Reviewerly/exHarmony huggingface dataset:
Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing

The explosive growth of users and applications in IoT environments has promoted the development of cloud computing. In the cloud computing environment, task scheduling plays a crucial role in optimizing resource utilization and improving overall performance. However, effective task scheduling remains a key challenge. Traditional task scheduling algorithms often rely on static heuristics or manual configuration, limiting their adaptability and efficiency. To overcome these limitations, there is increasing interest in applying reinforcement learning techniques for dynamic and intelligent task scheduling in cloud computing. How can reinforcement learning be applied to task scheduling in cloud computing? What are the benefits of using reinforcement learning-based methods compared to traditional scheduling mechanisms? How does reinforcement learning optimize resource allocation and improve overall efficiency? Addressing these questions, in this paper, we propose a Q-learning-based Multi-Task Scheduling Framework (QMTSF). This framework consists of two stages: First, tasks are dynamically allocated to suitable servers in the cloud environment based on the type of servers. Second, an improved Q-learning algorithm called UCB-based Q-Reinforcement Learning (UQRL) is used on each server to assign tasks to a Virtual Machine (VM). The agent makes intelligent decisions based on past experiences and interactions with the environment. In addition, the agent learns from rewards and punishments to formulate the optimal task allocation strategy and schedule tasks on different VMs. The goal is to minimize the total makespan and average processing time of tasks while ensuring task deadlines. We conducted simulation experiments to evaluate the performance of the proposed mechanism compared to traditional scheduling methods such as Particle Swarm Optimization (PSO), random, and Round-Robin (RR). The experimental results demonstrate that the proposed QMTSF scheduling framework outperforms other scheduling mechanisms in terms of the makespan and average task processing time.
Context-aware and part alignment for visible-infrared person re-identification

Visible-infrared person re-identification (VI-ReID) is a challenging problem of matching a person from visible and infrared modalities. Existing researches adopt the last convolutional layer features of the off-the-shelf backbone network as the representation to refine, which is unable to represent the heterogeneous cross-modality features with discriminant information. In this paper, we propose a novel graph-based aggregation learning network (GALNet) with visual Transformer embedding to mine both multi-layer features and part-level contextual cues for VI-ReID. We propose a novel feature memory module (FMM) to reserve global discriminative features of the low layers with the correlation modeling ability of the graph convolution network (GCN) which is supplemented to the final person representation. To learn more discriminant part-level features, an attentive part aggregation module (PAM) is designed to mine part relationships, leveraging the self-attention mechanism of the Transformer. By fusing these components, the global-level, and part-level discriminant information can be utilized. Extensive experiments on SYSU-MM01 and RegDB benchmarks demonstrate the effectiveness of our proposed methods compared with several state-of-the-art methods.
Feature subset selection in data-stream environments using asymmetric hidden Markov models and novelty detection

With the increase of computational power and memory capacity, it is possible to record and analyse lots of features of different nature in real time or in a data stream manner. Nonetheless, in many applications, not all variables may be relevant to explain the nature of the data. Feature subset selection strategies are therefore required to extract the relevant features to understand the data generating process. Much work has been performed in the area of supervised classification problems regarding feature selection, whereas for unsupervised environments, scarce studies have been conducted. In current feature subset selection methodologies for unsupervised data streams, the set of relevant variables are reevaluated or forgotten whenever a new instance or chunk of data arrives: this approach can be computationally expensive or unnecessary. Therefore, in this article, we provide a method to perform feature subset selection in unsupervised data streams. An embedded feature subset selection methodology based on asymmetric hidden Markov models and novel concept discovery strategies is used. This methodology can be used to detect novel concepts in data, and update the set of relevant features when a drift in the data stream is detected. Thus, the relevant variables are updated only when needed. To make this possible, we provide a model for batch and online problems, that is capable of dynamically determining the relevant variables to address feature selection in data streams. Additionally, the model provides domain insights using context-specific Bayesian networks which can be helpful to understand the dynamic process. To validate the proposed methodology, synthetic and real data from ball-bearings are used. Additionally, the proposed methodology is compared with other state of the art methodologies. The proposed method can be used to update the relevant features when needed and is more stable than its competitors.
Learning reward machines: A study in partially observable reinforcement learning

Reinforcement Learning (RL) is a machine learning paradigm wherein an artificial agent interacts with an environment with the purpose of learning behaviour that maximizes the expected cumulative reward it receives from the environment. Reward machines (RMs) provide a structured, automata-based representation of a reward function that enables an RL agent to decompose an RL problem into structured subproblems that can be efficiently learned via off-policy learning. Here we show that RMs can be learned from experience, instead of being specified by the user, and that the resulting problem decomposition can be used to effectively solve partially observable RL problems. We pose the task of learning RMs as a discrete optimization problem where the objective is to find an RM that decomposes the problem into a set of subproblems such that the combination of their optimal memoryless policies is an optimal policy for the original problem. We show the effectiveness of this approach on three partially observable domains, where it significantly outperforms A3C, PPO, and ACER, and discuss its advantages, limitations, and broader potential.1
Relay Hindsight Experience Replay: Self-guided continual reinforcement learning for sequential object manipulation tasks with sparse rewards

Learning with sparse rewards remains a challenging problem in reinforcement learning (RL). In particular, for sequential object manipulation tasks, the RL agent generally only receives a reward upon successful completion of the entire task, leading to low exploration efficiency. To address this sample inefficiency, we propose a novel self-guided continual RL framework, named Relay Hindsight Experience Replay (RHER). RHER decomposes a sequential task into several subtasks with increasing complexity, allowing the agent to learn from the simplest subtask and gradually complete the task. It is crucial that a Self-Guided Exploration Strategy (SGES) is proposed to use the already-learned simpler subtask policy to guide the exploration of a more complex subtask. This strategy allows the agent to break the barriers of sparse reward sequential tasks and achieve efficient learning stage by stage. As a result, the proposed RHER method achieves state-of-the-art performance on the benchmark tasks (FetchPush and FetchPickAndPlace). Furthermore, the experimental results demonstrate the superiority and high efficiency of RHER on a variety of single-object and multi-object manipulation tasks (e.g., ObstaclePush, DrawerBox, TStack, etc.). Finally, the proposed RHER method can also learn a contact-rich task on a real robot from scratch within 250 episodes.
Cross-Modal Transformer for RGB-D semantic segmentation of production workshop objects

Scene understanding in a production workshop is an important technology to improve its intelligence level, semantic segmentation of production workshop objects is an effective method for realizing scene understanding. Since the varieties of information of production workshop, making full use of the complementary information of RGB image and depth image can effectively improve the semantic segmentation accuracy of production workshop objects. Aiming at solving the multi-scale and real-time problems of segmenting the production workshop objects, this paper proposes Cross-Modal Transformer (CMFormer), a Transformer-based cross-modal semantic segmentation model. Its key feature correction and feature fusion parts are composed of the Multi-Scale Channel Attention Correction(MS-CAC) module and the Global Feature Aggregation(GFA) module. By improving Multi-Head Self-Attention(MHSA) in Transformer, we design Cross-Modal Multi-Head Self-Attention(CM-MHSA) to build long-range interaction between RGB image and depth image, and further design the MS-CAC module and the GFA module on the basis of the CM-MHSA module, to achieve cross-modal information interaction in the channel and spatial dimensions. Among them, the MS-CAC module enriches the multi-scale features of each channel and achieve more accurate channel attention correction between the two modals; the GFA module interacts with RGB feature and depth feature in the spatial dimension and fuses global and local features at the same time. In the experiments on the NYU Depth v2 dataset, the CMFormer reached 68.00% MPA(Mean Pixel Accuracy) and 55.75% mIoU(Mean Intersection over Union), achieves the state-of-the-art results. While in the experiments on the Scene Objects for Production workshop dataset(SOP), the CMFormer achieves 96.74% MPA, 92.98% mIoU and 43 FPS(Frames Per Second), which has high precision and good real-time performance. Code is available at: https://github.com/FutureIAI/CMFormer
Hyperledger fabric platform for healthcare trust relations—Proof-of-Concept

In recent years, blockchain technologies have expanded from the finance field to other areas that rely on trust-based solutions. The healthcare industry represents one such area, as digital transformation disrupts relationships between patients, healthcare professionals, and healthcare institutes. Patients and healthcare institutes lack a proficient tool to verify the credentials of medical professionals in a digital environment. Furthermore, healthcare professionals lack a tool where they are in control over their credentials. The first contribution of this paper is a proposal of a solution that leverages the private permissioned Hyperledger Fabric blockchain and smart contracts to provide a source of transparent trust for relationships within the healthcare industry. Second, we pave the ground for GDPR compliance by storing only the hash values on the blockchain. Third, we solve the problem of patient authentication by utilizing cryptographic techniques. Finally, we prove the usability of the proposed solution by implementing a user interface and creating a live deployment.
Electronic Evidence: A Framework for Applying Digital Forensics to Data Base

ABSTRACT Accounting data bases contain much information reflecting a company’s financial performance, clients, suppliers, and other relevant information about the company’s activities. Thus, attracting criminal activity is empowered by a wide range of new and evolving digital technologies. More interestingly, all of these electronic media create digital traces, providing useful information for the digital investigation process. As digital crime targeting millions of pieces of sensitive information from data bases is rising, specific expertise and an informed approach in data base forensics are required. This study suggests an enhanced meta-framework, named data base evidence finding investigation (DEFI), to investigate an accounting data base to detect tampering. DEFI offers more targeted and streamlined guidance for data base experts to perform investigations while preserving the digital evidence's integrity and leaving it unchanged. Using an example, we demonstrate that the proposed enhanced meta-framework can guide the detection of suspicious transactions in an accounting data base.
Research on obstacle avoidance motion planning method of manipulator in complex multi scene

In order to improve the efficiency and success rate of obstacle avoidance motion planning of industrial manipulator in complex multi scenes, a collision detection model between manipulator and obstacles based on cylinder and sphere bounding box is established, and an improved RRT * algorithm based on heuristic probability fusion artificial potential field method(P-artificial potential field RRT * , PAPF-RRT * ) is proposed. The probability target bias and random sampling point optimization strategy are introduced into the sampling, and the location optimization constraints are applied to the sampling points to enhance the sampling guidance and quality. In order to change the expansion direction of the traditional new node and the local optimization problem in special environment, the target gravity, obstacle repulsion and adaptive step size of the artificial potential field method are combined, so that the algorithm can guide the expansion direction and step size of the new node in real time within the resultant force range generated by APF, reducing excessive exploration and the expansion of the collision region. The Cubic B-spline is used to interpolate and optimize the planned path to reduce the complexity of the path and improve the flexibility of the path. The simulation results in two-dimensional and three-dimensional multi scenes show that the present algorithm reduces the average path search time by 31.22% and shortens the path length by 17.32% comparing with the traditional RRT * algorithm. The visual simulation results show that the present algorithm can make the manipulator successfully avoid obstacles and run to the target point quickly and smoothly.
Assisting Visually Impaired People Using Deep Learning-based Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems on Remote Sensing Images

Anomaly detection in pedestrian walkways of visually impaired people (VIP) is a vital research area that utilizes remote sensing and aids to optimize pedestrian traffic and improve flow. Researchers and engineers can formulate effective tools and methods with the power of machine learning (ML) and computer vision (CV) to identifying anomalies (i.e. vehicles) and mitigate potential safety hazards in pedestrian walkways. With recent advancements in ML and deep learning (DL) areas, authors have found that the image recognition problem ought to be devised as a two-class classification problem. Therefore, this manuscript presents a new sine cosine algorithm with deep learning-based anomaly detection in pedestrian walkways (SCADL-ADPW) algorithm. The proposed SCADL-ADPW technique identifies the presence of anomalies in the pedestrian walkways on remote sensing images. The SCADL-ADPW techniques focus on the identification and classification of anomalies, i.e. vehicles in the pedestrian walkways of VIP. To accomplish this, the SCADL-ADPW technique uses the VGG-16 model for feature vector generation. In addition, the SCA approach is designed for the optimal hyperparameter tuning process. For anomaly detection, the long short-term memory (LSTM) method can be exploited. The experimental results of the SCADL-ADPW technique are studied on the UCSD anomaly detection dataset. The comparative outcomes stated the improved anomaly detection results of the SCADL-ADPW technique.
Misalignment Estimation in Non-Aligned Double JPEG Scenario Based on AC Histogram Analysis

In forensics investigation, the estimation of the misalignment occurred between consecutive JPEG compressions can be considered an important step to recover the manipulation history of an image under analysis and localize forgeries. Exploiting statistics computed from the AC histograms obtained applying a third compression, an effective method to estimate the misalignment has been designed. Finally, to assess the performance of the proposed approach a series of tests has been conducted considering scenarios involving different patch sizes and quantization matrices, performing also comparisons with state-of-the-art solutions.
Constructing Design Knowledge for Postdigital Science and Education

In this chapter we draw upon our program of research into interdisciplinary science and education to explore some relations between postdigital research and the construction of locally-useful design knowledge. We introduce the Activity-Centred Analysis and Design framework (ACAD) and explain how design knowledge – knowledge that is useful for design work – can be constructed through alternating processes of collaborative analysis and design. ACAD uses a dual ontology that permits reasoning about both complex entangled human activities and the physical, social and epistemic phenomena that situate those activities. A postdigital disposition entails an interest in things digital and simultaneously in the other material, social, and epistemic things that constitute, motivate, complicate, and conceal the digital. We are interested in finding out more about how people learn to flourish, and indeed help each other flourish, in the complex situations and practices that can be characterised as postdigital, postdisciplinary science and education. We argue for focusing on how people work together when they are trying to understand what shapes their current epistemic (knowledge-making) practices, how they analyse problems and find opportunities for change and how they create and agree designs for better ways of working.
Understanding Digital Inequality: A Theoretical Kaleidoscope

The pandemic affected more than 1.5 billion students and youth, and the most vulnerable learners were hit hardest, making digital inequality in educational settings impossible to overlook. Given this reality, we, all educators, came together to find ways to understand and address some of these inequalities. As a product of this collaboration, we propose a methodological toolkit: a theoretical kaleidoscope to examine and critique the constitutive elements and dimensions of digital inequalities. We argue that such a tool is helpful when a critical attitude to examine ‘the ideology of digitalism’, its concomitant inequalities, and the huge losses it entails for human flourishing seems urgent. In the paper, we describe different theoretical approaches that can be used for the kaleidoscope. We give relevant examples of each theory. We argue that the postdigital does not mean that the digital is over, rather that it has mutated into new power structures that are less evident but no less insidious as they continue to govern socio-technical infrastructures, geopolitics, and markets. In this sense, it is vital to find tools that allow us to shed light on such invisible and pervasive power structures and the consequences in the daily lives of so many.
Algorithmic Systems Claim Education and The (Re)Production of Education

As education grows its dependency on digital technologies, their business owners have the capacity to claim control over not only the distribution of knowledge but also over individuals with regards to their future prospects and life chances. Algorithmic systems are increasingly used to infer, predict, and steer the education experience of children. Such systems are data-intensive, often proprietary in nature, and near impossible for their end-users to understand how they work or even resist. These systems therefore build their privilege in education to acquire pedagogic power and authority over the educational process and over one’s future life chances. But concerns grow as to whom this authority benefits the most and how to prevent their possible negative impact. For that, this chapter calls for more and systematic postdigital research to identify the needs of governance, scrutiny, and oversight of these advancing data-intensive systems and their owners to ensure that they are held accountable in their influence and impact on education.
What Would Happen if Hackers Attacked the Railways? Consideration of the Need for Ethical Codes in the Railway Transport Systems

With the development of Internet and the spread of digital systems, cybersecurity has become an urgent issue for safeguarding businesses and society. The topic of cyber risks is of particular interest to governments, especially due to moral dilemmas arising in relation to concrete cases of cyberattacks against critical infrastructures. As data breach occurrence could lead to serious security problems and loss of reputation, sensitive critical infrastructure information must be safeguarded. While the expertise of engineers might be adequate to avert the risk of cyberattacks in most cases, the likelihood of their occurrence cannot be zeroed, and the resulting consequences should be addressed beyond the purely technical aspects. At a time in history when cybersecurity is one of the most hotly debated topics in engineering and law, it seems to also have a strong link to ethics. Moral difficulties arise precisely in such contexts, as far as how to appropriately behave in resistance to cyber blackmail against critical infrastructure. Social responses to these risk situations are by no means obvious and reflect the difficulties of companies in responding quickly to cyber blackmail. As well as other critical infrastructures, the railway system has increased the use of information and operational technology systems and it may face hard-to-solve moral dilemmas occasionally. The attack on the Italian state railways, Ferrovie dello Stato, a huge group and—supposedly—with a high level of protection, is a rather emblematic case as it shows the serious vulnerability of companies and the risks associated with increasing cybercrime. This position paper’s main purpose is to raise awareness of possible cybersecurity social risks, related to railway systems, and highlight the need of developing ethical guidelines to respond quickly to security breaches.
Dual Deep Clustering

Deep clustering is a branch of deep learning, in which the dimensionality reduction capabilities of deep networks are exploited for clustering data. In this sense, the deep part of clustering has to be considered more as a preprocessing step than a perfectly integrated module of the neural network. This paper proposes the idea of dual neural network in the framework of gradient-based competitive learning. The theory is based on the intuition that neural networks are able to learn topological structures by working directly on the transpose of the input matrix. In this sense, the dual layer is better suited for handling high-dimensional data, because the weight estimation is driven by a constraining subspace which does not depend on the input dimensionality, but only on the dataset cardinality. This approach allows an exact integration with the deep neural networks in such a way to output the input data prototypes.