Reviewerly/exHarmony huggingface dataset:
7S Model for Technology Protection of Organizations

Given the importance of technologies to organizations, technology leakages can cause considerable financial losses and threaten the survival of firms. Although organizations use technology protection diagnostic models to prevent such leakages, most diagnostic models focus on cybersecurity, and the evaluation system is complex, making it difficult for SMEs to use it. This makes them unsuitable for the general technology protection diagnosis of companies. Hence, this study proposes a diagnostic model that assesses these technology protection capabilities of organizations from personnel and administrative perspectives. Drawing upon the individual elements of the 7S model—shared values, strategy, structure, systems, staff, style, and skills—our model analyzes the influence of the elements on the technology protection capabilities of organizations. To determine this influence, the study conducts a questionnaire survey among 435 employees from large, larger medium-sized, and small and medium enterprises. Using the partial least squares and the artificial neural network methods, the study determines the ranking of the relative importance of the 7s elements. The results show that the shared values element most significantly influences these capabilities. The remaining elements influence the technology protection capabilities in the following order from the greatest to the least effect: staff, strategy, structure, systems, style, and skills. These findings highlight the significance of developing an awareness of the necessity of technology protection among all the members of an organization.
Learning Deep Latent Variable Models by Short-Run MCMC Inference with Optimal Transport Correction

Learning latent variable models with deep top-down architectures typically requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference step typically relies on either time-consuming long-run Markov chain Monte Carlo (MCMC) sampling or a separate inference model for variational learning. In this paper, we propose to use a shortrun MCMC, such as a short-run Langevin dynamics, as an approximate flow-based inference engine. The bias existing in the output distribution of the non-convergent short-run Langevin dynamics is corrected by the optimal transport (OT), which aims at transforming the biased distribution produced by the finite-step MCMC to the prior distribution with a minimum transport cost. Our experiments not only verify the effectiveness of the OT correction for the short-run MCMC, but also demonstrate that the latent variable model trained by the proposed strategy performs better than the variational auto-encoder (VAE) in terms of image reconstruction/generation and anomaly detection.
Cyber legislation and cyber-related legal issues in Bangladesh: inadequacies and challenges

Technology has inevitably evolved, driving significant human progress in every sphere of life. Amid this technological development, laws and regulations have been enacted to control offences relating to global advances in technology, including in Bangladesh. Bangladesh government has taken many initiatives to make the country digital. But in Bangladesh, the cyber-related laws are full of shortcomings and the customary laws are extremely outdated and most of the laws do not fit the era of ICT. Although Bangladesh government passed the ICT Act and the Digital Security Act to control cybercrime, the country's existing laws for fighting the menace are inadequate due to certain limitations. The article examines contemporary cyber legislation and legal issues in Bangladesh, together with the customary laws and existing policies, with a view to identifying their inadequacies and challenges. This article makes use of secondary data, i.e., books, journals, customary laws, acts etc.
Implications of the China–US trade agreement on the civil protection of trade secrets in China: is it a game changer?

China and the United States (US) signed the China–US trade agreement in early 2020 as a means to pause trade tensions between these two large economies. The protection of trade secrets is a priority issue that the trade agreement aims to address. In this regard, the current article examines the implications of the China–US trade agreement in relation to civil protection of trade secrets in China. This article first analyses the traditional civil protection method of trade secrets in China, which relies on the European approach of anti-unfair competition law. It then decodes several different and improved protection measures for trade secrets provided in the China–US trade agreement. Lastly, this article identifies some implications of the trade agreement for China’s legal practices. China has amended its Anti-Unfair Competition Law and enacted relevant judicial interpretations regarding preliminary injunctions in an endeavour to fulfil its commitments. Arguably, according to provisions in the China–US trade agreement, the protection of trade secrets in China faces a game changer from the traditional unfair-competition approach to the intellectual property approach. Additionally, the authors of this article believe that China’s current provisions in the Anti-Unfair Competition Law are not sufficient. Rather, China needs to enact a standalone trade secret law containing provisions of trade secret protection and the legal use of trade secrets.
Deterministic Video Streaming with Deep Learning Enabled Base Station Intervention for Stable Remote Driving System

A remote driving system (RDS) via wireless net- works is a promising solution to guarantee the safety of autonomous vehicles. In the system, a remote operator controls vehicles while watching video frames transmitted from the controlled vehicles. The conventional video streaming method decides the video resolution using the statistical quality of services (QoS) to guarantee the delay constraints; however, it may yield a long delay in best-effort wireless networks if the quality of the wireless channel suddenly changes. To cope with the issue, we propose a deterministic networking approach. A base station (BS) predicts a future QoS using a radio map and driving route of vehicles to decides the adequate video resolution that satisfies the delay constraints of RDS. BS also has a super-resolution (SR) function to enhance the quality of experience (QoE) in video streaming. Thanks to the proposed QoS prediction and video frame resolution decision, BS can use the adequate SR model for each video frame, further enhancing QoE. Using the measurement datasets of radio access networks, we confirm that the proposed RDS can provide high-quality video streaming while satisfying the delay constraints in any time-series wireless channel situations.
Cloud deployment of game theoretic categorical clustering using apache spark: An application to car recommendation

Personal vehicles are invariably being preferred over public transport nowadays. Contact-less feature inspection and analysis based on personal preferences will be in high demand among customers in the post-pandemic world. A comprehensive online car recommendation system will be the customers’ spontaneous choice to understand and select the features of vehicles. However, the clustering of such categorical features is a challenging task as it is difficult to compare two textual attributes. In this paper, we have designed a cloud-based system that will automatically address this issue. Motivated by the cooperative game theory and fuzzy technique, and integrating the concept of Shapley theorem, a categorical data clustering algorithm has been developed. At the same time, to overcome the major limitation of having a high time complexity of the order O (n2) associated with the Shapley computation, the proposed algorithm has been distributed using Apache Spark’s Map Reduce architecture in Google Cloud Platform. The model has been thoroughly validated based on its performance on several synthetic as well as real data sets. Finally, a car recommendation system has been proposed and tested on three car sell data sets. The proposed approach outperforms the corresponding existing categorical clustering approaches in terms of various clustering validity indices. To the best of the authors’ knowledge, this is the first attempt to apply Map Reduce based Shapley computation over the categorical clustering, which can find its application beyond the proposed car recommendation system as well.
Creating and Visualising Cognitive Maps of Knowledge Diagnosis During the Processing of Learning Digital Footprint

The paper describes the problem of creating a single mapping of the learning situation during the processing of digital footprint and decision making in intelligent tutoring systems. We propose creating a Cognitive Map of Knowledge Diagnosis as a way of summarising data in the digital footprint. The elements of this cognitive map and the details of their visualisation are described. The results of the use of cognitive maps are demonstrated on the example of personalising the content of an online learning course and providing detailed feedback.
A graph-based recommendation approach for highly interactive platforms

Highly interactive and dynamic marketplaces shake the underlying hypothesis of established recommendation approaches assuming static offerings and sparse interaction data. Such markets show different dynamics due to goods/contents volatility and public accessibility. This imposes new challenges to recommender systems since they are required to handle unbounded data streams with high velocity, volume and variability while operating at scale and in real-time. Moreover, due to the high rates of new offerings introduction and obsolescence, low latency modeling and inference are needed to keep an up-to-date understanding of the market. In this paper, we propose a recommendation approach addressing the specific challenges of real-time stream recommendation in highly interactive marketplaces. The approach is based on several psychological theories to model consumers’ perceived value towards items. Besides, a ranking measure is defined on a heterogeneous information network to infer the factors that drive consumers’ decisions. With data relationships at its center, this data model is strongly efficient while ensuring free and flexible knowledge evolution as data evolves. It allows low latency incremental learning at scale while providing dynamic recommendations. Several comparative experiments were conducted to validate the potential of this approach in different use cases requiring offline, online, static or dynamic recommendations.
Adoptability of machine learning and its relationship to learning style preferences

This research has empirically examined the association between the implementation of M-learning and the interests of Malaysian EFL students in a course in business English. The report used a systematic method for association analysis. The data were randomly collected from 125 Malaysian EFL students from a total of 184 cell phone students enrolled in an English business class at one of the universities in Shanghai, Malaysia. Two structured testing instruments, Mobile Learning Questionnaire (MLQ) and the Choice Questionnaire for Visual Learning Style (PLSPQ), were introduced. The findings revealed that students display a high degree of M-learning in business Language. It was found that the participants had a significant preference for visual learning, group learning, individual learning, tactile learning and auditory learning, while being the least concerned with kinesthetic learning. Tests of disparities showed that males respondents have a higher appeal for utility, acceptability, satisfaction, ease-of-use and behavioural intent than their peers, if students are divided by age and gender. Regarding their age, it was noted that respondents understood the benefits of using M-learning regardless of their age. Likewise, there are no major variations in the interests of respondents in their learning styles when clustered according to class. The younger group of respondents however shows preference for group study, while the older group favors individual schooling. Finally, a significant association between acceptance of M-learning and student expectations was demonstrated. The optimistic correction indicates that the higher the recognition of M-learning by students, the greater their expectations for touch, physical, visual, auditory, kinesthetic and touch learning activities. This research provides scientific and educational ramifications.
Elections, Civic Trust, and Digital Literacy: The Promise of Blockchain as a Basis for Common Knowledge

Abstract Few recent developments in information technology have been as hyped as blockchain, the first implementation of which was the cryptocurrency Bitcoin. Such hype furnishes ample reason to be skeptical about the promise of blockchain implementations, but I contend that there’s something to the hype. In particular, I think that certain blockchain implementations, in the right material, social, and political conditions, constitute excellent bases for common knowledge. As a case study, I focus on trust in election outcomes, where the ledger records not financial transactions but vote tallies. I argue that blockchain implementations could foster warranted trust in vote tallies and thereby trust in the democratic process. Finally, I argue that if the promise of blockchain implementations as democratic infrastructure is to be realized, then democracies first need to ensure that these material, social, and political conditions obtain.
Intention Recognition in Human Robot Interaction Based on Eye Tracking

In human robot interaction any input that might help the robot to understand the human behaviour is valuable, and the eyes and their movement undoubtedly hold valuable information. In this paper we propose a novel algorithm for intention recognition using eye tracking in human robot collaboration. We first explore how the Cascade Effect hypothesis and a LSTM-based machine learning model perform to classify intent from gaze. Second, an algorithm is proposed, which can be used in a real time interaction to infer intention from the human user with a small uncertainty. A data collection with 30 participants was conducted in virtual reality to train and test the algorithm. The algorithm allows to detect the user intention up to two seconds before any user action with a success rate of up to 75%. These results open the possibility to study human robot interaction, where the robot can take the initiative based on the intention recognition.
Predictive Two-timescale Resource Allocation for VoD Services in Fast Moving Scenarios

With the burgeoning video applications in 5G/B5G, the requirements for video frame quality and playback smoothness are equally strict but challenging to satisfy simultaneously, especially in fast moving scenarios. To tackle this issue, in this paper, we propose a predictive two-timescale resource allocation scheme for video-on-demand (VoD) services by leveraging the long-term channel prediction. The scheme addresses two critical concerns. One is how to schedule packets over a large timescale to avoid transmission in poor channel states. The other is how to guarantee the delay-quality of service (QoS) over a small timescale to satisfy the high-quality requirements of video services. In the framework of network slicing, we split the VoD slice into two logical sub-slices to support the video contents that play immediately after downloading and the video contents to be pre-cached, respectively. To perform an efficient resource reservation, we propose a martingales-based resource estimation method for the video streams with statistical delay-QoS requirements. Based on this, our scheme is divided into two stages. First, on the time scale of prediction time slots, we propose a low-complexity heuristic algorithm to find a spectrum-efficient video delivery pattern. Then, in each transmission time interval, we develop a utility theory based resource allocation algorithm to balance the metrics of spectrum efficiency, fairness, and delay-QoS. The simulation results demonstrate the capability of the QoS guarantee and the promising gain of spectrum efficiency brought by our scheme.
Text Recognition of Cardboard Pharmaceutical Packages by Utilizing Machine Vision

In this paper, text recognition of variably curved cardboard pharmaceutical packages is studied from the photometric stereo imaging point-of-view with focus on developing a method for binarizing the expiration date and batch code texts. Adaptive filtering, more specifically Wiener filter, is used together with haze removal algorithm with fusion of LoG-edge detected sub-images resulting an Otsu thresholded binary image of expiration date and batch code texts for future analysis. Some results are presented, and they appear to be promising for text binarization. Successful binarization is crucial in text character segmentation and further automatic reading. Furthermore, some new ideas will be presented that will be used in our future research work.
Can Teleseismic Travel-Times Constrain 3D Anisotropic Structure in Subduction Zones? Insights from Realistic Synthetic Experiments

&amp;lt;p&amp;gt;Despite the well-established anisotropic nature of Earth&amp;amp;#8217;s upper mantle, the influence of elastic anisotropy on teleseismic tomographic images remains largely ignored. In subduction zones, unmodeled anisotropic heterogeneity can lead to substantial isotropic velocity artefacts that may be misinterpreted as compositional heterogeneities (e.g. Bezada et al., 2016). Recent studies have demonstrated the possibility of inverting P-wave delay times for the strength and orientation of seismic anisotropy assuming a hexagonal symmetry system (e.g. Huang et al., 2015; Munzarov&amp;amp;#225; et al., 2018). However, the ability of P-wave delay times to constrain complex anisotropic patterns, such as those expected in subduction settings, remains unclear as the aforementioned methods are tested using ideal self-consistent data (i.e. data produced using the assumptions built into the tomography algorithm) generated from simplified synthetic models. Here, we test anisotropic P-wave imaging methods on data generated from geodynamic simulations of subduction. Micromechanical models of polymineralic aggregates advected through the simulated flow field are used to create an elastic model with up to 21 independent coefficients. We then model the teleseismic wavefield through this fully anisotropic model using SPECFEM3D coupled with AxiSEM. P-wave delay times across a synthetic seismic array are measured using conventional cross-correlation techniques and inverted for isotropic velocity and the strength and orientation of anisotropy using travel-time tomography methods. We propose and validate approximate analytic finite-frequency sensitivity kernels for the simplified anisotropic parameters. Our results demonstrate that P-wave delays can reliably recover horizontal and vertical changes in the azimuth of anisotropy. However, substantial isotropic artefacts remain in the solution when only inverting for azimuthal anisotropy parameters. These isotropic artefacts are largely removed when inverting for the dip as well as the azimuth of the anisotropic symmetry axis. Due to the relative nature of P-wave delay times, these data generally fail to reconstruct anisotropic structure that is spatially uniform over large scales. To overcome this limitation, we propose a joint inversion of SKS splitting intensity with P-wave delay times. Preliminary results demonstrate that this approach improves the recovery of the magnitude and azimuth of anisotropy. We conclude that teleseismic P-wave travel-times are a useful observable for probing the 3D distribution of upper mantle anisotropy and that anisotropic inversions should be explored to better understand the nature of isotropic velocity anomalies in subduction settings.&amp;lt;/p&amp;gt;
Maximizing Last-Minute Backup in Endangered Time-Varying Inter-Datacenter Networks

Natural disasters are time-varying in nature. They adversely affect backbone datacenter (DC) networks, thereby resulting in huge loss of data within a short span of time. Maximizing last-minute data backup in an endangered DC network hit by a progressive disaster is, therefore, of utmost importance to ensure data protection and service continuation. Thus, in this article, we propose a novel Mixed-Integer Linear Program (MILP) and a heuristic (D-RADDAR) with reduced time-complexity to address risk-aware emergency backup in endangered DC networks hit by dynamic disasters. The mathematical framework for maximizing the overall last-minute backup percentage is developed. A quantitative analysis of the risk associated with such last-minute backups is carried out. The proposed algorithms are evaluated in terms of the DC backup success percentage and compared to existing dynamic backup algorithms in the literature. Extensive simulations indicate that among all existing heuristics, the D-RADDAR achieves maximum data evacuation while maintaining minimal risk and having a polynomial time-complexity.
An Evaluation ALgorithms for Classifying Leukocytes Images

An extremely significant component of the blood that arranges the immune system responsible for combating foreign elements is white blood cells (leukocytes). Neutrophils, eosinophils, lymphocytes, monocytes, and basophils in common are the five specific types of white blood cells. Each class receives a different proportion and typically performs various cognitive functions. It is significant for monitoring patients' health and awareness of the health to be capable of distinguishing and therefore recognizing these various constituents. The discoloration procedure and visual examination for raw images obtained merely are repetitive and based on human errors under the standard microscope. Besides, the distinct lack of training samples investing in white blood cells' spatial differences, such that suitable classification methods can make assumptions effectively, is a significant challenge. Blood cells are correctly examined in a clinical diagnosis through experienced clinicians from potential patients' epithelial cell samples. This considerable research primarily focuses on the morphological characteristics and features of white blood cells and their nuclei and cytoplasm, including forms, proportions, shades, textures, phases of maturity, and staining processes. The proposed method can be appropriately implemented in four stages: segmentation, scanning, feature extraction, and blood cell classification. Next, the cell images are segmented, including the social categorization of white blood cells into specific clusters. The second distinct stage typically includes each segmented image being carefully scanned and the dataset being adequately prepared. The third necessary step is carefully extracting the modern form and complex texture from a scanned image. Typically apply various machine learning algorithms at the final stage (Backpropagation (BP), Multilayer Perceptron (MP), and Support Vector Machine (SVM).
PonderNet: Learning to Ponder

In standard neural networks the amount of computation used grows with the size of the inputs, but not with the complexity of the problem being learnt. To overcome this limitation we introduce PonderNet, a new algorithm that learns to adapt the amount of computation based on the complexity of the problem at hand. PonderNet learns end-to-end the number of computational steps to achieve an effective compromise between training prediction accuracy, computational cost and generalization. On a complex synthetic problem, PonderNet dramatically improves performance over previous adaptive computation methods and additionally succeeds at extrapolation tests where traditional neural networks fail. Also, our method matched the current state of the art results on a real world question and answering dataset, but using less compute. Finally, PonderNet reached state of the art results on a complex task designed to test the reasoning capabilities of neural networks.1