sentence Ordering For Prioritization Of Journalistic Notes

In the stages of pre-writing and writing of a news article, journalists require to process the gathered data to identify important points and events which will predominantly support the main theme of the news story. In relation to the field of computer science, there is a lack of intelligent systems to help organize unstructured journalist data and optimize the news data pre-processing stage. There are existing research projects in the area of natural language processing which are focusing on text ordering and main theme identification of textual documents. However, there is no system, which is fine-tuned for the journalism domain, that can utilize the main theme of an unstructured textual document (journalistic notes) to semantically organize and prioritize text.
TLV-to-MUC Express: Post-quantum MACsec in VXLAN

MACsec in VXLAN is an end-to-end security protocol for protecting Ethernet frames traveling over IP networks. It can provide a high-speed Ethernet encryption while supporting the virtualization of a large network such as data center network. Although MACsec addresses most of security threats, it is not immune against quantum attacks which are a future, yet disastrous threat against public-key cryptography in use. In this paper, we demonstrate a new solution for a MACsec protocol over VXLAN in a post-quantum setting. Instead of a standard MACsec key agreement protocol, we use an ephemeral key exchange protocol and an end-to-end authentication scheme, both of which are based on post-quantum cryptography. To measure the impact on the performance, we established a quantum-secure link between Germany and Israel using MACsec in VXLAN over public IP networks. We verified that the impact on the latency and throughput is minimal. Our experiment confirms that quantum-secure virtualized links can be already established in a long-distance without changing their infrastructure.
A Machine Learning Based Plagiarism Detection in Source Code

Converting source codes to feature vectors can be useful in programming-related tasks, such as plagiarism detection on ACM contests. We present a brand-new method for feature extraction from C++ files, which includes both features describing syntactic and lexical properties of an AST tree and features characterizing disassembly of source code. We propose a method for solving the plagiarism detection task as a classification problem. We prove the effectiveness of our feature set by testing on a dataset that contains 50 ACM problems and ∼90k solutions for them. Trained xgboost model gets a relative binary f1-score=0.745 on the test set.
Combining ICT Technologies To Serve Societal Challenges

European counties continue to receive an increasing number of migrants and refugees from an also increasing number of both European and non-European countries.This results in a huge societal challenge which is societal inclusion of people speaking different languages and of diverse backgrounds.Key for their inclusion is job finding which comes with hurdles like the language, the difficulty in assessing and certifying their skills and many more.In this paper, we present the architecture of a novel platform that aspires to provide migrants with a) assistance in discovering and assessing their hard and soft skills by employing Artificial Intelligence technologies, b) recommendations of appropriate job sectors and positions based on their profile, c) recommendations about training that would allow them to find jobs in the country/region they are located and d) practical information regarding the integration process.Furthermore, the proposed platform aims at assisting host authorities, non-governmental organizations and companies in detecting the needs of the target populations (migrants and refugees) through data analytics and supports them in reaching them.Apart from the technical architecture, we provide the results from the initial testing of the platform in real-life pilots in two countries.
An Empirical Study on Robustness of DNNs with Out-of-Distribution Awareness

The state-of-the-art deep neural network (DNN) achieves impressive performance on the input that is similar to training data. However, it fails to make reasonable decisions on the input that is quite different from training data, i.e., out-of-distribution (OOD) examples. Although many techniques have been proposed to detect OOD examples in recent years, it is still a lack of a systematic study about the effectiveness and robustness of different techniques as well as the performance of OOD-aware DNN models. In this paper, we conduct a comprehensive study to unveil the mystery of current OOD detection techniques, and investigate the differences between OOD-unaware/-aware DNNs in model performance, robustness, and uncertainty. We first compare the effectiveness of existing detection techniques and identify the best one. Then, evasion attacks are performed to evaluate the robustness of techniques. Furthermore, we compare the accuracy and robustness between OOD-unaware/-aware DNNs. At last, we study the uncertainty of different models on various kinds of data. Empirical results show OOD-aware detection modules have better performance and are more robust against random noises and evasion attacks. OOD-awareness seldom degrades the accuracy of DNN models in training/test datasets. In contrast, it makes the DNN model more robust against adversarial attacks and noisy inputs. Our study calls for attention to the development of OOD-aware DNN models and the necessity to take data distribution into account when robust and reliable DNN models are desired.
Education in the library: luxury or a citizen’s guaranteed quality of life

The article studies the role of city libraries in school education based on expert interviews carried out with Moscow teachers, librarians, teenagers, and their parents. The study is relevant due to the importance of city libraries in the educational process and their insufficient use in practice. This is the first comprehensive study of standardized interaction between schools and city libraries. The research has demonstrated that ties between these institutions and the process of their interaction have a personal and informal nature. Today, public libraries are information and education resources for school only to a small extent. Most teachers do not see any point in collaborating with libraries since their communication with librarians is not properly regulated. As a rule, successful examples of such interaction are not associated with established rules but rather with a personal interest conditioned by subjective, often random reasons. An essential characteristic of this communication is event-based learning, the use of verbal practices and educational games, as well as a stable educational orientation. Children as participants in the educational process have minimum engagement in this field. It is a rare case that libraries are regarded as a place for independent learning where students work with the necessary sources to conduct scientific research and engage in project activity. In most cases, city libraries attract teachers with their student-oriented events or activities. At the same time, libraries are rarely used in the educational process. Their role does not always involve working with collections or developing children’s need for independent reading. The latter can be satisfied by visiting a local library and addressing reading lists.
The Myth of the Chilling Effect

The chilling effect — historically associated with protecting First Amendment rights — has more recently become a tool used to argue against social media platform (e.g., Facebook, Twitter) policies of restricting content. The 70-year-old principle invalidates regulations if censoring the unwanted speech will also deter or “chill” related but valuable speech. Despite its longstanding use and recent significance, little to no empirical work has been done on whether this phenomenon exists.This Article finally fills this gap. It employs an empirical study using social media speech restrictions analyzed with text analysis to conclude that, in the social media context, the chilling effect has little to no impact on the content of the message; at most, it subtly alters the specific style or tone used. This study therefore confirms the existence of the phenomenon but simultaneously raises serious concerns about its usefulness as a guiding constitutional legal principle when assessing speech regulations.
Context-Based Zero Database Watermarking Scheme Based on Evolutionary Mechanism

Because of the increasingly dispersed existence of universal access to databases, it is important to establish security measures that allow the delicate properties of privacy and copyright rights of outsourcing databases. Researchers started studying how watermarking software would allow pervasive datasets accessing more secure work environments. Initially, most analyses in this area relied on distortion-based watermark, whilst the few remaining studies centered on distortion-free. But there are also drawbacks in previous studies; most importantly some depend on inserting watermark as additional attributes or tuples, raising the sample capacity. Other approaches like permutation and abstract representation system need significant effort to validate the watermark. The aim of this research is to implement an advanced distortion-free watermarking focused on fake tuples embedded in a non-domain separate file to verify material quality for pervasive database entry. The proposed scheme uses the GA, which reduces its function to establish the values of false tuples as a watermark nearest to actual values. But an intruder can hardly infer the watermark. The latest technique provides imperceptibility and stability. Experimental findings suggest that the proposed algorithm is feasible, reliable, and resilient against several attacks.
Automatic Test Case Generation for Vulnerability Analysis of Galois Field Arithmetic Circuits

The research work proposes a framework for checking the correctness of Galois field arithmetic operations in digital circuits. The authors propose to automatically generate the test cases from the user input, avoiding reliance upon predesigned test cases, comprising Galois field-width and respective choice of irreducible polynomial. We do this through the use of polynomial arithmetic to verify the circuits. To the best of author's knowledge, though extensive work has been carried out in optimising the performance of arithmetic operations in Galois field, there exist no testbench to evaluate the efficacy of hardware circuits incorporating this concept. By automating the process of generating test cases, the work can be scaled to test circuits of arbitrarily large field widths, thus providing a flexible architecture that guarantees correctness of the underlying design under test. We present simulation results for Galois field polynomials of width GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )), GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ). This work can be applied to test and prevent intentional tampering of data bit stream and safeguarding it against malicious activities, especially in applications such as cryptography that heavily relies on Galois field arithmetic.
Development of Colorization of Grayscale Images Using CNN-SVM

Nowadays, there is a growing interest in colorizing many grayscales or black and white images dating back to before the colored camera for historical and aesthetic reasons. Image and video colorization can be applied to historical images, natural images, astronomical photography. This paper proposes a fully automated image colorization using a deep learning algorithm. First, the image dataset was selected for training and testing purposes. A convolutional neural network (CNN) was designed with several layers of convolutional and max pooling. Support Vector Machine (SVM) regression was used at the final stage. The proposed algorithm was implemented using Python with Keras and Tensorflow libraries in Google Colab. Results showed that the proposed system could predict the colored image from the training process's learning knowledge. A survey was then conducted to validate our findings.
An Innovative Information Retrieval Model Implementing Particle Swarm Optimization Technique

The Internet has enormous information and it is growing rapidly. The vast amount of data creates challenges in relation to effective Information Retrieval (IR). The scope of the Information Retrieval System (IRS) is to provide the most relevant data for user query from large datasets. However the current IR system fails to provide the hidden and up to date data. This paper focused on soft computing techniques to overcome the above mentioned issues. Particle Swarm Optimization (PSO) is used to compute the fitness function to optimize the retrieval result. PSO has an efficient capability in global search and the implementation is easy to develop. The implementation result of the present study is feasible, that improves the retrieval effect and the accuracy of hidden data retrieval.
Data Mining Approach for Learning management system

Abstract During the pandemic, COVID-19 had an impact on education. All academic activities are hampered, teaching and learning activities must continue to run, and the efforts made are to do distance learning or e-learning. This relates to the integration of systems that must be synchronized with computers, mobile, and technology that can manage learning media electronically. In this study, the LMS in the COVID-19 pandemic period aims to optimize a learning system and provide convenience in the use of technology to deal with this pandemic. The results of this study are explaining the testing of the COVID-19 dataset with the concept of data mining and providing convenience in the application of Moodle-based e-learning so that the development of LMS can be maximized.
Classifying insincere questions on Question Answering (QA) websites: meta-textual features and word embedding

The power of information and information exchange defines the current Internet and Online Social Networks (OSNs). With such power and influence, individuals and entities expose those networks to different types of false information. This paper proposes several classification models based on Quora insincere questions; a dataset released by Kaggle. We evaluated several models including word embeddings based on meta and word-level features. Best results were achieved using the BERT transformer with an overall accuracy of more than 95% on several individual classifiers. Overall, results indicated that the meta-textual features are important predictors for whether a question is sincere or not. In one implication, we noticed that users are putting more cognitive efforts into writing more readable sincere questions compared to insincere questions. Moreover, a dictionary is assembled from several explicit dictionaries and significant words selected from Quora questions. The dictionary showed a good performance in predicting insincere questions.
Analysis of Risks Assessment in Multi Software Projects Development Environment Using Classification Techniques

Risk Assessment contributes to optimizing the allocation of resources at the enterprise level, which achieves its goals, so the matter needs centralized management of risks on the enterprise level, not for each project. The risk assessment is carried out through several stages and by using various methods. This research provides an analytical view of risk assessment in concurrent multiple software projects environment. Research experiment has proven high levels of accuracy, reaching almost from 93% by using Simple Logistic into 98% using REP Tree technique. in determining risk levels in a concurrent multi-project environment.