Reviewerly/exHarmony huggingface dataset:
Empirical AI Simulation Algorithm Research on the Impact of Floating Population Status on Sentencing

This paper conducts the empirical AI simulation algorithm research on the impact of floating population status on sentencing. Stable, startling advances in computer hardware have led to a massive supply of powerful computers, data collection devices, and storage media. With this advancement, the analysis of the large-scale AI simulation becomes real and possible. This research study considers the scenario of the impact of floating population status on sentencing. Firstly, a novel remote sensing model is designed for the estimation of the population status with the integration of the novel structure data pattern. Then, a novel AI simulation tool with the SQL is designed to treat the situation of the complex data. Further, the application scenario is designed with the numerical simulation. The results prove that the designed model is efficient.
Agricultural Economic Risk Forecast Based on Data Mining Technology

In order to improve the effect of agricultural economic risk forecast, this paper studies the agricultural economic risk forecast combined with data mining technology and builds an intelligent agricultural economic risk forecast system. Moreover, this paper employs a dynamic factor model to estimate common factors that drive changes in target topics. In order to construct a sentiment index that can reflect the overall operating situation of the macroeconomy, this paper improves the agricultural economic risk mining algorithm and standardizes the sentiment value corresponding to the target theme. In addition, this article analyzes the sentiment changes of its individual topics one by one in combination with the specific economic environment. The simulation study shows that the agricultural economic risk forecast system based on data mining technology proposed in this paper has a good effect.
Enabling Tangible Interaction on Non-touch Displays with Optical Mouse Sensor and Visible Light Communication

This paper presents Centaur, an input system that enables tangible interaction on displays, e.g., untouchable computer monitors. Centaur’s tangibles are built from low-cost optical mouse sensors, or can alternatively be emulated by commercial optical mice already available. They are trackable when put on the display, rendering a real-time and high-precision tangible interface. Even for ordinary personal computers, enabling Centaur requires no new hardware and installation burden. Centaur’s cost-effectiveness and wide availability open up new opportunities for tangible user interface (TUI) users and practitioners. Centaur’s key innovation lies in its tracking method. It embeds high-frequency light signals into different portions of the display content as location beacons. When the tangibles are put on the screen, they are able to sense the light signals with their optical mouse sensors, and thus determine the locations accordingly. We develop four applications to showcase the potential usage of Centaur.
Smart Mental Health Guiding in Colleges: from HCI Data Collection to Fuzzy Information Mining

This paper studies the smart mental health guiding in colleges from HCI data collection to fuzzy information mining. The HCI firmware implements HCI commands to the Bluetooth hardware by accessing base-band commands, link manager commands, hardware status registers, basic control registers, and event registers. Considering this feature, this paper design the novel information fuzzy analysis framework to apply to the scenario of the smart mental health guiding. Similarly, the flow control of the HCI-UART adopts event-based stop and other flow control methods, but this flow control method is completely controlled by the host. Hence, novel host is designed to analyze the data. Through the experimental analysis, the performance of the model is validated.
Robust and Uncertainty-Aware VAE (RU-VAE) for One-Class Classification

One-class classification (OCC) methods for abnormality detection learn either a generative model of the inlier class (e.g., using variants of kernel principal component analysis) or a decision boundary to encapsulate the inlier class (e.g., using one-class variants of the support vector machine). Recent methods use deep-neural-network models to learn (for the inlier class) either latent-space distributions or autoencoders, but not both. OCC learning typically relies solely on inlier-class data, but some recent semi-supervised versions also leverage some outlier-class training data. We propose a robust and uncertainty-aware variational framework for OCC, leveraging data-adaptive generalized-Gaussian (GG) models leading to distribution modeling in both latent space and image space. We propose a reparameterization for samples from the latent-space GG to enable backpropagation. Results on publicly available real-world datasets show the benefits of our method over others.
A Hyperelliptic Curve Cryptosystem Based Proxy Promised Signcryption Scheme

Signcryption is the method of combining a digital signature with encryption in a single logical step to get the cryptographic primitives of a public key. However, in addition to signcryption, we sometimes require anonymity and delegation of rights. In this paper, we propose a scheme called proxy promised signcryption to address all of these objectives simultaneously. In this scheme, the actual sender or signer delegates authority to an agent known as signcrypter for signcryption of the actual ciphertext. The agent entity is facilitated to generate a promised signcrypted text. The signcrypted text is then communicated to the intended recipient. The proposed work aims to reduce computation and communication costs, which has been tested by comparing it to existing schemes. The results obtained from this comparison support the aims of our scheme. The proposed scheme’s security has also been evaluated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The results validate that our scheme resists well-known cyberattacks.
How to get away with cyberattacks

The possibility that common users are successfully recruited in cyberattacks represents a considerable vulnerability because it implies that citizens can legitimize cyberattacks instead of condemning them. We propose to adopt an argumentative approach to identify which premises allow such legitimization. To showcase this approach, we created four short narratives describing cyberattacks involving generic users and covering different motives for the attacks: profit, recreation, revenge, and ideology. A sample of 16 participants read the four narratives and was afterward interviewed to express their position on the attacks described. All interview transcripts were then analyzed with an argumentative approach, and 15 premises were found to account for the different positions taken. We describe the premises, their distribution across the four narratives, and discuss the implications of this approach for cybersecurity.
Spatial Analysis For Histopathology: A Statistical Approach

A pattern analysis approach is proposed to model spatial interaction between the immune cells and the islets of cancer cells within the tumor microenvironment. Embedded in a statistical null hypothesis paradigm, the proposed solution provides a novel mechanism to extract quantitative, system-level snap-shot of the tumor microenvironment during immunotherapy. Experimental results on synthetic and real histopathological data suggest the potential of our technique in mining robust information from the spatial distribution of immune cells at different time-points during cancer treatment. This statistical framework could be potentially adopted in future large-scale experimental studies to objectively monitor disease progression and the patient’s response to immune therapy.
Improved Histology Image Classification under Label Noise Via Feature Aggregating Memory Banks

Medical images are often subjectively labeled or weakly supervised, which introduces both closed-set and open-set label noise. Deep learning (DL) models underperform when the quality of supervisory labels degrades. We propose a unified framework for robust training of DL models that takes into account both closed-set and open-set noise, whereas the latter has been largely overlooked. We use a contrastive learning framework and feature aggregating memory banks to identify and increase the emphasis on clean training samples. Based on two publicly available datasets we demonstrate improved results for both synthetically introduced and naturally occurring (unknown) label noise.
A Study on Linguistic Management in Online and Offline Communities

There are many conflicts among online communities' users and there are diverse types of online communities which have their own conflict management. Also, there are many users who perceive their online communities useful both in their cyber and real lives. This study clarifies the effects of jargons and focuses on finding out whether there are considerable relationships among individual inclination, linguistic habits, and conflict management types to affect the user's intention to participate in communities. There are significant relationships among jargons, conflict managements, selfishness, and perceived usefulness. This study also explains whether the frequency usage of jargon in online communities would affect the types of management, perceived usefulness, and community members' participation. Moreover, it may be clear that online jargon effect community members' participation and perceived usefulness.
Deep Non-Linear Embedding Deformation Network for Cross-Modal Brain MRI Synthesis

Multimodal MRI (e.g. T1, T2, and Flair) can provide rich anatomical and functional information, thereby facilitating clinical diagnosis and treatment. However, multimodal MRI takes a long scan time, easily leading to artifacts or corruption in certain modalities. Therefore, it is of great value to synthesize a new MRI modality from a complete MRI modality to obtain complementary information for clinical diagnosis. Existing GAN-based approaches treat cross-modal MRI synthesis as an end-to-end learning process without explicit consideration of the inherent correlations between different modalities, leading to inaccurate anatomical and lesion structure in the synthesized modality. In this paper, we propose a deep non-linear embedding deformation network (NEDNet) for cross-modal brain MRI synthesis. NEDNet represents each modality as a non-linear embedding based w.r.t. its own atlas, and learns a deformation feature that is assumed to be the same across modalities. The modality-specific atlas and multi-modal shared deformation are jointly used for generating the new MRI modality. Experiments show that our approach can obtain better cross-modality synthesis results than several baseline methods.
Generating and visualizing trace link explanations

Recent breakthroughs in deep-learning (DL) approaches have resulted in the dynamic generation of trace links that are far more accurate than was previously possible. However, DL-generated links lack clear explanations, and therefore non-experts in the domain can find it difficult to understand the underlying semantics of the link, making it hard for them to evaluate the link's correctness or suitability for a specific software engineering task. In this paper we present a novel NLP pipeline for generating and visualizing trace link explanations. Our approach identifies domain-specific concepts, retrieves a corpus of concept-related sentences, mines concept definitions and usage examples, and identifies relations between cross-artifact concepts in order to explain the links. It applies a post-processing step to prioritize the most likely acronyms and definitions and to eliminate non-relevant ones. We evaluate our approach using project artifacts from three different domains of interstellar telescopes, positive train control, and electronic healthcare systems, and then report coverage, correctness, and potential utility of the generated definitions. We design and utilize an explanation interface which leverages concept definitions and relations to visualize and explain trace link rationales, and we report results from a user study that was conducted to evaluate the effectiveness of the explanation interface. Results show that the explanations presented in the interface helped non-experts to understand the underlying semantics of a trace link and improved their ability to vet the correctness of the link.
Analysis of Information Quality and Security Factors that Affect the use of Pet Apps during COVID-19

Information quality factors are important factors in the formation of applications. Currently, there are many animal lovers who really need pets application to take care of their pets, such as how to care for pets, pet salon treatments, health care and even pet care. Pet applications already exist, but it found that there are still many animal lovers who have not used animal lover applications during COVID-19, however, the loyal customer who used the application quit a lot (58%) but not for the new user especially during COVID-19. Therefore, this study was conducted to see how the information quality factor affects the use of pet applications during COVID-19 situation. This research was conducted using the application of R Statistics with Structural Equation Model and Partial Least Square Techniques. The GoF value of the model formed is quite good, which is 66%. The results of this study show that information quality and security factors are very influential for the use of pet applications.
A Two-Stage Mass-to-drag Ratio Estimation Algorithm for Reentry Vehicles

Abstract Ballistic target tracking and identification is a key task for missile defense systems. A vital problem in ballistic target identification is how to estimate the target mass-to-drag ratio fast and accurately. The reentry phase of a ballistic missile is the last stage of its flight, and the reentry trajectory is significantly affected by the atmospheric drag. Based on the constraints of space flight dynamics, this paper proposes a two-stage algorithm to estimate the mass-to-drag ratio. This algorithm first estimates the position and velocity of the target in the free flight stage by using the least-squares method, and then estimates the mass-to-drag ratio of the target by using the radial range measurements from the reentry stage. Several trajectories of a ballistic target under different mass-to-drag ratios are simulated, and the proposed algorithm is verified using these simulated trajectories.
Delve: Neural Network Feature Variance Analysis

Designing neural networks is a complex task.Deep neural networks are often referred to as "black box" models -little insight in the function they approximate is gained from looking at the structure of layer outputs.Delve is a tool for looking at how a neural network represents data, and how these representations, or features, change throughout training.This tool enables deep learning researchers to understand the limitations and suggest improvements for the design of their networks, such as removing or adding layers.
New data on the formation conditions of the Golets Vysochaishiy gold deposit (Bodaibinskiy district, Russia)

The Golets Vysochaishy deposit has a sub-tabular orebody hosted by black carbonaceous fine-grained aleuropelitic rocks. We have studied fluid inclusions in quartz samples taken from sulfide-quartz veins in the ore zone of the deposit — performed studies made it possible to establish the parameters of the ore-forming fluid. Thus, the fluids of the deposit are characterized by the typical features of orogenic vein gold deposits’ ore-forming fluids. The process of carbon dioxide degassing from the fluid may have contributed to the deposition of native gold.
Improving Finger Stroke Recognition Rate for Eyes-Free Mid-Air Typing in VR

We examine mid-air typing data collected from touch typists to evaluate the features and classification models for recognizing finger stroke. A large number of finger movement traces have been collected using finger motion capture systems, labeled into individual finger strokes, and classified into several key features. We test finger kinematic features, including 3D position, velocity, acceleration, and temporal features, including previous fingers and keys. Based on this analysis, we assess the performance of various classifiers, including Naive Bayes, Random Forest, Support Vector Machines, and Deep Neural Networks, in terms of the accuracy for correctly classifying the keystroke. We finally incorporate a linguistic heuristic to explore the effectiveness of the character prediction model and improve the total accuracy.
A Method Based on Roberta_Seq2Seq for Chinese Text Multi Label Sentiment Analysis

People’s comments on goods or services are more and more faceted, so multi-label text sentiment analysis has become a hot research topic. Compared with single-label sentiment analysis, it is more complex to analyze the sentimental polarity contained in different labels from the text. A multi-label sentiment analysis method of Chinese text based on the combination of Roberta and Seq2Seq is proposed. Roberta is used for text vectorization to improve the Semantic Connotation of vectorization. And Seq2Seq is used to learn more relationships among labels to obtain better results. Experiments show that the accuracy and F1 value of our method are improved compared with some other methods such as TextCNN, BiGRU-DAtt and ATT-TexRNN.
An Image Matching Algorithm Based on Local Sensitive Hash

In image matching, the oriented FAST and rotated BRIEF algorithm (ORB) has fast matching speed and less space. However, local features extracted do not have scale invariance. Its matching accuracy cannot meet some application requirements. In this paper, we present a novel matching algorithm, LSH-based image matching (LBIM), which is on the basis of local sensitive hash (LSH) to solve the problems mentioned above. In this work, there are three steps in image matching. First, contrast limited adaptive histogram equalization (CLAHE) is adopted to promote image quality. Then, improved ORB algorithm is used to extract and describe key points to meet the requirements of scale invariance and rotation invariance. Finally, LSH algorithm is employed on the descriptor to obtain the bucket number of each cluster. After pre-matching of LSH, progressive sample consensus (PROSAC) algorithm is used for rematching of key points to further improve the accuracy. The experiment results demonstrate LBIM proposed is more robust and accurate in different scenarios.
On the Impact of Competitive Gameplay on Text Entry Performance - A Study Based on a Mobile Typing Game

Text entry is a fundamental part of human computer interaction and typing games are a popular way to train and improve text entry skills. To assess the impact of competitive gameplay on text entry performance we conducted a public app store trial. For this purpose TypeClash, a competitive multiplayer mobile typing game was designed, developed and publicly distributed. The results demonstrate a significant effect of competitive gameplay mechanics on text entry performance regarding both speed and accuracy, with competitive gameplay resulting in better text entry performance.
CoBlok: Collaborative Performance in Virtual Reality and Face-to-Face

Computer-mediated communication is being adopted in work and personal life around the world. Measuring collaborative performance would be useful for evaluating and optimizing social computing applications, but there is a lack of methods for it. For this purpose, we have developed a collaborative block design task, which requires collaboration and depends on simple and abstract rules. The task is about constructing three-dimensional structures out of primitive shapes based on cards representing two-dimensional flat projections of the complete structure. The task is presented as a physical version, which can be manufactured using a 3D printer and a laser cutter, as well as a virtual version which is released as an open source VRChat world created in Unity, and as task components which can be imported into any virtual 3D environment. The task can be used for evaluating systems and augmentations for their fitness for collaboration, as well as to investigate other phenomena which seem to be linked with better cooperation, such as inter-individual and inter-brain synchronization.
Improving Feature Generalizability with Multitask Learning in Class Incremental Learning

Many deep learning applications, like keyword spotting [1], [2], require the incorporation of new concepts (classes) over time, referred to as Class Incremental Learning (CIL). The major challenge in CIL is catastrophic forgetting, i.e., preserving as much of the old knowledge as possible while learning new tasks. Various techniques, such as regularization, knowledge distillation, and the use of exemplars, have been proposed to resolve this issue. However, prior works primarily focus on the incremental learning step, while ignoring the optimization during the base model training. We hypothesise that a more transferable and generalizable feature representation from the base model would be beneficial to incremental learning.In this work, we adopt multitask learning during base model training to improve the feature generalizability. Specifically, instead of training a single model with all the base classes, we decompose the base classes into multiple subsets and regard each of them as a task. These tasks are trained concurrently and a shared feature extractor is obtained for incremental learning. We evaluate our approach on two datasets under various configurations. The results show that our approach enhances the average incremental learning accuracy by up to 5.5%, which enables more reliable and accurate keyword spotting over time. Moreover, the proposed approach can be combined with many existing techniques and provides additional performance gain.
Blockchain-Based Incentive Mechanism for Spectrum Sharing in IoV

In this paper, we design a blockchain-based incentive mechanism for the problem of low-level participation of primary users caused by location privacy leakage during spectrum data sharing in the Internet of Vehicles (IoV). First, we propose a <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -anonymous location protection scheme for multiuser cooperation, which can protect the location privacy of primary users by generalizing their location information through the construction of anonymous areas. Then, we design an incentive mechanism, which performs reporting and adjudication strategy through the transaction stored in blockchain. Simulation results indicate that the proposed scheme can effectively prevent the privacy leakage of primary users’ location and encourage them to actively participate in spectrum sharing in IoV.
A New Automatic User Calibration Algorithm for Three-Dimensional Gaze Estimation

Gaze estimation systems use subject-specific parameters to estimate the user’s gaze direction. Advanced remote gaze estimation systems obtain the parameters including the angles between the visual axis and the optical axes of each eye through the automatic calibration algorithms. In these automatic calibration algorithms, the coordinates of the observation surface should be known when calculating the angles between the visual axis and optical axis. However, in head-mounted systems, the coordinates of the observation surface cannot be determined. This paper proposes a new automatic calibration algorithm that does not require the coordinates of the observation surface and subject' active participation in calibration. In order to estimate the angles between the visual and optical axes of each eye, differential evolution algorithms are used to minimize the distance between the visual axes of the left and right eyes while subjects naturally look at the point of gaze (PoG). Simulation results have demonstrated the effectiveness of the algorithm.