Reviewerly/exHarmony huggingface dataset:
An Event-driven and User Behavior-empowered Hybrid Recommendation System Based on Voluntary Service

Despite the popularity of voluntary service, the voluntary recommendation domain is less explored. Besides, compared with pop recommendation fields, the dataset from the Chinese voluntary service information system shows specific features of voluntary service, summarised as instability of volunteers and time-effectiveness of volunteer projects. These features lead to problems such as data sparsity and cold start, which makes it difficult for traditional recommendation algorithms to achieve good results, while regular hybrid systems also face the problem of scalability with the data accumulating. Based on such findings, we propose a layered multi-graphs structure and construct a novel hybrid recommendation system. The proposed system helps to alleviate the data sparsity problem and improve algorithm execution efficiency through graph-based methods such as community detection, and solve the cold start problem and enhance the recommendation performance by combining the advantages of proper algorithms. By simulating the actual daily activities of volunteers in our experiments, it is shown that the recommendation success rate of the proposed system reaches 53% with a better ability to deal with massive data.
Modeling and Simulation of Unstable High Resistance Grounding Fault based on RTDS

RTDS is an effective simulation method for mining and processing the hidden criterion of protection device. Based on the case of invisible criterion blocking line protection under unstable high resistance grounding fault, this paper analyzes the test requirements, platform, equipment parameters and test items of modeling and simulation of unstable high resistance grounding fault based on RTDS, on the basis of accurately grasping the characteristics of the change of unstable high resistance ground resistance. The test results show that the proposed simulation model can accurately fit the characteristics of unstable high resistance grounding fault. Based on the summary of the test results, this paper further puts forward engineering suggestions for optimizing the operation performance of the protection device.
Research on Efficient Retrieval Technology of Optical Characteristics of Image Resources in Digital Library

Abstract In order to improve the intelligence of digital library image resources, this paper proposes an efficient retrieval technology of digital library image resources based on deep learning and semantic feature extraction, which uses infrared and radio frequency transverse scanning technology to scan the front cover and back cover of digital library image resources, and divides the text and image regions of scanned digital library image resources infrared and radio frequency images. In the infrared and radio frequency transmission space, the collected scanned image resources of digital library are subjected to semantic diversification feature decomposition and feature separation, and the binary semantic ontology information features of digital library image resources are extracted. Combined with the image semantic feature extraction method, the convergence control in the process of extracting the structural features of digital library image resources is realized, and the digital library image resources are retrieved based on the extracted library resource information feature components. Simulation results show that this method has higher precision and shorter retrieval time, which improves the retrieval efficiency of digital library.
On small perturbations of Markov cyber threat models

Abstract In this work, we consider Markov chain-based stochastic modeling of cyber threats acting on computer systems. In the framework of this approach, computer systems are considered as systems with failures and recoveries by analogy with technical system models in reliability theory. Under the assumption that the cyber threats are independent random events, we derive the explicit analytic formulas for the state probabilities of the corresponding Markov chain and the mean time to security failure (MTSF). Then we investigate the case of dependent cyber threats and derive the approximate expressions for the state probabilities and MTSF within the framework of the first-order perturbation theory. As an illustration of our results, we consider a few simple examples.
A Neighbourhood Encoding Framework for Deep Mining Heterogeneous Texts in Recipe-image Retrieval

Abstract Cross-modal retrieval usually fills the semantic gap between different modalities by sharing subspaces. However, existing methods rarely consider that the data in a certain modality may be heterogeneous when mapping multimodal data into a shared subspace. In addition, most existing methods focus on semantic associations between different modalities, while few approaches consider the semantic associations within a single modality. To address the above two deficiencies, we propose a Neighbourhood Encoding (NE) framework that mines the semantic association of data in the same modality, solves the problem of data heterogeneity by improving the semantic expression of a single modality. To verify the effectiveness of the proposed framework, we use two types of recurrent neural networks to instantiate the framework. Experiments show that the instantiated approaches outperform existing advanced methods in both text-to-image and image-to-text retrieval directions.
Risk Management Analysis on Organizational Website Using Octave Allegro Method

The Website Portal of XYZ Organization has a crucial function for the sustainability of the organization's business processes. Applications are required to always meet stakeholder expectations which include confidentiality, integrity and availability. IT risk management implementation is needed to achieve these expectations. This research implements the Octave Allegro method to manage existing risks of XYZ's Organizational Website. Concern areas of this application are server room access, server access, security vulnerabilities, backup power and electricity which is also related to the services availability. The results showed that the availability of services have a Relative Risk Score (RRS) of 31 so it is necessary to mitigate and monitor continuously. The recommended mitigation is identification of risk of modification and theft of database, as well as identification of risk from backup data failure, identification of human / technician error risk, identification of hardware and software failure risk, identification of power failure risk, identification of network failure risk, identification of fire and natural disaster risks, also including identification of risk of theft important media or documents.
slepice: Towards a Verified Implementation of Type Theory in Type Theory

Dependent types have proven a useful technique for development of verified software. Despite the existence of many systems based in dependent type theory, mostly interactive theorem provers but also programming languages, there is no system that would itself be implemented using dependent types. Recently, a new approach to type inference and term synthesis for type theory with dependent types emerged that separates the process into an analysis phase that is carried out in type theory, and a search phase that is carried out in a logic programming engine. We describe an architecture of type inference and term synthesis engine for a language with dependent types that is based on the new approach and that is feasible to implement using a dependently typed language. We demonstrate the architecture by describing slepice, its particular implementation.
ForestDSH: a universal hash design for discrete probability distributions

In this paper, we consider the problem of classification of high dimensional queries to high dimensional classes from discrete alphabets where the probabilistic model that relates data to the classes is known. This problem has applications in various fields including the database search problem in mass spectrometry. The problem is analogous to the nearest neighbor search problem, where the goal is to find the data point in a database that is the most similar to a query point. The state of the art method for solving an approximate version of the nearest neighbor search problem in high dimensions is locality sensitive hashing (LSH). LSH is based on designing hash functions that map near points to the same buckets with a probability higher than random (far) points. To solve our high dimensional classification problem, we introduce distribution sensitive hashes that map jointly generated pairs to the same bucket with probability higher than random pairs. We design distribution sensitive hashes using a forest of decision trees and we analytically derive the complexity of search. We further show that the proposed hashes perform faster than state of the art approximate nearest neighbor search methods for a range of probability distributions, in both theory and simulations. Finally, we apply our method to the spectral library search problem in mass spectrometry, and show that it is an order of magnitude faster than the state of the art methods.
Composition-aware Learning for Aesthetic Assessment of Edited Photos

It is common that people prefer to edit photos before posting, and the aesthetic assessment of edited photos is increasingly important, which has applications in photo recommendation, retrieval, and automatic beautification. To our knowledge, this is the first attempt to study the problem of aesthetic assessment for edited photos. Since no related public dataset is available, we create a large-scale Edited photo Aesthetic Data set (EAD) including 25,000 images by incorporating a diversity of personal judgments from the photo-sharing platform. Moreover, in this paper, we propose a new deep learning framework MSNet to obtain the composition characteristics of the edited photos in aesthetic assessment. Specifically, there are two main modules in MSNet. The global composition information describing the overall picture is extracted through the Multi-level feature Fusion Module while the Spatial Attention-aware Module is used to get the local composition information expressing the dependence between visual elements. Finally, the experimental results show the superiority of our proposed approach against the traditional methods designed for non-edited photos.
Image Non-significant Target Detection Based on Depth Metric Particle Swarm Optimization

In the problem of target detection, the degree of discrimination between the target area and the background area will directly affect the detection result. The existing target detection algorithms relies too much on the difference between the target area and the background area. When the boundary between the target region and the background region is fuzzy or even intersects, the detection effect is not good. In order to overcome this problem, this paper proposes a depth measurement particle swarm optimization method for target detection of blurred images. By constructing a depth measurement model, the similarity between the target and the detection area is solved, combined with PSO (particle swarm search algorithm). Search the target area. Compared with the existing target detection algorithms, the proposed in this paper is more effective in dealing with medical image target detection problems.
Development of a dual-stage and visual-servo filming robot

This article presents the development of a visual-servo filming robot for dolly &amp; truck style camera movement in filming applications. The robot was implemented with a fast-response slider as the upper stage on top of the slow-response tracked robot body as the lower stage, to improve target tracking performance. A new switching controller was developed, which controlled the stages’ motions by balancing and adjusting the weights of vision error and slider’s noncentering error of the upper stage, thus achieving tracking performance better than the traditional master–slave control strategy. The simulations were carried out to evaluate the tracking performance of the model, particularly focusing on evaluating how the dual stage improves the overall response of the model. The similar evaluation was executed experimentally as well. Both results confirm that the fast-response characteristics of the upper stage can compensate the slow dynamics of lower stage, the tracked robot which is inevitably heavy due to its composition.
Matrix-DSP back-end support based on TVM compilation structure

The emergence of deep learning frameworks has greatly facilitated the construction of network models, but it has not solved the problem of network models deployed in different hardware backends. TVM combines hardware-independent optimization and hardware-related optimization decoupling ideas to provide excellent solutions. By analyzing the basic structure of TVM and the basic process of neural network deployment on hardware, TVM has realized the basic support of the independently developed chip Matrix-DSP, which provides a foundation for further exploring the performance of the chip and enriching the application scenarios of the chip.
FINSTIX: A Cyber-Physical Data Model for Financial Critical Infrastructures

Cyber-physical security of financial institutions is a critical and sensitive topic. In this context, the FINSEC project aims to design and build a reference architecture for the integrated physical and cyber security of financial institutions. To make feasible, the interactions among the different services of the FINSEC platform, a proper data model defining the exchanged information semantic is fundamental. One of the objectives of the FINSEC project is to integrate cyber and physical security measures in the financial services industry. To do so, the data model must consider both cyber and physical systems. In this paper, the authors present FINSTIX, namely the data model adopted in the FINSEC platform. In particular, they extended the Structured Threat Information eXpression (STIX) standard creating custom objects to describe the financial organization’s infrastructure and then to integrate cyber and physical security measures. The paper also reports an example of the use of FINSTIX in a relevant use case scenario.
Secure Spatial Query Scheme with Access Pattern Obfuscation

The existing secure spatial query methods allow authorized users to generate spatial query tokens that are executed in ciphertext mode by the service provider. However, since only the correct query results that match the query tokens are returned, the service provider can observe clearly which encrypted data is accessed and returned to the client when a query is launched. It results in the leakage of data access pattern. Existing studies have shown that the attacker can use the leaked access patterns to recover query information with some prior knowledge. In this paper, we propose a secure spatial range query scheme with access pattern obfuscation to protect the location data from attackers. This scheme not only prevents access pattern leakage but also ensures the feasibility of spatial range query on encrypted data, which makes up for the deficiencies of existing privacy protection methods. Extensive experiments are conducted on real-world datasets, and experimental results demonstrate that performing range query on the obfuscated dataset can still obtain acceptable query accuracy. Besides, we find both privacy budget and query area are proportional to query accuracy. Moreover, we prove our scheme is more suitable for circular range queries than rectangular ones. In addition, the choosing of privacy budget is studied to enable datasets with different sizes to achieve similar obfuscation performance.
Reprogramming GANs via Input Noise Design

The goal of neural reprogramming is to alter the functionality of a fixed neural network just by preprocessing the input. In this work, we show that Generative Adversarial Networks (GANs) can be reprogrammed by shaping the input noise distribution. One application of our algorithm is to convert an unconditional GAN to a conditional GAN. We also empirically study the applicability, feasibility, and limitation of GAN reprogramming.
The Knowledge Service System for Information Classification and Coding Standards Management

Information classification and coding is a fundamental standard type that plays an important role in information sharing and exchange. Due to current standards management systems only provide data sharing service for users, we have developed the knowledge service system for information classification and coding standards management. The system based on standard knowledge-elements database can provide users more powerful tools to analyze or extract knowledge from standards. In this paper, the whole system architecture and three main functionalities are introduced, and more future development works are proposed.
Feature Learning of Patent Networks Using Tensor Decomposition

In the age of big data, the graph clustering algorithms are visual analytics can help the decision-makers to have a precise description of the relationships between data information in the technological areas and companies. Considering the patent as a source of science and technologies this paper presents a new road map for the patent landscape begins with searching and recognizing related text data in similar patent using a deep learning approach to build a model that can exhibit the relationships between text patent in the form of a graph and constructing large patent networks, to complete the last pass in a patent network project and owing to the complexities of the data structure involve the application of the basic tensor concept and their properties to perform automatic unsupervised learning without taking account of the noisy patent data, throw this process the visualization of graphs knowledge much time more powerful and helpful could prove very useful in improving the decision-making process within an organization.
Physical Layer Authentication Utilizing Carrier Frequency Offset in Dynamic MIMO Systems

This paper focuses on the problem of authenticating transmitters in dynamic MIMO systems. In particular, we exploit carrier frequency offset (CFO) to design an authentication scheme at physical layer. Then, we take CFO caused by hardware impairments as well as Doppler shift into account to accurately analyze authentication performance. An extended Kalman filter (EKF) is employed to track CFO, and the variance of estimation errors is modeled analytically. Moreover, the authentication decision criterion is viewed as a composite hypothesis testing problem. The analytic expressions for false alarm and detection rates are also derived by using tools from hypothesis testing and signal processing. Finally, we provide simulation results to illustrate the authentication performance for the proposed scheme.
Sentiment is an Attitude not a Feeling

Sentiment analysis' attempts to measure the strength of the relationship between a person and an object, sometimes a concrete object such as a product and sometimes an abstract object such as a brand. There is considerable confusion about the form of this relationship - it is typically assumed to be a feeling (and so connected to emotions and moods). Here we argue, and demonstrate, that the relationship is better modelled as a cognitive one, and so connected to attitudes. We demonstrate that the more a lexicon avoids mood and emotion words, the greater its prediction accuracy for reviews of Amazon products.
Scientific Persona Performance Through Online Biographies and Their Relationship to Historical Models

In this chapter, Barbour et al. explore the relationship between historical models of scientific personas and the ways in which present-day scientists represent themselves through their online biographies. The core argument is that identifiable features of the personas produced by historical scientists continue to be reproduced, at least in part, by scientists working in the present day. These personas, which were based on the sociocultural expectations of their times, particularly in the period in which ‘science’ became formalized and recognized, have become part of the discourse about what it means to be a scientist on an international scale. Hence, some key themes are shared between historical scientific personas and contemporary personas constructed through online biographies by scientists who seek to communicate to, and engage, with the broader public.
Research on tort liability of self-driving cars

Since the 21st century, artificial intelligence technology has developed rapidly and is gradually being applied to people’s lives. For example, the emergence of autonomous vehicles in the automobile industry has brought many conveniences to people’s travel. But at the same time, there are also some problems in the application of current laws to the civil liability for accidents in autonomous vehicles. This article mainly studies the tort liability of self-driving cars, taking the world’s first self-driving car death case as the starting point, and analyzing the behavior of self-driving cars from the perspective of the concept, characteristics, and specificity of torts. It then points out that in the field of autonomous driving, there are legal issues such as complicated identification of the subject of motor vehicle accident liability, difficulty in identifying defects in product quality liability, and producers’ claims to develop risk defense to avoid liability. Finally, in response to the above-mentioned problems, it proposes the use of diversified liability subjects, the determination of differentiated liability principles, and the establishment of a social shared risk mechanism to fully protect the legitimate interests of the infringed.
Research on Natural Scene Text Detection and Recognition

Abstract In recent years, as the importance of scene text detection and recognition in real life has gradually become prominent, its research methods have become mature, and more attention has been placed on improving accuracy. Due to the complex background in natural scenes, different font arrangements, lighting, etc., various methods are difficult to meet the requirements of the application. In this paper, the detection and recognition of curved text are studied, and the research method based on the ESIR framework [1] is improved, specifically in the following two aspects: (1) TPS transform fiducial points are predicted by the positioning network, and the positioning network uses CNN to return to the required fiducial points [2] The x and y coordinates. (2) Using ACE (aggregated cross entropy) method for sequence recognition, its loss function is realized faster and it occupies less memory than CTC and attention mechanism, and it performs well in the one-dimensional prediction of scene text recognition. In this paper, the irregular data set is sampled by reference points, and the parameter matrix T of TPS is calculated to transform the points in the picture, and cyclically transform until the image text is level, and then use the CNN-BLSTM model to perform one-dimensional prediction and output the recognition results. The selected data set is irregular text data set: for example, SVT-Perspective, CUTE80.
Tuning a Mamdani Fuzzy Controller with an Imperialist Competitive Algorithm

We have implemented a fuzzy controller with a view to regulating a single-input and single-output second-order linear system. The fuzzy controller was a Mamdami proportional-derivative controller. To determine the parameters of the fuzzy controller we have used an imperialist competitive algorithm. This type of algorithm has a long running time so we implemented also a parallel version of the algorithm that we run on HPC Zamolxes located at the Engineering Faculty of “Lucian Blaga” University from Sibiu. Because we did not have on this computer a version of MATLAB allowing to write parallel algorithms, we implemented the entire application in the C language using the MPI library.