Reviewerly/exHarmony huggingface dataset:
A Multi-Core Controller for an Embedded AI System Supporting Parallel Recognition

Recent advances in artificial intelligence (AI) technology encourage the adoption of AI systems for various applications. In most deployments, AI-based computing systems adopt the architecture in which the central server processes most of the data. This characteristic makes the system use a high amount of network bandwidth and can cause security issues. In order to overcome these issues, a new AI model called federated learning was presented. Federated learning adopts an architecture in which the clients take care of data training and transmit only the trained result to the central server. As the data training from the client abstracts and reduces the original data, the system operates with reduced network resources and reinforced data security. A system with federated learning supports a variety of client systems. To build an AI system with resource-limited client systems, composing the client system with multiple embedded AI processors is valid. For realizing the system with this architecture, introducing a controller to arbitrate and utilize the AI processors becomes a stringent requirement. In this paper, we propose an embedded AI system for federated learning that can be composed flexibly with the AI core depending on the application. In order to realize the proposed system, we designed a controller for multiple AI cores and implemented it on a field-programmable gate array (FPGA). The operation of the designed controller was verified through image and speech applications, and the performance was verified through a simulator.
Learning the Incremental Warp for 3D Vehicle Tracking in LiDAR Point Clouds

Object tracking from LiDAR point clouds, which are always incomplete, sparse, and unstructured, plays a crucial role in urban navigation. Some existing methods utilize a learned similarity network for locating the target, immensely limiting the advancements in tracking accuracy. In this study, we leveraged a powerful target discriminator and an accurate state estimator to robustly track target objects in challenging point cloud scenarios. Considering the complex nature of estimating the state, we extended the traditional Lucas and Kanade (LK) algorithm to 3D point cloud tracking. Specifically, we propose a state estimation subnetwork that aims to learn the incremental warp for updating the coarse target state. Moreover, to obtain a coarse state, we present a simple yet efficient discrimination subnetwork. It can project 3D shapes into a more discriminatory latent space by integrating the global feature into each point-wise feature. Experiments on KITTI and PandaSet datasets showed that compared with the most advanced of other methods, our proposed method can achieve significant improvements—in particular, up to 13.68% on KITTI.
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT’s Heads

Vision-and-Language (VL) pre-training has shown great potential on many related downstream tasks, such as Visual Question Answering (VQA), one of the most popular problems in the VL field. All of these pre-trained models (such as VisualBERT, ViLBERT, LXMERT and UNITER) are built with Transformer, which extends the classical attention mechanism to multiple layers and heads. To investigate why and how these models work on VQA so well, in this paper we explore the roles of individual heads and layers in Transformer models when handling 12 different types of questions. Specifically, we manually remove (chop) heads (or layers) from a pre-trained VisualBERT model at a time, and test it on different levels of questions to record its performance. As shown in the interesting echelon shape of the result matrices, experiments reveal different heads and layers are responsible for different question types, with higher-level layers activated by higher-level visual reasoning questions. Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions. Our dynamic chopping module can effectively reduce the parameters of the original model by 50%, while only damaging the accuracy by less than 1% on the VQA task.
Right to be Wrong: Examining the (Im) possibilities of Regulating Fake News while Preserving the Freedom of Expression in Kenya

With the proliferation of peer-to-peer networks as a source of information, concerns on the accuracy of information shared have been raised, necessitating attempts by governments to regulate fake news. Kenya’s Computer Misuse and Cybercrimes Act, for instance, criminalises the intentional dissemination of false or misleading data. However, such regulation has resulted in a different set of concerns, particularly its potential to bring about undue limitation on the freedom of expression. In appraising the approach taken in Kenya of imposing liability on perpetrators, and that taken in some jurisdictions of imposing intermediary liability, the article posits that similar difficulties are faced in regulating fake news – the freedom of expression could be curtailed. This is fuelled by ambiguity in the definition of ‘fake news’. Consequently, this article seeks to find out if indeed, it is possible to regulate fake news while preserving the freedom of expression in Kenya. Further, the article delves into some of the effects the proliferation of fake news has had on the democratic process in Kenya, thereby requiring regulation. In doing so, it tackles fake news from two general conceptions: fake news as calculated disinformation campaigns by individuals for certain purposes, and fake news as an overarching culture of misinformation that enables the spread of false information. Regarding the former, it finds that legislative measures may prove sufficient. However, the latter requires a combination of non-legislative measures such as collaborative measure, awareness initiatives and fact-checking.
Cost Benefit Analysis of Incorporating Security and Evaluation of Its Effects on Various Phases of Agile Software Development

This article addresses the costs and benefits of integrating security into the development of applications and gives formulas for calculating security costs and benefits. The lack of safe application might lead to safety issues. Increasingly, there are accidents recorded that expose security flaws in many major software systems. It results in significant losses for consumer companies. While software businesses are working to produce secure software, the utility of secure software is quite limited. In contrast to the traditional manufacturers of commodities, for example, automakers, software developers have no legal responsibility if their products include flaws. The market reacts adversely to software manufacturers with serious vulnerabilities in their products. This is because of the loss of credibility, cost of patches, and so on. The study shows that the market is ready to penalize the supplier for insecurity and therefore offers the chance to deliver safer technologies. To improve cost/efficiency, the vulnerabilities are connected by accessible fixes. Significant savings are gained when security shortcomings are corrected during designing requirements instead of fixing security failures after deploying software. For suppliers, updates are more expensive to produce and publish. In addition, development costs can be reduced by plugging security issues in the early stages of development.
Development of an Anomaly Alert System Triggered by Unusual Behaviors at Home

In many countries, the number of elderly people has grown due to the increase in the life expectancy of the population, many of whom currently live alone and are prone to having accidents that they cannot report, especially if they are immobilized. For this reason, we have developed a non-intrusive IoT device, which, through multiple integrated sensors, collects information on habitual user behavior patterns and uses it to generate unusual behavior rules. These rules are used by our SecurHome system to send alert messages to the dependent person’s family members or caregivers if their behavior changes abruptly over the course of their daily life. This document describes in detail the design and development of the SecurHome system.
Data and Digital Intelligence Commons (Making a Case for their Community Ownership)

Digital technologies once held the promise to be a great equalizer. As a digital economy takes shape, global inequalities however are rising sharply. Vertically integrated global digital corporations are set to rule every sector of the economy based on their exclusive control over its data, further concentrating economic power. There exists a political paralysis over regulation of such digital corporations. Any shift in the dominant digital model and practices towards greater fairness and sustainability requires first of all an examination of the political economy of data, and the digital intelligence that it contributes. Policy makers around the world are evidently becoming eager to explore ways for wide data sharing, with a view to its easy availability for domestic businesses.But there is a dearth of understanding and political will at the highest levels to develop the required new policies and laws for this purpose, as well as of viable practical models for data sharing. Very little theoretical work explores alternative models for economic governance of data. This paper attempts some new directions in this regard.The digital economy can be understood as comprising intelligent systems running whole sectors, employing data based digital intelligence to re-organize and coordinate them. Within such a macro understanding, it is possible to apply the framework of Institutional Analysis and Development (IAD) developed by Elinor Ostrom to examine the management of data and digital intelligence resources at the community level in a given sector, like transport, under the dominant model. Such an analysis reveals very suboptimal results on almost all the key IAD evaluation parameters; from efficiency and equity to accountability and sustainability. The paper then proposes treating data and digital intelligence as common pool resources, under common property regimes.It briefly considers the kind of data governance arrangements that may be possible and necessary for a robust and fair digital economy. The discussion also subsumes key contemporary data related issues like the contestations around free global flows of data and the data rights of platform dependent actors, like taxi drivers and traders.
Energy Efficient Scheduling in Cloud Computing using Black Widow Optimization

Abstract The delivery of services at IaaS, SaaS, and PaaS levels in Cloud Computing through internet proves to a promising technology. The increase in demand of services of cloud computing increased the creation of cloud datacenters in the world. This leads to increase in demand of energy consumption by datacenter. The integration of green computing with cloud tries to minimize the consumption of power, to minimize their energy costs, and increases their profit. Minimizing the energy consumption with promise of better Quality of Service (QoS) seems to be mutually explosive task for cloud service providers (CSP). To deal with these, a meta-heuristic technique called Energy Efficient Black Widow Optimization based Scheduling algorithm (EEBWOSA) is developed and analyzed in this paper. The special stage of cannibalism which excludes bad solutions will not be used for generating new solution which leads to early convergence. The performance of EEBWOSA is tested on workload taken from HPC2N dataset in CloudSim tool. It exhibits reduction in energy consumption by 25.69% and 13.52% as compared to GA and PSO.
USE OF VIRTUAL REALITY TECHNOLOGIES IN EDUCATION

The purpose of the article is to highlight the virtual reality technologies that are already used to modernize the education system, to review and determine the relevance of existing augmented and virtual reality applications used in the modern educational process. The choice of research methods is determined by the purpose of the article and the subject of research, in particular, a systematic approach to the analysis of works on virtualization of education, general scientific and empirical techniques based on a systematic approach, and general research methods such as generalization and comparison. The scientific novelty of the obtained results lies in the review of VR-systems and applications used today in the educational process, in particular in Ukraine, focusing on the problems and prospects for their further use for educational purposes. The article describes the possibilities and advantages of using virtual reality technologies in modern education. It is noted that today there are already several thousand different applications for the use of virtual reality in education, and their number is increasing daily – in the European market alone there are more than 300 companies engaged in the development of these technologies. Thanks to new challenges and virtual reality technologies, the education sector is on the threshold of a new stage of technological and effective development. New curricula will appear in the near future, and virtual reality technology will be improved and implemented in an increasing number of educational processes. It is emphasized that a significant prerequisite for the development of the education sector is the intensive spread of virtual reality technologies, while the development of VR programs is still expensive and time-consuming, labour-intensive, and not all learning materials can be transferred to VR. Effective use of VR-technologies requires a significant change in curricula at the state level, so the introduction of VR-technologies in the education sector is at the level of experiments.
Field-programmable gate array (FPGA) hardware design and implementation of a new area eﬀicient elliptic curve crypto-processor

Elliptic curve cryptography provides a widely recognized secure environment for information exchange in resource-constrained embedded system applications, such as Internet-of-Things, wireless sensor networks, and radio frequency identification. As the elliptic-curve cryptography (ECC) arithmetic is computationally very complex, there is a need for dedicated hardware for efficient computation of the ECC algorithm in which scalar point multiplication is the performance bottleneck. In this work, we present an ECC accelerator that computes the scalar point multiplication for the NIST recommended elliptic curves over Galois binary fields by using a polynomial basis. We used the Montgomery algorithm with projective coordinates for the scalar point multiplication. We designed a hybrid finite field multiplier based on the standard Karatsuba and shift-and-add multiplication algorithms that achieve one finite field multiplication in $\frac{m}{2}$ clock cycles for a key-length of m. The proposed design has been modeled in Verilog hardware description language (HDL), functionally verified with simulations, and implemented for field-programmable gate array (FPGA) devices using vendor tools to demonstrate hardware efficiency. Finally, we have integrated the ECC accelerator as an AXI4 peripheral with a synthesizable microprocessor on an FPGA device to create an elliptic curve crypto-processor.
Twitter Sentiment Analysis using Deep Learning

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Attention-Based Overall Enhance Network for Chinese Semantic Textual Similarity Measure

ABSTRACTSemantic text similarity(STS) measure plays an important role in the practical application of natural language processing. However, due to the complexity of Chinese semantic comprehension and the lack of currently available Chinese text similarity dataset, present research on Chinese semantic text similarity still exists many limitations. In this paper, we construct a new private self-built Chinese semantic similarity (NCSS) dataset and propose a new method called Attention-based Overall Enhance Network (ABOEN) for measuring semantic textual similarity. This model takes advantage of convolutional neural network upon soft attention layers to capture more fine-grained interactive features between two sentences. Besides, inspired by the channel attention mechanism in image classification, we adopt a channel attention mechanism to enhance the critical overall interactive features between two sentences. The experimental results show that compared with other baseline models, the accuracy based on our model on the NCSS and LCQMC datasets has increased by 1.38% and 1.49%, respectively, which proves the effectiveness of our proposed model.
SEDIBLOFRA: A Blockchain-Based, Secure Framework for Remote Data Transfer in Unmanned Aerial Vehicles

Security attacks on Cyber-Physical Systems with operations that involve Unmanned Aerial Vehicles (UAVs) are a matter of great concern due to their major impact in the deployed systems, and a deal-breaker for their utilization; if a system is not perceived as secure, either it will not be used or its capabilities will be underutilized, regardless of how good they could be.This happens with particular intensity in missions with UAVs, as they can be hacked to tamper with their collected data or taken away by unauthorized parties.Development of security countermeasures is extensive both in theoretical and practical implementations, but the integration of these measures can be difficult, and performance might be affected because of it.This manuscript puts forward a SEDIBLOFRA (Secure, Distributed, Blockchainbased Framework) for remote maneuvering of UAVs, based on several distributed systems technologies that pile up to provide complementary functionalities.Asymmetric cryptography encrypts the data.Publish/Subscribe communications offer a way to enhance data delivery.Finally, blockchain provides a way to keep all the transferred data in a redundant and immutable manner.The proposed framework is also extensible to other kinds of unmanned vehicles, like Unmanned Ground Vehicles or Autonomous Underwater Vehicles.
Dual-mode Fizeau interferometer with four-step phase-tilting iteration for dynamic optical measurement

For a conventional Fizeau interferometer, accommodating both anti-vibration and conventional measurement modes is a challenge. Here, a dual-mode Fizeau interferometer (DFI) for dynamic optical measurement is proposed, which retains the conventional measurement mode. A vibration information measurement system is added to the conventional Fizeau interferometer for vibration-phase extraction. To better extract the vibration phase, a four-step phase-tilting iteration algorithm is proposed. The two combined systems separate the vibration information while retaining the Fizeau interferometry accuracy. Simulations and experiments prove that the two DFI modes both can accurately recover the phase. Thus, the DFI has considerable application potential for optical topography measurement.
Ultrasonic Logging Image Denoising Based on CNN and Feature Attention

Various kinds of noise will be produced during the process of ultrasonic logging in high temperature and high-pressure environment under oil wells, which is blurring the logging image. This paper presents a novel end-to-end denoising model (ULNet) based on CNN and feature attention to address this problem and remove the noise from ultrasonic logging images. Our method mainly includes feature attention, feature enhancement based on residual model and reconstruction for ultrasonic logging image. Feature enhancement based on a residual model integrates global and local features to increase the expressive ability of the denoising model. Feature attention is used to distinguish the channel feature weights, and effective for blind denoising of actual images. Kernel dilation and skip-connection is used to reduce the computational cost during training. The Noise mapping results are used to reconstruct a clean image. Comprehensive quantitative and qualitative evaluations of results for selected study datasets collected at six oil wells in China show that this model is a feasible and effective means for denoising ultrasonic logging images. Overall, ULNet shows potential for practical ultrasonic logging images denoising.
Business Data Science

Data science is a combination of various tools, algorithms and machine learning principles aimed at discovering hidden patterns of raw data. As you can see in the image above, a data analyst usually describes what is happening by processing the data history. Data scientists, on the other hand, not only perform exploratory analysis to discover insights, but also use various advanced machine learning algorithms to detect the occurrence of a particular event in the future. A data scientist looks at data from different angles, sometimes from angles that have not been addressed before. Thus, data science is primarily used for decision making and prediction using causal analysis (prediction), prescriptive analysis (prediction plus decision making), and machine learning.
KnowRU: Knowledge Reuse via Knowledge Distillation in Multi-Agent Reinforcement Learning

Recently, deep reinforcement learning (RL) algorithms have achieved significant progress in the multi-agent domain. However, training for increasingly complex tasks would be time-consuming and resource intensive. To alleviate this problem, efficient leveraging of historical experience is essential, which is under-explored in previous studies because most existing methods fail to achieve this goal in a continuously dynamic system owing to their complicated design. In this paper, we propose a method for knowledge reuse called “KnowRU”, which can be easily deployed in the majority of multi-agent reinforcement learning (MARL) algorithms without requiring complicated hand-coded design. We employ the knowledge distillation paradigm to transfer knowledge among agents to shorten the training phase for new tasks while improving the asymptotic performance of agents. To empirically demonstrate the robustness and effectiveness of KnowRU, we perform extensive experiments on state-of-the-art MARL algorithms in collaborative and competitive scenarios. The results show that KnowRU outperforms recently reported methods and not only successfully accelerates the training phase, but also improves the training performance, emphasizing the importance of the proposed knowledge reuse for MARL.
How do properties of data, their curation, and their funding relate to reuse?

Abstract Despite large public investments in facilitating the secondary use of data, there is little information about the specific factors that predict data's reuse. Using data download logs from the Inter‐university Consortium for Political and Social Research (ICPSR), this study examines how data properties, curation decisions, and repository funding models relate to data reuse. We find that datasets deposited by institutions, subject to many curatorial tasks, and whose access and preservation is funded externally, are used more often. Our findings confirm that investments in data collection, curation, and preservation are associated with more data reuse.