Reviewerly/exHarmony huggingface dataset:
Adaptation, Collaboration, Contention

When the COVID pandemic struck Colombia, the first academic semester of 2020 was already underway. Neither the government nor higher education institutions (HEI s) were prepared, but they adapted to the new reality. Adaptation, collaboration, and contention marked Colombian higher education’s response to the pandemic. The Colombian government, HEI s, faculty members, staff, and students adapted to the crisis in the best possible manner. The government issued regulations, revisited policies and procedures and provided financial resources within its capacities. HEI s offered to the government and society their support in efforts to mitigate the impact of the pandemic and swiftly adjusted their schedules and methodologies to a remote teaching mode. However, the crisis highlighted the country’s weaknesses in higher education financing, and a contention dynamic between mostly students and the government, which predated the pandemic and gained relevance when the needs of students and HEI s became more pressing. This chapter explores the impact of COVID in the Colombian higher education system during 2020 and 2021 and focuses on the financial challenges of the system. The first part highlights the prompt but uncoordinated and insufficient collaboration of different stakeholders. The second part addresses the major financial challenges from the perspectives of institutions and the students and provides examples of what HEI s and the government were able to provide in response to the crisis. Finally, in the third part, we analyse the country’s response to the pandemic and its challenges in higher education financing in the next few years.
BiInfGCN: Bilateral Information Augmentation of Graph Convolutional Networks for Recommendation

The current graph-neural-network-based recommendation algorithm fully considers the interaction between users and items. It achieves better recommendation results, but due to a large amount of data, the interaction between users and items still suffers from the problem of data sparsity. To address this problem, we propose a method to alleviate the data sparsity problem by retaining user–item interactions while fully exploiting the association relationships between items and using side-information enhancement. We constructed a “twin-tower” model by combining a user–item training model and an item–item training model inspired by the knowledge distillation technique; the two sides of the structure learn from each other during the model training process. Comparative experiments were carried out on three publicly available datasets, using the recall and the normalized discounted cumulative gain as evaluation metrics; the results outperform existing related base algorithms. We also carried out extensive parameter sensitivity and ablation experiments to analyze the influence of various factors on the model. The problem of user–item interaction data sparsity is effectively addressed.
English to Tamil Multi-Modal Image Captioning Translation

Bridging the gap between the latest technologies and the local languages, Machine Translation has had a big impact on countless non-English speakers who do not get access to these latest technologies coming out which are exclusive only to English Speakers. One such new research field which requires the intervention of Translation is the domain of Image Captioning. With the potential to impact 75 million worldwide users of the language, we have created a one-of-a-kind unique Tamil Image Captioning Dataset, translated from Microsoft’s Common Objects in Context Dataset or commonly called the COCO Dataset, for Captioning of Images in the language of Tamil. With the help of the dataset created, this research work will explore several multi-modal architectures to provide captions of images directly in the language of Tamil for any given input Image. The results of the captions generated have been discussed and evaluated using the popular evaluation metric, Bilingual Evaluation Understudy Score (BLEU).
Hardware-assisted Neural Network IP Protection using Non-malicious Backdoor and Selective Weight Obfuscation

Neural networks (NNs) are already deployed in hardware today, becoming valuable intellectual property (IP) as many hours are invested in their training and optimization. Therefore, attackers may be interested in copying, reverse engineering, or even modifying this IP. The current practices in hardware obfuscation, including the widely studied LL technique, are insufficient to protect the actual IP of a well-trained NN: its weights. Simply hiding the weights behind a key-based scheme is inefficient (resource-hungry) and inadequate (attackers can exploit knowledge distillation). This paper proposes a two-step technique that addresses these issues: the obfuscation overhead is kept under control by applying only selective weight obfuscation, while distillation-based attack is prevented by prediction poisoning using non-malicious backdoor such that an attacker with access to an oracle cannot accurately train his/her model. This is the first work to consider such a poisoning approach in HW-implemented NNs. The poisoning occurs in score part before SoftMax. In the score poisoning, the accuracy and prediction distribution are maintained without disturbing the functionality or incurring high overheads. Finally, we elaborate a threat model which highlights the difference between random logic obfuscation and the obfuscation of NN IP. Based on this threat model, our security analysis shows that the proposed technique successfully and significantly reduces the accuracy of the stolen NN model on various representative datasets. Finally, we highlight that our proposed approach is flexible and does not require manipulation of the NN toolchain, and is coded in a flexible high-level language (e.g., C++).
An Adaptive Cubature Kalman Filtering Process for Two-Dimensional Target Tracking

In this paper, a novel adaptive approach of Cubature Kalman filter (CKF) is implemented for two-dimensional target tracking problem. This novel approach uses residual-innovation approach for adjusting the noise covariances for process. The measurement noise covariance is adjusted based on the covariance matching method. The novelty exists with the formulation of adaptation law for process and measurement noise covariances. The effectiveness of proposed algorithm is compared with traditional CKF, UKF and EKF by calculating root mean square error (RMSE) of states in 500 Monte Carlo runs. The relative computational time, mean and standard deviation of RMSEs for considered filtering techniques are also presented. The simulation results show improvement in estimation performance with adaptive CKF (ACKF) approach than others for target tracking system dealing with Gaussian and non-Gaussian noise distributions.
AELGA-FHE: An Augmented Ensemble Learning Based Genetic Algorithm Model for Efficient High Density Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) is defined as an encryption scheme that allows arithmetic & logical operations on ciphers, and yields the same effect on decrypted data. Resulting in the widespread use of FHE model for enforcing data privacy in organizations using polynomial arithmetic during encryption and decryption process, and use of different moduli for plain and cipher data. A wide variety of FHE algorithmic implementations are existing and each of these have their own nuances and limitations. It is observed that most of the existing approaches are context-insensitive and do not consider application-specific encryption strength requirements. In order to integrate context-sensitivity and improve encryption strength, These texts offer a Genetic Algorithm design (GA) model for FHE parameter optimization. Results of the proposed GA-FHE model are validated on multiple applications and are stored with respect to their strength classes. These classes, along with their respective FHE configurations are used for training an ensemble deep learning model. This model uses a combination of k Nearest Neighbors (kNN), random forest (RF), linear support vector machine (LSVM), linear regression (LR), and customized 1D Convolutional neural network (CNN) classifiers for strength estimation. Each incoming FHE request is evaluated on these models and their results are augmented to evaluate final FHE moduli parameters. The proposed AELGA-FHE model is tested on a wide variety of textual datasets including ‘Cipher text challenge’, ‘National cipher challenge’, & ‘Secondary cipher challenge’, and strength selection accuracy results are evaluated, strength of encryption, and computational delay. The proposed model outperforms existing FHE methods in terms of these parameters, thereby showcasing its superior deployment capabilities with respect to improved FHE encryption strength and reduced delay needed for high security encryption.
Augmented Reality for Seamless Human-Robot Interaction in Surgery

In the field of computer-assisted orthopaedic surgery (CAOS), new display technologies such as augmented reality (AR), defined as the superposition of computer- generated imagery onto the unoccluded, real environ- ment [1], have the potential to transform the way clinicians manage patient-specific data during surgery. Modern CAOS systems require surgeons to visualise and interact with 3D medical data (e.g. MRI/CT scans, surgical cutting plans etc.) in the 2D environment of touchscreen monitors. Such user interfaces can divert the surgeon’s attention from the operating site and require a mental coordinate mapping process to determine the spa- tial transformation between a surgeon’s real viewpoint and the monitor’s virtual view. In contrast, specialised AR user interfaces capable of directly projecting 3D in- formation onto anatomy can allow a surgeon to maintain their line-of-sight to the operating site, and also improve the quality of human-robot interaction of CAOS systems through reductions in visual clutter and sight diversion, and improvements in situational awareness, as investi- gated in literature [2]. A novel, AR-centric workflow was designed to exploit the reported benefits of AR in the context of a simulated computer-assisted patellofemoral arthroplasty (PFA), using the Navio® (Smith &amp; Nephew, UK), a commercially available CAOS platform. The constructed system co-registered the Navio® platform’s optical tracker with the fixed virtual world frame of a HoloLens 2 (Microsoft, USA) headset, and provided in-situ holographic guidance during two surgical tasks carried out during a PFA, patient-registration and robot- assisted cutting (cutting accuracy previously reported at [3]). A pre-clinical study was carried out to investigate the impact of the new AR workflow on mental-workload and usability measures when compared to the standard workflow delivered by the platform.
INTERPRETAÇÃO SIMULTÂNEA INTERMODAL DA LIBRAS PARA O PORTUGUÊS EM CONFERÊNCIA ACADÊMICA: EM FOCO OS NOMES PRÓPRIOS NACIONAIS OU NACIONALIZADOS DE PESSOAS

simultaneous interpretation.This will demonstrate the diversity of occurrences in the source language and of responses in the target language of people's names in an intermodal linguistic pair.
The Impact of Institutional Governance on Perceived Organizational Reputation in the Jordanian Public Sector

This paper aims to identify the impact of institutional governance on perceived organizational reputation in the Jordanian public sector. A quantitative approach was adopted through designing a questionnaire which was distributed to 600 employees in 6 governmental institutions. A total of 556 valid questionnaires were retrieved in the first stage and 97 questionnaires (items) were removed in the second SEM stage, so that 459 questionnaires were subject to analysis. In light of the data analysis, the study revealed a number of findings. Most notably, there is a significant impact of justice and rule of law, integrity and work ethics and social responsibility upon perceived organizational reputation in the Jordanian public sector. Justice and rule of law came first (79%), then, integrity and work ethics (65%), followed by social responsibility (61%). This means the endeavor of Jordanian public sector organizations towards adherence to laws and resorting to them in their functions, operations and services. Also, this result reflects the level of awareness of the importance of justice as a core value and an essential input in building a good organizational reputation for Jordanian public-sector organizations. This paper provides evidence from the public sector employees and concludes the impact of institutional governance factors on organizational reputation, which has not been researched enough previously in the public-sector context. These are important factors underlying organizational reputation and trust from the users of public services. Finally, new insights have been provided into the organizational reputation theory in the public sector.
Video analysis and intelligent signal processing and diagnosis system for sports dance training items

Since the 1980s, various art and cultural activities from the West have been widely disseminated in our country, and sports dance is one of the most popular projects among them. However, the training for sports dance mostly stays in the traditional classroom, and is not combined with deep-learning techniques. We focus on the video analysis and intelligent diagnosis system of sports dance training projects. During the research process, we developed an intelligent platform dedicated to training projects and combined it with the signal processing diagnostic system. The effect of training will ultimately lead to better competition results. From the experimental data, in this test of ten dance styles, the highest comprehensive score is Waltz’s 8.93 points, and the lowest comprehensive score is Samba’s 8.312 points. Moreover, the system designed is superior to the traditional multimedia system in performance comparison, which can provide better help for sports dance training. On the whole, the application of the intelligent diagnosis system developed in sports dance training projects meets the expected demand.
The Electronic Digital Instrument

Music education has relied on the basic assumption that to learn music, you need to learn to play an instrument. This disenfranchises and marginalizes many musicians who produce music using non-traditional approaches. As the computer, in all its various configurations, assumes a greater role in all musical activities, we need to rethink what learning music really means. Traditional music pedagogy is built on our understanding of music theory and practice from the perspective of the piano as our point of reference. In a time when any sound imaginable can be used as musical material, we see the computer becoming the focal point of musical activity and study. In this chapter, the author showcases how Berklee College of Music has made efforts to acknowledge the central role the computer plays in all aspects of musical life and to bring the computer into a program of instrumental study that includes private lesson, lab, and ensemble experiences. While computing devices in all manifestations are the central appliance in modern life, the electronic digital instrument (EDI) is a system we define as a computing device, physical controllers, and software working together to enable musical performance. While we see many students coming to music by producing it, we see that virtuosity in this field is not bound by the roles of producer and player, and that mastery comes from the place where they intersect. This chapter will highlight key features of the EDI and the approaches, constructivist in design, instructors at Berklee College of Music use to facilitate student musicianship in these contexts, while showcasing how barriers of access are reduced and a diversification of musicianship backgrounds is accepted.
A Corpus-Based Computational Study on Translators' Styles Based on Three Chinese Translations of the Old Man and the Sea

The study of translators’ styles is an important research topic that has attracted increasing attention in translation studies (cf. Baker, 2000; Huang, 2015; Li and Shao, 2016; Marco, 2004, among others). However, to date, virtually all these studies have relied on manual identification of linguistic features characteristic of a translator. To bridge this gap, this study introduces state-of-the-art tools in computational linguistics that can facilitate semi-automatic identification of translators’ styles. We demonstrate how to draw on natural language processing (NLP) tools to construct a paragraph-aligned parallel corpus based on The Old Man and the Sea and its three Chinese translations by Eileen Chang, Shaowei Zhao, and Kwang-Chung Yu. Through a series of corpus processing procedures including Chinese tokenization and bilingual alignment at paragraph and word level, lexical divergences among different translators can be automatically derived. The statistical information from bilingual word alignment and Keyword List empowers researchers to explore the systematic stylistic differences of word choices among the three translators in a straightforward manner, facilitating the identification of translators’ styles in a way that was difficult to obtain through traditional methods. Compared with previous approaches, our proposed corpus-based computational approach can address translators’ styles more efficiently and effectively by presenting both quantitative and qualitative evidence.
Performance Analysis of Wiener filter in Restoration of Covid-19 Chest X-Ray Images, Ultrasound Images and Mammograms

Medical images such as X-Ray images, Mammograms and Ultrasound images are very useful diagnostic techniques used for understanding the functions of different internal organs, bones, tissues, etc. Most of the times these medical images are degraded by some noises and different kinds of blur. Image blurring and degradation leads to loss of quality of images which in hand causes difficulty in proper diagnosis. This paper emphases on the efficacy of Wiener filter in image de blurring and denoising Chest X-Ray of Covid-19 patients, ultrasound images of fetal abdominal cyst, umbilical cord cyst and Common Carotid Artery, Mammogram of both pathological and non-pathological breasts. Performance of Wiener filter is analyzed using image restoration parameters like Structural Similarity (SSIM), Histogram, Peak Signal to Noise Ratio and Mean Square Error.
Image processing algorithms for self-driving cars navigation

The paper describes research and development of navigation algorithms for self-driving cars using multimodal input. The basis of such algorithms is the integration of the image processing results obtained from high-resolution cameras, as well as a lidar data. Such fusion method based on nonlinear filtering of detected and identified fixed landmarks. The paper substantiates the consistency of the solution and provides characteristics of its effectiveness.
Improved KCF algorithm and its application to target lost prediction

In order to solve the tracking drift problem in the obscured environment and reduce the failure rate of target tracking in the obscured scene, this paper proposes a target loss warning mechanism in the obscured situation based on the traditional KCF algorithm, which uses the analysis of the 3D response map during the target tracking process, and uses the response maximum (Fmax) and the average value of the response between 2 adjacent frames to measure the tracking status of the target and determine whether the target has tracking drift. At the same time, the APCE evaluation criterion is used to reduce unnecessary model updates and increase the speed of computation. The simulation results demonstrate that the target loss warning mechanism can accurately warn the KCF algorithm when tracking drift occurs, and the tracking success rate and tracking accuracy can be improved by 9.5% and 5.3% respectively compared to the traditional target tracking algorithm in the occlusion scenario.
Semantically Valid Integration of Development Processes and Toolchains

As an indispensable component of today’s world economy and an increasing success factor in production and other processes, as well as products, software needs to handle a growing number of specific requirements and influencing factors that are driven by globalization. Two common success factors in the domain of Software Systems Engineering are standardized software development processes and process-supported toolchains. Development processes should be formally integrated with toolchains. The sequence and the results of toolchains must also be validated with the specifications of the development process on several levels. The outcome of a conceptual deductive analysis is that there is neither a formal general mapping nor a generally accepted validation mechanism for the challenges that such an integrated concept faces. To close this research gap, this paper focuses on the core issue of the integration of development processes and toolchains in order to create benefits for modeling and automatization in the domain of systems engineering. Therefore, it describes a self-developed integration approach related to the recently introduced prototypical technical implementation TOPWATER. A unified metamodel specifies how processes and toolchains are linked by a general mapping mechanism that considers test options for the structural, content, and semantic levels.
On correctness of conditions for the CSIDH algorithm implementation on Edwards curves

Incorrect formulation and incorrect solution of the problem of the CSIDH algorithm implementation on Edwards curves was revealed in one of the famous works. The purpose of this paper is to present a detailed critique of such concept with a proof of its inconsistency. Specific properties of three non-isomorphic classes of super singular curves in the generalized Edwards form are considered: full, quadratic, and twisted Edwards curves. Conditions for existence of curves of all 3 classes with order of curves over a prime field are determined. The implementation of the CSIDH algorithm on isogenies of odd prime degrees is based on the use of quadratic twist pairs of elliptic curves. To this end, the CSIDH algorithm can be built both on complete Edwards curves with quadratic twist within this class, and on quadratic and twisted Edwards curves forming pairs of quadratic twist. In contrast to this, the authors of a well-known work are trying to prove theorems that state that there is a solution within one class of curves with a parameter which is a square. The critical analysis of theorems, lemmas, erroneous statements in this work is carried out. Theorem 2 on quadratic twist in classes of Edwards curves is proved. The CSIDH algorithm modification based on isogenies of quadratic and twisted Edwards curves is presented. To illustrate the correct solution of the problem, an example of Alice and Bob calculations in the secret sharing scheme according to the CSIDH algorithm is considered for.
Blockchain Analytics - Real-time Log Management in Healthcare

With the growing dependence on information technologies in healthcare organizations, it is crucial to ensure data security since its sensitiveness. To guarantee this requirement, a system was developed consisting of a private blockchain network designed to support the recording of hospital logs and an interface to facilitate data analysis. This article covers the development of a private blockchain solution. For its development were used Hyperledger technologies and Sockets.io. Apart from that, web technologies that were used to interface real-time logs. The objective of the developed solution is to allow the visualization of logs in real time, where analysis methods have applied.