Reviewerly/exHarmony huggingface dataset:
A Sampling Based Approach to Robust Planning for a Planetary Lander

Planning for autonomous operation in unknown environments poses a number of technical challenges. The agent must ensure robustness to unknown phenomena, un-predictable variation in execution, and uncertain resources, all while maximizing its objective. These challenges are ex-acerbated in the context of space missions where uncertainty is often higher, long communication delays necessitate robust autonomous execution, and severely constrained computational resources limit the scope of planning techniques that can be used. We examine this problem in the context of a Europa Lander concept mission where an autonomous lander must collect valuable data and communicate that data back to Earth. We model the problem as a hierarchical task network, framing it as a utility maximization problem constrained by a strictly monotonically decreasing energy resource. We propose a novel deterministic planning framework that uses periodic replanning and sampling-based optimization to better handle model uncertainty and execution variation, while remaining computationally tractable. We demonstrate the efficacy of our framework through simulations of a Europa Lander concept mission in which our approach outperforms several baselines in utility maximization and robustness.
A Secure Image Stegnography using LSB and XOR Operation

Since it serves as a substitute for secure transmission, data concealing has received a lot of attention recently. Several image embodiment approaches, like the Least Significant Bit (LSB) replacement, are used to embody a secret content with a picture. There would be difficult to assert the uniqueness and calibre of the content embodiment. In the suggested article, each of the red, green, and blue layer blocks of the image have been subjected to a scrambling process utilising the Sudoku scheme. To create the scrambling image, the image layers would be divided into blocks, and throughout the blocks, special scrambling techniques would be used. Then, using the conventional LSB substitution strategy and the Queen Tour traversing pattern from the chess game, the jumbled image was revealed to the confidential stream embodiment. Just after embedding procedure is complete, the scrambled image will be broken up into blocks, and the exact descrambling on the blocks will be done to recover the image that looks the most like the original image, known as the stego image. There at receiving side, the stego image is divided into blocks in order to apply precise scrambling to the corresponding blocks and recover the hidden stream that use the Queen tour pattern. Following the tests,the predicted strategy is determined to be less likely to be highly predictive and distinctive in its patterns.
Lightweight Self-Attention Network for Semantic Segmentation

The deep neural network model based on self-attention (SA) for obtaining rich contextual information has been widely adopted in semantic segmentation. However, the computational complexity of the standard self-attentive module is high, which partly limits the use of this module. In this work, we propose the lightweight self-attention network (LSANet) for semantic segmentation. Specifically, the Lightweight Self-Attentive Module (LSAM) captures information using a hand-designed compact feature representation, and weighted fusion of position information. In the decoder structure, an improved up-sampling module is proposed. Compared with the bilinear upsampling, this method achieves better results in restoring image details. The experimental results on PASCAL VOC 2012, and Cityscapes datasets show the effectiveness of our method, which simplifies operations and improves performance.
Multi-Scale Inter-Communication Spatio-Temporal Network for Video Compression Artifacts Reduction

Video compression artifacts are widespread in online videos, which greatly affects the quality of the videos. In recent years, the deep learning-based methods for video compression artifacts reduction have made impressive achievements. However, most of existing methods ignore the rich multi-scale information contained in video frames. In this brief, we propose a new multi-scale inter-communication spatio-temporal network (MSICSTN) for video compression artifacts reduction by fully exploring contextual multi-scale information. MSICSTN is constructed on the basis of the multi-scale feature inter-communication module (MSFICM) and the source feature selection enhancement module (SFSEM). Specifically, the MSFICM adaptively generates respective attention weights using channel statistical information after the inter-communication of different scale features, and recalibrates the input multi-scale features to enhance the multi-scale features. The SFSEM utilizes the statistical information of the shallow features of the target frame to fuse the shallow features with the deep features for better dense pixel predictions. The experimental results on test videos demonstrate that our method outperforms the existing methods.
Magic Mirror on the Wall: Reflecting the Realities of Lower Limb Rehabilitation in Virtual Reality

Virtual reality (VR) could increase access, improve effectiveness, and reduce costs for at-home physical rehabilitation. However, there are open questions regarding the design, development, and usability of VR applications, particularly for lower limb rehabilitation protocols. This work addresses data capture and usability questions by developing and testing a low-cost lower limb rehabilitation system in VR. Our prototype demonstrates a viable design strategy for creating an exercise paradigm capable of tracking movement input from standing, seated, or supine positions. With the prototype application, we advance the following goals: (1) Achieve accurate data capture of user performance during lower limb rehabilitation in VR, and (2) Deliver relevant performance feedback to users in real-time. We tested our prototype's strengths and limitations using Unity3D and HTC Vive VR hardware with six participants.
Trade-Off Analysis of Pruning Methods for Compact Neural Networks on Embedded Devices

Pruning of neural networks is a technique often used to reduce the size of a machine learning model, as well as to reduce the computation cost for model inference. This research provides an analysis on four current pruning techniques that theoretically efficiently reduce the machine learning model size, where efficiency is defined by the relation between the compression of the model and the accuracy of the model. Furthermore, this research will assess in what way these four neural network pruning techniques affect the total energy consumption during model inference on a Raspberry Pi 4B board, applied to MobileNetV2, a machine learning model architecture optimized for image classification on embedded devices. Lastly, the research will analyze the trade-offs between energy consumption, model size and model accuracy for each of the assessed pruning algorithms applied to one of the most commonly used neural network architectures, MobileNetV2, on a Raspberry Pi 4B prototyping board. The research is expected to provide engineers a reference providing guidance upon deciding what pruning technique to use for a machine learning model to be deployed on an embedded device.
Research Productivity in Economics and Business Disciplines in Emerging Economies: Insights from Kazakhstan

The research productivity of a country is positively associated with the development of its scientific capacity, which, in turn, contributes to the nation’s economic growth. In this paper, we explore the research productivity of Kazakhstan in economics and business disciplines and reveal its specific characteristics. Our methodology followed two steps. First, applying scientometric (descriptive statistics and network analysis) methods, we analyzed 3225 articles from Kazakhstan and published in international journals during 1991–2020. We focus on three subject areas in Scopus: Business, management, and accounting; Decision sciences; and Economics, econometrics, and finance. Second, we conduct comprehensive interviews with four experts representing the science sector and government. The study found that research in economics and business disciplines was growing, emphasizing agriculture, agro-industrial complex, cluster development, entrepreneurship, innovative development, investments, and sustainable development areas. The interviews reveal the issues of doing research in higher education institutions (from the bottom-up) and improving the government’s science sector (from the top-down). Among others, these challenges are related to developing critical research areas, science funding, and their impact on economic growth. Our findings may serve as exemplary for other emerging economies that face similar challenges in developing their global research profile.
From Attack to Identification: MEMS Sensor Fingerprinting Using Acoustic Signals

A device pretending to be behaving normally can carry out malicious acts that result in the leakage of sensitive information, invasion of privacy, damage property, or even loss of life. The first step to detect these malicious acts is to identify whether a device is authorized or not. There are existing methods for identifying devices based on cryptographic schemes or physical unclonable function (PUF), but each of these methods has disadvantages in that key management costs are incurred or additional chips or circuits are required. In this article, we propose a device identification method that overcomes these shortcomings. Based on the fact that MEMS sensors are built-in to most devices due to their various applications and the property that MEMS sensors are sensitive to acoustic signals with resonant frequencies, the proposed method uses MEMS sensor readings to acoustic signals. Many attack methods have been studied using this property to disable the normal function of the sensor or eavesdrop on speech around a device. On the other hand, our method is the first to fingerprint MEMS sensors using raw sensor readings to acoustic signals from a security point of view rather than from this conventional attack point of view. Furthermore, our method is based on a challenge-response structure to be secure from replay attacks; here, a challenge is a random acoustic signal, and a response is the unique MEMS sensor reading to the challenge. In our evaluation, we use commercial MEMS sensors and a low-cost speaker that costs less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula> 1. The results show that under various conditions that may affect MEMS sensor readings, the macro F1-scores when identifying authorized devices is 1.0, and the macro accuracy of detecting attacks using devices other than authorized ones is 0.994. As a result, our method can identify devices well at a low cost.
Dirichlet Mixture of Gaussian Processes with Split-kernel: An Application to Predicting Disease Course in Multiple Sclerosis Patients

In many machine learning applications, data are collected from multiple sources and thus may suffer from id-iosyncratic biases. A typical approach to modeling such a dataset is via multi-task learning in which a group of tasks are trained simultaneously by exploiting their similarities. However, because different types of bias are often reflected in disjoint subsets of features and may not conform to the same parametric form, traditional multi-task learning algorithms are not sufficient to capture the individualized impact of these subsets. In this paper, we develop a Dirichlet Mixture of Gaussian Processes with Split-kernel (S-DPM) to address this challenge. We establish a mixture model in which each component consists of instances with similar bias characteristics. The prediction task for each component is modeled by a Gaussian process whose kernel is derived from two sub-kernels operating on separate feature spaces. The number of mixing components is inferred automatically by invoking Dirichlet process based clustering on the data. We apply our model to a clinical dataset to predict disease course in Multiple Sclerosis patients and demonstrate its efficacy by comparing it to standard Dirichlet mixture model (DPM) and the non-mixture single Gaussian process model.
Agreement: An Argumentation Perspective

The Chapter offers an account of how agreement is dealt with in the pragma-dialectical approach and in the Argumentum Model of Topics. The account sets off with a discussion of the opening stage of the critical discussion model, as the one most inherently related to agreement. It is the stage dedicated to establishing starting points, i.e. facts and values that are, or are cast as, objects of agreement which are exploited argumentatively. In real-discourse counterparts of the critical discussion model, the opening stage often remains implicit, because premises are simply entailed, as is typically the case in enthymemes. Such implicitness may be exploited by the protagonist to leave controversial starting points out of focus, surreptitiously treating them as agreed facts or opinions. From the opening stage the discussion passes to argument schemes, with the related notion of topoi, as the inferential patterns whereby the agreement elicited or tacitly assumed on the premises should be transferred to the conclusion. It is in schemes/topoi that starting points, whether preliminarily established or not, are brought to bear in the defense of standpoints/conclusions. It is argued that reconstructing argumentation through formal models favors the identification of those nodes of discourse where efforts to secure agreement concentrate.
Augmentation Based Synthetic Sampling and Ensemble Techniques for Imbalanced Data Classification

The imbalance data problem appears in data mining fields and has recently attracted the attention of researchers. In order to solve this problem, scholars proposed various approaches such as undersampling majority class, oversampling minority class, synthetic Minority Oversampling (SMOTE) technique, Proximity Weighted Random Affine Shadowsampling (ProWRAS), etc. However, this work proposes a new method called Augmentation Based Synthetic Sampling (ABS) for imbalanced data classification that concatenates data to predict features with imbalance problems. The proposed study integrates sampling and concatenated features to generate synthetic data. This study shows the ability of the proposed method and the average of the AUC (area under the curve) to generate good data samples while experimenting compared to the previous study. In addition, this study merged the proposed method with the boosting to create a technique known as ABSBoost. Therefore, the experimental outcomes show that the proposed ABS method and ABSBoost are effective on the given datasets.
An Efficient and Distributed Data Storage and Sharing Method Based on Blockchain

With the development of intelligent medical, electronic medical records (EMR) promote the information management of Internet of Medical Things (IoMT) data. As the need for sharing IoMT data between organizations grows, EMR faces challenges in terms of reliability of the third-party storage, availability of cross-domain authentication, and efficiency of access control in mobile network. In this paper, we propose a blockchain-based medical data sharing model to guarantee the security and efficiency of cross-domain sharing of medical privacy data. Firstly, heterogeneous data of IoMT are classified, and the lightweight data are mixed encrypted and stored in the blockchain full nodes. Then considering the availability and scalability, we introduce the light nodes deployed on mobile edge computing (MEC) platforms to provide distributed access control proxy service. In the CP-ABE access control algorithm, we decouple the relationship between attributes and decryption time, and further design a token mechanism in improved CP-ABE algorithm. Finally, security analysis proves that our model performs well in terms of privacy. Simulation results show that the encryption and decryption performance and key storage space of our CP-ABE access control algorithm are better than traditional algorithms.
A Collaborative Approach to Mobile Crowdsourcing Based on Data Stream Learning

Mobile crowdsourcing refers to systems where task completion necessarily involves physical movement of crowd workers. Crowdsourced parcel delivery, also called crowdshipping, is a particularly relevant example to this respect. Evidence exists that in such systems tasks are frequently abandoned, indicating that crowd workers accept tasks that they misjudge and later prefer to discontinue. In this paper we evaluate as to how far on-the-fly task transfers between crowdworkers can alleviate this problem in a cooperative setting. Its contribution to this respect is twofold. Firstly, it analyses different data stream learning approaches for service quality prediction in mobile crowdshipping. Secondly, it embeds this prediction model into a collaborative agent-based crowdshipping approach where task transfer decisions are taken in a peer-to-peer fashion with limited overhead.
GCNTrain: A Unified and Efficient Accelerator for Graph Convolutional Neural Network Training

Graph convolutional neural networks (GCNs) have been emerging as a promising category of neural network models for extending deep learning to graph data analytics. Serving as a type of semi-supervised models, GCNs need training before being used to extract any input graph’s features. The challenge is that the existing GCN accelerators often target the sparse-dense matrix multiplications (SpDM) in GCN inference while ignoring the compute-intensive GCN training. Obviously, this poses momentous performance demands and design challenges.In this paper, we categorize the computations of GCN training into sparse-sparse matrix multiplications (SpSpM) and sparse-dense matrix multiplications (SpDM); and then introduce the GCNTrain-v1 architecture that uniformly performs both SpSpM and SpDM by the column-wise-product-based method. To ad-dress the bank conflict problem in the GCNTrain-v1 architecture, we further propose the GCNTrain-v2 architecture with the conflict-free bank access strategy. This strategy is able to coalesce all requests to one bank by broadcasting elements. Moreover, to alleviate the workload imbalance problem in the GCNTrain-v2 architecture, we offer the GCNTrain-v3 architecture with the offline reshuffle technique that offline reshuffles and balances the non-zero elements in the matrix before GCN training. Overall, the GCNTrain-v3 architecture implements both SpSpM and SpDM for accelerating GCN training without bank conflict and work-load imbalance problems. On five graph datasets, experiment results demonstrate considerable performance speedups over CPU (80.47×), GPU (10.88×), and GCNAX (1.65×).
High Capacity Reversible Data Hiding Scheme based on Interpolation and Tetris Matrix

Interpolation technology has evolved into a powerful tool for reversible data hiding in the image processing field. However, existing interpolated algorithms only have a trivial impact on image quality. In this paper, an innovative interpolation and matrix-based algorithm is proposed. A novel concept of the difference between interpolated pixels is represented to dramatically improve the visual quality of the image, which lays a solid foundation for the subsequent data hiding stage. There is growing evidence that the Tetris matrix plays a vital role in improving embedding capacity. It is worth mentioning that our scheme can intensely resist different attacks of various degrees. Numerous experimental findings demonstrate that the effect of our proposed scheme is unprecedentedly perfect even though a higher capacity is embedded than with traditional steganography approaches.
Self-Challenging Mask for Cross-Domain Few-Shot Classification

Few-shot classification (FSC) aims to recognize novel classes with few labeled samples in each class. Currently, meta-learning methods have achieved great success in few-shot classification tasks. However, most methods assume that base classes and novel classes share a single domain, and their performance can be greatly reduced when comes to domain-shift problem. To further improve the generalization of the existing FSC models, we propose a novel Self-Challenging Module including the Self-Challenging Mask and random noise. Self-Challenging Mask exploits the relationship between mid-level feature maps and high-level feature vectors to challenge (greatly weaken) the dominant mid-level local descriptors which are extracted in focus. Therefore, the model is forced to discover the residual information that correlates with the classification task from the remaining mid-level local descriptors. This method appears to expand the feature distribution for generalizing on unseen domains. We combine our method with three existing metric-based FSC model and conduct a large number of classification experiments in five datasets under the setting of cross-domain few-shot classification. The result shows that our Self-Challenging Module can significantly improve the classification accuracy in both seen and unseen domains.
Block-structured echo state network based on error reduction mechanism

The echo state network (ESN) is a special recurrent neural network, which is a powerful method of time series prediction. However, the traditional ESN with single reservoir cannot fully mine the feature information of complicated time series. In this article, a block-structured echo state network (BESN) with cascaded modules is proposed to solve this problem based on error reduction mechanism. In BESN, the external inputs and the outputs of the previous module form the inputs of the next adjacent module, and the prediction errors of the previous module are defined as the target outputs of the next module. Meanwhile, the number of modules is determined by a self-organizing method for BESN. Finally, the performance of BESN is tested on two benchmarks.
An Efficient Visual Place Recognition System by Predicting Unique Features

Visual place recognition (VPR) is the challenge of determining whether a sensor is visiting a previously recorded location or exploring new locations using visual inputs. VPR approaches typically presume that the appearance remains the same as the moment the map (reference) was produced. This presents a significant challenge, since the premise of static appearance is invalid. Instead, the environment is constantly changing because of weather, time of day, building sites, the upgrading of facades and billboards, and so on. A prominent way to deal with the resilience of environmental change is to demand the selection of features from unique and non-unique objects. By doing this, a method can properly discriminate the image, but it is computationally expensive. In this paper, we seek to recognize a place efficiently by reducing the number of its features. In particular, we predict unique features and avoid using features from non-unique objects by taking advantage of geo-tags. Our method provides increased accuracy with lower computational costs compared with other state-of-the-art methods.
Geographic Analysis for Determining the Value of Different Photovoltaic Performance Factors

Geographic analysis of photovoltaic (PV) performance factors across large regions can help relevant stakeholders make informed, and reduced risk decisions. High temporal and spatial resolution meteorological data from the National Solar Radiation Database are used to investigate performance and cost as an effect of varying system characteristics such as the module temperature coefficients, mounting configurations and coatings. The results demonstrated the strong climatic dependence that these characteristics have on annual energy yield whereas the revenues were dominated by the electricity price.