Reviewerly/exHarmony huggingface dataset:
Research on Monster Path Planning Scheme in Horror Game Based on Whale optimization Algorithm

This paper proposes a path planning algorithm based on the whale algorithm, which when used with A* algorithm can effectively control path-finding progress. The algorithm introduces the Bezier curve, the algorithm can better describe the curve path between two points, to replace the logarithmic spiral in the spiral update part of the original whale algorithm. In addition, temporarily changing target point put the path planned by the algorithm far away from the shortest path, and appropriately surrounds the target point. This paper's research object is the monster path-finding problem in horror games, and realizes the controllable non-shortest path planning under the special path environment. The algorithm planning result has better performance than the path planned by the A* algorithm.
Recommendation-Based Interactivity Through Cross Platform Using Big Data

A recommendation is a suggestion or proposal as to the best course of action, especially one put forward by an authoritative body. Shopping is a necessity of every human being, and when we do shop, it is definitely either the product we like or our friends like. Aim of the research paper is to give recommendation to the user based on the user’s interest in a single cross platform. Design a cross-platform layers. Currently provide the public centres. Provide a platform for mining user interest content across different social networks. In the proposed system, we develop a Web application where in the user has to subscribe by giving the users credentials along with his interest in different fields. Based on user’s priority and interest, recommendations are displayed. A cross-platform Web application which is purely based on the user interest and interactivity obtain big data based on the user interactivity and use it for future recommendations. Content is recommend based on user interest.
Refinement of matching costs for stereo disparities using recurrent neural networks

Abstract Depth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume that is composed of the distances between the possible pixel points on the pre-aligned horizontal axis of stereo images. Most approaches use matching costs to identify matches between stereo images and obtain depth information. Recently, researchers have been using convolutional neural network-based solutions to handle this matching problem. In this paper, a novel method has been proposed for the refinement of matching costs by using recurrent neural networks. Our motivation is to enhance the depth values obtained from matching costs. For this purpose, to attain an enhanced disparity map by utilizing the sequential information of matching costs in the horizontal space, recurrent neural networks are used. Exploiting this sequential information, we aimed to determine the position of the correct matching point by using recurrent neural networks, as in the case of speech processing problems. We used existing stereo algorithms to obtain the initial matching costs and then improved the results by utilizing recurrent neural networks. The results are evaluated on the KITTI 2012 and KITTI 2015 datasets. The results show that the matching cost three-pixel error is decreased by an average of 14.5% in both datasets.
Labeled Triangle Indexing for Efficiency Gains in Distributed Interactive Subgraph Search

Subgraph search in a massive background graph, i.e., pattern matching in graphs, is a challenging problem, particularly in an interactive usage scenario where fast response time is important. Our approach, Prune.Juice [1], is based on two intuitions: first, rather than directly searching for individual matches, it is cheaper to eliminate vertices and edges of the background graph that do not participate in any match. Second, to perform this pruning process, it is possible to decompose a search pattern into a set of constraints, and use each of these constraints to eliminate non-matching vertices and edges. This paper explores the feasibility of indexing the background graph to accelerate the checking of non-local constraints (e.g., cycles and paths), the most expensive phase of pruning. In particular, we demonstrate that indexing labeled triangle (i.e., a 3-Cycle) participation is a valuable acceleration technique. Additionally, we observe that labeled triangle counts at edges can be employed to detect 3-Paths that have terminal points with non-unique labels, at relatively little extra cost. Such paths and triangles are basic sub-constraints that can be used to rapidly prune non-matching vertices and edges. As proof of concept, we show that, using triangle information alone, indexing further accelerates expensive queries in Prune.Juice by up to 500× on billion-edge graphs.
Fundamental limits on concentrating and preserving tensorized quantum resources

Quantum technology offers great advantages in many applications by exploiting quantum resources like nonclassicality, coherence, and entanglement. In practice, an environmental noise unavoidably affects a quantum system and it is thus an important issue to protect quantum resources from noise. In this paper, we investigate the manipulation of quantum resources possessing the so-called tensorization property and identify the fundamental limitations on concentrating and preserving those quantum resources. We show that if a resource measure satisfies the tensorization property as well as the monotonicity, it is impossible to concentrate multiple noisy copies into a single better resource by free operations. Furthermore, we show that quantum resources cannot be better protected from channel noises by employing correlated input states on joint channels if the channel output resource exhibits the tensorization property. We address several practical resource measures where our theorems apply and manifest their physical meanings in quantum resource manipulation.
Insider Threat Detection using Binary Classification Algorithms

Abstract The Insider Threat Detection(ISTD), is commonly referred to as the silent killer of organizations. The impact is greatly felt because it is usually perpetrated by existing staff of the organization. This makes it very difficult to detect or can even go undetected. Several authors have researched into this problem but no best solution has been discovered. This study therefore considers the insider problem as a classification problem. It provides a lay man’s understanding of a typical classification problem as faced in the insider threat detection research scope. It then highlights five (5) commonly used binary classification algorithms, stating their strengths and weaknesses. This work will help researchers determine the appropriate algorithm to consider for the employee dataset available for classification.
Identifying rename refactoring opportunities based on feature requests

Refactoring is a widespread practice of improving the quality of software systems by applying changes on their internal structures without affecting their observable behaviors. Rename is one of the most recurring and widely used refactoring operation. A rename refactoring is often required when a software entity was poorly named in the beginning or its semantics have changed and therefore should be renamed to reflect its new semantics. However, identifying renaming opportunities is often challenging as it involves several aspects including source code semantics, natural language understanding and developer's experience. To this end, we propose a new approach to identify rename refactoring opportunities by leveraging feature requests. The rationale is that, when implementing a feature request there are chances that the semantics of software entities could significantly change to fulfill the requested feature. Consequently, their names should be modified as well to portray their latest semantics. The approach employs textual similarity to assess the similarity between a feature request description and identifiers. The approach has been validated on the dataset of 15 open source Java applications by comparing the recommended renaming opportunities against those recovered from the refactoring history of the involved subject applications. The evaluation results suggest that, the proposed approach can identify renaming opportunities on up to 66% precision and 72% recall.
Automatic liver segmentation using U-net in the assistance of CNN

Liver segmentation is widely used in medicine for volumetric or morphological analysis. This paper presents a system to automatically segment liver from the CT scans for real clinical application. The proposed system uses one of the most suitable networks in biomedical segmentation U-net in the assistance of Convolutional Neural Network (CNN). The preprocessed CT scans with and without liver are divided into five position groups and labelled with group number. In the next step, we train CNN for automatic localization of the CT scans. Afterwards, three networks based on U-net are fed with images in the same group having liver and then trained for liver segmentation separately. The proposed system is evaluated on the scans from LiTS2017 and 3Dircadb database. The average IOU score improves to 92.8% compared with state-of-the-art. Meanwhile, the novel system demonstrates its flexibility in the choice of training data and its universality in testing data, which is suitable for both scans with liver and scans without liver. The experimental results indicate that the proposed system facilitates the procedure of liver segmentation and has its potential for accurate liver segmentation for clinical application.
On Publicly-Accountable Zero-Knowledge and Small Shuffle Arguments

Constructing interactive zero-knowledge arguments from simple assumptions with small communication complexity and good computational efficiency is an important, but difficult problem. In this work, we study interactive arguments with noticeable soundness error in their full generality and for the specific purpose of constructing concretely efficient shuffle arguments.To counterbalance the effects of a larger soundness error, we show how to transform such three-move arguments into publicly-accountable ones which allow the verifier to convince third parties of detected misbehavior by a cheating prover. This may be particularly interesting for applications where a malicious prover has to balance the profits it can make from cheating successfully and the losses it suffers from being caught.We construct interactive, public-coin, zero-knowledge arguments with noticeable soundness error for proving that a target vector of commitments is a pseudorandom permutation of a source vector. Our arguments do not rely on any trusted setup and only require the existence of collision-resistant hash functions. The communication complexity of our arguments is independent of the length of the shuffled vector. For a soundness error of \(2^{-5}=1/32\), the communication cost is 153 bytes without and 992 bytes with public accountability, meaning that our arguments are shorter than shuffle arguments realized using Bulletproofs (IEEE S&P 2018) and even competitive in size with SNARKs, despite only relying on simple assumptions.
Behavior control-based approach to influencing user's cybersecurity actions using mobile news app

In this paper, we propose that the theory of planned behavior (TPB) with the additional factors of awareness and context-based information can be used to positively influence users' cybersecurity behavior. A research model based on TPB is developed and validated using a user study. As a proof-of-concept, we developed a mobile cybersecurity news app that incorporates context-based information such as location, search history, and usage information of other mobile apps into its article recommendations and warning notifications to address user awareness better. Through a survey of 100 participants, the proposed research model was validated, and it was confirmed that context-based information positively influences users' awareness in cybersecurity.
Crowdsourcing or Network KPIs? A Twofold Perspective for QoE Prediction in Cellular Networks

Monitoring the Quality of Experience (QoE) of the customer base is a key task for Mobile Network Operators (MNOs), and it is generally performed by collecting users feedbacks through directed surveys. When such feedbacks are few in number, a MNO may predict the users QoE starting from objective network measurements, gathered directly from the users equipments through crowdsourcing. In this work, we compare such a traditional approach with a different one, where the data used for predicting the users QoE is gathered directly at the network access, using Key Performance Indicators (KPI) available on each base station. Although such KPIs are aggregated by design (i.e., they refer to the distribution of a population of users rather than to a single individual), we show through experiments with a country-wide dataset that their predictive power is comparable and in some cases superior than the one of crowdsourcing. Such a result is particularly attractive for MNOs, since network KPIs are generally much easily obtainable than crowdsourcing data.
The Signal Intelligent Analysis System of Eventlog

This paper describes the composition of the Eventlog of Guangzhou Pumped Storage Power Station, and based on it, to further study the signal intelligent analysis system of Eventlog. The Eventlog information intelligent analysis system can extract abnormal non-alarm signals from a large amount of data by extracting the Eventlog signals and using Excel VBA to carry out intelligent mining analysis on the signals, thus providing important references for the maintenance of unit equipment, daily management and even the selection of unit operation mode. This paper also uses practical examples to prove the huge effect of the system in analyzing the Non-alarm signals.
Everlasting UC Commitments from Fully Malicious PUFs

Abstract Everlasting security models the setting where hardness assumptions hold during the execution of a protocol but may get broken in the future. Due to the strength of this adversarial model, achieving any meaningful security guarantees for composable protocols is impossible without relying on hardware assumptions (Müller-Quade and Unruh, JoC’10). For this reason, a rich line of research has tried to leverage physical assumptions to construct well-known everlasting cryptographic primitives, such as commitment schemes. The only known everlastingly UC secure commitment scheme, due to Müller-Quade and Unruh (JoC’10), assumes honestly generated hardware tokens. The authors leave the possibility of constructing everlastingly UC secure commitments from malicious hardware tokens as an open problem. Goyal et al. (Crypto’10) constructs unconditionally UC-secure commitments and secure computation from malicious hardware tokens, with the caveat that the honest tokens must encapsulate other tokens. This extra restriction rules out interesting classes of hardware tokens, such as physically uncloneable functions (PUFs). In this work, we present the first construction of an everlastingly UC-secure commitment scheme in the fully malicious token model without requiring honest token encapsulation. Our scheme assumes the existence of PUFs and is secure in the common reference string model. We also show that our results are tight by giving an impossibility proof for everlasting UC-secure computation from non-erasable tokens (such as PUFs), even with trusted setup.
Optimizing Spatial Shift Point-Wise Quantization

It is no longer an option but a necessity to enhance the efficiency of deep learning models regarding energy consumption, learning time, and model size as the computational burden on deep neural networks increases. To improve the efficiency of deep learning, this study proposes a lightweight spatial shift point-wise quantization (L-SSPQ) model to construct a ResNet-like CNN model with significantly reduced accuracy degradation. L-SSPQ adds efficiency with the last linear layer weight reduction technology to SSPQ, which combines compact neural network design and quantization technology. To reduce weight and optimize performance, the learning time and system-required resources in the L-SSPQ are minimized. Accuracy could be improved with the warm-up interval and a step-size optimal value, both of which are hyper-parameters of the cosine learning rate. A two-stage optimization method that divides quantization learning into two steps is applied to further minimize loss. The size of the proposed L-SSPQ50 model is only 3.55 MB with an accuracy loss rate of 2.42%. This is just 3.56% of the size of ResNet50. In addition, the L-SSPQ50 score was 1.318 for information density, surpassing the SOTA models, including MobileNet V.2, MobileNet V.3, ReActNet-A, and FracBNN.
Monthly Rainfall Components in Ambon City: Evidence from the Serious Time Analysis

Abstract The purpose of this study is to prove whether rainfall in Ambon City contains seasonal components based on the characteristics of monthly rainfall history data in Ambon City. The data used is monthly rainfall data in Ambon City 2005.01-2017.12. The data is time series data from the BMKG Meteorology Station observation in Maluku Province. The time series analysis method used is ARIMA / SARIMA and Holt-Winter Exponential Smoothing. The results of this study have a good level of accuracy, namely using the results of analysis based on the values of information criteria, Ljung-Box Test, and RMSE. The results of the analysis prove that rainfall in Ambon City has a seasonal pattern.
Dependency tree positional encoding method for relation extraction

Dependency trees are recently used for relation extraction tasks to capture long-range relations among entities. Many studies have proposed various methods to apply dependency tree features to relation extraction models. However, most of these approaches are difficult to apply to different models especially when the overall structure of a model is changed or newly suggested for inserting information of the dependency trees. Existing approaches, which are a structure to use dependency trees such as graph convolutional networks (GCNs), do not use these information into input representations. In this paper, we propose a new method of injecting dependency tree information into the input representations of a model with positional encoding of dependency trees. Specifically, to incorporate dependency tree information with input vectors, we present a novel strategy to convert dependency tree features to positional encoding. The existing model (BERT) that applied our method achieves a state-of-the-art performance without changing the original model on the TACRED and SemEval-2010 Task 8, the standard benchmark dataset in relation extraction. Through detailed analysis, we show that our method is effective complementarily for relation extraction models, and is also easy to use for other models.
The Development Tendency of Artificial Intelligence in Command and Control: A Brief Survey

Abstract Google’s DeepMind’s AlphaStar has achieved great success in the real-time strategy game StarCraft 2, and it has also made the combination of artificial intelligence and command and control, attracted widespread attention. Artificial intelligence theory and its engineering practice accumulated over many years have injected new development momentum into traditional fields, such as command and control. We download more than 900 high-level papers from Web of Science (WoS) that combine artificial intelligence and command and control as the research object, and employ bibliometric, CiteSpace, VOSviewer, and other bibliometric analysis software. From the number of documents, co-citing, co-occurrence of keywords and research hotspots, the four aspects are studied and visualized analysis, which clarifies the research status and key documents of artificial intelligence in command and control, and reveals the frontier hotspots and research directions in this field. Finally, a summary of the four visual analysis aspects points out important references for choosing research directions, exploring the frontiers of disciplines, and assisting scientific decision-making in this field.
A hidden-password online password manager

The most commonly adopted password management technique is to store web account passwords on a password manager and lock them using a master password. However, current online password managers do not hide the account passwords or the master password from the password manager itself, which highlights their real-world vulnerability and lack of user confidence in the face of malicious insiders and outsiders that compromise the password management service especially given its online nature. We attempt to address this crucial vulnerability in the design of online password managers by proposing HIPPO, a cloud-based password manager that does not learn or store master passwords and account passwords. HIPPO is based on the cryptographic notion of device-enhanced password authenticated key exchange proven by Jarecki et al. to resist online guessing attacks and dictionary attacks. We introduce the HIPPO protocol design and report on a full implementation of the system.
Towards a Secure Smart Parking Solution for Business Entities

This paper proposes a parking management system that is geared towards business entities. The proposed system will focus on privacy for the different entities who use the system. In a business setup, performance, scalability, and privacy should be at the centre stage. The paper aims to improve on already existing research on smart parking using blockchain. Most of the research done focuses on single entity privacy, single point of failure for the systems deployed on cloud databases, and geographical censorship. This paper proposes a parking management system that will be based on JPMorgan Quorum. The system will focus on privacy, performance, scalability and multiple entities interaction.
Design of Feature Selection Algorithm Based on Improved FCBF

Feature selection is an indispensable step in data mining, and its core is to analyze and quantify the correlation and redundancy between features and classes. Firstly, in the FCBF feature selection method, we use the symmetric uncertainty measurement criterion to evaluate the correlation of each feature and class label, sort each correlation measure from small to large, select the appropriate threshold, and eliminate the features below the threshold; secondly, use the approximate Markov blanket principle to screen irrelevant features and redundant features, and finally select the optimal feature subset. In this feature selection method, the complementarity between features is rarely taken into account, and it is easy to have some complementary features which are helpful to classification in the eliminated redundant features. Therefore, a feature selection method based on correlation, redundancy and complementarity is proposed, which enters the complementarity measurement criterion on the basis of FCBF algorithm to select complementary features and improve the accuracy of feature selection.