Reviewerly/exHarmony huggingface dataset:
A blockchain solution for the privacy of patients’ medical data

In the era of electronic health records, various advanced technologies are being used to monitor the patients with their health records. These records are shared by the hospitals with the patients for their further follow-ups. In some cases, these records are shared with other agencies for medical advices. Sharing of electronic data can expose it to vulnerabilities and misuse. Also, access control strategies and mechanisms are required to be embedded with data sharing to prevent manipulation and breeching of patient’s sensitive data and maintain its confidentiality and integrity. Blockchain technology can be a solution to the vulnerabilities associated with data sharing and data manipulation. In this chapter, we discuss the scope, applicability, and usage of blockchain technology to preserve the sensitive medical data of patients. A framework is also proposed which allows patients and hospitals to store medical records. The framework allows patients to share it by providing access to its own data and to invoke smart contract for automatic payments for their medical claims.
Learnings from a Retail Recommendation System on Billions of Interactions at bol.com

Recommender systems are ubiquitous in the modern internet, where they help users find items they might like. We discuss the design of a large-scale recommender system handling billions of interactions on a European e-commerce platform.We present two studies on enhancing the predictive performance of this system with both algorithmic and systems-related approaches. First, we evaluate neural network-based approaches on proprietary data from our e-commerce platform, and confirm recent results outlining that the benefits of these methods with respect to predictive performance are limited, while they exhibit severe scalability bottlenecks. Next, we investigate the impact of a reduction of the response latency of our serving system, and conduct an A/B test on the live platform with more than 19 million user sessions, which confirms that the latency reduction of the recommender system correlates with a significant increase in business-relevant metrics. We discuss the implications of our findings with respect to real world recommendation systems and future research on scalable session-based recommendation.
Knife Detection using Indoor Surveillance Camera

Security and safeness are constant topics to be solved in cities around the world, finding ways to detect weapons threatening human beings is an important challenge. This paper presents an approach for solving knife detection in Close Circuit Television (CCTV) cameras videos. In this sense, knife detection is the goal in this work and through a combination of color and invariant moments techniques, the system presented reaches the objective and turns reliable in indoor environments, with or without environmental illumination. The work has been proved under white and infrared lighting, with a range between 0.5 to 4.0 meters of distance from camera to knives with positive qualities of detection. Supporting this work, videos were uploaded on web. The reported error in them runs from 0% to 1.192%. This system may be useful at convenience stores, banks, theatres and some others public places with commercial surveillance cameras from a relatively long distance. Results offer a simple classification because to important features found. For instance, when the system may be executed by parallel processors or in pipeline method it could be detecting more than one knife on scene.
Data Privacy and System Security for Banking on Clouds using Homomorphic Encryption

In recent times, the use of cloud computing has gained popularity all over the world in the context of performing smart computations on big data. The privacy of sensitive data of the client is of utmost important issues. Data leakage or hijackers may theft significant information about the client that ultimately may affect the reputation and prestige of its owner (bank) and client (customers). In general, to save the privacy of our banking data it is preferred to store, process, and transmit the data in the form of encrypted text. But now the main concern leads to secure computation over encrypted text or another possible way to perform computation over clouds makes data more vulnerable to hacking and attacks. Existing classical encryption techniques such as RSA, AES, and others provide secure transaction procedures for data over clouds but these are not fit for secure computation over data in the clouds. In 2009, Gentry comes with a solution for such issues and presents his idea as Homomorphic encryption (HE) that can perform computation over encrypted text without decrypting the data itself. Now a day's privacy-enhancing techniques (PET) are there to explore more potential benefits in security issues and useful in historical cases of privacy failure. Differential privacy, Federated analysis, homomorphic encryption, zero-knowledge proof, and secure multiparty computation are a privacy-enhancing technique that may useful in financial services as these techniques provide a fully-fledged mechanism for financial institutes. With the collaboration of industries, these techniques are may enable new data-sharing agreements for a more secure solution over data. In this paper, the primary concern is to investigate the different standards and properties of homomorphic encryption in digital banking and financial institutions.
Practical Security and Privacy for Database Systems

Computing technology has enabled massive digital traces of our personal lives to be collected and stored. These datasets play an important role in numerous real-life applications and research analysis, such as contact tracing for COVID 19, but they contain sensitive information about individuals. When managing these datasets, privacy is usually addressed as an afterthought, engineered on top of a database system optimized for performance and usability. This has led to a plethora of unexpected privacy attacks in the news. Specialized privacy-preserving solutions usually require a group of privacy experts and they are not directly transferable to other domains. There is an urgent need for a generally trustworthy database system that offers end-to-end security and privacy guarantees. In this tutorial, we will first describe the security and privacy requirements for database systems in different settings and cover the state-of-the-art tools that achieve these requirements. We will also show challenges in integrating these techniques together and demonstrate the design principles and optimization opportunities for these security and privacy-aware database systems. This is designed to be a three hour tutorial.
A Tailored Regression for Learned Indexes: Logarithmic Error Regression

Although linear regressions are essential for learned index structures, most implementations use Simple Linear Regression, which optimizes the squared error. Since learned indexes use exponential search, regressions that optimize the logarithmic error are much better tailored for the use-case. By using this fitting optimization target, we can significantly improve learned index’s lookup performance with no architectural changes.
Towards Low-Bit Quantization of Deep Neural Networks with Limited Data

Recent machine learning methods use increasingly large deep neural networks to achieve state-of-the-art results in various tasks. Network quantization can effectively reduce computation and memory costs without modifying network structures, facilitating the deployment of deep neural networks (DNNs) on cloud and edge devices. However, most of the existing methods usually need time-consuming training or fine-tuning and access to the original training dataset that may be unavailable due to privacy or security concerns. In this paper, we present a novel method to achieve low-precision quantization with limited data. Firstly, to reduce the complexity of per-channel quantization and degeneration of per-layer quantization, we introduce group quantization that separates the output channels into groups and processes each group independently. Secondly, to better distill knowledge from the pre-trained FP32 model with limited data, we introduce a two-stage knowledge distillation method that divides the optimization process into blockwise optimization and joint optimization to address the limitation of layer-wise supervision and global supervision. Extensive experiments on ImageNet2012 (ResNet18/50, ShuffleNetV2, and MobileNetV2) demonstrate that the proposed approach can significantly improve the quantization model's accuracy when only a few training samples are available. We further show that the method also extends to other computer vision architectures and tasks such as object detection.
Intragroup and intergroup secret image sharing based on homomorphic Lagrange interpolation

In this paper, a ( k , n , m ) -intragroup and intergroup secret image sharing (I 2 SIS) scheme for the multi-group (i.e., m -group) environment is investigated. ( 2 m − 1 ) secret images are shared within groups (intragroups) and between groups (intergroups). Differing from existing SIS methods, any k or more qualified participants among different groups (i.e., intergroup) can collaboratively recover the respective secret image via the homomorphic Lagrange interpolation. The proposed scheme consists of three major procedures: preprocessing, sharing, and recovering. In the preprocessing procedure, an approach is designed to partition the coefficients in the m ( k − 1 ) -degree polynomials for sharing the ( 2 m − 1 ) secret images. In addition, an optimal algorithm for the preprocessing is given to let all the percentages of recovering secret images be the same as possible. Shadows belonging to all participants in the m groups are generated in the sharing procedure. In the recovering procedure, the secret images can be reconstructed in intragroups and intergroups. Moreover, experiments and comparisons are demonstrated to show the merits of the proposed scheme.
Unifying Node Labels, Features, and Distances for Deep Network Completion

Collected network data are often incomplete, with both missing nodes and missing edges. Thus, network completion that infers the unobserved part of the network is essential for downstream tasks. Despite the emerging literature related to network recovery, the potential information has not been effectively exploited. In this paper, we propose a novel unified deep graph convolutional network that infers missing edges by leveraging node labels, features, and distances. Specifically, we first construct an estimated network topology for the unobserved part using node labels, then jointly refine the network topology and learn the edge likelihood with node labels, node features and distances. Extensive experiments using several real-world datasets show the superiority of our method compared with the state-of-the-art approaches.
Blockchain-Based Secure Outsourcing of Polynomial Multiplication and Its Application in Fully Homomorphic Encryption

The efficiency of fully homomorphic encryption has always affected its practicality. With the dawn of Internet of things, the demand for computation and encryption on resource-constrained devices is increasing. Complex cryptographic computing is a major burden for those devices, while outsourcing can provide great convenience for them. In this paper, we firstly propose a generic blockchain-based framework for secure computation outsourcing and then propose an algorithm for secure outsourcing of polynomial multiplication into the blockchain. Our algorithm for polynomial multiplication can reduce the local computation cost to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>O</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> </mrow> </mfenced> </math> . Previous work based on Fast Fourier Transform can only achieve <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>O</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> <mi mathvariant="normal">log</mi> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mi>n</mi> </mrow> </mfenced> </mrow> </mrow> </mfenced> </math> for the local cost. Finally, we integrate the two secure outsourcing schemes for polynomial multiplication and modular exponentiation into the fully homomorphic encryption using hidden ideal lattice and get an outsourcing scheme of fully homomorphic encryption. Through security analysis, our schemes achieve the goals of privacy protection against passive attackers and cheating detection against active attackers. Experiments also demonstrate our schemes are more efficient in comparisons with the corresponding nonoutsourcing schemes.
Educational performance and ICTs: Availability, use, misuse and context

The relationship between technology and learning is an issue without robust evidence. Previous studies have presented varied results depending on the learning area, the type of use and the context. This study examines this relationship and the influence of the context, by means of a multilevel analysis of the information in the PISA Report for the Spanish regions. The results show that there is a positive relationship for access to technology both at school and at home, and that its educational uses are not more beneficial than its recreational uses. The study confirms the misuse of technology as a distraction, with saturation thresholds above which their availability and use are detrimental, and that technological devices have different impacts depending on the guidance provided in their use. Finally, the influence of the students' environment on their ICT use is confirmed according to contextual characteristics that affect that guidance.
An Improved Coarse-To-Fine Motion Estimation Scheme For Lidar Point Cloud Geometry Compression

Light detection and ranging (LiDAR) plays a vital role in autonomous driving applications. Due to the enormous volume of point cloud data from LiDAR acquisition, LiDAR point cloud compression schemes are desirable under limited storage and transmission bandwidth. This paper proposes an improved coarse-to-fine motion estimation scheme to significantly reduce the spatial redundancy among LiDAR point clouds, combining registration-based global motion estimation and block-based local motion estimation. First, in the global motion estimation process, an efficient scheme is introduced to segment ground level and object level from original point clouds. Then we use an ICP-based registration method for extracted object point cloud to estimate global motion transformation. Second, an irregular prediction unit (PU) partition method is introduced in the block-based local motion estimation part, which provides more flexibility to estimate complex motions. Experimental results have shown significant gains of our proposed model over the state-of-the-art test model (TMC13) of MPEG Geometry-based PCC (GPCC) standard. For the dynamically acquired point clouds, average coding gains of 8.0% and 1.2% are obtained respectively in lossy and lossless geometric coding.
A Study on MPLS Vs SD-WAN

Internet service providers and techno enterprises evolved with CISCO IOS called MPLS built a strong and diligent network for succeeding generations to utilize all sorts of full-fledged amnesties over a solitary structure. MPLS is considered as a routing method, and it is not a facility or a service. MPLS can be encased with any prevailing infrastructures, namely digital subscriber line, asynchronous transfer mode, frame relay, and IP. MPLS is not platform dependent. It can work seamlessly without making any change in the current environment of these technologies. But implementing MPLS is quite expensive, so with the support of SD-WAN, ISPs and enterprises are attempted to enhance its usages using inexpensive Internet connection. This survey articled aimed to compare the pros and cons of both the technologies MPLS and SD-WAN.
Cyber 9/11 Will Not Take Place: A User Perspective of Bitcoin and Cryptocurrencies from Underground and Dark Net Forums

Background. There is a historical narrative of fear surrounding cybercrime. This has extended to cryptocurrencies (CCs), which are often viewed as a criminal tool. Aim. To carry out the first user study of CCs for illicit activity, from the perspective of underground and dark net forums. Method. We conducted a qualitative study, using a content analysis method, of 16,405 underground and dark net forum posts selected from CrimeBB, a dataset of 100 million posts curated by the Cambridge Cybercrime Centre. Results. Firstly, finality of payments emerged as a major motivator for the use of CCs. Second, we propose an Operational Security Taxonomy for Illicit Internet Activity to show that CCs are only one part of several considerations that combine to form security in illicit internet transactions. Third, the dark net is hard to use and requires significant study, specialist equipment and advanced knowledge to achieve relative security. Conclusion. We argue that finality is the main advantage of CCs for this user group, not anonymity as widely thought. The taxonomy shows that banning CCs is unlikely to be effective. Finally, we contend that the dark net is a niche for criminal activity and fears over cybercrime cause the threat to be exaggerated.
<i>MemOReL</i>

Reinforcement Learning (RL) represents the machine learning method that has come closest to showing human-like learning. While Deep RL is becoming increasingly popular for complex applications such as AI-based gaming, it has a high implementation cost in terms of both power and latency. Q-Learning, on the other hand, is a much simpler method that makes it more feasible for implementation on resource-constrained embedded systems for control and navigation. However, the optimal policy search in Q-Learning is a compute-intensive and inherently sequential process and a software-only implementation may not be able to satisfy the latency and throughput constraints of such applications. To this end, we propose a novel accelerator design with multiple design trade-offs for implementing Q-Learning on FPGA-based SoCs. Specifically, we analyze the various stages of the Epsilon-Greedy algorithm for RL and propose a novel microarchitecture that reduces the latency by optimizing the memory access during each iteration. Consequently, we present multiple designs that provide varying trade-offs between performance, power dissipation, and resource utilization of the accelerator. With the proposed approach, we report considerable improvement in throughput with lower resource utilization over state-of-the-art design implementations.
An Efficient Pseudo-Sequence-Based Light Field Video Coding Utilizing View Similarities for Prediction Structure

Light Field (LF) video technology is a step towards offering a better immersive experience through on-demand refocusing and perspective viewing. However, the significant increase in captured data makes the need for efficient compression of paramount importance. In this paper, we proposed two prediction structures and coding orders that efficiently compress LF video content using the existing HEVC standard. This is achieved by utilizing horizontal and vertical correlation among the views for better inter-view prediction. To assess the performance of the schemes, ten publicly available and widely used LF video sequences were used. Our first method is highly suitable for applications demanding high compression efficiency. It outperforms the best pseudo-sequence-based compression technique to date by up to 17% in bitrate reduction while being scalable in the number of views. The second method is proficient for low computational and random-access complexity to any arbitrary view in the light field video. It offers 10% faster decoding and 20% lower random-access complexity compared to the best existing technique.
An Op-amp Sharing Folded Bernoulli Mapping TRNG for Low-power Applications

An operational amplifier sharing (Op-amp sharing), folded Bernoulli mapping chaos-based true random number generator (TRNG) is proposed in this paper. Based on the concept of Op-amp sharing for non-flipped folded Bernoulli mapping, the number of stages is reduced from two stages to one, which reduces power consumption and improves the stability of the system. The entire system has been implemented in a standard 180nm process with power supply of 1.2V, and the output speed of random number is 400kbit/s with power consumption of only 21μW. The generated random bits pass National Institute of Standards and Technology (NIST) Pub-800.22 randomness tests successfully.
Digital education facing COVID - 19 pandemic: technological university experience

The article deals with the aspects of electronic and distance education under the COVID19 pandemic. It is noteworthy that there are common features in managing electronic and online education in higher school, as well as the problems of applying these formats of education. The tools of the educational process of digital education, the effectiveness of their use, the reaction of students and teachers to the new learning environment and technologies are described. The author argues that information and communication technologies are increasingly melting into various areas of education. This is facilitated by both external factors related to the widespread computerization of the society and the need for appropriate training of specialists, and internal factors related to the spread of modern digital technologies and software in school, the adoption of governmental and intergovernmental programs for the computerization of education, the emergence of the relevant digital experience of a large number of instructors. In most cases, the use of digital tools produces a real positive impact on the intensification of the work of the educators both in high school and university, as well as on the effectiveness of students' training, despite a number of shortcomings of the existing distant education system.
An Automatic Detection Method of Bird's Nest on Electric Tower Based on Attention Full Convolutional Neural Networks

Automatic detection of the bird's nest on the transmission line tower is of great significance to ensure the stable operation of the power grid. The main challenge is that the diversification and complexity of the environment will have a greater impact on the detection results. In addition, the real-time performance of the detection algorithm is the key to the successful deployment of the algorithm. In order to solve the above challenges, this paper proposes an Attention Complete Convolutional Neural Network (AFCNN) to detect the bird's nest on the electric tower. The specific content is as follows: First, design a shallow feature extraction model to shorten the calculation time of the model. Subsequently, the attention network is added to the feature extraction model to reduce the interference of the surrounding environment on the detection results and improve the detection accuracy. The algorithm uses natural images collected by drones for training and testing. Experimental results show that the algorithm can be applied to bird nest detection in complex environments and has high real-time performance. Our work provides a solution to ensure the safe operation of the grid.