Reviewerly/exHarmony huggingface dataset:
Transparent Value Alignment

As robots become increasingly prevalent in our communities, aligning the values motivating their behavior with human values is critical. However, it is often difficult or impossible for humans, both expert and non-expert, to enumerate values comprehensively, accurately, and in forms that are readily usable for robot planning. Misspecification can lead to undesired, inefficient, or even dangerous behavior. In the value alignment problem, humans and robots work together to optimize human objectives, which are often represented as reward functions and which the robot can infer by observing human actions. In existing alignment approaches, no explicit feedback about this inference process is provided to the human. In this paper, we introduce an exploratory framework to address this problem, which we call Transparent Value Alignment (TVA). TVA suggests that techniques from explainable AI (XAI) be explicitly applied to provide humans with information about the robot's beliefs throughout learning, enabling efficient and effective human feedback.
A modified GNN architecture with enhanced aggregator and Message Passing Functions

Graph neural networks (GNN) uphold the essence of irregularly structured information embedded in a graph via message passing among the nodes and aggregating the node features at various levels of the graph. In the past, researchers have extensively used the GNN models for several semi-supervised node classification tasks. Existing GNN models do not use nodes’ information sufficiently. The use of inter-node feature-level correlational information with the existing GNN models might lead to more powerful learning models. Here, a weighting scheme has been developed for message passing and aggregation functions. This model has been named “Vector GNN”, or in short, “VecGNN”, due to its relationship with vector space. VecGNN takes into consideration the relative position of a node with respect to its neighboring nodes in the feature space, which influences the weight of features passed to the information aggregation phase. These weights are assigned using two different statistical measures: Jaccard’s coefficient and Cosine similarity. The proposed weighting scheme uses a generalized approach that can be easily incorporated into several GNN frameworks. VecGNN is evaluated using three citation datasets: Citeseer, Pubmed, and Cora. On these datasets, three sets of experiments have been conducted with varying numbers of training and testing nodes. We have used training, validation, and test set nodes with ratios of 1:1:8, 2:1:7, and 3:1:6. Experimenting on these, we observe an improvement of 2%–4% over the baseline models: Graph Convolution Network (GCN), Graph Attention Network (GAT), and Jumping Knowledge Networks (JKNets). The source code is available at the link https://github.com/sourodeeproy/VecGNN.
Explanation leaks: Explanation-guided model extraction attacks

Explainable artificial intelligence (XAI) is gradually becoming a key component of many artificial intelligence systems. However, such pursuit of transparency may bring potential privacy threats to the model confidentially, as the adversary may obtain more critical information about the model. In this paper, we systematically study how model decision explanations impact model extraction attacks, which aim at stealing the functionalities of a black-box model. Based on the threat models we formulated, an XAI-aware model extraction attack (XaMEA), a novel attack framework that exploits spatial knowledge from decision explanations is proposed. XaMEA is designed to be model-agnostic: it achieves considerable extraction fidelity on arbitrary machine learning (ML) models. Moreover, we proved that this attack is inexorable, even if the target model does not proactively provide model explanations. Various empirical results have also verified the effectiveness of XaMEA and disclosed privacy leakages caused by decision explanations. We hope this work would highlight the need for techniques that better trade off the transparency and privacy of ML models.
Multi-ant colony optimization algorithm based on finite history archiving and boxed pigs game

The ant colony algorithm has good performance in solving small-scale discrete problems, but still has the shortcomings of poor convergence and tendency to fall into local optimum when dealing with large-scale data. In order to solve these problems, we propose a multi-ant colony algorithm based on finite history archiving and the boxed pigs game model. Firstly, the heterogeneous multi-ant colony system is constructed by ant colony system and max–min ant colony system to enrich the diversity of populations. Second, the limited history archiving strategy is used to enhance the path guidance to enable faster convergence. In addition, the co-evolution of populations is achieved by constructing the boxed pigs game model to increase the performance of the algorithm. Finally, the leveling mechanism is used to help the algorithm escape from the local optimum when it stagnates, thereby improving the accuracy of the solution. The effectiveness of the algorithm is verified by testing benchmark instances of traveling salesman problem. The experimental results show that the improved algorithm has significantly improved in terms of convergence and solution accuracy.
The Matrix: connecting and re-using digital records and archives of archaeological investigations

Stratigraphic data and relationships form the backbone of all the related archaeological records from each excavated site and are essential for integrated analysis, wider synthesis and accessible archiving of the growing body of archaeological data and reports generated through the commercial archaeological sector in the UK and internationally. The stratigraphic record, usually in the form of a stratigraphic matrix, with associated relationships and data, acts as a primary, if not the primary 'evidence' for how, and in what order, the site was excavated. As such the stratigraphic matrix can be the key mechanism that enables anyone less familiar with the site, to re-visit and re-use the excavation records, understand what data is most relevant for addressing certain research questions, or problems encountered, and piece together the underlying details of how the excavator(s) arrived at their interpretations. However such records are often only held on paper or as scanned image copies (as PDFs) of matrix diagrams that cannot easily be re-used with all the associated data. This article presents outcomes from The Matrix project (AHRC AH/T002093/1) that address the current problems caused by the lack of standardized approaches to digital archiving of archaeological data using the case study of stratigraphic and phasing data.
Industrial applications of software defect prediction using machine learning: A business-driven systematic literature review

Machine learning software defect prediction is a promising field of software engineering, attracting a great deal of attention from the research community; however, its industry application tents to lag behind academic achievements. This study is part of a larger project focused on improving the quality and minimising the cost of software testing of the 5G system at Nokia, and aims to evaluate the business applicability of machine learning software defect prediction and gather lessons learnt. The systematic literature review was conducted on journal and conference papers published between 2015 and 2022 in popular online databases (ACM, IEEE, Springer, Scopus, Science Direct, and Google Scholar). A quasi-gold standard procedure was used to validate the search, and SEGRESS guidelines were used for transparency, reporting, and replicability. We have selected and analysed 32 publications out of 397 found by our automatic search (and seven by snowballing). We have identified highly relevant evidence of methods, features, frameworks, and datasets used. However, we found a minimal emphasis on practical lessons learnt and cost consciousness — both vital from a business perspective. Even though the number of machine learning software defect prediction studies validated in the industry is increasing (and we were able to identify several excellent papers on studies performed in vivo), there is still not enough practical focus on the business aspects of the effort that would help bridge the gap between the needs of the industry and academic research.
Environment-Aware Rendering and Interaction in Web-Based Augmented Reality

This work presents a novel framework for web-based environment-aware rendering and interaction in augmented reality based on WebXR and three.js. It aims at accelerating the development of device-agnostic Augmented Reality (AR) applications. The solution allows for a realistic rendering of 3D elements, handles geometry occlusion, casts shadows of virtual objects onto real surfaces, and provides physics interaction with real-world objects. Unlike most existing state-of-the-art systems that are built to run on a specific hardware configuration, the proposed solution targets the web environment and is designed to work on a vast range of devices and configurations. Our solution can use monocular camera setups with depth data estimated by deep neural networks or, when available, use higher-quality depth sensors (e.g., LIDAR, structured light) that provide a more accurate perception of the environment. To ensure consistency in the rendering of the virtual scene a physically based rendering pipeline is used, in which physically correct attributes are associated with each 3D object, which, combined with lighting information captured by the device, enables the rendering of AR content matching the environment illumination. All these concepts are integrated and optimized into a pipeline capable of providing a fluid user experience even on middle-range devices. The solution is distributed as an open-source library that can be integrated into existing and new web-based AR projects. The proposed framework was evaluated and compared in terms of performance and visual features with two state-of-the-art alternatives.
Crossroads: Collaboration at the Intersection of Pandemic and Post-Pandemic Times

Discussions on the potential for technology to disrupt education have appeared at regular intervals for many years [...]
ARTD-Net: Anchor-Free Based Recyclable Trash Detection Net Using Edgeless Module

Due to the sharp increase in household waste, its separate collection is essential in order to reduce the huge amount of household waste, since it is difficult to recycle trash without separate collection. However, since it is costly and time-consuming to separate trash manually, it is crucial to develop an automatic system for separate collection using deep learning and computer vision. In this paper, we propose two Anchor-free-based Recyclable Trash Detection Networks (ARTD-Net) which can recognize overlapped multiple wastes of different types efficiently by using edgeless modules: ARTD-Net1 and ARTD-Net2. The former is an anchor-free based one-stage deep learning model which consists of three modules: centralized feature extraction, multiscale feature extraction and prediction. The centralized feature extraction module in backbone architecture focuses on extracting features around the center of the input image to improve detection accuracy. The multiscale feature extraction module provides feature maps of different scales through bottom-up and top-down pathways. The prediction module improves classification accuracy of multiple objects based on edge weights adjustments for each instance. The latter is an anchor-free based multi-stage deep learning model which can efficiently finds each of waste regions by additionally exploiting region proposal network and RoIAlign. It sequentially performs classification and regression to improve accuracy. Therefore, ARTD-Net2 is more accurate than ARTD-Net1, while ARTD-Net1 is faster than ARTD-Net2. We shall show that our proposed ARTD-Net1 and ARTD-Net2 methods achieve competitive performance in mean average precision and F1 score compared to other deep learning models. The existing datasets have several problems that do not deal with the important class of wastes produced commonly in the real world, and they also do not consider the complex arrangement of multiple wastes with different types. Moreover, most of the existing datasets have an insufficient number of images with low resolution. We shall present a new recyclables dataset which is composed of a large number of high-resolution waste images with additional essential classes. We shall show that waste detection performance is improved by providing various images with the complex arrangement of overlapped multiple wastes with different types.
Multi-Stage Network for Event-Based Video Deblurring with Residual Hint Attention

Video deblurring aims at removing the motion blur caused by the movement of objects or camera shake. Traditional video deblurring methods have mainly focused on frame-based deblurring, which takes only blurry frames as the input to produce sharp frames. However, frame-based deblurring has shown poor picture quality in challenging cases of video restoration where severely blurred frames are provided as the input. To overcome this issue, recent studies have begun to explore the event-based approach, which uses the event sequence captured by an event camera for motion deblurring. Event cameras have several advantages compared to conventional frame cameras. Among these advantages, event cameras have a low latency in imaging data acquisition (0.001 ms for event cameras vs. 10 ms for frame cameras). Hence, event data can be acquired at a high acquisition rate (up to one microsecond). This means that the event sequence contains more accurate motion information than video frames. Additionally, event data can be acquired with less motion blur. Due to these advantages, the use of event data is highly beneficial for achieving improvements in the quality of deblurred frames. Accordingly, the results of event-based video deblurring are superior to those of frame-based deblurring methods, even for severely blurred video frames. However, the direct use of event data can often generate visual artifacts in the final output frame (e.g., image noise and incorrect textures), because event data intrinsically contain insufficient textures and event noise. To tackle this issue in event-based deblurring, we propose a two-stage coarse-refinement network by adding a frame-based refinement stage that utilizes all the available frames with more abundant textures to further improve the picture quality of the first-stage coarse output. Specifically, a coarse intermediate frame is estimated by performing event-based video deblurring in the first-stage network. A residual hint attention (RHA) module is also proposed to extract useful attention information from the coarse output and all the available frames. This module connects the first and second stages and effectively guides the frame-based refinement of the coarse output. The final deblurred frame is then obtained by refining the coarse output using the residual hint attention and all the available frame information in the second-stage network. We validated the deblurring performance of the proposed network on the GoPro synthetic dataset (33 videos and 4702 frames) and the HQF real dataset (11 videos and 2212 frames). Compared to the state-of-the-art method (D2Net), we achieved a performance improvement of 1 dB in PSNR and 0.05 in SSIM on the GoPro dataset, and an improvement of 1.7 dB in PSNR and 0.03 in SSIM on the HQF dataset.
FOREIGN DIRECT INVESTMENT, INFORMATION TECHNOLOGY, AND TOTAL FACTOR PRODUCTIVITY DYNAMICS IN SUB-SAHARAN AFRICA

The potential for information technology penetration in sub-Saharan Africa (SSA) is very high compared to other regions. Unfortunately, productivity levels in the area are also deficient. This study investigates the importance of information technology in influencing the effect of foreign direct investment (FDI) on total factor productivity (TFP) dynamics. The focus is on 25 countries in SSA. Information technology is measured with mobile phone and internet penetration, while the engaged TFP productivity dynamics are TFP, real TFP, welfare TFP, and real welfare TFP. The empirical evidence is based on the Generalized Method of Moments. The findings show that except regressions about real TFP growth for which the estimations do not pass post-estimation diagnostic tests, it is apparent that information technology modulates FDI to positively influence TFP dynamics (i.e., TFP, welfare TFP, and welfare real TFP). Policy and theoretical implications are discussed.
A Dynamic Multiple-Query RRT Planning Algorithm for Manipulator Obstacle Avoidance

Manipulator motion planning for real-time obstacle avoidance in a dynamic environment is explored in this article. To address obstacle avoidance problems, a multiple-query and sampling-based motion replanning algorithm with the dynamic bias-goal factor, rapidly exploring random tree (DBG-RRT), is proposed to achieve a rapid response and a high success rate. Differently from other studies on path planning, a relay-node method is adopted on the basis of motion planning to generate a new collision-free trajectory. Subsequently, an un-interrupt strategy is embraced to judge whether the generated trajectory would be interfered with by dynamic obstacles. In the end, the DBG-RRT algorithm is applied, and the results demonstrate its effectiveness for manipulator motion planning in a dynamic environment.
Research on Intelligent Perception Algorithm for Sensitive Information

In the big data era, a tremendous volume of electronic documents is transmitted via the network, many of which include sensitive information about the country and businesses. There is a pressing need to be able to perform intelligent sensing of sensitive information on these documents in order to be able to discover and guarantee the security of sensitive information in this enormous volume of documents. Although the low effectiveness of manual detection is resolved by the current method of handling sensitive information, there are still downsides, such as poor processing effects and slow speed. This study creatively proposes the Text Sensitive Information Intelligent Perception algorithm (TSIIP), which detects sensitive words at the word level and sensitive statements at the statement level to obtain the final assessment score of the text. We experimentally compare this algorithm with other methods on an existing dataset of sensitive Chinese information. We use the metrics measuring the accuracy of the binary classification model, where the F1 score reaches 0.938 (+0.6%), and the F2 score reaches 0.946 (+1%), and the experimental results fully demonstrate the superiority of this algorithm.
A Novel Joint Time-Frequency Spectrum Resources Sustainable Risk Prediction Algorithm Based on TFBRL Network for the Electromagnetic Environment

To protect the electromagnetic environment and understand its current state in a timely manner, monitoring the electromagnetic environment has great practical significance, while massive amounts of data are generated. It is crucial to utilize data mining technology to extract valuable information from these massive amounts of data for effective spectrum management. Traditional spectrum prediction methods do not integrate the prior information of spectrum resource occupancy, so that the prediction of the channel state of a single frequency point is of limited significance. To address these issues, the paper describes a dynamic threshold algorithm which mines bottom noise and spectrum resource occupancy from massive electromagnetic environment data. Moreover, the paper describes a joint time-frequency spectrum resource prediction algorithm based on the time-frequency block residual LSTM (TFBRL) network, which utilizes hourly time closeness, daily period, and annual trend as prior knowledge of spectrum resources. The TFBRL network comprises three main parts: (1) a residual convolution network with a squeeze-and-excitation (SE) attention mechanism, (2) a long short term memory (LSTM) model with memory ability to capture sequence latent information, and (3) a feature fusion module based on a matrix to combine time closeness, daily period, and annual trend feature components. Experimental results demonstrate that the TFBRL network outperforms the baseline networks, improving by 31.37%, 16.00% and 13.06% compared with the best baseline for MSE, RMSE and MAE, respectively. Thus, the TFBRL network has good risk prediction performance and lays the foundation for subsequent frequency scheduling.
A Comprehensive Methodology for the Statistical Characterization of Solar Irradiation: Application to the Case of Morocco

The prediction and characterization of solar irradiation relies mostly on either the use of complex models or on complicated mathematical techniques, such as artificial neural network (ANN)-based algorithms. This mathematical complexity might hamper their use by businesses and project developers when assessing the solar resource. In this study, a simple but comprehensive methodology for characterizing the solar resource for a project is presented. It is based on the determination of the best probability distribution function (PDF) of the solar irradiation for a specific location, assuming that the knowledge of statistical techniques may be more widely extended than other more complex mathematical methods. The presented methodology was tested on 23 cities across Morocco, given the high interest in solar investments in the country. As a result, a new database for solar irradiation values depending on historical data is provided for Morocco. The results show the great existing variety of PDFs for the solar irradiation data at the different months and cities, which demonstrates the need for undertaking a proper characterization of the irradiation when the assessment of solar energy projects is involved. When it is simply needed to embed the radiation uncertainty in the analysis, as is the case of the techno-economic valuation of solar energy assets, the presented methodology can reach this objective with much less complexity and less demanding input data. Moreover, its application is not limited to solar resource assessment, but can also be easily used in other fields, such as meteorology and climate change studies.
Mineral Resource Classification Using Geostatistical and Fractal Simulation in the Masjed Daghi Cu–Mo Porphyry Deposit, NW Iran

Mineral resource classification is an important step in mineral exploration and mining engineering. In this study, copper and molybdenum resources were classified using a combination of the Turning Bands Simulation (TBSIM) and the Concentration–Volume (C–V) fractal model based on the Conditional Coefficient of Variation (CCV) for Cu realizations in the Masjed Daghi porphyry deposit, NW Iran. In this research, 100 scenarios for the local variability of copper were correspondingly simulated using the TBSIM and the CCVs were calculated for each realization. Furthermore, various populations for these CCVs were distinguished using C–V fractal modeling. The C–V log–log plots indicate a multifractal nature that shows a ring structure for the “Measured”, “Indicated”, and “Inferred” classes in this deposit. Then, the results obtained using this hybrid method were compared with the CCV–Tonnage graphs. Finally, the results obtained using the geostatistical and fractal simulation showed that the marginal parts of this deposit constitute inferred resources and need more information from exploration boreholes.