Reviewerly/exHarmony huggingface dataset:
Category Exemplar Production Norms for Hong Kong Cantonese: Instance Probabilities and Word Familiarity

The lexical system of Hong Kong Cantonese has been heavily shaped by the local trilingual environment. The development of cultural- and language-specific norms for Hong Kong Cantonese is fundamental for understanding how the speaker population organize semantic memory, how they utilize their semantic resources, and what information processing strategies they use for the retrieval of semantic knowledge. This study presents a normative database of 72 lexical categories in Hong Kong Cantonese produced by native speakers in a category exemplar production task. Exemplars are enlisted under a category label, along with the instance probabilities and word familiarity scores. Possible English equivalents are given to the exemplars for the convenience of non-HKC speaker researchers. Statistics on categories were further extracted to capture the heterogeneity of the categories: the total number of valid exemplars, the number of exemplars covering 90% of the occurrence and the probabilities of the most frequent exemplars in each category. The database offers a direct lexical sketch of the vocabulary of modern Hong Kong Cantonese in a categorical structure. The category-exemplar lists and the comparative statistics together lay the foundations for further investigations on the Hong Kong Cantonese speaking population from multiple disciplines, such as the structure of semantic knowledge, the time-course of knowledge access, and the processing strategies of young adults. Results of this norm can be also used as a benchmark for other age groups. The database can serve as a crucial resource for establishing initial screening tests to assess the cognitive and psychological functioning of the Cantonese-speaking Hong Kong population in both educational and clinical settings. In sum, this normative study provides a fundamental resource for future studies on language processing mechanisms of Hong Kong Cantonese speaking population, as well as language studies and other cross-language/culture studies on Hong Kong Cantonese.
Deep Learning Based Cell Segmentation Using Cascaded U-Net Models

In this study, a deep learning-based cell segmentation method is proposed. Since existing methods have difficulty in separating touching cells, problem is solved in two parts. First, the position of each cell on the image is determined using a convolutional neural network in U-Net architecture. Then, a second U-Net network is trained to determine the cell boundaries by using first network output as prior information. Finally, complete segmentation is achieved by applying the Watershed algorithm to the output of the second network. The proposed method achived 0.958 detection and 0.85 segmentation accuracies in the tests performed in the DIC-C2DH-HeLa data set. These scores are very close to the state-of-art methods in this data set.
Шкідливі програми в контексті розуміння комп’ютерної вірусології та техніко-правової змагальності: міждисциплінарне дослідження

The article examines interdisciplinary connections in the detection of malicious programs that make up the computer Virology system. It shows how, depending on the mechanism of action of malicious programs, they can be divided into appropriate classes (computer viruses, "worms", Trojan horses, "keygens", "cracks", "droppers", backdoors, advertising modules, root-kits, "password invaders", etc.). The process of virus infection of Program Files is considered. The most well-known among the known "pests" are computer viruses, which are small programs and have the property of spreading and self-reproducing in computer systems. Knowledge of the mechanisms of action of "pests", in particular viruses, allows you to effectively organize their counteraction, minimize the likelihood of infection and losses from their action. The features of trojans that cause destructive actions regardless of the conditions in which they operate are noted. So, they destroy information on disks at each launch, "hang" the system, etc. The effectiveness of measures in the field of organized cybercrime was discussed, defining the line between the technical and legal components in general and in the fight against it. It is shown that the levels of technical protection are not always established by law by introducing special standards. However, there is an interaction between the level of development of technical means and technologies and information legislation. It is determined that the faster pace of development of the technical component in society is only getting stronger. It is noted that the legislative level is "programmed" to constantly lag the technological development of the industry. It is noted that crimes in the field of computer information processing are characterized by high secrecy, difficulties in collecting evidence to establish the facts of their commission, and difficulty in proving them in court. Attention is paid to interdisciplinary connections between malware, computer virology, and technical and legal competition.
Security and Privacy for Distributed Optimization &amp; Distributed Machine Learning

The tutorial will include an introduction to (i) distributed optimization and distributed machine learning, (ii) security or fault-tolerance for distributed optimization and learning, and (iii) privacy in distributed optimization and learning. The presentation will cover the basic principles, and some representative solutions. Server-based and peer-to-peer solutions will be discussed. In particular, Byzantine fault-tolerant algorithms for distributed optimization and learning will be discussed. Privacy mechanisms to be discussed include differential privacy and its variations for systems based on multiple servers.
THE IMPLEMENTATION OF GAMIFICATION CONCEPT INSIDE ONLINE CLASSROOM ACTIVITIES TO PROMOTE STUDENTS’ ENGAGEMENT

Online learning has made massive development nowadays. Due to the spread of Covid-19, the government made a policy to prohibit physical meetings in schools. However, this new policy caused a problem of engagement in online learning. During the lesson, students are giving slow and minimum responses to the teacher’s instruction. This happens in most of the school that operates online learning. Therefore, a solution to improve engagement in online classroom is needed. The implementation of the gamification concept is one of the great alternatives. The implementation of this concept has reaped positive results from various fields, including education. With the right application, this method can be a powerful tool to teach in online learning.
Knowledge Transfer in Genetic Programming Hyper-heuristics

Genetic Programming Hyper-heuristics (GPHHs) have been successfully applied in various problem domains for automatically designing heuristics such as dispatching rules in scheduling and routing policies in vehicle routing. In the real world, it is normal to encounter related problem domains, such as the vehicle routing problem with different objectives, constraints, and/or graph topology. On one hand, different heuristics are required for different problem domains. On the other hand, the knowledge learned from solving previous related problem domains can be helpful for solving the current one. Most existing studies solve different problem domains in isolation, and train/evolve the heuristic for each of them from scratch. In this chapter, we investigate different mechanisms to improve the effectiveness and efficiency of the heuristic retraining by employing knowledge transfer. Specifically, in the context of GPHH, we explored the following two transfer strategies: (1) useful subtrees and (2) importance of terminals, and verified their effectiveness in a case study of the uncertain capacitated arc routing problem.
The Biggest Problem of Blockchains: Key Management

The mechanisms of blockchains are based on cryptographic technologies such as digital signatures and hash functions.
Enhancing CPS Architectures with SOA for Industry 4.0 Enterprise Systems

Cyber-Physical Systems (CPS) integrates computing and communication resources with sensing and actuation functionalities, for physical world entities and processes. CPS realizes connections of the computational and communications infrastructures with the physical reality. Integrating the Internet of Things (IoT) technology, CPS is already involved in various applications in building industrial systems, according to the Industry 4.0 concept. Internal and cross-organizational services associated with the production value chains can also be realized. Service Oriented Architecture (SOA) proved to be an efficient approach for CPS architectures, being able to cope with the heterogeneity of the industrial systems, to assure interoperability. In this paper we propose a CPS architecture, using a SOA approach, integrating a Soap ad Rest and based gateway, which enables a seamless interoperability within the industrial and manufacturing processes and services providing in business environment. Through the gateway, the CPS resources and services can be accessed by mobile clients through Smartphones.
THE SHIFT IN TEACHING PEDAGOGY FOR INDEPENDENT LANDSCAPE DESIGN DURING THE PANDEMIC: AN ANALYSIS OF STUDENT PERFORMANCE IN ODL

The teaching and learning environment have subsequently expanded. For the Independent Landscape Design course offered at UiTM Perak Branch, the online distance learning (ODL) has demonstrated the changes of delivery approach in teaching landscape design process. This design-based syllabus is more acquainted with face to face or physical teaching learning environment. Aligned with the Educational 5.0 @ Universiti Teknologi MARA that embraces values and future progressive thinking, this course has adopted MOOCLAA350 to engage and equip these design students with significant understanding, graphic presentation skills as well as technical aspects related to design developments and constructions. This paper analyses student performance throughout the ODL implementation, adopting MOOC and other relevant online platforms during this Covid-19 pandemic outbreak. An online survey supported with a comparative analysis between semesters was conducted to evaluate the students’ readiness, challenges and performance throughout the semester. Some tools and techniques to ensure the continuity of learning during the current pandemic are described. The findings revealed factors contributing to student performance and the reality behind the success of this new teaching strategy which is practical for landscape architecture programs and may also be relevant to other design-based programs or courses.
Blinding quantum computation using alternative sources

Quantum computation has seen rapid progress in the past years. Due to implementation complexity and cost, cloud-based quantum computing is strongly believed to be the most feasible framework for individual users. Universal blind quantum computing (UBQC) provides the protocol for secure delegation of arbitrary quantum computations and has received significant attention. However, one of the major challenges in UBQC is a secure and reliable transmission of a quantum state over a long distance. Herein, we improve the efficiency of UBQC using alternative photon sources. Our analysis shows that a heralded single-photon source can significantly improve the transmitted distance of UBQC and a modified coherent source can reduce the required transmitted number of pulses. Furthermore, we study the finite-data-size effect, which is a crucial for real-world applications. Our proposed scheme can be a promising alternative for the future development of UBQC.
Learning to communicate about shared procedural abstractions.

Author(s): McCarthy, William P; Hawkins, Robert; Wang, Haoliang; Holdaway, Cameron; Fan, Judith E. | Abstract: Many real-world tasks require agents to coordinate their behavior to achieve shared goals. Successful collaboration requires not only adopting the same communicative conventions, but also grounding these conventions in the same task-appropriate conceptual abstractions. We investigate how humans use natural language to collaboratively solve physical assembly problems more effectively over time. Human participants were paired up in an online environment to reconstruct scenes containing two block towers. One participant could see the target towers, and sent assembly instructions for the other participant to reconstruct. Participants provided increasingly concise instructions across repeated attempts on each pair of towers, using more abstract referring expressions that captured each scene's hierarchical structure. To explain these findings, we extend recent probabilistic models of ad hoc convention formation with an explicit perceptual learning mechanism. These results shed light on the inductive biases that enable intelligent agents to coordinate upon shared procedural abstractions.
Securing Big Data Processing With Homomorphic Encryption

The arrival of Big Data era has challenged the conventional end-to-end data protection mechanism due to its associated high volume, velocity and variety characteristics. This paper reviews the security mechanisms of dominated Big Data processing platform – Hadoop and examines its capabilities on providing the end-to-end data protection: data-in-transit, data-at-rest and data-in-transform. While Hadoop is limited to protect data-in-transit with its built-in security mechanism and relies on third-party vendor tools (e.g. HDFS disk level encryption or security-enhanced Hadoop security distribution) for securing data-at-rest, the homomorphic encryption scheme that capable of performing computation on encrypted data serve as a promising tool to provide end-to-end data protection Big Data processing. However, existing circuit-based homomorphic encryption schemes still insufficient enough for supporting Big Data applications due to their high complexity of computation, huge generated ciphertext and public key size. To address this problem, this paper proposed homomorphic encryption from a non-circuit-based approach. Our result shows that the newly proposed non-circuit based homomorphic encryption has greatly reduced the computation time and ciphertext size as compared to existing circuit-based homomorphic encryption schemes, therefore amenable to support the high volume and high-velocity requirement of Big Data processing.
CompNA at SemEval-2021 Task 1: Prediction of lexical complexity analyzing heterogeneous features

This paper describes the CompNa model that has been submitted to the Lexical Complexity Prediction (LCP) shared task hosted at Se-mEval 2021 (Task 1).The solution is based on combining features of different nature through an ensembling method based on Decision Trees and trained using Gradient Boosting.We discuss the results of the model and highlight the features with more predictive capabilities.
Learning Embeddings for Rare Words Leveraging Internet Search Engine and Spatial Location Relationships

Word embedding techniques depend heavily on the frequencies of words in the corpus, and are negatively impacted by failures in providing reliable representations for low-frequency words or unseen words during training.To address this problem, we propose an algorithm to learn embeddings for rare words based on an Internet search engine and the spatial location relationships.Our algorithm proceeds in two steps.We firstly retrieve webpages corresponding to the rare word through the search engine and parse the returned results to extract a set of most related words.We average the vectors of the related words as the initial vector of the rare word.Then, the location of the rare word in the vector space is iteratively finetuned according to the order of its relevances to the related words.Compared to other approaches, our algorithm can learn more accurate representations for a wider range of vocabulary.We evaluate our learned rare-word embeddings on the word relatedness task, and the experimental results show that our algorithm achieves state-of-the-art performance.
A Tabu Search Algorithm for General Threshold Visual Cryptography Schemes

In Visual Cryptography Schemes (VCSs), for message n transparencies are generated, such that the original message is visible if any k of them are stacked. VCS especially for large values of k and n, the pixel expansion’s reduction and enhancement of the recovered images’ display quality continue to be critical issues. In addition to this, it is challenging to develop a practical and systematic approach to threshold VCSs. An optimization-based pixel-expansion-free threshold VCSs approach has been proposed for binary secret images’ encryption. Along with contrast, blackness is also treated as a performance metric for assessing the recovered images’ display quality. An ideally secure technique for a secret image’s protection through its partition into shadow images (known as shadows) is the Visual Secret Sharing (VSS) scheme. Acquirement of a smaller shadow size or a higher contrast is the VSS schemes’ latest focus. The white pixels’ frequency has been utilized to demonstrate the recovered image’s contrast in this work. While the Probabilistic VSS (ProbVSS) scheme is non-expansible, it can also be readily deployed depending upon the traditional VSS scheme. Initially, this work has defined the problem as a mathematical optimization model such that, while contingent on blackness and density-balance constraints, there is the maximization of the recovered images’ contrast. Afterward, an algorithm dependent on the Tabu Search (TS) is devised in this work for this problem’s resolution. Multiple complicated combinatorial problems have been successfully resolved with the powerful TS algorithm. Moreover, this work has attempted to bolster the contrast through the density-balance constraint’s slight relaxation. Compared to the older techniques, the proposed optimization-based approach is superior regarding the recovered images’ display quality and the pixel expansion factor from the experimental outcomes.
Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks

Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world.How can this wealth of information be leveraged?Using such interaction logs in an offline reinforcement learning (RL) setting is a promising approach.However, due to the nature of NLP tasks and the constraints of production systems, a series of challenges arise.We present a concise overview of these challenges and discuss possible solutions.
IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task

This paper describes our contribution to Se-mEval 2021 Task 1: Lexical Complexity Prediction.In our approach, we leverage the ELECTRA model and attempt to mirror the data annotation scheme.Although the task is a regression task, we show that we can treat it as an aggregation of several classification and regression models.This somewhat counterintuitive approach achieved an MAE score of 0.0654 for Sub-Task 1 and MAE of 0.0811 on Sub-Task 2. Additionally, we used the concept of weak supervision signals from Gloss-BERT in our work, and it significantly improved the MAE score in Sub-Task 1.
Experience in designing a system for monitoring and assessing the results of vocational education of a future educator at a university

In the modern system of higher vocational education, special attention is paid to modernizing approaches to building the educational process, changing the system of monitoring and assessing the results of students' vocational training. Scholars and practitioners pay special attention to identifying opportunities for self-development of students, including within the framework of assessment activities [1, 2, 3, 4, 5]. At the same time, the emphasis is shifted to the need to choose assessment tools that contribute to the reflective assessment of students' achievements in educational activities, building their own educational route in mastering the necessary competencies. The paper shows the problematic nature of solving the problem under consideration in the direction of substantiating conceptual approaches to the organization of control as a function of managing the vocational training of future educators, choosing the forms and methods of monitoring and assessing educational activities. The purpose of the paper is to show conceptual approaches and their practical application in the organization of control and assessing the results of the educational activities of future educators. Based on the experience of their own professional activities in the preparation of future preschool teachers and supplementary education teachers, the authors have shown the design system of assessment tools, demonstrated conceptual approaches to solving this problem.
Application of fuzzy modelling to predict the earthquake damage degree of buildings based on field data

Prediction of structural damage prior to earthquake occurrence provides an early warning for stakeholders of building such as owners and urban managers and can lead to necessary decisions for retrofitting of structures before a disaster occurs, legislating urban provisions of execution of building particularly in earthquake prone areas and also management of critical situations and managing of relief and rescue. For proper prediction, an effective model should be produced according to field data that can predict damage degree of local buildings. In this paper in accordance with field data and Fuzzy logic, damage degree of building is evaluated. Effective parameters of this model as an input data of model consist of height and age of the building, shear wave velocity of soil, plan equivalent moment of inertia, fault distance, earthquake acceleration, the number of residents, the width of the street for 527 buildings in the city. The output parameter of the model, which was the damage degree of the buildings, was also classified as five groups of no damage, slight damage, moderate damage, extensive damage, and complete damage. The ranges of input and output classification were obtained based on the supervised center classification (SCC-FCM) method in accordance with field data.
Online Machine Learning Techniques for Coq: A Comparison

We present a comparison of several online machine learning techniques for tactical learning and proving in the Coq proof assistant. This work builds on top of Tactician, a plugin for Coq that learns from proofs written by the user to synthesize new proofs. Learning happens in an online manner, meaning that Tactician’s machine learning model is updated immediately every time the user performs a step in an interactive proof. This has important advantages compared to the more studied offline learning systems: (1) it provides the user with a seamless, interactive experience with Tactician and, (2) it takes advantage of locality of proof similarity, which means that proofs similar to the current proof are likely to be found close by. We implement two online methods, namely approximate k-nearest neighbors based on locality sensitive hashing forests and random decision forests. Additionally, we conduct experiments with gradient boosted trees in an offline setting using XGBoost. We compare the relative performance of Tactician using these three learning methods on Coq’s standard library.
The NiuTrans End-to-End Speech Translation System for IWSLT 2021 Offline Task

This paper describes the submission of the Ni-uTrans end-to-end speech translation system for the IWSLT 2021 offline task, which translates from the English audio to German text directly without intermediate transcription.We use the Transformer-based model architecture and enhance it by Conformer, relative position encoding, and stacked acoustic and textual encoding.To augment the training data, the English transcriptions are translated to German translations.Finally, we employ ensemble decoding to integrate the predictions from several models trained with the different datasets.Combining these techniques, we achieve 33.84 BLEU points on the MuST-C En-De test set, which shows the enormous potential of the end-to-end model.