Reviewerly/exHarmony huggingface dataset:
Threat intelligence and sharing for collaborative defense

Collaborative defense and the exchange of threat intelligence have emerged as important weapons in the fight against cyber threats.Cooperation and the exchange of threat intelligence data across organizations can help businesses improve their level of cybersecurity and reduce their susceptibility to cyber-attacks.To embrace such initiatives, however, presents a number of challenges, including concerns around privacy and trust, incompatibility between systems, organizational pushback, the have to comply with laws and regulations, issues in operations, and scalability.Recent technological advancements, such as federated threat intelligence sharing, automated threat intelligence sharing, artificial intelligence and machine learning, industry-specific collaborative defense initiatives, and threat intelligence sharing via blockchain, have helped to ease some of these issues.Within the scope of this research, we investigate the practices that are now in use and propose a structure for the sharing of threat intelligence and the coordination of defenses.We also discuss the challenges that come along with implementing such a system, as well as the possible benefits of doing so.Before making a final decision to install a system for collaborative defense and threat intelligence sharing in their industry and environment, businesses should first determine its potential costs, advantages, and risks.This should be done before making a commitment to using the system.
eCubeLand: An Intelligent Multiview Video Data Modeling

The extensive use of surveillance systems, particularly those installed in Internet of Things environments, leads to the continuous harvesting of tremendous amounts of video data. The effective analysis and management of these data are challenging tasks for surveillance experts due to unstructured storage and variability. We propose an intelligent modeling framework, offering a convenient representation with indexing for real-world objects and solving complicated computer vision problems, such as anomaly detection and person re-identification. Moreover, our framework generates grids and assigns indexing to visual sensors and real-world entities, allowing efficient information retrieval with better resource allocation. The proposed framework consists of four major modules: 1) mapping, 2) data analysis, 3) information sharing, and 4) data storage. The mapping module is responsible for analyzing the environment, followed by the data analysis module, which detects, analyzes, and indexes the entities. Furthermore, video data from these modules are passed to the information sharing module, which generates alerts in the case of undesirable scenes and broadcasts the meaningful information toward adjacent visual sensors. The final module is used to preserve anomalous data along with the identified person’s information from distributed vision sensors. To validate the proposed framework, we perform experiments on real-world complex tasks, including anomaly detection and person re-identification, showing promising performance on surveillance video data.
Real-World Authentication of Feasible and Readily Collectable Person Using In-Ear EEG Biometrics

We suggest a generic "one-size-fits-all" in-ear EEG monitor (collectability) made of viscoelastic material as the basis for a readily deployable EEG biometrics system that doesn't need specialised help or timeconsuming setup.Since training and test portions are not derived over the similar tracking days, we examine data that was collected for multiple individuals and over multiple recording days.The state of rest with closed eyes scenario, the application of non-parametric (spectral) features and parametric (autoregressive model) , and support through quick and easy cosine distance, support vector machine models, and linear discriminant analysis are all examined as part of a solid strategy.The findings are comparable to studies built on theoretical on-scalp recordings and take into account the validation as well as recognition forensics situations.The suggested ear-EEG biometrics' viability is shown through thorough analysis across a variety of subjects, settings, and analytic features.It also shows its potential to address the important collectability, reliability, and reproducibility problems connected with existing EEG biometrics.A useful biometric system should also optimise the trade-off among performance, approval, and circumvention; basically, it should be built with precision, rapidity, and resource needs in mind.Goal is to increase identification protection, to establish identity using the EEG data and to prevent biometric theft.
A Location-independent Human Activity Recognition Method Based on CSI: System, Architecture, Implementation

In the application of human activity recognition (HAR) based on channel state information (CSI), due to the high dynamic characteristics of wireless channel to different environments, the features of human activity samples in different locations are different. In addition, the existing CSI-based HAR approaches limit the extraction of activity features to the Euclidean space and ignores the rich relational information between samples, categories and locations, which result in insufficient generalization performance for location-independent HAR. To address this challenge, this paper proposes a CSI-based location-independent HAR system CSI-MTGN. The system represents the classification task under each training sample collection location (TSCL) as a task, which is composed of three interactive parts: sample hidden representation, activity features extraction based on hierarchical graph neural network (HGNN) and information exchange based on multi-task learning. The proposed system improves the sample hidden representation, which is benefit for activity feature extraction and classification. The HGNN is designed to express various relationship information between samples, categories and locations in the form of graph structure, and the classification task under each TSCL is constructed through data augmentation, so as to improve the knowledge understanding and inference capabilities of the recognition model. The multi-task learning is used to achieve implicit data augmentation by sharing parameters among tasks through soft parameter sharing, and improves the generalization performance of the system. To validate the performance of the proposed system, experiments were conducted in a hall and a conference room, where samples of 10 categories of activities under 7 TSCLs were used for training the system, and the HAR accuracy rates at any locations were 94.1% and 93.3%, respectively.
Curve-based Depth Map Image Adjustment for Eyeball Phantom Measurement

Light field images have much more information than regular images. One of the advantages is that this processing can produce depth map images. However, there is a problem, the resulting depth map image is inconsistent due to the influence of lighting variations around the object. Therefore it is necessary to make adjustments so that the depth map value matches the actual size. The method proposed in this paper is to adjust the depth map image with the slit lamp guide curve. The object being measured is a ground truth reference object with some defined surface height used for measurement and calibration, and the other object is an eyeball phantom. First, a consumer light field camera is attached to a slit lamp for image acquisition. In the next step, the depth map obtained from the light field image is adjusted to increase the range of pixel values. The third step is the depth calculation of the projected slit light. The last step is the adjustment of the object surface curve based on the depth obtained from the projected slit light. Based on testing on both objects, the proposed method can improve the curvature of the depth map.
Sentiment Analysis of Flipkart Product Reviews using Natural Language Processing

In this contemporary world, people depend more on ecommerce sites or applications to purchase items on-line. People purchase items on-line based upon the scores and evaluates offered by individuals that purchased items previously which identifies the success or failing of the item. Furthermore, business suppliers or manufacturers identify the success or failing of their item by evaluating the evaluates offered by the clients. In current system, a number of techniques were utilized to evaluate a dataset of item evaluates. It likewise provided belief category formulas to use a monitored discovering of the item evaluates situated in 2 various datasets. The proposed speculative methods examined the precision of all belief category formulas, and ways to identify which formula is more precise. Additionally, the existing system unable to spot phony favorable evaluates and phony negative reviews with discovery procedures. One of the most popular works was done "Bad" and "Outstanding" seed words are utilized by him to determine the semantic positioning, factor smart shared info technique is utilized to determine the semantic positioning. The belief positioning of a file was determined as the typical semantic positioning of all such expressions. Semantic Positioning of context independent viewpoints is identified and the context reliant viewpoints utilizing linguistic guidelines to infer positioning of context unique reliant viewpoint are thought about. Contextual info from various other evaluates that discuss the exact same item function to identify the context indistinct-dependent viewpoints were drawn out.
Autonomous Robot Navigation in Known Environment

Autonomous robot navigation is one of the challenging researched topic in robotics. A secure and optimal path in known environment is required for any mobile robot navigation for navigation purpose. In this work, a Simulink model is proposed based on Pure Pursuit and path following controllers for solving the problem of mobile robot navigation in a known environment is presented. Pure Pursuit controller is used to find the linear and angular velocities of the robot. Moreover, (x, y) coordinate position of robot and waypoints are input to the pure pursuit block. Velocity commands are sent to drive robot on the given path. The main aim of the proposed mod el is to find the obstacle free path for the mobile robot navigation. However, the robot is to navigate from start to target location without hitting obstacles. For experimental results, Turtle Bot Gazebo simulator is used. "Robotic system Toolbox" of the MATLAB is used to program the navigation process.
Training effects of set- and repetition-interval rest time on recumbent-boxing exercise: Could virtual reality improve further?

This study examined the influence of set-interval and repetition-interval rest time of virtual reality (VR) boxing game in supine-lying posture. Fifty healthy middle-aged adults were randomly assigned into VR and non-VR groups to perform six different exercise protocols with varying set-interval and repetition-interval rest times (S0R0, S0R1/3, S0R2/3, S40R0, S40R1/3, and S40R2/3). Analysis on the non-VR group showed significant differences between exercise protocols for average heart rate (p < 0.001), maximum ventilation volume (p < 0.001), respiratory quotient (p < 0.001), oxygen pulse (p < 0.001), and excess post-exercise oxygen consumption (EPOC) (p = 0.003). VR appeared to have no further improvement on physical training effects in middle-aged adults, while the participants reported negative experience that might be associated with the over-exertion. Future study might need to explore game design elements that can accommodate high-exertion exercises.
Ten lectures on corpora and cognitive linguistics

Click to increase image sizeClick to decrease image size Additional informationFundingThis study was supported by the Shanghai Top Discipline of Type I (Foreign Languages and Literature), China (上海市I类高峰学科（外国语言文学）建设项目) [Grant no. 41004525/001], the Fundamental Research Funds for the Central Universities, China [Grant no. 2019114030] and the Fifth Academic Mentoring Program of Shanghai International Studies University [Grant no. 2022113033].
SiamTDNN: Enhancing Discriminative Embeddings for Speaker Diarization

Recent advances in speaker embeddings promote a great development of speaker diarization. However, determining ‘who spoke when’ in the meeting scenarios is still challenging due to similar speaker voices and unknown speaker quantity. In this paper, this research proposes enhanced discriminative features for speaker diarization, including discriminative speaker-specific features based on Siamese networks, and a speaker re-verification method. With Siamese architecture, SiamTDNN, this research first explores latent representations which is capable of modeling intra-class and inter-class differences between speakers, by training with audio pairs. Then, the re-verification method is introduced with a local-global strategy to identify speakers in a multi-person talking scene. Our method provides a novel speaker embedding with enhanced discriminative power for disambiguated speakers and achieves an elevated upper bound on the number of speakers. The proposed speaker embedding achieved an EER of 1.1% and a minDCF of 0.1192 on VoxCeleb1 for the speaker verification task. Extensive experiments on AiShell-4, ICSI, AMI and VoxConverse demonstrate the effectiveness of the proposed method with an average DER reduction of 3% and an RTF of 0.0792.
Bipolar-Valued Fuzzy Filters of Sheffer Stroke BL-Algebras

The bipolar-valued fuzzy filter in Sheffer stroke BL-algebras is defined, and several properties are investigated. Various characterizations of the bipolar-valued fuzzy filter are discussed. It is shown that the intersection of bipolar-valued fuzzy filters is also a bipolar-valued fuzzy filter, and an example to show that the union of bipolar-valued fuzzy filters is not a bipolar-valued fuzzy filter is provided.
Analyzing Process Quality Control Variables Using Fuzzy Cognitive Maps

Meeting quality characteristics of products and processes is an important issue for customer satisfaction and business competitiveness.It is necessary to integrate new techniques and tools that improve and complement traditional process variables analysis.This paper proposes a new methodological approach to analyze process quality control variables using Fuzzy Cognitive Maps.Application of the methodology in the production process of carbonated beverages allowed identifying process variables with the greatest influence on finished product quality.The process variables with the greatest impact on carbon dioxide content in the beverage were the beverage temperature in the filler, the carbo-cooler pressure, and the filler pressure.
LOVE SCAMS ISSUES IN MALAYSIA: HOW TO OVERCOME IT FROM AN ISLAMIC PERSPECTIVE

Love scams are nothing new, as this case has been ongoing since 2008 to the present day. Malaysia is one of the countries that has been identified as a destination or base for this cybercrime syndicate. In Malaysia, 1,535 love scams were reported in 2019, with a total loss of RM79.11 million, while 1582 cases were reported in 2020, with a total loss of RM58.33 million. The common target of a scammer who is easily duped consists of highly educated, still-single professional women with a high income. This increasing number of cases demonstrates that the community must remain vigilant and pay close attention to these cases. Therefore, this paper aimed at finding the factors, impacts and solutions for this issue from an Islamic perspective. Library research is used to discuss the issue from the three dimensions. It is found that the increasing use of social media is one of the main contributing factors to the existence of love scams. The victims of the love scam will thereafter suffer a significant financial loss. The main solution to this problem is to get closer to Allah SWT and family members, as this can help Muslims avoid becoming victims of love scams.
Federal work program on the academic subject "History" (basic level)

The federal work program on the academic subject "History" (subject area "Social and scientific subjects") (hereinafter, respectively, the history program, history) includes an explanatory note, the content of the training, the planned results of the development of the history program.
Rainfall Variability in East Kalimantan from Impact of El Nino and La Nina for Effort Disaster Prevention to Support National Security

Rainfall has an important influence on human life. One of the global influences that have an impact on rainfall conditions in Indonesia is the El Nino and La Nina phenomena which can result in disasters. Efforts to reduce disaster risk require good disaster management to protect the entire nation from all threats, especially non-military threats in the disaster aspect in East Kalimantan. One effort is to recognize East Kalimantan's rainfall variability in La Nina and El Nino conditions. This study uses rainfall data for 1991 – 2020 at three locations at the Sepinggan Meteorological Station, Balikpapan, Samarinda and Kalimarau Berau, East Kalimantan. El Nino conditions in East Kalimantan show the influence of rainfall during the dry season, rainfall is less than normal conditions. Reduced rainfall from normal conditions can result in drought. La Nina conditions in East Kalimantan show the influence of the dry season, there is an increase in rainfall compared to normal conditions. Increased rainfall from normal conditions can cause flooding. La Nina and El Nino conditions affect rainfall in East Kalimantan which can have an impact on disasters. This condition must be anticipated in order to reduce the risk of an impending disaster. Anticipation efforts can guarantee the safety and welfare of citizens, communities, La Nina and El Nino conditions affect rainfall in East Kalimantan which can have an impact on disasters. This condition must be anticipated in order to reduce the risk of an impending disaster. Anticipation efforts can guarantee the safety and welfare of citizens, communities, La Nina and El Nino conditions affect rainfall in East Kalimantan which can have an impact on disasters. This condition must be anticipated in order to reduce the risk of an impending disaster. Anticipation efforts can guarantee the safety and welfare of citizens.
Fides: A System for Verifiable Computation Using Smart Contracts

Verifiable computation allows a resource-constrained client to outsource their computation to powerful servers, and efficiently verify their received results. Cryptographic verifiable computation systems, despite their elegant designs, have limited application in practice because of the computational cost and difficulty of correct and flexible implementation of complex cryptographic systems. An attractive approach to verifiably compute general functions is to use more than one server to compute the same function, and decide the computation result based on the submitted results of all servers. In this paper, we propose a system for delegation of computation to two cloud servers using a smart contract (SC), that guarantees correct computation results as long as at least one of the two servers is honest. Our work adapts the Refereed Delegation of Computation (RDoC) model of Canetti, Riva and Rothblum (ACM CCS’11) to the SC setting. This was first considered by Avizheh et al. (ACM CCSW’19) who showed that the direct employment of RDoC in the smart contract setting will be insecure because of the copy attack, where a server copies the result of the other server, and becomes possible due to the transparency of SC. However, the implementation of their protocol was left as future work. Our work is a new SC-aided RDoC design with proved security that significantly reduces the computation of the smart contract. Additionally, it provides security against the misbehaviours of the client and an implementation of the design over the Ethereum blockchain. The proposed system, which is called Fides, is the first implementation of SC-aided RDoC. We discuss the challenges of this implementation and our design decisions, and present the cost analysis of the system for an example computation. We also propose extensions of our work and directions for future research.
Not All Code are Create2 Equal

We describe the impact and measure the adoption of the CREATE2 instruction introduced to the Ethereum Virtual Machine in the Constantinople upgrade. This change to Ethereum’s execution environment is fundamental because it enables to modify the program stored on a given address after deployment, making it much harder to reason about the immutability of smart contracts. We enumerate six use cases and novel attack vectors, and present empirical evidence from all 32 million code accounts created between March 2019 and July 2021. The data shows that the main beneficiaries of the upgrade are wallet contracts, which can now use predictable addresses. But they do not require the more risky feature of mutable smart contracts. So far, the only applications that use the latter are front-running bots and gas tokens.
A First Approach to Risk-Limiting Audits for Single Transferable Vote Elections

Risk-limiting audits (RLAs) are an increasingly important method for checking that the reported outcome of an election is, in fact, correct. Indeed, their use is increasingly being legislated. While effective methods for RLAs have been developed for many forms of election—for example: first-past-the-post, instant-runoff voting, and D’Hondt elections—auditing methods for single transferable vote (STV) elections have yet to be developed. STV elections are notoriously hard to reason about since there is a complex interaction of votes that change their value throughout the process. In this paper we present the first approach to risk-limiting audits for STV elections, restricted to the case of 2-seat STV elections.
Accuracy improvement of carcinoma detection on screening diagnostic procedure by using deep learning method

This paper presents a comprehensive analysis of the application of deep learning methods to enhance the precision of carcinoma identification during screening diagnostic procedures.Recent research has put forth a range of models and approaches, encompassing lung cancer, breast cancer, cervical cancer, gastric cancer, and liver cancer.The aforementioned models have exhibited encouraging outcomes in enhancing the precision of cancer identification and mitigating instances of erroneous positive diagnoses.The optimal choice of model is contingent upon the particular circumstances, encompassing the cancer subtype, the diagnostic modality, and the accessible data.In general, the application of deep learning techniques in the detection of carcinoma holds promise for enhancing the accuracy of cancer diagnosis and improving treatment efficacy.