Reviewerly/exHarmony huggingface dataset:
Group Shared Mobility Platform Activated via Blockchain

Web sharing is a centralized trust system in order to trust users to establish, track, cancel, fare calculate, etc., the service providers. Any malicious activities performed on a centralized server system or vehicle or riders destroy the fairness of the ride and cause the party discomfort. The drivers are assessed by the riders after the ride has been completed. There can be a malicious driver who can claim reimbursement with a fake complaint and give the driver a poor rating. The current system cannot determine the correctness of both parties' objections and delivers the biased outcome of each objection in accordance with the centralized marketing strategies of the company. In order to ensure fair travel, a workaround that can be blocked is presented. The creation, completion, unrest, or abortion of a ride will be published in the block chain booklet so that the peer-to-peer network is available to all participants. This means that the interconnected credibility structure is managed equally at the same time. In Ethereal secure network as well as the KOVAN test network, that have implemented a prototype, and the analysis is included
CBSeq: A Channel-Level Behavior Sequence for Encrypted Malware Traffic Detection

Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and benign traffic. Compared with signature-based methods, they have higher scalability and flexibility. However, affected by the frequent variants and updates of malware, current methods suffer from a high false positive rate and do not work well for unknown malware traffic detection. It remains a critical task to achieve effective malware traffic detection. In this paper, we introduce CBSeq to address the above problems. CBSeq is a method that constructs a stable traffic representation, behavior sequence, to characterize attacking intent and achieve malware traffic detection. We novelly propose the channels with similar behavior as the detection object and extract side-channel content to construct behavior sequence. Unlike benign activities, the behavior sequences of malware and its variant’s traffic exhibit solid internal correlations. Moreover, we design the MSFormer, a powerful Transformer-based multi-sequence fusion classifier. It captures the internal similarity of behavior sequence, thereby distinguishing malware traffic from benign traffic. Our evaluations demonstrate that CBSeq performs effectively in various known malware traffic detection and exhibits superior performance in unknown malware traffic detection, outperforming state-of-the-art methods.
ARIVA: Artificial Intelligence Enabled Voice Assistance System using Natural Language Processing

In this modern era, the regular life of people have become more competent and are interlinked with technology. There are many voice assistants like Google and Siri. etc., getting used in the present day. Here, the proposed Artificial Intelligence Enabled Voice Assistance System (ARIVA) using Natural Language Processing facilitates domains such as drug prescribers, to-do lists, note-writing, calculators, and searching tools. The ARIVA model takes input as a voice signal and yields output in numerous ways, like voice and a visual display. This virtual assistant aims to provide users with instant, accomplished results. The proposed ARIVA system accepts the command via the microphone of the executing system and converts human voice into computer-understandable language, and then generates the required solutions for the said queries. The speech recognition and processing are carried out using Natural Language Processing (NLP) algorithm that aids computer systems in interacting through natural human speech in different forms. ARIVA can connect with the world wide web to procure the results of the individual's query. The ARIVA aids the user with everyday actions such as general verbal interaction, exploring Google for query processing, video searching, searching for videos, playing songs, predicting future weather reports, analyzing synonyms, searching for health and hygiene, and assisting the user by scheduling tasks and events. In terms of performance, ARIVA, when compared to IntelliAssistant, it is seen that ARIVA consumes lesser time and exhibits lesser memory requirement.
Unified Modeling Method of Heterogeneous Resources for Multi-platform Avionics System Based on AADL

Currently, single-platform avionics system modeling often uses service-oriented modeling methods to describe service-oriented architecture (SOA). However, for the multi-platform avionics system, the traditional modeling method can not represent the heterogeneous resources uniformly; and the description method of the relationship between services and resources is lacking. To solve these problems, this paper proposes a modeling method based on architecture analysis and design language (AADL). The heterogeneous resource pool model is constructed by unified description of heterogeneous resources internally and externally. In addition, a service-oriented approach is used to match function servitization with resources. This method is used to model a multi-platform avionics system. The example shows that this method can abstract heterogeneous resources in the same way, support multiple types of resources, and describe the relationship between services and resources in the task scenario.
Wearable Devices for Patient Monitoring System using IoT

The most crucial components of life is health. People could still not access adequate healthcare, nevertheless. It is brought on by restrictions on the technology used in hospitals and on how people may travel to hospitals. One of today's most popular topics, the Internet of Things (IoT), already provides many solutions, including in the healthcare industry. The field of wearable medical technology has emerged as one of the promising healthcare technologies. By actively collecting physiological indicators and monitoring metabolic status, these intelligent sensors help individuals lead healthier lifestyle and provide continuous healthcare information for disease diagnosis and treatment. Wearable technology is a new technique to track patients' post-intervention progress. It may lead to better patient outcomes and less demand for healthcare resources by enabling a faster and safer discharge from the hospital. Current findings have looked into the usage of wearable technology for monitoring. The ability to incorporate gadgets that can connect to the internet, provide data on patient health, and give real-time data to doctors who can help is becoming more and more possible due to the Internet of Things (IoT). It is obvious that health indicators like temperature, heartbeat, high blood pressure, and humidity represents a significant global economic and social issue. The patient's health is being monitored for health issues, and details can be viewed for obtaining the live patient data on a mobile web server at the correct time.
A Hybrid Autoscaling Algorithm for Microservices with Federated Load Balancing

Since edge clouds do not have more computational processing power than central clouds and can easily lead to scaling timeout violations and poor resource handling when dealing with dynamic loads. This paper conducts an experimental evaluation of microservice applications using two real workloads in a real edge cloud environment and proposes a hybrid autoscaling approach for microservices with federated load balancing (LB-HVPA). The approach enables centralised unmonitored load awareness by integrating the results of load balancing into the hybrid autoscaling module, avoiding additional scheduling due to unknown load distribution scenarios. Simulation results show that this approach can reduce the number of timeout violations and container scaling for microservice applications in edge cloud environments.
Enabling Fraud Prediction on Preliminary Data Through Information Density Booster

In online lending services, fraud prediction is an especially critical step to control loss risk and improve processing efficiency. Unfortunately, it is definitely challenging since the ex-ante prediction actually needs to be made only based on the most basic information of applicants. This work figures out that the essential difficulty here is the low information density of data associations which contain the useful information for fraud prediction. Accordingly, we propose a novel multi-stage data representation scheme, called AI2Vec (Applicant Information Vectoring), as an information density booster. It can gradually boost information density of associations by simultaneously decreasing the scale of information carriers and increasing the amount of useful information. The qualified performance of our AI2Vec is validated by the experiments over real-life data from a prestigious online lending platform. It can help commonly-used machine learning classifiers outperform the state-of-the-art methods, including the method of pilot platform with manual feature engineering by the subject matter experts.
Weakest Link in Formal Argumentation: Lookahead and Principle-Based Analysis

In this paper, we introduce a new definition of weakest link attack relation assignment based on lookahead, and compare this new lookahead definition with two existing ones in the literature using a principle-based analysis. We adopt a formal framework for such attack relation assignments that was introduced by Dung in 2016. We show that our lookahead definition does not satisfy context independence, we introduce a new principle called weak context independence, and we show that lookahead weakest link satisfies weak context independence. We also show that lookahead weakest link is the closest approximation to Brewka’s prioritised default logic PDL, also known as the greedy approach. For PDL, we prove an impossibility result under Dung’s axioms. Our results generalise earlier findings restricted to total orders to the more general case of modular orders.
FedStream: Prototype-Based Federated Learning on Distributed Concept-Drifting Data Streams

Distributed data stream mining has gained increasing attention in recent years since many organizations collect tremendous amounts of streaming data from different locations. Existing studies mainly focus on learning evolving concepts on distributed data streams, while the privacy issue is little investigated. In this article, for the first time, we develop a federated learning framework for distributed concept-drifting data streams, called FedStream. The proposed method allows capturing the evolving concepts by dynamically maintaining a set of prototypes with error-driven representative learning. Meanwhile, a new metric-learning-based prototype transformation technique is introduced to preserve privacy among participating clients in the distributed data streams setting. Extensive experiments on both real-world and synthetic datasets have demonstrated the superiority of FedStream, and it even achieves competitive performance with state-of-the-art distributed learning methods.
Enabling Fine-Grained Spatial Multitasking on Systolic-Array NPUs Using Dataflow Mirroring

Neural Processing Units (NPUs) frequently suffer from low hardware utilization as the efficiency of their systolic arrays heavily depends on the characteristics of a deep neural network (DNN). Spatial multitasking is a promising solution to overcome the low NPU hardware utilization; however, the state-of-the-art spatial-multitasking NPU architecture achieves sub-optimal performance due to its coarse-grained systolic-array distribution and incurs significant implementation costs. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dataflow-mirroring NPU (DM-NPU)</i> , a novel spatial-multitasking NPU architecture supporting fine-grained systolic-array distribution. The key idea of DM-NPU is to reverse the dataflows of co-located DNNs in horizontal and/or vertical directions. DM-NPU can place allocation boundaries between any adjacent processing elements of a systolic array, both horizontally and vertically. We then propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DM-Perf</i> , an accurate systolic-array NPU performance model, to maximize the spatial-multitasking performance of DM-NPU. Utilizing the existing performance models achieves sub-optimal performance as they cannot accurately capture the resource contention caused by spatial multitasking. DM-Perf, on the other hand, exploits the per-layer performance profiles of a DNN to accurately capture the resource contention. Our evaluation using MLPerf DNNs shows that DM-NPU and DM-Perf can greatly improve the performance by up to 35.1% over the state-of-the-art NPU architecture and performance model.
Designing n-D Non-Degenerate Hyperchaotic Systems via a Simple Circulant Matrix

To counteract the dynamic deterioration of digitized chaotic systems, we propose a mathematical model for generating n-D non-degenerate hyperchaotic systems (NHS) with n positive Lyapunov exponents (LEs) and complex dynamic behaviors. In this brief, we analyze the internal relations among the coefficients, eigenvalues, and singular values of a circulant matrix. Based on singular value decomposition, the n-D NHS can be constructed by presetting a conjugate symmetric vector, with a theoretical proof provided. The LEs of the n-D NHS can be adjusted arbitrarily by changing a preconfigured eigenvalue vector. We demonstrate the feasibility and efficacy of the proposed scheme with two examples, a 4-D NHS and a 5-D NHS. Based on the 5-D NHS, we design a simple pseudorandom number generator (PRNG) with desirable statistical properties. The proposed NHS model has fewer control coefficients, making it suitable for applications in IoT security and lightweight chaotic cryptography.
Uncovering the Dark Side of Artificial Intelligence in Electronic Markets

The dark sides of artificial intelligence (AI) have attracted immense attention in recent years. This study produces a synthesis of current research on six dark sides of AI in electronic markets through a systematic literature review. The authors searched five different databases and summarized the dark sides of AI in electronic markets from six aspects: privacy concerns, security issues, ethical challenges, criminals and terrorists enabled by AI, trust issues between humans and machines, and AI biases. The literature review presented in this study has provided a rigorous and structured overview of research on AI's dark sides in the electronic markets through a combination of quantitative and qualitative analysis of the AI literature. As AI has made rich contributions to a variety of applications in electronic markets, special care should be taken regarding the dark side of AI. Governments and policymakers are recommended to establish legislation to ensure that AI-powered innovation and implementation are beneficial to the social good while limiting the threats caused by the dark side of AI.
Quantum computing in automated invoice factoring control systems

Abstract. The aim of this paper is to describe existing approaches to automation in enterprise management systems of invoice factoring operations. The relevance of this problem is estimated by the volume and dynamics of growth of the invoice factoring market in the Russian Federation. The existing performance problems inherent in software built on the basis of classical algorithms are considered, and their optimization by using quantum algorithms is proposed. Attention is focused on the problem of parallel computing as the main feature, which allows to achieve a significant increase in computing power. Possible way of solution, based on the joint use of quantum and classical approach within one software complex is given. Application of quantum Monte Carlo method for the purpose of improvement of the automated information system of the enterprise is described. The received result allows to draw a conclusion about perspective of use of the given technique for construction of the combined quantum and classical algorithms in systems of management of factoring operations.
Dynamic Modal Logic with Counting: When Reduction Axioms Work and Fail

In the present paper, we study the dynamic aspect of modal logic with counting ML $$(\#)$$ . We study several kinds of model updates where we have reduction axioms, namely two kinds of public announcements, preference upgrade and deleting arrows from $$\varphi _1$$ to $$\varphi _2$$ . We also show that certain PDL program constructions cannot be defined in the basic modal logic with counting ML $$(\#)$$ .
SMAR: Summary-Aware Multi-Aspect Recommendation

Extracting user preferences and item features from reviews to assist recommendations is becoming increasingly popular. However, on the one hand, existing works generally select reviews based on how well user reviews match item reviews. They ignore that reviews may contain noise such as irrelevant phrases, which will affect the accuracy of selecting important reviews. In contrast, summaries written by users are abstracts of reviews that contain critical item feature information. They can be adopted to identify crucial reviews and further capture user’s fine-grained preferences from reviews. In addition, current methods do not consider that different items have different aspects in the same domain. They normally set a fixed number of aspects of the entire domain to get coarse-grained user preferences and item features. However, when modeling the user’s preferences for the current item, it might be more important to capture the corresponding aspects of the item preferences. Therefore, in this paper, we are motivated to propose a Summary-Aware Multi-Aspect Recommendation (SMAR). Specifically, we first construct a Summary-Aware Review Selection Module which adopts summaries to alleviate noise in reviews, identifying key reviews accurately. We then design a Summary-Aware Multi-Aspect Module which captures targeted user preferences towards the current item’s aspects. Finally, we employ Latent Factor Model to complete the recommendation process. The experimental results on Amazon datasets show that our method significantly outperfoms state-of-art approaches in terms of rating prediction accuracy.
Multi-Source Monitoring Data Fusion Comprehensive Evaluation Method for the Safety Status of Deep Foundation Pit

Construction of the deep foundation pit (DFP) in subway stations is fraught with significant uncertainties, which may cause project delays due to discrepancies between single-indicator monitoring warning information and actual conditions at the site. Therefore, this article proposes a safety assessment method for DFP based on the Game-Cloud Model. An entirely quantitative assessment index system is established with on-site monitoring projects according to the design safety classification of DFP. Considering the one-sidedness of using a single method to determine the weights of assessment indices, game theory is introduced to calibrate the subjective and objective weights determined by the grey decision-making trial and evaluation laboratory (GDEMATEL) and the entropy method, respectively. Next, we use the forward cloud generator of the cloud model (CM) to generate the safety level membership function of the evaluation indicators. Finally, we quantitatively calculate the synthetic safety level of DFP using the comprehensive evaluation approach. A 19-day dynamic assessment was conducted on the actual engineering project by the proposed method. The results indicated that the synthetic safety level of the assessed area ranged between grades Ⅰ and Ⅱ, corresponding to Negligible and Acceptable in the acceptance criteria. Compared with the single-indicator monitoring warning results, it was more in line with on-site observation, which verified its reliability and practicality.
A universal lesion detection method based on partially supervised learning

Partially supervised learning (PSL) is urgently necessary to explore to construct an efficient universal lesion detection (ULD) segmentation model. An annotated dataset is crucial but hard to acquire because of too many Computed tomography (CT) images and the lack of professionals in computer-aided detection/diagnosis (CADe/CADx). To address this problem, we propose a novel loss function to reduce the proportion of negative anchors which is extremely likely to classify the lesion area (positive samples) as a negative bounding box, further leading to an unexpected performance. Before calculating loss, we generate a mask to intentionally choose fewer negative anchors which will backward wrongful loss to the network. During the process of loss calculation, we set a parameter to reduce the proportion of negative samples, and it significantly reduces the adverse effect of misclassification on the model. Our experiments are implemented in a 3D framework by feeding a partially annotated dataset named DeepLesion, a large-scale public dataset for universal lesion detection from CT. We implement a lot of experiments to choose the most suitable parameter, and the result shows that the proposed method has greatly improved the performance of a ULD detector. Our code can be obtained at https://github.com/PLuld0/PLuldl.
Detection and Prevention of Insecure Direct Object References (IDOR) in Website-Based Applications

IDOR (Insecure Direct Object References) is a security vulnerability that occurs when a web application does not validate or authorize access to direct objects, such as data or resources, in an adequate manner. In the context of web application security, objects can be files, database records, or other resources identified by a parameter or direct reference. The IDOR technique allows an attacker to manipulate parameters passed to a web application to gain unauthorized access to objects he or she should not have access to. By exploiting this vulnerability, attackers can access, modify, or delete data that should only be accessible to authorized users. One of the dangers in accessing data on websites, data retrieval techniques from object IDs are often vulnerable to Insecure Direct Object References (IDOR) attacks. Therefore, the data retrieval technique from $_SESSION can be a safer alternative to avoid the IDOR security vulnerability. Using this technique, only the account in use can be accessed and does not allow access to other technician accounts. The use of additional query parameters can also increase website security and protect the data and information contained therein. Thus, adding additional validation to the code can help prevent IDOR vulnerabilities from occurring in web applications.