Reviewerly/exHarmony huggingface dataset:
Engineering Explainable Agents: An Argumentation-Based Approach

Explainability has become one of the most important concepts in Artificial Intelligence (AI), resulting in a complete area of study called Explainable AI (XAI). In this paper, we propose an approach for engineering explainable BDI agents based on the use of argumentation techniques. In particular, our approach is based on modelling argumentation schemes, which provide not only the reasoning patterns agents use to instantiate arguments but also templates for agents to translate arguments in an agent-oriented programming language to natural language. Thus, using our approach, agents are able to provide explanations about their mental attitudes and decision-making not only to other software agents but also to humans. This is particularly useful when agents and humans carry out tasks collaboratively.
A Dynamic Scheduling Framework for Byproduct Gas System Combining Expert Knowledge and Production Plan

Effective scheduling for byproduct gas systems of steel industry is becoming increasingly vital for maintaining their safe operating and improving energy utilization. Considering that the existing studies failed to capture the dynamic changes in the production environment, a novel dynamic scheduling framework is proposed that seamingly integrates expert knowledge with a dynamic programming process. Given the phase characteristics of the steelmaking processes, data series are first partitioned into information granules based on the production plan to form the knowledge-based initial policies. To achieve dynamic scheduling process, a two-stage value function approximation method is proposed, where in the first stage one learns an event-driven Q-function by the fuzzy rule-based states, and then an action fitting strategy is developed for evaluating continuous actions. Considering the difficulties of establishing a mechanism-based model, the state transition process is described by a granular prediction model to simulate taking actions. On their basis, a dynamic compensation for the initial policies is finally achieved. A number of comparative experiments are conducted by utilizing the practical data coming from a steel plant. The results show that the proposed method can deliver effective solutions for long-term scheduling scenarios. Note to Practitioners—Given that the steelmaking process is a discontinuous one and the byproduct gas system can hardly be described by a physical or mechanism-based model, its energy scheduling works is usually performed by manual approach or using static optimization methods, which would lead to low accuracy and a waste of energy. Since a large number of real-time data had been accumulated by the SCADA system implemented in most steel plants, a data-driven dynamic scheduling approach is proposed in this study. The proposed method takes advantages of the expert knowledge and production plan data, and produces dynamic scheduling solutions by utilizing an actor-critic learning process. The application system on the basis of the proposed method can adapt to different scenarios and ensure long-term safety operations of the gas tanks. Furthermore, since there may be missing data or outliners in the acquired data collected by the SCADA onsite, it is necessary to perform data imputation and filtering methods to guarantee the data integrity and reliability. This study avoids the redundant introduction of such preliminary preprocessing methods for the sample data.
Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

Cervical cancer is screened by pap smear methodology for detection and classification purposes.Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues.In this paper, we proposed the first system that it ables to classify the pap smear images into a seven classes problem.Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells.Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine (SVM) classifier.The success of this proposed system in distinguishing between the levels of normal cases with 100% accuracy and 100% sensitivity.On top of that, it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high accuracy.On the other hand, the low level of abnormality is studied separately and classified into two classes, mild and moderate dysplasia, with ∼ 92% accuracy.The proposed system is a built-in cascading manner with five models of polynomial (SVM) classifier.The overall accuracy in training for all cases is 100%, while the overall test for all seven classes is around 92% in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer, which may lead to an increase in the survival rate in women.
An effective nonlocal means image denoising framework based on non-subsampled shearlet transform

Image denoising is a fundamental task in computer vision and image processing system with an aim of estimating the original image by eliminating the noise and artefact from the noise-corrupted version of the image. In this study, a nonlocal means (NLM) algorithm with NSST (non-subsampled shearlet transform) has been designed to surface a computationally simple image denoising algorithm. Initially, NSST is employed to decompose source image into coarser and finer layers. The number of decomposition levels of NSST is set to two, resulting in set of low-frequency coefficients (coarser layer) and four sets high-frequency coefficients (finer layers). The two number of levels of decomposition are used in order to preserve memory, reduce processing time, and mitigate the influence of noise and misregistration errors. The finer layers are then processed using NLM algorithm, while the coarser layer is left as it is. The NL-Means algorithm reduces noise in finer layers while maintaining the sharpness of strong edges, such as the image silhouette. When compared to noisy images, this filter preserves textured regions, resulting in retaining more information. To obtain a final denoised image, inverse NSST is performed to the coarser layer and the NL-means filtered finer layers. The robustness of our method has been tested on the different multisensor and medical image dataset with diverse levels of noise. In the context of both subjective assessment and objective measurement, our method outperforms numerous other existing denoising algorithms notably in terms of retaining fine image structures. It is also clearly exhibited that the proposed method is computationally more effective as compared to other prevailing algorithms.
An Adversarial Approach to Protocol Analysis and Selection in Local Differential Privacy

Local Differential Privacy (LDP) is a popular standard for privacy-preserving data collection. Numerous LDP protocols have been proposed in the literature which differ in how they provide higher utility in different settings. Yet, few have engaged in analyzing the privacy relationships of these protocols under varying settings, and consequently, it is non-trivial to select which LDP protocol is best to use in a newly emerging application. In this paper, we present an adversarial approach to protocol analysis and selection and make three original contributions. First, we introduce a Bayesian adversary to analyze the privacy relationships of LDP protocols under varying settings. We show that different protocols have substantially different responses to the attack effectiveness of the Bayesian adversary, measured in terms of Adversarial Success Rate (ASR). Second, we provide a formal and empirical analysis on a set of privacy and utility-critical factors, including encoding parameters, privacy budget, data domain, adversarial knowledge, and statistical distribution. We show that different settings of these factors have significant effects on the ASRs of LDP protocols, and no protocol provides consistently low ASR across all settings. Third, we design and develop LDPLens, a prototype implementation of our proposed framework. Given a data collection scenario with various factors and constraints, LDPLens enables optimized selection of a desirable LDP protocol for the given scenario. We evaluate the effectiveness of LDPLens using three case studies with real-world datasets. Results show that LDPLens recommends a different protocol in each case study, and the protocol recommended by LDPLens can yield up to 1.5–2 fold reduction in utility loss, ASR or privacy budget compared to a randomly selected protocol.
LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Accelerator

Graph convolutional networks (GCNs) have been introduced to effectively process non-Euclidean graph data. However, GCNs incur large amounts of irregularity in computation and memory access, which prevents efficient use of traditional neural network accelerators. Moreover, existing dedicated GCN accelerators demand high memory volumes and are difficult to implement onto resource limited edge devices. In this work, we propose LW-GCN, a lightweight FPGA-based accelerator with a software-hardware co-designed process to tackle irregularity in computation and memory access in GCN inference. LW-GCN decomposes the main GCN operations into Sparse Matrix-Matrix Multiplication (SpMM) and Matrix-Matrix Multiplication (MM). We propose a novel compression format to balance workload across PEs and prevent data hazards. Moreover, we apply data quantization and workload tiling, and map both SpMM and MM of GCN inference onto a uniform architecture on resource limited hardware. Evaluation on GCN and GraphSAGE are performed on Xilinx Kintex-7 FPGA with three popular datasets. Compared to existing CPU, GPU, and state-of-the-art FPGA-based accelerator, LW-GCN reduces latency by up to 60×, 12×, and 1.7× and increases power efficiency by up to 912×, 511×, and 3.87×, respectively. Furthermore, compared with NVIDIA’s latest edge GPU Jetson Xavier NX, LW-GCN achieves speedup and energy savings of 32× and 84×, respectively.
Social Network Identification of Laundered Videos Based on DCT Coefficient Analysis

Identifying the originating social network of a digital video is considered a relevant task to support law enforcement agencies and intelligence services in tracing producers of deceptive visual contents. Recent advances in video forensics highlighted how the structure of video containers can be extremely effective in determining the social network of provenance. However, current studies do not consider that a malicious user could easily launder the traces of the social network by rebuilding the container without transcoding. In this letter, we propose a method to identify a video’s originating social network, even when the video container structure is completely unreliable. The proposed method exploits the statistics of DCT coefficients to characterize the different social media encoding properties. With this work, we also built and made available over 1000 videos of different provenance (native, manipulated, exchanged through social networks) to aid the forensic community further researching this topic.
MobileAR-GAN: MobileNet-Based Efficient Attentive Recurrent Generative Adversarial Network for Infrared-to-Visual Transformations

Deep learning has recently shown outstanding performance for different applications, including image-to-image translation by generative adversarial networks (GANs). However, GAN models are very complex as build with multiple deep networks and require huge computational resources for the training and inference. Hence, the real-time deployment of GAN models is not feasible at present. In this article, we propose MobileNet-based efficient attentive recurrent GAN (MobileAR-GAN) for resource-constrained infrared-to-visual translation. The proposed model utilizes the lightweight MobileNet and enhances its capacity using the attention and recurrent modules, leading to an efficient yet effective model. We consider the infrared to visible image translation task to validate the efficiency and performance of the proposed model. The proposed MobileAR-GAN outperforms most of the existing GAN models in terms of both the efficiency and the quality of the generated images. We also test the MobileAR-GAN model over the resource-limited Jetson TX2 board with very compelling results. The proposed model shows promising results over state-of-the-art methods. Compared to lightweight models, such as Pix2pix and GAN-Compression methods, an average improvement gain of 19.19% and 17.05% is observed by the proposed model in terms of SSIM metric. It is observed that the proposed model can be deployed on edge devices to transform the images taken at night time using an infrared camera to the corresponding visible images with satisfactory performance.
Safe and Robust Motion Planning for Dynamic Robotics via Control Barrier Functions

Control Barrier Functions (CBF) are widely used to enforce the safety-critical constraints on nonlinear systems. Recently, these functions are being incorporated into a path planning framework to design safety-critical path planners. However, these methods fall short of providing a realistic path considering both the algorithm’s run-time complexity and enforcement of the safety-critical constraints. This paper proposes a novel motion planning approach using the well-known Rapidly Exploring Random Trees (RRT) algorithm that enforces both CBF and the robot Kinodynamic constraints to generate a safety-critical path. The proposed algorithm also outputs the corresponding control signals that resulted in the obstacle-free path. The approach also allows considering model uncertainties by incorporating the robust CBF constraints into the proposed framework. Thus, the resulting path is free of any obstacles and accounts for the model uncertainty from robot dynamics and perception. Result analysis indicates that the proposed method outperforms various conventional RRT-based path planners, guaranteeing a safety-critical path with minimal computational overhead. We present numerical validation of the algorithm on the Hamster V7 robot car, a micro autonomous Unmanned Ground Vehicle that performs dynamic navigation on an obstacle-ridden path with various uncertainties in perception noises and robot dynamics.
Accurate thermal prediction model for building-integrated photovoltaics systems using guided artificial intelligence algorithms

Progress in development of building-integrated photovoltaic systems is still hindered by the complexity of the physics and materials properties of the photovoltaic (PV) modules and its effect on the thermal behavior of the building. This affects not only the energy generation, as its active function and linked to economic feasibility, but also the thermal insulation of the building as part of the structure’s skin. Traditional modeling methods currently presents limitations, including the fact that they do not account for material thermal inertia and that the proposed semi-empirical coefficients do not define all types of technologies, mounting configuration, or climatic conditions. This article presents an artificial intelligence-based approach for predicting the temperature of a poly-crystalline silicon PV module based on local outdoor weather conditions (ambient temperature, solar irradiation, relative outdoor humidity and wind speed) and indoor comfort parameters (indoor temperature and indoor relative humidity) as inputs. A combination of two algorithms (Grammatical Evolution and Differential Evolution) guides to the creation of a customized expression based on the Sandia model. Different data-sets for a fully integrated PV system were tested to demonstrate its performance on three different types of days: sunny, cloudy and diffuse, showing relative errors of less than 4% in all cases and including night time. In comparison to Sandia model, this method reduces the error by up to 11% in conditions of variability of sky over short time intervals (cloudy days).
D-PPSOK clustering algorithm with data sampling for clustering big data analysis

Clustering is an essential data mining and tool for investigating big data. There are difficulties in applying clustering techniques to big data due to new drawbacks which are elevated with big data. As Big Data is referring to terabytes and petabytes of data and basically the clustering algorithms use great computational costs, here have to consider that the question is how to cope with this problem and how to deploy clustering methods to big data and acquire the outcomes in a reasonable time. Clustering is an essential analysis area in the data processing. In several decades, k-means lingers the most popular clustering algorithm because of its simplicity. Recently, as data volume continues to raise called large data (Big Data); many researchers address various clustering algorithms for big data to get high performance. This chapter proposes the distributed-parallel particle swarm optimization with k-means (D-PPSOK) clustering algorithm with data sampling on large data sets for getting the clusters with less computational steps. According to the experimental results, the proposed D-PPSOK with data sampling on large-scale data set gives high performance compared to the existing algorithms.
Multi-Oriented Rotation-Equivariant Network for Object Detection on Remote Sensing Images

Object detection has attracted a lot of attention in the field of image automatic interpretation. Detectors based on convolution neural networks (CNNs) applied in natural scene images encode detection results with horizontal bounding boxes (HBBs), which can not accurately calibrate the position and shape of the arbitrary-orientation objects on remote sensing images (RSIs). To solve these issues, we propose an object detection framework named multi-oriented rotation-equivariant network (MORE-Net) in this letter. The MORE-Net consists of a multi-oriented rotating filter (MORF) and a multi-oriented ground objects detector. The MORF generates rotation-equivariant features by rotating canonical filter according to the predefined discrete directions. Equipped with MORF, the multi-oriented ground objects detector extracts rotation-invariant semantic representations for each decoded rotated region of interest (RRoI). We also propose area smooth (AS) L1 loss to impose tighter shape and position constraints to the RROIs. Extensive experiments and comprehensive evaluations on the large-scale DOTA dataset demonstrate the effectiveness of the proposed framework, which achieves a mean average precision (mAP) value of 81.27 on the DOTA-v1.0 dataset and a mAP value of 78.03 on the DOTA-v1.5 dataset.
Towards Sustainable ICTD in Bangladesh: Understanding the Program and Policy Landscape and Its Implications for CSCW and HCI

Historically, Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction for Development (HCI4D) researchers in the Global South have advocated for a community-based approach to technology design and development. However, even with this "bottom-up" emphasis, the sustainability and scalability of the resulting innovations remain major challenges, and are poorly understood. To address this gap, we take the case of Bangladesh as a typical Global South context in which development work is carried out by a complex intertwined network of stakeholders across governments, NGOs, donors, and industries. To better understand the current development landscape and its priorities for digital technologies, we conducted interviews with 14 influential decision-makers in Bangladesh who play significant roles in the development of nutrition strategies. Our findings highlight a disconnect between the Bangladesh government's "digital mandate" and the reality of digital innovation practice within the nutrition development sector. Our paper contributes to the debate on factors that affect decision-making processes. We explore the dynamics of diverse actors and institutions who are intended to participate in, but can act as obstacles to sustained bottom-up innovations. Our findings expand understanding of institutional priorities, the dynamics of intermediaries, techno-solutionism, postcolonialism, bureaucracy, competition, and other important topics in CSCW scholarship. We suggest understanding the factors that guide the decision-making process of digital innovation practices in terms of four dimensions: internal, external, vertical, and horizontal. Consequently, we recommend CSCW and HCI researchers become mediators to connect decision-makers and communities and bring their voices in ICT innovations for global development. Finally, we offer recommendations for proactive engagement with decision-making stakeholders, enabling researchers to design community-centered sustainable digital innovations for development.
An Indirect Rate-Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation

A new source model, which consists of an intrinsic state part and an extrinsic observation part, is proposed and its information-theoretic characterization, namely its rate-distortion function, is defined and analyzed. Such a source model is motivated by the recent surge of interest in the semantic aspect of information: the intrinsic state corresponds to the semantic feature of the source, which in general is not observable but can only be inferred from the extrinsic observation. There are two distortion measures, one between the intrinsic state and its reproduction, and the other between the extrinsic observation and its reproduction. Under a given code rate, the tradeoff between these two distortion measures is characterized by the rate-distortion function, which is solved via the indirect rate-distortion theory and is termed the semantic rate-distortion function of the source. As an application of the general model and its analysis, the case of Gaussian extrinsic observation is studied, assuming a linear relationship between the intrinsic state and the extrinsic observation, under a quadratic distortion structure. The semantic rate-distortion function is shown to be the solution of a convex programming problem with respect to an error covariance matrix, and a reverse water-filling type of solution is provided when the model further satisfies a diagonalizability condition.
Contribution- and Participation-Based Federated Learning on Non-IID Data

The learning process takes place inside clients in federated learning (FL). How to effectively motivate clients and avoid the impact of statistical heterogeneity are challenges in FL. This article proposes contribution- and participation-based federated learning (CPFL) to address these challenges. CPFL can effectively allocate client incentives and aggregate models according to client contribution ratios, by which it can reduce the impact of heterogeneous data. To get effective and approximately fair client contributions faster, we propose an extended Raiffa solution (ERS). Compared to the conventional solution Shapley value, the time complexity of ERS goes from $\mathscr {O}(2^{n})$O(2n) down to $\mathscr {O}(n)$O(n). We perform extensive experiments with the MNIST/EMNIST datasets, heterogeneous datasets, and with different ratios of participation reward. Experimental results demonstrate that CPFL generally has a better learning effect in the heterogeneous case.