Reviewerly/exHarmony huggingface dataset:
Web Bot Detection Evasion Using Deep Reinforcement Learning

Web bots are vital for the web as they can be used to automate several actions, some of which would have otherwise been impossible or very time consuming. These actions can be benign, such as website testing and web indexing, or malicious, such as unauthorised content scraping, scalping, vulnerability scanning, and more. To detect malicious web bots, recent approaches examine the visitors’ fingerprint and behaviour. For the latter, several values (i.e., features) are usually extracted from visitors’ web logs and used as input to train machine learning models. In this research we show that web bots can use recent advances in machine learning, and, more specifically, Reinforcement Learning (RL), to effectively evade behaviour-based detection techniques. To evaluate these evasive bots, we examine (i) how well they can evade a pre-trained bot detection framework, (ii) how well they can still evade detection after the detection framework is re-trained on new behaviours generated from the evasive web bots, and (iii) how bots perform if re-trained again on the re-trained detection framework. We show that web bots can repeatedly evade detection and adapt to the re-trained detection framework to showcase the importance of considering such types of bots when designing web bot detection frameworks.
Instinct-Based Decision-Making in Interactive Narratives

This paper examines the expressive potential of instinct-based decision-making as a method to enhance narrative immersion in interactive storytelling. One of the key challenges to propose leaned-back interactive narratives lies in the methods through which users and system exchange inputs and outputs. While explicit interfaces tend to disrupt leaned-back participation – demanding a leaned-forward type of agency – and, thus, immersion in the narrative environment, this model proposes interactions based on diegetic stimuli that avoid interfaces and encourage instinctive and immediate reactions from the user in order to navigate a narrative immersive environment. The notion of instinct-based decision-making has been observed in three stages: (1) Conceptualization through previous practices and literature review, (2) Design and production of a Cinematic Virtual Reality (CVR) interactive prototype, and (3) system-testing of the model’s key functional aspects.
Scientific Perspectives: Challenges for Human Cognition

AbstractOne of key issues in the study of Earth’s habitability concerns the origin and preservation of volatiles, which are related either to Earth accretion processes associated with some special giant impacts or to solidification of magma ocean and vertical tectonics, which were responsible for volatile transport and distribution.
Getting Started with Java Development for Azure

Cloud is the new generation hosting environment. All modern applications developed using cutting-edge frameworks are hosted on the cloud. Cloud hosting helps you save cost and address concerns. You need not worry about building and managing the underlying infrastructure hosting your application. You build your application and host it on the cloud, thus easing the development and maintenance efforts. Microsoft Azure, Amazon Web Services, and Google Cloud Platform are the popular cloud vendors. However, there are a lot of other vendors providing cloud hosting.
A Near-Production Azure-Based Java Application

Now we have come to the end of the book. We have learned the basics of the essential Azure services that will come in handy while working with Java-based applications on Azure. We learned how to deploy Java-based applications on Azure services like Azure WebApp, Azure Kubernetes Service, Azure Spring Cloud, and Azure Functions. We also learned how to work with Azure SQL, Azure Storage, and Azure Redis Cache. We explored how to monitor, secure, and use Azure DevOps for automated infrastructure creation.
Storing Runtime Data in Azure Redis Cache

Modern applications generate and work on runtime data. They keep the runtime data either in memory or in a persistent disk or data store. Storing the data in the memory enables you faster access to the temporary runtime data stored. However, it gets slower while accessing data from persistent storage like disk or database than the in-memory store. Azure Redis Cache helps you store and manage runtime data for your application in Azure. Azure Redis Cache helps you access the runtime data quickly and increases your application's performance.
Work with Azure Cosmos DB

Modern applications use NoSQL databases for storing data. These databases should be highly scalable and responsive and have very low latency. These applications are globally available, and this needs their databases to be available globally. Data replication should happen in no time across the globe for such applications. Azure Cosmos DB is a cloud-based NoSQL database on Azure that can meet these requirements.
Markov-based genetic algorithm with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.svg" display="inline" id="d1e1107"><mml:mi>ϵ</mml:mi></mml:math>-greedy exploration for Indian classical music composition

Recent technological advancements have made it possible to automate some of the intricate activities involved in music production. In this paper, a heuristic method is proposed for automatic composition of Indian Classical Music (ICM). Composing ICM necessitates consideration of raga constraints which distinguishes it from other genres of music. The raga-based music cannot be generated through random exploration of search space. More specifically, certain disordering in note combinations can violate the raga constraints. To address the aforesaid issue, the paper proposes a novel method by combining genetic algorithm with Markov chain model attuned to ICM sequences. Moreover, the proposed method uses ϵ-greedy strategy to balance the exploration and exploitation of musical search space. In order to preserve the musical characteristics, domain-specific genetic operators (i.e., crossover and mutation) are defined. The evaluation function based on music theory is used to direct the search to favorable tracks. The Kullback Liebler (KL) divergence metric is used to analyze the probability distribution of output and sample data sequence. The results indicate that by regulating the exploration rate, the method is capable of generating melodic sequences while maintaining the desired musical note distribution. Moreover, the method converges faster than the conventional genetic algorithm.
An Optimal Algorithm for Renewable Energy Generation Based on Neural Network

Solar energy is a costless and readily available form of energy that has shown to be one of the cleanest and most plentiful renewable energy sources. Various large-scale solar photovoltaic (PV) facilities are being utilized to minimize pollution and carbon emissions generated by fossil energy in many nations across the world. The power sequence of PV is influenced by a variety of diverse variables, and it is very unpredictable and volatile. Unlike the distributed PVs, the centralized PVs have the same intensity and location. The obstruction of clouds causes minor variations in the output power of the PV, making the power forecasting more difficult. To solve the aforementioned difficulties, this article provides a new neural network-based technique for PV power optimization and forecasting. The first stage is to create a cloud trajectory tracking system based on cloud photos taken from the ground. Second, a cloud trajectory tracking-based irradiance coefficient prediction model was built. Then, to increase forecast accuracy, build an error correcting model. For verification, data from a centralized solar power station was used. The results show that the proposed algorithm has technological applications and may greatly improve prediction accuracy.
Simplified Risk-aware Decision Making with Belief-dependent Rewards in Partially Observable Domains

With the recent advent of risk awareness, decision-making algorithms' complexity increases, posing a severe difficulty to solve such formulations of the problem online. Our approach is centered on the distribution of the return in the challenging continuous domain under partial observability. This paper proposes a simplification framework to ease the computational burden while providing guarantees on the simplification impact. On top of this framework, we present novel stochastic bounds on the return that apply to any reward function. Further, we consider simplification's impact on decision making with risk averse objectives, which, to the best of our knowledge, has not been investigated thus far. In particular, we prove that stochastic bounds on the return yield deterministic bounds on Value at Risk. The second part of the paper focuses on the joint distribution of a pair of returns given a pair of candidate policies, thereby, for the first time, accounting for the correlation between these returns. Here, we propose a novel risk averse objective and apply our simplification paradigm. Moreover, we present a novel tool called the probabilistic loss (PLoss) to completely characterize the simplification impact for any objective operator in this setting. We provably bound the cumulative and tail distribution function of PLoss using PbLoss to provide such a characterization online using only the simplified problem. In addition, we utilize this tool to offer deterministic guarantees to the simplification in the context of our novel risk averse objective. We employ our proposed framework on a particular simplification technique - reducing the number of samples for reward calculation or belief representation within planning. Finally, we verify the advantages of our approach through extensive simulations.
Cross-Regional Friendship Inference via Category-Aware Multi-Bipartite Graph Embedding

This paper proposes a novel problem of cross-regional friendship inference to solve the geographically restricted friends recommendation. Traditional approaches rely on a fundamental assumption that friends tend to be co-location, which is unrealistic for inferring friendship across regions. By reviewing a large-scale Location-based Social Networks (LBSNs) dataset, we spot that cross-regional users are more likely to form a friendship when their mobility neighbors are of high similarity. To this end, we propose Category-Aware Multi-Bipartite Graph Embedding (CMGE for short) for cross-regional friendship inference. We first utilize multi-bipartite graph embedding to capture users’ Point of Interest (POI) neighbor similarity and activity category similarity simultaneously, then the contributions of each POI and category are learned by a category-aware heterogeneous graph attention network. Experiments on the real-world LBSNs datasets demonstrate that CMGE outperforms state-of-the-art baselines.
Inline Traffic Analysis Attacks on DNS over HTTPS

Even though end-to-end encryption was introduced to Domain Name System (DNS) communications to ensure user privacy and there is an increase in adoption of DNS over HTTPS (DoH), prior research has demonstrated that encrypted DNS traffic is vulnerable to traffic analysis attacks. However, these attacks were demonstrated under strong assumptions such as handling only closed-set classification or doing only post-event analysis. In this work we demonstrate traffic analysis attacks on DoH without such strong assumptions. We first show the feasibility of website fingerprinting over DoH traffic and present an inline traffic analysis attack that achieve over 90% accuracy using DoH traces of length as short as ten packets. Next, we propose a novel open-set classification method and achieve over 75% accuracy on both closed-set and open-set samples for the open-set scenario. Finally, we demonstrate that the same attack can be performed without any knowledge on the start of the activity.
Service-Based Identification of Highly Coupled Mobile Applications

Identifying mobile applications from network traffic is important for Internet service providers (ISPs) to manage their networks at a fine-grained level. However, the rise of public services has led to a gradual increase in service coupling among applications, making it more difficult to identify applications. Existing methods produce classification ambiguities when identifying highly coupled mobile applications, resulting in low application identification accuracy. In this paper, we propose a service-based method to quickly identify highly service coupling applications after the applications are launched. It can accurately identify highly coupled mobile applications based on the features of the services accessed by the applications. Experiments on a real network traffic dataset of highly coupled mobile applications verify that our method can identify applications within 25s after the mobile applications are launched, and the identification accuracy is over 99%.
MicroTL: Transfer Learning on Low-Power IoT Devices

Deep Neural Networks (DNNs) on IoT devices are becoming readily available for classification tasks using sensor data like images and audio. However, DNNs are trained using extensive computational resources such as GPUs on cloud services, and once being quantized and deployed on the IoT device remain unchanged. We argue in this paper, that this approach leads to three disadvantages. First, IoT devices are deployed in real-world scenarios where the initial problem may shift over time (e.g., to new or similar classes), but without re-training, DNNs cannot adapt to such changes. Second, IoT devices need to use energy-preserving communication with limited reliability and network bandwidth, which can delay or restrict the transmission of essential training sensor data to the cloud. Third, collecting and storing training sensor data in the cloud poses privacy concerns. A promising technique to mitigate these concerns is to utilize on-device Transfer Learning (TL). However, bringing TL to resource-constrained devices faces challenges and trade-offs in computational, energy, and memory constraints, which this paper addresses. This paper introduces MicroTL, Transfer Learning (TL) on low-power IoT devices. MicroTL tailors TL to IoT devices without the communication requirement with the cloud. Notably, we found that the MicroTL takes 3x less energy and 2.8x less time than transmitting all data to train an entirely new model in the cloud, showing that it is more efficient to retrain parts of an existing neural network on the IoT device.
Multiagent Architecture for a Codebook-Based Bug Management System

The management of software incidents is a process that involves a significant cost in the context of software development. The introduction of artificial intelligence techniques in the issue management process can help to reduce these costs, covering tasks such as the detection of duplicate issues, related issues, prioritization or assignment. This work presents an agent architecture for the introduction of artificial intelligence in software issue management, taking as a central axis the use of codebook-based retrieval models for the representation of the textual component of the codebooks. The resulting system will allow the systematic study of the hyperparameters that need to be adjusted to make use of such a system.
A Novel Component of Decision-Making for Context-Aware Applications in Pervasive Environments

In pervasive computing environments, context-aware applications face multiple challenges to keep high performance. One-challenge faced by context-aware applications is the frequent changing situations and environments around users at runtime. These changes could affect the quality of context reasoning and decision-making owing to the fact that decision-making rules defined a priori could lose their efficiency in dynamic environments. Consequently, it is certain that the quality of services provided for users would be decreased. Therefore, it is important to address context reasoning and decision-making problems leveraged by dynamic environments and context models evolution at runtime. In this paper, we propose a decision adaptation component to deal with the evolution of a rule knowledge base and subsequently the generation of appropriate adaptations and services more related to changes occurring around users at runtime. A case study is conducted to illustrate the implementation of the rule generation module for rule knowledge base enrichment and decision-making improvement.
Uncertainty Identification in Context-Aware Systems Using Public Datasets

Uncertain situations are naturally embedded in intelligent environments due to a series of factors. Changes in users’ behaviour and the lack of context data are some of the reasons for this phenomenon to happen. Computing solutions for such domains should consider strategies to cope with the uncertainty problem, once it may influence the behaviour of services and, consequently, affect the way users interact with the environment and with the system. This paper presents the validation of an approach to tackle this obstacle. The main goal is to provide subsidies for the identification of uncertainty. The proposal includes the definition of a decision tree to classify context data and use it to reduce the level of uncertainty while building situations. The conduction of the experiments was through case studies created based on two public datasets containing Activity Daily Living data from smart houses.
A Data-Locality-Aware Distributed Learning System

Machine Learning problems are significantly growing in complexity, either due to an increase in the volume of data, to new forms of data, or due to the change of data over time. This poses new challenges that are both technical and scientific. In this paper we propose a Distributed Learning System that runs on top of a Hadoop cluster, leveraging its native functionalities. It is guided by the principle of data locality. Data are distributed across the cluster, so models are also distributed and trained in parallel. Models are thus seen as Ensembles of base models, and predictions are made by combining the predictions of the base models. Moreover, models are replicated and distributed across the cluster, so that multiple nodes can answer requests. This results in a system that is both resilient and with high availability.
Design and Development of Smart Wearable Products for Basketball Dribble Teaching Training Posture Monitoring

Basketball is one of the most popular sports, but apart from a small number of sports specialties, ordinary people rarely have the opportunity to receive professional basketball training, let alone coaches who provide one-on-one dribbling posture guidance. Dribbling is a very basic and important technique in basketball. Mastering the correct dribbling posture can help people further improve their basketball skills. In response to this problem, this article designed a smart wearable product to monitor the user’s posture in basketball dribbling training. If the user has a wrong dribble posture, the product will automatically prompt and give relevant suggestions. This article focuses on user demand research, product conceptual design, prototype development, and dribbling posture determination experiments and elaborates the design and development process of the product. Based on the experimental data, this article believes that the optimal parameters of the monitoring standard for the “head down too long” during basketball dribbling are the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>x</mi> </math> -axis angle value of the motion sensor is critical for -120°, and the duration of exceeding the critical value is 1 second. The optimal parameters of the “dribbling wrist flip” monitoring standard are the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>x</mi> </math> -axis angle value of the motion sensor has a critical value of 105°, and the duration of exceeding the critical value is 0.4 seconds. Judging from the end user’s experience and rating of the product’s trial experience, it can be seen that the smart product can indeed play a very good auxiliary effect in the field of dribbling posture monitoring. Popularizing it in the daily training of basketball players can effectively promote the full informatization and intelligent development of sports.
Blockchain With the Internet of Things

The internet of things (IoT) is ushering a new age of technology-driven automation of information systems into the manufacturing industry. One of the main concerns with IoT systems is the lack of privacy and security preserving schemes for controlling access and ensuring the safety of the data. Many security issues arise because of the centralized architecture of IoT-based information systems. Another concern is the lack of appropriate authentication and access control schemes to moderate the access to information generated by the IoT devices in the manufacturing industry. Hence, the question that arises is how to ensure the identity of the manufacturing machinery or the communication nodes. This chapter presents the advantages of blockchain technology to secure the operation of the modern manufacturing industry in a trustless environment with IoT applications. The chapter reviews the challenges and threats in IoT applications and how integration with blockchain can resolve some of the manufacturing enterprise information systems (EIS).
Blockchain Technology for IoT

In the internet era, data is considered to be the primary asset, and the host or applications in a network are vulnerable to various attacks. Traditional network architectures have centralized authority to provide authentication, authorization, and access control services. In this case, there is a possibility of data mishandling activities from the valuable information available in the given network application. To avoid this type of mishandling, a new technology came into existence known as blockchain. Implementing blockchain technology in the internet of things (IoT) will ensure data integrity, stability, and durability. The authors present a detailed investigation of various IoT applications with blockchain implementation. Blockchain-based mechanisms will improve the security aspects in the traditional network applications related to IoT like insurance policies claiming, personal identification, and electronic health records.
Blockchain and the Research Libraries

This chapter will examine the theoretical uses of blockchain technologies in research libraries. Technology has enhanced the services and operations of research libraries since the early implementation of computerized cataloging systems. Blockchain technology provides research libraries with the opportunity to decentralize services, while also maintaining and strengthening digital rights management. Research libraries will be able locate services that can be decentralized to provide patrons with a more effective and efficient service. The blockchain technology has the potential to expand library collections through distributed verifiable sovereign identity, which would allow patrons to securely access information from multiple libraries while maintaining their privacy. Libraries will be able to evaluate services and programs to determine best uses for blockchain technology.