Reviewerly/exHarmony huggingface dataset:
Hybrid Recommendation Algorithm based on User Behavior

Due to the large amount and strong sparsity of data of e-commerce users' behaviors, the traditional single recommendation algorithm cannot meet the requirement of the recommendation system for accuracy. This paper proposes a hybrid recommendation algorithm (KMFSCF) that integrates K-means and Funk-SVD into the collaborative filtering algorithm. Use K-means to cluster users, and then decompose the scoring matrix of user-item after clustering, calculate user similarity to make scoring prediction, and finally make recommendations according to the ranking of ratings. Experiments show that this algorithm can effectively reduce the influence of data sparsity, reduce the recommendation error and improve the recommendation accuracy.
Awareness Logic: A Kripke-Based Rendition of the Heifetz-Meier-Schipper Model

Heifetz, Meier & Schipper (HMS) present a lattice model of awareness. The HMS model is syntax-free, which precludes the simple option to rely on formal language to induce lattices, and represents uncertainty and unawareness with one entangled construct, making it difficult to assess the properties of either. Here, we present a model based on a lattice of Kripke models, induced by atom subset inclusion, in which uncertainty and unawareness are separate. We show the models to be equivalent by defining transformations between them which preserve formula satisfaction, and obtain completeness through our and HMS’ results.
SFSSD: A Select Feature Single-shot Detector for Real-time Target detection

We present Select Feature Single-shot Detection (SFSSD), a lightweight object detector framework that can optimize feature fusion. State-of-the-art extract features rely heavily on a very deep or large network structure, so it's difficult to apply to miniaturized embedded devices. Designing a good lightweight network instead of a large network can solve this problem. Lightweight networks using shallow features to detect small targets do not work well, so needs fuse deep semantic information to shallow layers, we design a feature fusion selector to determine the optimal network structure. In the case of guaranteed speed, compared with Tiny-SSD, the mean Average Precision (mAP) increases 3%, and the speed is 3 times faster than DSOD. At 512*512 inputs, mAP was achieved 67.4% without pre-training. After using the feature fusion selector improved the network structure, mAP rise 0.6%. Our code and models are available at: https://github.com/976912560/SFSSD.
Research On Wireless Intelligent Propagation Model Based On Deep Learning

In order to better meet the growing and diversified market demand of users, mobile operators need to deploy a large number of base stations to improve the quality of radio wave propagation. Accurate and efficient network estimation is of great significance for accurate 5G network deployment. In this study, PCRR and RMSE of the reference signal receiving power (RSRP) of the observation points are taken as the optimization objectives, and the features are extracted from the perspective of model analysis and data analysis. Based on these features, a neural network is established for multi-objective learning and prediction of radio wave signal strength. Finally, the test data set of the radio wave signal strength prediction model based on deep learning achieved root mean square error of 8.59 and PCRR of 23.6%, which has a strong practical reference value for engineering practice.
Development of the mathematic model of disymmetric bigram cryptosystem based on a parametric solution family of multi-degree system of Diophantine equations✱

This paper shows the objective necessity of improving the information security systems (ISS) under the development of information and telecommunication technologies. The paper also shows the theorems that describe properties of the parametric solutions of multi-degree systems of Diophantine equations (MSDE) necessary for development of the mathematical models of the information security systems (ISS) based on MSDE solutions. The Frolov’s theorem is generalized, and the author’s theorem is given that allows developing the mathematical model of ISS containing Diophantine difficulties and the author’s mathematical model of alphabetic cryptosystem in the form of tuple.
Applying Code Transform Model to Newly Generated Program for Improving Execution Performance

The existing programs inside the voice assistant machine prompt human-machine interaction in response to a request from a user. However, the crucial problem is that the machine often may not give a proper answer to the user or cannot work out the existing program execution efficiently. Therefore, this study proposes a novel transform method to replace the existing programs (called sample programs in this paper) inside the machine with newly generated programs through code transform model GPT-2 that can reasonably solve the problem mentioned above. In essence, this paper introduces a theoretical estimation in statistics to infer at least a number of generated programs as required so as to guarantee that the best one can be found within them. In addition, the proposed approach not only imitates a voice assistant system with filtering redundant keywords or adding new keywords to complete keyword retrieval in semantic database but also checks code similarity and verifies the conformity of the executive outputs between sample programs and newly generated programs. According to code checking and program output verification, the processes can expedite transform operations efficiently by removing the redundant generated programs and finding the best-performing generated program. As a result, the newly generated programs outperform the sample programs because the proposed approach reduces the number of code lines by 32.71% and lowers the program execution time by 24.34%, which is of great significance.
Just TYPEical: Visualizing Common Function Type Signatures in R

Data-driven approaches to programming language design are uncommon. Despite the availability of large code repositories, distilling semantically-rich information from programs remains difficult. Important dimensions, like run-time type data, are inscrutable without the appropriate tools. We contribute a task abstraction and interactive visualization, TYPEICAL, for programming language designers who are exploring and analyzing type information from execution traces. Our approach aids user understanding of function type signatures across many executions. Insights derived from our visualization are aimed at informing language design decisions - specifically of a new gradual type system being developed for the R programming language. A copy of this paper, along with all the supplemental material, is available at osf.io/mc6zt.
Study and Analysis of Virtual Reality and its Impact on the Current Era

In these days, virtual reality (VR) technology is widely using in many fields and becoming the mainstream due to its features (e.g. experience, personalization and entertainment). With development, it provides a new platform to make the technology more conventional, exciting and progressively make changes in people’s way of creation and life. The real-life impacts of VR and its effects on users are not currently well-understood. Therefore, it is important to comprehend its possible positive and negative impacts on users while using this technology. This paper first provides the concept of VR technology with a framework for better understanding its features, importance and its implications in many fields. Built based on existing literature, it observes the VR experiences offered as well as VR user involvement based on position, location and interactivity. Previous studies have shown mixed responses from VR users in different situations that must also be analyzed. In this context, we wrap up this paper with the analysis of five different areas (Entertainment, Education, Healthcare, Automobile and Aviation) where VR in function with the present and future development level. In addition, this paper will also present suggestions for improvements by considering the drawbacks of this technology which could be harmful to users and thus need to be overcome.
Patches Based Multichannel Weighted Nuclear Norm Prior for Color Image Denoising

Image denoising is a fundamental problem in the field of signal processing and computer vision. A lot of work has been proposed for gray image denoising. Because there are different noise variances in different color channels, it is not easy to extend these methods for color image denoising directly. In this paper, we propose a novel method for color image denoising by introducing a multichannel weighted nuclear norm prior into the patches based image recovery framework. The different noise statistics of three color channels are taken into account by using a weight matrix to data fidelity in multichannel weighted nuclear norm prior. After each patch is updated, then the whole image can be recovered by global convex optimization. Each alternative updating step has a closed form solution and the problem can be solved efficiently. We demonstrate the superiority of proposed method over some state-of-the-art denoising algorithms on synthesis and real color image datasets.
Soft Policy Optimization using Dual-Track Advantage Estimator

In reinforcement learning (RL), we always expect the agent to explore as many states as possible in the initial stage of training and exploit the explored information in the subsequent stage to discover the most returnable trajectory. Based on this principle, in this paper, we soften the proximal policy optimization by introducing the entropy and dynamically setting the temperature coefficient to balance the opportunity of exploration and exploitation. While maximizing the expected reward, the agent will also seek other trajectories to avoid the local optimal policy. Nevertheless, the increase of randomness induced by entropy will reduce the train speed in the early stage. Integrating the temporal-difference (TD) method and the general advantage estimator (GAE), we propose the dual-track advantage estimator (DTAE) to accelerate the convergence of value functions and further enhance the performance of the algorithm. Compared with other on-policy RL algorithms on the Mujoco environment, the proposed method not only significantly speeds up the training but also achieves the most advanced results in cumulative return.
Smart Elderly Care Robot

This paper describes the implementation of a low cost service robot to help the elderly to improve their quality of life. It includes different technologies such as Simultaneous Localization and Mapping (SLAM), Open Source Computer Vision Library (OpenCV) and ChatterBot (a machine learning, conversational dialog engine for creating chat bots) to develop intelligence for autonomous control of the robot itself and easy-to-use interfaces for the elderly. The robot equipped with a 2D low cost laser sensor and SLAM technology would be able to build a map in an unknown area and navigate the mapped area after mapping. The ChatterBot provides the learning and conversational dialog engine which provides user-friendly interaction and feedbacks as a robotic companion to the elderly. The OpenCV is mainly used to perform the face detection, face tracking and motion detection tasks. A mobile application software (App) with streaming service is also developed to allow elderly's close relatives to monitor the health status and condition of the elderly.
F-Measure Optimization of Forest Flame Salient Object Detection Based on Boundary Perception

Relaxed F-measure is applied to the improved boundary perception model to optimize c. Shorten the model of training time, after one hundred iterations can study to the characteristics of forest fire; Highlight the significant areas quickly. With immediate proximity activation, Floss also maintains a large gradient; Make network model produce polarized activations, speeds up the model convergence. Solve forest fires due to wide dispersion of targets; A problem where foreground and background are not easily distinguishable.
Research of Low-light Image Enhancement Method for Enclosed Tank

Due to closed environment of gas insulated switchgear (GIS) chamber, low-light images are easily produced when taking pictures under weak lighting conditions. The lost details and low contrast not only cause unpleasant subjective feelings, but also hurt the performance of many computer vision systems which are designed for normal-light images. Thus, a low-light image enhancement algorithm based on improved conditional generative adversarial network (GAN) is proposed here to solve this poor visual perception problem. Decom-Net referenced to RetinexNet used here decompose image into reflection map and illumination map. This study proposed an encode-decode convolutional neural network model as the generative model and a lightweight convolutional neural network(CNN) as the discriminative model. After joint training of generative model and discriminative model, the network outputs the final enhanced images. The experimental results demonstrate that the proposed method is more effective than existing methods in perception.
VMware ESXi and Microsoft Hyper-V Hypervisor Performance Comparison

The focus of this paper is the evaluation of two typical type 1 hypervisors: ESXi and Microsoft Hyper-V. The test procedures relied on the use of HD Tune Pro and Filebench environments, providing same testing conditions, while MS Windows Server and Centos 7 were used as the guest operating systems. The results have shown that the assumed hypotheses for the hypervisor behavior comply with the expectations.
Household Garbage Classification: A Transfer Learning Based Method and a Benchmark

Household garbage images are highly diverse in color, texture and geometry, which poses significant challenges to garbage classification. Deep convolutional neural network (DCNN) have recently achieved remarkable progress due to their ability to learn high-level feature representations. It usually requires a large number of labelled image data for training a DCNN model. However, there are few public and mature data sets concerned on household garbage images. This severely limits the progress of research and the state of the art is not entirely clear. To address this problem, we introduce a new benchmark data set for household garbage image classification. This data set is called 30 Types of Household Garbage Images (HGI-30), which contains 6′000 images of 30 household garbage types, with complex backgrounds, different resolutions, and complicated variations in sample, pose, illumination and background. The publicly available HGI-30 data set allows researchers to develop more accurate and robust methods for both household garbage image processing and interpretation analysis of household garbage object. We further study the classification problem on this data set and propose a transfer learning based method, also provide a performance analysis, which serves as baseline result on this benchmark.
Learning Symbolic Action Definitions from Unlabelled Image Pairs

Task planners and goal recognisers often require symbolic models of an agent’s behaviour. These models are usually manually developed, which can be a time consuming and error prone process. Therefore, our work transforms unlabelled pairs of images, showing the state before and after an action has been executed, into reusable action definitions. Each action definition consist of a set of parameters, effects and preconditions. To evaluate these action definitions, states were generated and a task planner invoked. Problems with large state spaces were solved using the action definitions learnt from smaller state spaces. On average, the task plans contained 5.46 actions and planning took 0.06 seconds. Moreover, when 20 % of transitions were missing, our approach generated the correct number of objects, action definitions and plans 70 % of the time.
Optimization of Personal Website: Solution for Learning in The New Normal Era

Abstract The education sector is one of the sectors most affected by the spread of the Covid-19 pandemic. Lecturers must ensure that teaching and learning activities continue even though students are at home. Learning innovation in the era of the industrial revolution 4.0 is a solution that needs to be designed and implemented by lecturers by maximizing existing media such as websites. Optimizing the Lecturer Personal Website is a learning model solution that can be used to deal with the current New Normal Era, especially in the field of education. The learning model through lecturer personal websites aims to optimize the role and function of the website and get added value or benefits that students get from using the Web (E-Learning). This research was conducted at STAIN Mandailing Natal using qualitative descriptive methods. The research data were obtained through recorded interviews with 8 informants, classroom observation, documentation, and literature study. This study used the interactive model data analysis technique of Miles and Huberman. The results showed that the personal role of the lecturer website was as a facility to support the quality of Islamic Law learning at STAIN Mandailing Natal. It has function as means of accessing E-Learning that is easier, more cost-effective, attractive and interactive and serves as a discussion tool for students. Students are more enjoy and full spirit in the learning process because they are cover in getting information and learning materials needed in learning Islamic Law.
Knowing what to look for: A Fact-Evidence Reasoning Framework for Decoding Communicative Visualization

Despite the widespread use of charts as a medium for communicating data in science domains, we lack a systematic understanding of the goals and principles of effective visual communication. Existing research mostly focuses on the means, i.e. the encoding principles, and not the end, i.e. the key takeaway of a chart. To address this gap, we start from the first principles and aim to answer the fundamental question: how can we describe the message of a scientific chart? We contribute a fact-evidence reasoning framework (FaEvR) by augmenting the conventional visualization pipeline with the stages of gathering and associating evidence for decoding the facts presented in a chart. We apply the resulting classification scheme of fact and evidence on a collection of 500 charts collected from publications in multiple science domains. We demonstrate the practical applications of FaEvR in calibrating task complexity and detecting barriers towards chart interpretability.
weWeb Page Re-Ranking using Squirrel Search Rank Algorithm

Ranking of web page is one of the key factors in search engine. However, ranking is intended to offer the list of ranked web documents that user is more likely to desire, but retrieving the relevant documents at faster rate based on the user query poses a great challenge in web environments. Hence, an effective web page re-ranking mechanism named Squirrel Search Rank algorithm (SqSRank) is developed in this research for re-ranking web pages more effectively with re-ranking measure. However, the proposed SqSRank algorithm is designed using the Squirrel Search Algorithm (SSA), which considers the foraging feature of flying squirrels in search of food source. The filtered web pages make the proposed ranking algorithm to effectively perform well in web page re-ranking process. The features associated with the web documents are extracted and enable the re-ranking measure to find the ranking score using the fitness measure. The fitness with the minimal distance of retrieval is accepted as the best solution. However, the proposed SqSRank algorithm obtained better performance in terms of the metrics, like precision, F-measure, and recall, which acquired the values of 0.964, 0.980, and 0.996, respectively.
The BERT-BiLSTM-CRF Question Event Information Extraction Method

The purpose of this research is to further improve the accuracy of the entity information extraction of customer voice consultation questions, enhance the overall effect of information extraction technology in the knowledge map of the intelligent question and answer system, optimize information from the aspect of semantic labelling, and add the BERT model on the basis of BiLSTM-CRF to study sentences in depth, thus enhancing the semantic information extraction of questions. Entity extraction and semantic labelling experiments are carried out with a data source of customer's voice consultation questions and question text. The results show that the semantic label extraction tuning average of the BERT model is higher than that of the BiLSTM-CRF method on the basis of semantic label optimization, and the model tuning average is 91.53%, which can provide practical significance for enhancing event entity information extraction.
FlexSketch: Estimation of Probability Density for Stationary and Non-Stationary Data Streams

Efficient and accurate estimation of the probability distribution of a data stream is an important problem in many sensor systems. It is especially challenging when the data stream is non-stationary, i.e., its probability distribution changes over time. Statistical models for non-stationary data streams demand agile adaptation for concept drift while tolerating temporal fluctuations. To this end, a statistical model needs to forget old data samples and to detect concept drift swiftly. In this paper, we propose FlexSketch, an online probability density estimation algorithm for data streams. Our algorithm uses an ensemble of histograms, each of which represents a different length of data history. FlexSketch updates each histogram for a new data sample and generates probability distribution by combining the ensemble of histograms while monitoring discrepancy between recent data and existing models periodically. When it detects concept drift, a new histogram is added to the ensemble and the oldest histogram is removed. This allows us to estimate the probability density function with high update speed and high accuracy using only limited memory. Experimental results demonstrate that our algorithm shows improved speed and accuracy compared to existing methods for both stationary and non-stationary data streams.
Closest separable state when measured by a quasi-relative entropy

Abstract It is well known that for pure states the relative entropy of entanglement is equal to the reduced entropy, and the closest separable state is explicitly known as well. The same holds for Renyi relative entropy per recent results. We ask the same question for a quasi-relative entropy of entanglement, which is an entanglement measure defined as the minimum distance to the set of separable state, when the distance is measured by the quasi-relative entropy. First, we consider a maximally entangled state, and show that the closest separable state is the same for any quasi-relative entropy as for the relative entropy of entanglement. Then, we show that this also holds for a certain class of functions and any pure state. And at last, we consider any pure state on two qubit systems and a large class of operator convex function. For these, we find the closest separable state, which may not be the same one as for the relative entropy of entanglement.
Re-Identification for Multi-Object Tracking Using Triplet Loss

Assigning a consistent identification(ID) number is a chronic problem in the tracking model. However, recent tracking models lose the ID because it focuses only on the previous frame. This paper constructed a tracking deep learning model using triplet loss to give consistent ID to objects detected while tracking. We also show the best way for pre-processing the input for the triplet-tracking model, which inputs various image sizes. The experimental result of 97.76% accuracy on KITTI shows the effectiveness of our result.