Reviewerly/exHarmony huggingface dataset:
Enhancing Personalized Recommendation by Implicit Preference Communities Modeling

Recommender systems aim to capture user preferences and provide accurate recommendations to users accordingly. For each user, there usually exist others with similar preferences, and a collection of users may also have similar preferences with each other, thus forming a community. However, such communities may not necessarily be explicitly given, and the users inside the same communities may not know each other; they are formally defined and named Implicit Preference Communities (IPCs) in this article. By enriching user preferences with the information of other users in the communities, the performance of recommender systems can also be enhanced. Historical explicit ratings are a good resource to construct the IPCs of users but is usually sparse. Meanwhile, user preferences are easily affected by their social connections, which can be jointly used for IPC modeling with the ratings. However, this imposes two challenges for model design. First, the rating and social domains are heterogeneous; thus, it is challenging to coordinate social information and rating behaviors for a same learning task. Therefore, transfer learning is a good strategy for IPC modeling. Second, the communities are not explicitly labeled, and existing supervised learning approaches do not fit the requirement of IPC modeling. As co-clustering is an effective unsupervised learning approach for discovering block structures in high-dimensional data, it is a cornerstone for discovering the structure of IPCs. In this article, we propose a recommendation model with Implicit Preference Communities from user ratings and social connections. To tackle the unsupervised learning limitation, we design a Bayesian probabilistic graphical model to capture the IPC structure for recommendation. Meanwhile, following the spirit of transfer learning, both rating behaviors and social connections are introduced into the model by parameter sharing. Moreover, Gibbs sampling-based algorithms are proposed for parameter inferences of the models. Furthermore, to meet the need for online scenarios when the data arrive sequentially as a stream, a novel online sampling-based parameter inference algorithm for recommendation is proposed. To the best of our knowledge, this is the first attempt to propose and formally define the concept of IPC.
Fast and Efficient Non-Contact Ball Detector for Picking Robots

Object detectors based on deep learning requires high-performance computing and large run-time memory footprint to maintain good detection performance. They bring high computation overhead and power consumption to on-board embedded devices of non-contact ball picking robot. Furthermore, it is difficult to deploy on the machine because the model size is so big. The accuracy of the existing simplified detectors deployed on embedded devices cannot meet the requirements of practical applications. Therefore, how to reduce floating point operations (FLOPs) and the size of model without notably sacrificing detection precision becomes an urgent problem to be solved. To solve this problem, a shuttle residual block which is more efficient network unit based on depthwise separable convolution was proposed. And we designed a non-contact ball object detector for picking robots, which is shallower than YOLOv3 and has narrower structure. We evaluate the proposed method on non-contact Ball dataset and compelling results are achieved by the proposed method. Compared with YOLOv3, the proposed method reduces FLOPs by 86.2%, declines parameter size by 89.5%. Overall, the proposed method achieves comparable detection accuracy than YOLOv3, and its speed is 2.2 times faster than YOLOv3.
Is Sampling Heuristics Necessary in Training Deep Object Detectors

In training deep object detectors, the problem of foreground-background imbalance has been addressed by several heuristic methods, such as online hard example mining (OHEM), Focal Loss, and gradient harmonizing mechanism (GHM). These methods either re-sample the training examples (i.e. hard sampling), or re-weight them discriminatively (i.e. soft sampling). In this paper, we challenge the necessity of such hard/soft sampling heuristics in training deep object detectors. First, without hard/soft sampling, we reveal that the scale and the stability of the classification loss greatly influence the final accuracy. Thus, we propose a guided loss scaling technique to control the classification loss during training, without using any hyper-parameter. We also propose to optimally initialize the model to ensure the stability of the classification loss. Moreover, we propose an adaptive thresholding technique to refine predictions during inference. These three ingredients constitute our Sampling-Free mechanism, which is fully data diagnostic and avoids the laborious hyper-parameter search for hard/soft sampling. We verify the effectiveness of our Sampling-Free mechanism in training one-stage, two-stage, multi-stage, and anchor-free object detectors, where our method always achieves higher accuracy on COCO and PASCAL VOC datasets. We also use the Sampling-Free mechanism for instance segmentation to demonstrate its generalization ability. Code is released at: this https URL
Multi-level analysis and recognition of the text sentiment on the example of consumer opinions

In this article, we present a novel multidomain dataset of Polish text reviews, annotated with sentiment on different levels: sentences and the whole documents.The annotation was made by linguists in a 2+1 scheme (with inter-annotator agreement analysis).We present a preliminary approach to the classification of labelled data using logistic regression, bidirectional long short-term memory recurrent neural networks (BiLSTM) and bidirectional encoder representations from transformers (BERT).
Distorted Building Image Matching with Automatic Viewpoint Rectification and Fusion

Building image-matching plays a critical role in the urban applications. However, finding reliable and sufficient feature correspondences between the real-world urban building images that were captured in widely separate views are still challenging. In this paper, we propose a distorted image matching method combining the idea of viewpoint rectification and fusion. Firstly, the distorted images are rectified to the standard view with the transform invariant low-rank textures (TILT) algorithm. A local symmetry feature graph is extracted from the building images, followed by multi-level clustering using the mean shift algorithm, to automatically detect the low-rank texture region. After the viewpoint rectification, the Oriented FAST and Rotated BRIEF (ORB) feature is used to match the images. The grid-based motion statistics (GMS) and RANSAC techniques are introduced to remove the outliers and preserve the correct matching points to deal with the mismatched pairs. Finally, the matching results for the rectified views are projected to the original viewpoint space, and the matches before and after distortion rectification are fused to further determine the final matches. The experimental results show that both the number of matching pairs and the matching precision for the distorted building images can be significantly improved while using the proposed method.
Numerical study of Wigner negativity in one-dimensional steady-state resonance fluorescence

In a numerical study, we investigate the steady-state generation of nonclassical states of light from a coherently driven two-level atom in a one-dimensional waveguide. Specifically, we look for states with a negative Wigner function, since such nonclassical states are a resource for quantum information processing applications, including quantum computing. We find that a waveguide terminated by a mirror at the position of the atom can provide Wigner-negative states, while an infinite waveguide yields strictly positive Wigner functions. Moreover, our paper reveals a connection between the purity of a quantum state and its Wigner negativity. We also analyze the effects of decoherence on the negativity of a state.
A New High Capacity Separable Reversible Data Hiding in Encrypted Images Based on Block Selection and Block-Level Encryption

Reversible data hiding in encrypted image (RDHEI) is a technology that can simultaneously implement privacy protection and reversible information hiding. For RDHEI, the main problem is the conflict between embedding capacity and real reversibility. In this paper, to solve such a conflict, a new block-level image encryption method is proposed to fulfill privacy protection while preserving the spatial correlation of pixels inside pixel block. On this basis, a new reversible data hiding method based on block selection is also proposed. Specifically, pixel blocks are classified into embeddable block (EB) and non-embeddable block (NEB). Then, additional data is embedded into only EBs and the position file locating EB is also embedded using a new self-embedding method. Experimental results demonstrate that the proposed method not only achieves high embedding capacity, but also guarantees lossless data extraction and perfect image recovery.
Noise-tolerant Z-type neural dynamics for online solving time-varying inverse square root problems: A control-based approach

Assuming that the solving process is free of measurement noises, the zeroing neural dynamics (Z-type) based on different zeroing dynamics for online solving time-varying inverse square root problems (TVISRPs) are revisited from the perspective of control technique and unified into a control framework. However, noises are ubiquitous and unavoidable in the application of the real-time system. Therefore, the modified Z-type models (MZTMs) with different measurement noises are needed for online solving TVISRPs. In this study, the MZTMs are first developed, analyzed and verified for online solution of the TVISRPs with different measurement noises. Furthermore, theoretical analyses infer that the MZTMs globally and exponentially converge to the theoretical solution. Compared with the traditional Z-type neural dynamics model (ZTNDM) and gradient neural dynamics model (GNDM), two illustrative examples are described and investigated to substantiate the efficiency and superiority of the developed MZTMs. Finally, a systematic method is proposed via exploring control framework to construct MZTMs for online solving TVISRPs with great robustness and accuracy.
Discrete gradients for computational Bayesian inference

In this paper, we exploit the gradient flow structure of continuous-time formulations of Bayesian inference in terms of their numerical time-stepping. We focus on two particular examples, namely, the continuous-time ensemble Kalman–Bucy filter and a particle discretisation of the Fokker–Planck equation associated to Brownian dynamics. Both formulations can lead to stiff differential equations which require special numerical methods for their efficient numerical implementation. We compare discrete gradient methods to alternative semi-implicit and other iterative implementations of the underlying Bayesian inference problems.
Технологія блокчейн у бухгалтерському обліку й аудиті: сучасний стан, можливості та перспективи застосування

Blockchain is today one of the main technologies of the digital economy, which in a general sense is a distributed database, such as an accounting book that stores information about all transactions performed by its participants. The article presents data on the status and expected results of blockchain development in the world and in Ukraine as well, and illustrates that the implementation of the blockchain technology is now occurring in various areas, one of the most promising of which is accounting and auditing. It is emphasized that domestic accounting and auditing are based on outdated methods and techniques, and the blockchain technology, in the context of its key properties, can significantly transform their capabilities, improve the efficiency and innovativeness of such accounting and auditing. Particular attention is focused on the triple entry concept as a basic component of the blockchain technology in the accounting context. Among the most promising areas of blockchain implementation are accounting of accounts receivable and accounts payable and their repayment, assessment and payment of taxes, operational accounting and reporting, as well as work with documents and their storage. The advantages and disadvantages of the blockchain technology use in accounting and auditing practice are considered. It is established that on the level with the undeniable advantages of blockchain (saving resources, ensuring the reliability and transparency of data, the ability to carry out rapid and automated real-time audit, simplifying transactions, minimizing accounting errors, etc.), critical evaluation of all the disadvantages associated with reliability, speed, security and scalability of this technology has to be done. There should not be ignored the fact that in order to implement the blockchain, it is necessary to make changes in the organizational culture of an enterprise, change the way of employees’ thinking, modify business processes, which require additional time and financial costs.
Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters

This paper focuses on convolution neural network quantization problem. The quantization has a distinct stage of data conversion from floating-point into integer-point numbers. In general, the process of quantization is associated with the reduction of the matrix dimension via limited precision of the numbers. However, the training and inference stages of deep learning neural network are limited by the space of the memory and a variety of factors including programming complexity and even reliability of the system. On the whole the process of quantization becomes more and more popular due to significant impact on performance and minimal accuracy loss. Various techniques for networks quantization have been already proposed, including quantization aware training and integer arithmetic-only inference. Yet, a detailed comparison of various quantization configurations, combining all proposed methods haven't been presented yet. This comparison is important to understand selection of quantization hyperparameters during training to optimize networks for inference while preserving their robustness. In this work, we perform in-depth analysis of parameters in the quantization aware training, the process of simulating precision loss in the forward pass by quantizing and dequantizing tensors. Specifically, we modify rounding modes, input preprocessing, output data signedness, bitwidth of the quantization and locations of precision loss simulation to evaluate how they affect accuracy of deep neural network aimed at performing efficient calculations on resource-constrained devices.
An Efficient and Lightweight Deniably Authenticated Encryption Scheme for e-Mail Security

The most important security requirements to secure electronic mail (e-mail) systems are: confidentiality, authentication, non-repudiation and data integrity. In conventional e-mail systems, Secure/Multipurpose Internet Mail Extensions (S/MIME) and Pretty Good Privacy (PGP) digital envelops are used to satisfy these security requirements. However, confidentiality and authentication are performed in two different phases, which increases computations and leads to more energy consumption. Moreover, the receiver can easily reveal the source of the message, violating the sender's privacy. In this paper, we propose a low-cost deniably authenticated encryption scheme (DA-ENS), where all the cryptographic primitives are being performed in a single logical step to achieve these goals. Experimental results show that our scheme, DA-ENS, achieves low computational cost and communication overhead at 80-bit, 112-bit, 128-bit, 192-bit and 256-bit security levels. Energy consumption is shown to be reduced to 80%, 67%, 42%, 62% and 48% compared to similar schemes SL+BF, LXJ+BF, Fagen Li et al. (FL), AJL and CZJZJSZ respectively. Also, we have proven that, our scheme DA-ENS is provably secure in random oracle model.
An Analysis of Digital Library Publishing Services in Ukrainian Universities

Abstract&#x0D; Objective – The objective of this study was to assess the current state of digital library publishing (DLP) in university libraries in the Ukraine. The study was conducted in the hopes of gaining a better understanding of the DLP landscape, namely institutional operations, as well as their varying publishing initiatives, processes, and scope.&#x0D; Methods – The current study was conducted from January to June 2017 using a mixed methods approach, involving semi-structured interviews and an online questionnaire. Semi-structured interviews were conducted (n = 11) to gain insight into participants’ experiences with DLP. The interviews helped in the creation of the questions included in our online questionnaire. The questionnaire was distributed to 195 representatives (directors and leading specialists) of university libraries in the Ukraine. Replies were received from 111 of those institutions. The questionnaire consisted of 11 open- and closed-ended questions to allow the researchers to obtain a holistic picture of the process under investigation.&#x0D; Results – Analysis of the 111 questionnaires showed that for 26 libraries, DLP services were performed by employees of a separate structural unit of the library. For 34 libraries, employees of various departments were involved in performing certain types of services. The other 40 respondents’ libraries were planning to do this in the near future. Only 11 respondents replied that they did provide DLP services now nor planned to in the future. Among the libraries providing DLP services, the following results were observed: 54 of 60 work with digital repositories, 47 provide digital publishing platforms for journals, 26 provide digital publishing platforms for books, and 23 provide digital publishing platforms for conferences.&#x0D; Conclusions – The results obtained indicate a growing trend of expanding digital services in university libraries to support study, teaching, and research. Despite the still spontaneous, chaotic, and poorly explored nature of the development of the library publishing movement in the university libraries of the Ukraine, the readiness of librarians to implement publishing activities is notable. At the same time, the survey results point to specific aspects, such as organizational, economic, personnel, and motivational, that require further study.
Privacy-Preserving for Distributed Data Streams: Towards l-Diversity

Privacy-preserving data publishing have been studied widely on static data. However, many recent applications generate data streams that are real-time, unbounded, rapidly changing, and distributed in nature. Recently, few work addressed k-anonymity and l-diversity for data streams. Their model implied that if the stream is distributed, it is collected at a central site for anonymization. In this paper, we propose a novel distributed model where distributed streams are first anonymized by distributed (collecting) sites before merging and releasing. Our approach extends Continuously Anonymizing STreaming data via adaptive cLustEring (CASTLE) [4], a cluster-based approach that provides both k-anonymity and l-diversity for centralized data streams. The main idea is for each site to construct its local clustering model and exchange this local view with other sites to globally construct approximately the same clustering view. The approach is heuristic in a sense that not every update to the local view is sent, instead triggering events are selected for exchanging cluster information. Extensive experiments on a real data set are performed to study the introduced Information Loss (IL) on different settings. First, the impact of the different parameters on IL are quantified. Then k-anonymity and l-diversity are compared in terms of messaging cost and IL. Finally, the effectiveness of the proposed distributed model is studied by comparing the introduced IL to the IL of the centralized model (as a lower bound) and to a distributed model with no communication (as an upper bound). The experimental results show that the main contributing factor to IL is the number of attributes in the quasi-identifier (50%-75%) and the number of sites contributed about 1% and this proves the scalability of the proposed approach. In addition, providing l-diversity is shown to introduce about 25% increase in IL when compared to k-anonymity. Moreover, 35% reduction in IL is achieved by messaging cost (in bytes) of about 0.3% of the data set size
Steganographic Security Analysis From Side Channel Steganalysis and Its Complementary Attacks

Side channel steganalysis refers to detecting a steganographer in social websites via behavior analysis. In this paper, we first design a side channel steganalysis based on the correlation between image sequences of social users, which aims to find out the behaviorally anomalous steganographer. According to the experimental results of side channel steganalysis, it is intuitively secure for the steganographer to act identically to normal social users since she can avoid being detected by side channel steganalysis. However, when faced with various detection methods, is it still secure to behave similar to a normal user? To comprehensively consider the detection means and further explore the secure behavior region of the steganographer, we design a complementary attack of side channel steganalysis. Specifically, we take the correlation of contents of images as side information and take the images with similar content as references to calibrate steganalysis features, which helps improve traditional steganalysis. The proposed side channel steganalysis and its complementary attack efficiently detect steganographers from two different aspects. When the average rank of the steganographer is used to measure the performance, side channel steganalysis can rank the steganographer within the top ten in 100 actors, and the complementary attack can raise the average rank of the steganographer by three places compared with the previous method. From the perspective of the steganographer on social networks, it can help her behave in a more secure region, where her behavior should neither deviate from that of normal users nor be too similar to that of normal users.
Unidimensional continuous-variable measurement-device-independent quantum key distribution

Continuous-variable (CV) measurement-device-independent (MDI) quantum key distribution (QKD) is immune to imperfect detection devices, which can eliminate all kinds of attacks on practical detectors. Here we first propose a CV-MDI QKD scheme using unidimensional modulation (UD) in general phase-sensitive channels. The UD CV-MDI QKD protocol is implemented with the Gaussian modulation of a single quadrature of the coherent states prepared by two legitimate senders, aiming to simplify the implementation compared with the standard, symmetrically Gaussian-modulated CV-MDI QKD protocol. Our scheme reduces the complexity of the system since it ignores the requirement in one of the quadrature modulations as well as the corresponding parameter estimations. The security of our proposed scheme is analyzed against collective attacks, and the finite-size analysis under realistic conditions is taken into account. UD CV-MDI QKD shows a comparable performance to that of its symmetric counterpart, which will facilitate the simplification and practical implementation of the CV-MDI QKD protocols.