Reviewerly/exHarmony huggingface dataset:
Provision and Management of Educational Activities in the Conditions of Distance Education at the North-Eastern Federal University

This paper presents the experience and problems of forming electronic information educational environment in the North-Eastern Federal University, identifies the importance and urgency of developing management systems in a digital format and the impact of territorial, climatic, demographic and national characteristics of the region on the development of distance education. The peculiarities of the organization of electronic information educational environment in conditions of the identity of the requirements of federal state educational standards in educational institutions of higher education were disclosed. It is shown how the organization of work in digital format in conditions of self-isolation during the spread of a new coronavirus infection has shown itself in practice. This study predetermines the need for further theoretical analysis and practical use of academic disciplines implemented in online format, the development of scientific and methodological foundations for the organization of the educational process, the organization of different types of practices in the distance mode. All this requires improving and updating the systems of electronic information educational environment to provide individual educational trajectories, development of digital academic mobility of students to participate in educational courses and programs of leading domestic and foreign universities, creating their own online courses to develop inbound digital mobility.
Digital Assessment Literacy in Online Courses (Formative/Summative)

Assessment is a valuable process that assists teachers to identify teaching practices that are effective for all students since not every student learns or retains information in the same way. In the open, distance, and e-learning (ODeL) environment, specific assessment strategies can be employed to make a judgment and make the best decisions about the student's achievement. Due to the nature of the ODeL learning environment, assessments should be digital for powerful insights without the paperwork. This chapter employed digital assessment literacy to provide a particular perspective of assessment strategies in the ODeL.
Security in Digital Healthcare System

In this modern and technological world, medical field has also enhanced and digitized itself to help patients and medical professionals as per the need of the time. Most of the time, patients do not have serious medical problems. They only need some expert suggestions from a medical professional. In these times of crisis, medical professionals can help such patients via telecommunications. In serious medical conditions also, telecommunication plays an important role in helping patients as well as medicos. Thus, in the field of healthcare, conventional diagnostic methods are gradually being taken over by digital mode of diagnostics. Almost every component of medical field is in the process of digitization. This digital data can be used at convenient stages for the diagnosis of patients in the form of a patient medical record digitally stored over the Internet. Further, these medical data can also be used to predict future disease in patients and advise accordingly.
LCP of matrix product codes

In this paper, we firstly present a new criterion of linear complementary pairs (abbreviated to LCP) of codes over finite fields. Our result for the linear complementary pairs of codes extends the criterion of linear complementary dual (LCD) codes given by Massey. Then, two methods to construct LCP of codes from matrix product codes are provided, and concrete examples are presented to construct good parameters of LCP of codes.
DCQCN Advanced (DCQCN-A) : Combining ECN and RTT for RDMA Congestion Control

Since DCQCN was proposed in 2015, it has gradually become a common congestion control solution for remote direct memory access (RDMA), which is rapidly becoming a basic feature of high-speed clusters and data center networks. However, DCQCN has performance problems in the face of the ultralow latency and high bandwidth requirements of large data centers. As an explicit congestion notification (ECN) dominated congestion control (CC) algorithm, DCQCN is prone to queuing and causing delay and even packet loss in large-scale communications. Therefore, this paper proposes DCQCN-A, an algorithm that combines ECN and Round-Trip Time (RTT) to improve congestion control capabilities. In simulation, DCQCN-A can handle incast congestion of at least 2048 flows, which is 4 times more than that of DCQCN. The performance in microbenchmarks shows the convergence, fairness and adaptability of DCQCN-A. As for realistic loads, the average latency of DCQCN-A in Websearch load is 13.2µs, and it is 45% lower than DCQCN. In FB_Hadoop, DCQCN-A has a 15% lower average latency than DCQCN.
Enhancing Robots Navigation in Internet of Things Indoor Systems

In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer steps to reach the destination, ranging from 59 to 73 m on average, as opposed to the wall-following strategy, which requires an average of 732 m. On average, the robot with a constant speed and reflected virtual target approach takes 103 s, whereas the identical robot with a wall-following approach takes 907 s to complete the tasks. Using a fuzzy-speed robot, the duration for the wall-following approach is greatly reduced to 507 s, while the reflected virtual target may only need up to 20% of that time. More results and detailed comparisons are embedded in the subsequent sections.
NSGA-II-Based Granularity-Adaptive Control-Flow Attestation

Since the widespread adoption of edge computing and IoT technology, Control-Flow Hijacking (CFH) attacks targeting programs in resource-constrained embedded devices have become prevalent. While the Coarse-Grained Control-Flow integrity Attestation (CGCFA) lacks accuracy for the CFH attacks detection, the Fine-Grained Control-Flow integrity Attestation (FGCFA) detect the attacks more accurately but with high overheads, which can be a big burden (e.g., to industrial control system with strict performance requirements). In this paper, we propose a NSGA-II (Nondominated Sorting Genetic Algorithm-II) based Granularity-Adaptive Control-Flow Attestation (GACFA) for the programs in embedded devices. Specifically, we propose a Granularity-Adaptive Control-Flow representation model to reduce the complexity of programs’ control-flow graph and propose NSGA-II-based granularity-adaptive strategy generation algorithm to balance the security and performance requirements. Besides, runtime protection for the GACFA at the program end with SGX is proposed to protect the integrity and confidentiality of control-flow measurement data. The experiments show that our work can find out the best-so-far control-flow granularity with stability and provide secure program attestation for the verifier. In addition, the security/performance benefit of adopting our proposal over CGCFA is 13.7, 25.1, and 43.0 times that of adopting FGCFA over ours in different threat scenarios.
Human Factors in Interactive Machine Learning: A Cybersecurity Case Study

Cybersecurity is emerging as a major issue for many organizations and countries. Machine learning has been used to recognize threats, but it is difficult to predict future threats based on past events, since malicious attackers are constantly finding ways to circumvent defences and the algorithms that they rely on. Interactive Machine learning (iML) has been developed as a way to combine human and algorithmic expertise in a variety of domains and we are currently applying it to cybersecurity. In this application of iML, implicit knowledge about human behaviour, and about the changing nature of threats, can supplement the explicit knowledge encoded in algorithms to create more effective defences against cyber-attacks. In this paper we present the example problem of data exfiltration where insiders, or outsiders masquerading as insiders, who copy and transfer data maliciously, against the interests of an organization. We will review human factors issues associated with the development of iML solutions for data exfiltration. We also present a case study involving development of an iML solution for a large financial services company. In this case study we review work carried out on developing visualization dashboards and discussing prospects for further iML integration. Our goal in writing this paper is to motivate future researchers to consider the role of the human more fully in ML, not only in the data exfiltration and cybersecurity domain but also in a range of other applications where human expertise is important and needs to combine with ML prediction to solve challenging problems.
On Communication Models and Best-Achievable Security in Two-Round MPC

Recently, a sequence of works have made strong advances in two-round (i.e., round-optimal) secure multi-party computation (MPC). In the honest-majority setting – the focus of this work – Ananth et al. [CRYPTO’18, EC’19], Applebaum et al. [TCC’18, EC’19] and Garg et al. [TCC’18] have established the feasibility of general two-round MPC in standard communication models involving broadcast (\(\mathcal {BC}\)) and private point-to-point (\(\mathcal {P}\mathrm {2}\mathcal {P}\)) channels.In this work, we set out to understand what features of the communication model are necessary for these results, and more broadly the design of two-round MPC. Focusing our study on the plain model – the most natural model for honest-majority MPC – we obtain the following results: Dishonest majority from Honest majority: In the two round setting, honest-majority MPC and dishonest-majority MPC are surprisingly close, and often equivalent. This follows from our results that the former implies 2-message oblivious transfer, in many settings. (i) We show that without private point-to-point (\(\mathcal {P}\mathrm {2}\mathcal {P}\)) channels, i.e., when we use only broadcast (\(\mathcal {BC}\)) channels, honest-majority MPC implies 2-message oblivious transfer. (ii) Furthermore, this implication holds even when we use both \(\mathcal {P}\mathrm {2}\mathcal {P}\) and \(\mathcal {BC}\), provided that the MPC protocol is robust against “fail-stop” adversaries. Best-Achievable Security: While security with guaranteed output delivery (and even fairness) against malicious adversaries is impossible in two rounds, nothing is known with regards to the “next best” security notion, namely, security with identifiable abort (IA). We show that IA is also impossible to achieve with honest-majority even if we use both \(\mathcal {P}\mathrm {2}\mathcal {P}\) and \(\mathcal {BC}\) channels. However, if we replace \(\mathcal {P}\mathrm {2}\mathcal {P}\) channels with a “bare” (i.e., untrusted) public-key infrastructure (\(\mathcal {PKI}\)), then even security with guaranteed output delivery (and hence \(\texttt {IA} \)) is possible to achieve. These results “explain” that the reliance on \(\mathcal {P}\mathrm {2}\mathcal {P}\) channels (together with \(\mathcal {BC}\)) in the recent two-round protocols in the plain model was in fact necessary, and that these protocols couldn’t have achieved a stronger security guarantee, namely, \(\texttt {IA} \). Overall, our results (put together with prior works) fully determine the best-achievable security for honest-majority MPC in different communication models in two rounds. As a consequence, they yield the following hierarchy of communication models: $$\begin{aligned} \mathcal {BC}< \mathcal {P}\mathrm {2}\mathcal {P}< \mathcal {BC}+\mathcal {P}\mathrm {2}\mathcal {P}< \mathcal {BC}+\mathcal {PKI}. \end{aligned}$$This shows that \(\mathcal {BC}\) channel is the weakest communication model, and that \(\mathcal {BC}+\mathcal {PKI}\) model is strictly stronger than \(\mathcal {BC}+\mathcal {P}\mathrm {2}\mathcal {P}\) model.
Reconnaissance Mission Flight Modeling for Strategic Planning of Manned and Unmanned Vehicle Integrated Disaster Response

Disaster response activities include various aircraft, many of which operate under visual flight rules (VFR) when performing reconnaissance, search and rescue missions. When flying in visual conditions, the pilot keeps their eyes out of the cockpit and looks for other traffic to operate the aircraft and avoid collisions. The trajectory of the aircraft is mostly determined by the pilot’s manual control as well, and since the flight plan submitted before departure contains only very limited information, strategic trajectory planning is not possible. Recent technology developments, however, have shown the potential of small unmanned aircraft systems (sUAS) in reconnaissance. For both manned and unmanned aircraft to perform their missions safely and efficiently, a more efficient airspace allocation and potential segregation is necessary. This in turn requires strategic VFR trajectory modeling. In this research we consider potential parameters which define the VFR flight reconnaissance mission trajectory by analyzing helicopter flight data. As an initial step of the VFR flight model development, here we consider time, lateral and vertical trajectory modeling and identify necessary parameters to be distance from the target landmark, altitude, and view angle. The trajectory model assumptions are verified through flight data obtained from JAXA’s helicopter flight tests conducted in November 2020, which modeled real reconnaissance missions. Results confirm the feasibility of the model assumptions and set directions for future research.
eGAN: Unsupervised Approach to Class Imbalance Using Transfer Learning

Class imbalance is an inherent problem in many machine learning classification tasks. This often leads to learned models that are unusable for any practical purpose. In this study, we explore an unsupervised approach to address class imbalance by leveraging transfer learning from pre-trained image classification models. To this end, an encoder-based Generative Adversarial Network (eGAN) is proposed which modifies the generator of a GAN by introducing an encoder module and adopts the GAN loss function to directly classify the majority and minority class. To the best of our knowledge, this is the first work to tackle this problem using GAN-based loss function rather than augmenting the dataset with synthesized fake images. Our approach eliminates the epistemic uncertainty in the model predictions, as \(P(minority)\) and \(P(majority)\) need not sum up to 1. The impact of transfer learning and combinations of different pre-trained image classification models at the generator and the discriminator level is also explored. Best result of 0.69 F1-score was obtained on CIFAR-10 classification task with an enforced imbalance ratio of 1:2500. Our implementation code is available at - https://github.com/demolakstate/eGAN_addressing_class_imbalance_with_transfer_learning_on_GAN.git.
Cross-lingual embeddings with auxiliary topic models

Projection-based methods for generating high-quality Cross-Lingual Embeddings (CLEs) have shown state-of-the-art performance in many multilingual applications. Supervised methods that rely on character-level information or unsupervised methods that need only monolingual information are both popular and have their pros and cons. However, there are still problems in terms of the quality of monolingual word embedding spaces and the generation of the seed dictionaries. In this work, we aim to generate effective CLEs with auxiliary Topic Models. We utilize both monolingual and bilingual topic models in the procedure of generating monolingual embedding spaces and seed dictionaries for projection. We present a comprehensive evaluation of our proposed model through the means of bilingual lexicon extraction, cross-lingual semantic word similarity and cross-lingual document classification tasks. We show that our proposed model outperforms existing supervised and unsupervised CLE models built on basic monolingual embedding spaces and seed dictionaries. It also exceeds CLE models generated from representative monolingual topical word embeddings.
A Machine Learning Model to Prune Insignificant Attributes

In this research work, a machine learning model is proposed with only those features which are significantly contributing in prediction using multiple linear regression. The other insignificant features are pruned or eliminated using the concept of p-value. The research work will use p-value solely to determine which features are significant to the dataset and which are not so important. The results obtained are quite promising in terms of prediction power of the novel model as compared to the work done in literature till now.
FedLabCluster: A Clustered Federated Learning Algorithm Based on Data Sample Label

Federated learning is a method to train machine learning models in large-scale distributed devices without uploading local data to the central server. However, in the actual federated learning application environment, the Non-Independent and Identically Distributed(Non-IID) data will reduce the prediction accuracy and performance of federated learning. In this paper, we propose a novel clustered federated learning algorithm, FedLabCluster. This algorithm uploads the data sample labels of all clients participating in the training. The central server forms the sample label matrix and uses the clustering algorithm to cluster the clients. We evaluated the FedLabCluster algorithm based on five datasets and compared the FedLabCluster algorithm with the baseline algorithm. Compared with the FedAvg algorithm in the MNIST dataset, the accuracy of the FedLabCluster algorithm was improved by 6.5% and 6.9% on the CNN and MLP models. In the FEMNIST dataset, accuracy was improved by 15% and 28.6% on CNN and MLP models. Therefore, the FedLabCluster algorithm is feasible in Non-IID data and has batter federated learning performance and robustness.
Designing Augmented Reality Virtual Displays for Productivity Work

We must consider alternative displays for supporting productivity work in the context of an increasingly work-from-home world. Augmented reality virtual monitors can fulfill these needs by equipping users with large screen real estate, while maintaining portability, cost-effectiveness, and not occupying physical space. However, there are open questions regarding how to design virtual monitors. In my dissertation, I plan to investigate the design of virtual monitors to enhance productivity everywhere. This work comprises a group of design and user study contributions. I conducted a user study to understand the feasibility of virtual monitors and their tradeoffs when compared against physical monitors. I further propose investigating the design of static properties and dynamic behaviors that cannot be achieved through physical monitors.
Advancing Health Information System with System Thinking: Learning Challenges of E-Health in Bangladesh During COVID-19

Background: Public health system is connected to a complex dynamic of overall systematic processes of socio-political structure. Therefore, ensuring a high-quality digital healthcare system has inter-connected impacts on the war of COVID-19 prevention and beneficial for the health and welfare of people. Adopting an electronic vaccination registration system in Bangladesh has already made a huge impact in bringing the huge population under the vaccine scheme in a responsive manner. However, the country still has been struggling to achieve a systematic and technical strength due to a range of issues such as technical knowledge of healthcare professionals in providing services (for instance, e-healthcare services), use of tech tools and their application, developing and managing data, data visualization, and data availability. Objective: The advancement of the health information system (HIS) develops the entire healthcare service in a country. This paper aims to explore how a developing country like Bangladesh has been fighting the COVID-19 pandemic and how the adoption of a robust digital health information system can impact the overall health and living conditions of the large population at stake. Method: Based on system thinking and modeling, this study provided and described information based on existing data obtained from different inter-connecting sources with contributions to the systematic approach indicating a brief statistical data analysis. Results: This study finds out that adopting a rigorous digital health information system during COVID-19 helped Bangladesh immensely to fight the ongoing pandemic and minimized the overall risks, but she still has a lot of scope to improve.
Knowledge-based relational search in cultural heritage linked data

Abstract This article presents a new knowledge-based approach for finding interesting semantic relations between resources in a knowledge graph (KG). The idea is to characterize the notion of ‘interesting connection’ in terms of generic ontological explanation patterns that are applied to an underlying linked data repository to instantiate connections. In this way, (1) semantically uninteresting connections can be ruled out effectively and (2) natural language explanations about the connections can be created for the end-user. The idea has been implemented and tested based on a KG of biographical data extracted from the life stories of 13,144 prominent historical persons in Finland, enriched by data linking to collection databases of museums, libraries, and archives. The demonstrator is in use as part of the BiographySampo portal of interlinked biographies.
Security Management Suitable for Lifecycle of Personal Information in Multi-User IoT Environment

In recent years, as all actions of Internet users become information, the importance of personal information is emphasized, but in reality, the management of personal information is still insufficient. With the advent of the concept of sharing systems such as the sharing economy, the numbers of IoT application services (for example, a healthcare service using sharing IoT devices, or a vehicle sharing system with IoT devices) using users’ personal information are increasing, but the risk of using personal information is not managed. To solve this issue, the European GDPR stipulates the content of personal information protection. In this paper, we present a method to securely manage personal information in IoT devices in IoT application environments in accordance with the GDPR. We first describe the lifecycle stages of personal information occurring in IoT application services and propose a method to securely manage personal information at each stage of the lifecycle according to the flow of personal information in IoT devices. We also evaluated the usefulness and applicability of the proposed scheme through two service scenarios. Since the proposed method satisfies the requirements for personal information management in IoT application environments, it is expected to contribute to the development of the IoT business field that handles personal information.