Reviewerly/exHarmony huggingface dataset:
Constructing provable secure broadcast encryption scheme with dealership

Broadcast encryption with dealership (BED) introduced by Gritti et al. (2015) is a proficient cryptographic primitive which involves a third party called as a dealer and creates a business opportunity for the dealer in broadcast services. Designing a BED is a tough task due to its various security requirements. Existing BED of Gritti et al. (2015). requires to get the user’s response to detect a dishonest dealer which is not practical in many cases. Acharya and Dutta solved the problem and proposed a BED scheme using random oracles. This brings up the question of whether it is possible to generate a secure BED without using the random oracle. Fortunately, we have found positive results. We have proposed two constructions in the standard model. Our first scheme is semi-static secure whereas the second one is the first adaptive secure construction in the standard model. All of our constructions provide group privacy by hiding the group of users and provide the maximum number of accountability by restricting the dealer from generating a group token involving a set of users having cardinality greater than a threshold value. We have provided concrete security proof of both of our constructions under key indistinguishability security game. Moreover, we have implemented our schemes and compared with existing schemes.
Overdue: Collection development and deselection projects for a faculty-selected collection

Kent Library at Southeast Missouri State University is one of the only academic libraries in the United States to have a faculty-selected main collection. Three newly hired employees to the library updated a 28-year-old collection development policy and facilitated the reclassification of a half-million-item collection from the Dewey Decimal to the Library of Congress (LC) classification system. To complete these projects, the strengths and challenges of a faculty-selected collection were balanced through implementing research findings, collaborating, communicating, and being flexible.
A Robust Matching Method for Optical and SAR Images Based on Coarse-to-Fine Mechanism

Although image matching techniques have been developed in the last decades, automatic optical-synthetic aperture radar (SAR) image matching is still a challenging task due to significant nonlinear intensity differences between such images. In this paper, a new method of coarse-to-fine heterogeneous image matching based on contour features is proposed. After images preprocessing, a Canny operator is used to extract open-loop contour features, and the angle feature of local feature points is used to complete coarse matching. Finally, constructing contour tree descriptors of coarse matching pairs, and calculating the contour distance is proposed to obtain the fine matching result. The experimental results show the effectiveness and robustness of this method.
PrivacyScope: Automatic Analysis of Private Data Leakage in TEE-Protected Applications

Big data analytics is having a profound impact on many sectors of the economy by transforming raw data into actionable intelligence. However, increased use of sensitive business and private personal data with no or limited privacy safeguards has raised great concerns among individuals and government regulators. To address the growing tension between the need for data utility and the demand for data privacy, trusted execution environment (TEE) is being used in academic research as well as industrial application as a powerful primitive to enable confidential computation on the private data with only the result disclosed but not the original private data. While much of the current research has been focusing on protecting the TEE against attacks (e.g. side-channel information leakage), the security and privacy of the applications executing inside a TEE enclave has received little attention. The general attitude is that the application is running inside a trusted computing base (TCB), and therefore can be trusted. This assumption may not be valid when it comes to unverified third-party applications. In this paper, we present PrivacyScope, a static code analyzer designed to detect leakage of private data by an application code running in a TEE. PrivacyScope accomplishes this by analyzing the application code and identifying violations of a property called nonreversibility. We introduce nonreversibility since the classical noninterference property falls short of detecting private data leakage in certain scenarios, e.g., in machine learning (ML) programs where the program output is always related to (private) input data. Given its strict reliance on observable state, the noninterference falls short of detecting private data leakage in these situations. By design, PrivacyScope detects both explicit and implicit information leakage. The nonreversibility property is formally defined based on the noninterference property. Additionally, we describe the algorithms for PrivacyScope as extensions to the runtime semantics of a general language. To evaluate the efficacy of our approach and proof-of-feasibility prototype, we apply PrivacyScope to detect data leakage in select open-source ML code modules including linear regression, k-means clustering and collaborative filtering. Also, PrivacyScope can detect intentional data leakage code injected by a programmer. We responsibly disclosed all the discovered vulnerabilities leading to disclosure of private data in the open-source ML program we analyzed.
Mode merging for the finite mixture of <i>t</i>‐distributions

Finite mixture models can be interpreted as a model representing heterogeneous subpopulations within the whole population. However, more care is needed when associating a mixture component with a cluster, because a mixture model may fit more components than the number of clusters. Modal merging via the mean shift algorithm can help identify such multicomponent clusters. So far, most of the related works are focused on the Gaussian finite mixture. As the non‐Gaussian finite mixture models are gaining attention, the need to address the component‐cluster correspondence issue in these mixture models grows. Thus, we introduce a mode merging method via the mean shift for the finite mixture of t ‐distributions and its parsimonious variants. It can be framed as an expectation–maximization algorithm and enjoys similar theoretical properties as the mean shift for the Gaussian finite mixture. The performance of our method is demonstrated via simulated and real data experiments, where it shows a competitive performance against some of the existing methods.
Ciphertext-Policy Attribute-Based Encryption with Multi-keyword Search over Medical Cloud Data

Over the years, public health has faced a large number of challenges like COVID-19. Medical cloud computing is a promising method since it can make healthcare costs lower. The computation of health data is outsourced to the cloud server. If the encrypted medical data is not decrypted, it is difficult to search for those data. Many researchers have worked on searchable encryption schemes that allow executing searches on encrypted data. However, many existing works support single-keyword search. In this article, we propose a patient-centered fine-grained attribute-based encryption scheme with multi-keyword search (CP-ABEMKS) for medical cloud computing. First, we leverage the ciphertext-policy attribute-based technique to construct trapdoors. Then, we give a security analysis. Besides, we provide a performance evaluation, and the experiments demonstrate the efficiency and practicality of the proposed CP-ABEMKS.
Towards a Context-Dependent Framework for Visualizing Mental Models

Mental models describe an internal representation of knowledge, which can be used to describe, explain, and predict a person’s interactions with their environment. While there is no shortage on mental model research, a recent literature review revealed the majority of research on mental models does not include an actual model of any kind. Mental models are able to capture complex information, which is difficult to effectively convey through written word alone. A set of visual metaphors are needed to effectively convey the disparate information that can be identified in mental models. We have conducted initial research to develop a notional framework for visualizing mental models based on the context of the elicitation methods used (and the data collected).
Exploring the Encoding Specificity Principle and Context-Dependent Recognition in Virtual Reality

A virtual reality (VR) training system’s effectiveness is determined by how well the knowledge-and skills-gained in the virtual environment transfers to real-world performance. The purpose of this study was to examine the efficacy of virtual reality training by comparing semantic memorization in congruent (e.g., memorization task in VR and recognition task in VR) versus incongruent environments (e.g., memorization task in VR and recognition task in the real word). In the present study, we semi replicated Godden and Baddeley’s 1980 study on context-dependent recognition memory by using a photorealistic virtual reality environment in place of the underwater, scuba environment. Results revealed participants that learned semantic information in the virtual environment performed highly on the memory recognition task in the material, real-world environment (and vice versa). These findings replicate and extend Godden and Baddeley’s original results and provide evidence for the use of VR training to support semantic-based knowledge transfer.
Vehicle Tracking Using Morphological Properties for Traffic Modelling

In the proposed method, an innovative image processing technique for vehicle tracking at a roundabout is described. Background subtraction is applied to get the objects (vehicles) in the foreground. The objects are thus obtained and tracked using morphological operations and object properties. In the video stream, tracking is established by tracing the center of the target object. In present case,four different directions of incoming traffic are considered and four vehicle classes are defined. Implementation of above mentioned method achieved promising result of accuracy greater than 90 % for moderate traffic conditions where occlusion is not an issue.
Losing our Place

In 1986 four idealistic researchers at XEROX PARC, informally crossing organisational and spatial boundaries, spent two weeks working by video link. As this medium created a new space between them, they called it the Media Space. The field blossomed; within ten years researchers delineated place from space, and later work articulated principles for designing distributed place for collaborative creativity and recognised the Ba-Principle (場) of enabling contexts. In 2020 hundreds of millions of us find ourselves in a prolonged version of the same experiment, but rather than convivial workplaces we observe participants fatigued by bare virtual meeting rooms with ephemeral surfaces, void of boundary objects, only weakly affording ambient presence, staging, social signalling or situated action. By contrasting these observations with known design principles, we reveal which are most lacking in contemporary systems and propose a path back to constructing shared meaning in distributed place.
Minimal linear codes from Hermitian varieties and quadrics

Abstract In this note we investigate minimal linear codes arising from Hermitian varieties and quadrics. We study their parameters and formulate some open problems about their weight distribution.
Semi-automatic Construction of Sight Words Dictionary for Filipino Text Readability

Readability formulas consider word familiarity as one of the factors for predicting the readability of children’s books. Word familiarity is dependent on the frequency in which the words are encountered in daily reading. Often referred to as “sight words”, developing effective recognition of these high-frequency words can assist young readers to develop their reading fluency and comprehension. In this paper, we describe our work in building a dictionary of sight words for Filipino with the use of a corpus of Filipino literary materials written for children. We expanded the dictionary to a total of 664 words with the use of pre-trained word embedding model. The availability of such dictionary can facilitate the development of a readability formula for Filipino text, especially in the context of its lexical complexity.
A multiprocess Salp swarm optimization with a heuristic based on crossing partial solutions

The Salp swarm algorithm (SSA) is one of the most recent metaheuristic optimization algorithms. SSA has been used successfully to solve optimization problems in different research areas such as machine learning, engineering design, wireless networks, image processing, mobile robotics, and energy. In this article, we present a multi-threaded implementation of the SSA algorithm. Each thread executes an SSA algorithm that shares information among the swarms to get a better solution. The best partial solutions of each swarm intersect in a similar way of genetic algorithms. The experiments with nineteen benchmark functions (unimodal, multimodal, and composite) show the results obtained with this new algorithm are better than those achieved with the original algorithm.
Multi-Level Fusion Net for hand pose estimation in hand-object interaction

This work is about solving a challenging problem of estimating the full 3D hand pose when a hand interacts with an unknown object. Compared to isolated single hand pose estimation, occlusion and interference induced by the manipulated object and the clutter background bring more difficulties for this task. Our proposed Multi-Level Fusion Net focuses on extracting more effective features to overcome these disadvantages by multi-level fusion design with a new end-to-end Convolutional Neural Network (CNN) framework. It takes cropped RGBD data from a single RGBD camera at free viewpoint as input without requiring additional hand–object pre-segmentation and object or hand pre-modeling. Through extensive evaluations on public hand–object interaction dataset, we demonstrate the state-of-the-art performance of our method.
Distributed wind power virtual simulation experiment system for cultivating the ability to solve complex engineering problems

Abstract This paper presents a distributed wind power virtual simulation experiment system for cultivating students' ability to solve complex engineering problems. The experiment system was designed with complex engineering problems of real wind farms, both in a steady state and transient process. On the basis of the system, comprehensive practical contents were developed. According to engineering education certification, the virtual simulation practical contents provide support for improving students' ability to solve complex engineering problems. The system has been applied to a comprehensive practical course of renewable energy. From the designed assessment, the experiment system helped students to master the advanced technologies applied to real wind farms visually and enhanced their ability to solve complex engineering problems. After practical learning on the system, students' performance has been improved in graduation projects. It is proved that the virtual simulation experiment system is effective in cultivating students’ ability to solve complex engineering problems.
Robust and integrative Bayesian neural networks for likelihood-free parameter inference

State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference. Existing approaches for learning summarizing networks are mainly based on deterministic neural networks, and do not take network prediction uncertainty into account. This work proposes a robust integrated approach that learns summary statistics using Bayesian neural networks, and produces a proposal posterior density using categorical distributions. An adaptive sampling scheme selects simulation locations to efficiently and iteratively refine the predictive proposal posterior of the network conditioned on observations. This allows for more efficient and robust convergence on comparatively large prior spaces. The approximated proposal posterior can then either be processed through a correction mechanism, or be used in conjunction with a density estimator to arrive at the true posterior. We demonstrate our approach on benchmark examples.
Study on Wireless Signal Propagation in Residential Outdoor Activity Area Based on Deep Learning

As explosive growth of mobile data traffic brings great challenges to the mobile networks, how to reasonably deploy the latest generation mobile networks to meet the needs of users for communication becomes an urgent technical problem to be solved. Since machine learning technology has shown its superiority in processing big data in recent years, researches on the application of machine learning technology in wireless communication are expanded and explored gradually. In this paper, a wireless signal propagation model based on deep learning neural network is proposed to evaluate the wireless signal propagation characteristics and signal coverage status in different scenarios in the complex and changeable residential outdoor activity areas. The massive data generated in practical applications are reasonably utilized to predict the signal propagation characteristics of each cell with high accuracy and stability. The model has is of good adaptability to different complex scenarios and can providing provide a certain reference value and engineering guidance for further wireless network deployment and optimization development.
Fast-PPO: Proximal Policy Optimization with Optimal Baseline Method

Deep deterministic policy gradient (DDPG) is a useful deep reinforcement learning approach, but it tends to suffer from instability of gradient estimates. Recent methods such as PPO just limit the policy update under lower speed to keep stability by chance. In this paper, we model the problem under an advantage actor-critic(A2C) architecture. We first analyze the operation of the simplified analytic solution in A2C, where the instability of the policy update mainly be attribute to two factors: the variance of action estimate and the variance of accumulative rewards. To solve it, we propose a new method of PPO with the optimal baseline called Fast-PPO. In detail, our hybrid optimal baseline considers both the advantage of action estimate and the estimate of accumulative reward. By doing so, our method guarantees that not only the action estimate can be converged faster in the right direction, but also the accumulative reward can be under lower variance. The experimental results demonstrated that our Fast-PPO has the better performance than others methods.
Design of Open Source Personalized Information Recommendation System for Web Pages in Big Data Environment

The current web information recommendation system can not meet the personalized needs of users. To solve this problem, an open source personalized information recommendation system for web pages is proposed in the large data environment. The hardware of the system is PC-side web pages, and its development environment consists of the underlying database, software environment, development technology and user interface. Based on the hardware design of the system, the software is designed and the corresponding functions are realized, so that the software can run steadily on the platform. By collecting user data, user feature collaborative filtering, personalized correlation matching, recommendation ranking weighting and recommendation evaluation criteria, the software is designed and implemented by code. The experimental results show that the system can recommend suitable personalized information for users, and the system has advantages and reliability.
The Influence of Air Quality on Solar irradiances in most air polluted months in China

Abstract Solar energy, as a kind of clean and sustainable energy, is widely used in most countries, and the number of PV generators is increasing year by year in China. The solar energy generation directly depends on the amount of solar irradiance, which is easily affected by atmosphere situation. With the rapid development of urbanization and industrialization, the atmosphere situation in China become worse and worse, and the air is always heavily polluted in recent years, which will reduce the irradiance getting to the ground. In order to analyze the effect of air quality on the solar irradiance, we analyze three kinds of solar irradiance Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI), and Diffuse Irradiance (DIF). The influence of the Air Quality Index (AQI) on DNI, GHI, DIF are analyzed from November to February in the most air polluted months in recent four years for Beijing, Harbin, Shanghai and Guangzhou. The date when the AQI was the local maximum(minimum), DNI and GHI were the local minimum(maximum), and DIF was also the local maximum(minimum) are selected. The results shows that DNI and GHI decrease, and DIF increases with the increase of AQI,. The six-order polynomial is used to fit the difference of the three irradiance and the difference of AQI. The average fitting coefficient R 2 for GHI is 0.8855, DNI is 0.8529 and DIF is 0.8466, and the highest R 2 for GHI is 0.9748, DNI is 0.9794 and DIF is 0.9820.
Research on comprehensive evaluation of online stores based on user sentiment analysis

With the rapid development of the Internet, the e-commerce industry has become more and more popular, resulting in a large number of commodity review data, which provides users with reference value. However, the amount of comment data on the network is often huge, and it is difficult for consumers to extract useful information from these mixed opinions. Ther efore, how to effectively analyze the product comment data and extract the user’s emotion is the key issue at this stage. This paper proposes a comprehensive evaluation of online stores based on user sentiment analysis, which can accurately identify the evaluation of users on online shopping goods, point out the emotional trend of users, provide reference information for other users, and provide feedback information for businesses. As a reference index for e-commerce website to use recommendation algorithm for personalized recommendation.
An Arithmetic Differential Privacy Budget Allocation Method for the Partitioning and Publishing of Location Information

The rapid development of mobile Internet services and the wide application of intelligent terminals has accelerated the advent of the promising era of big data. A number of big data services based on location information bring convenience to users, however, it also results in serious leakage of personal privacy. The partitioning and publishing method combined with the differential privacy model can provide better range counting query results under the premise of ensuring the privacy of users' location. Nevertheless, most of the existing research studies only focus on the structural design during the partitioning process of location big data and ignore the impact of differential privacy budget allocation methods on the published results. This paper, therefore, proposes an efficient arithmetic privacy budget allocation strategy for the tree-based partitioning and publishing of location big data which satisfies the ε-differential privacy. Experimental results over a large number of real-world datasets prove that the proposed privacy budget allocation method is superior in contrast to the existing methods for improving the usability of the published data.