Reviewerly/exHarmony huggingface dataset:
The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations

To effectively improve system autonomy, increase fault-tolerant resilience, solve low payload capacity and short endurance time of unmanned surface vehicles (USVs), there's a trend to deploy multiple USVs as a formation fleet. The formation path planning algorithms are essential to generate optimal trajectories and provide practical collision avoidance maneuvers to efficiently navigate the USV fleet. To ensure the optimality, rationality and path continuity of the formation trajectories, this paper presents a novel deterministic algorithm named multiple sub-target artificial potential field (MTAPF) based on an improved APF. The MTAPF belongs to the local path planning algorithm, which refers to the global optimal path generated by an improved heuristic A* algorithm. and the optimal path is divided by this algorithm into multiple sub-target points to form sub-target point sequence. The MTAPF can greatly reduce the probability that USVs will fall into the local minimum and help USVs to get out of the local minimum by switching target points. As an underactuated system, the USV is restricted by various motion constraints, and the MTAPF is presented to make the generated path compliant with USV's dynamics and orientation restrictions. The proposed algorithm is validated on simulations and proven to work effectively in different environments.
A Scalable Blockchain Framework for Secure Transactions in IoT

Internet of Things (IoT) and blockchain (BC) technologies have been dominating their respective research domains for some time. IoT offers automation at the finest level in different fields, while BC provides secure transaction processing for asset exchanges. The capability of IoT devices to generate transactions prompts their integration with BC as the next logical step. The biggest challenges in this integration are the scalability of ledger and rate of transaction execution in BC. On one hand, due to their large numbers, IoT devices will generate transactions at a rate which current block chain solutions cannot handle. On the other hand, implementing BC peers onto IoT devices is impossible due to resource constraints. This prohibits direct integration of both technologies in their current state. In this paper, we propose a solution to address these challenges by using a local peer network to bridge the gap. It restricts the number of transactions which enters the global BC by implementing a scalable local ledger, without compromising on the peer validation of transactions at local and global level. The testbed evaluations show significant reduction in the block weight and ledger size on global peers. The solution also indirectly improves the transaction processing rate of all peers due to load distribution.
Investigating the impact of cybersecurity policy awareness on employees’ cybersecurity behavior

As internet technology and mobile applications increase in volume and complexity, malicious cyber-attacks are evolving, and as a result society is facing greater security risks in cyberspace more than ever before. This study has extended the published literature on cybersecurity by theoretically defining the conceptual domains of employees’ security behavior, and developed and tested operational measures to advance information security behavior research in the workplace. A conceptual framework is proposed and tested using survey results from 579 business managers and professionals. Structural equation modeling and ANOVA procedures are employed to test the proposed hypotheses. The results show that when employees are aware of their company’s information security policy and procedures, they are more competent to manage cybersecurity tasks than those who are not aware of their companies’ cybersecurity policies. The study also indicates that an organizational information security environment positively influences employees’ threat appraisal and coping appraisal abilities, which in turn, positively contribute to their cybersecurity compliance behavior.
BSIS: Blockchain-Based Secure Incentive Scheme for Energy Delivery in Vehicular Energy Network

Vehicular energy network (VEN), as an important part of the Internet of Things for the smart city, can facilitate the renewable energy (RE) transportation over a large geographical area by means of electric vehicles (EVs) through wireless power transfer technology. However, due to the potential security vulnerability in VEN, EV users can be attacked by external or internal adversaries. In addition, owing to the selfishness of EVs, it is a great challenge to optimally schedule the charging/discharging behaviors of EVs to realize regional energy balance in VEN. To tackle the above issues, this paper proposes a blockchain-based secure incentive scheme for energy delivery in VEN. First, a novel permissioned energy blockchain system is introduced in VEN to implement secure energy delivery services for EVs and energy nodes through the use of distributed ledgers and cryptocurrency. Second, a proof of reputation consensus protocol is proposed to efficiently reach consensus in energy blockchain, where the reputation derivation is constructed based on the local trust computing and credibility computing. Third, motivated by the pricing mechanism, an incentive model is developed to stimulate EVs to cooperatively deliver RE to various areas with different electricity loads while maximizing EVs' utilities. Finally, extensive numerical results are provided, which demonstrate the efficiency of the proposed scheme through the comparison with conventional schemes.
AlignedReID++: Dynamically matching local information for person re-identification

Person re-identification (ReID) is a challenging problem, where global features of person images are not enough to solve unaligned image pairs. Many previous works used human pose information to acquire aligned local features to boost the performance. However, those methods need extra labeled data to train an available human pose estimation model. In this paper, we propose a novel method named Dynamically Matching Local Information (DMLI) that could dynamically align local information without requiring extra supervision. DMLI could achieve better performance, especially when encountering the human pose misalignment caused by inaccurate person detection boxes. Then, we propose a deep model name AlignedReID++ which is jointly learned with global features and local feature based on DMLI. AlignedReID++ improves the performance of global features, and could use DMLI to further increase accuracy in the inference phase. Experiments show effectiveness of our proposed method in comparison with several state-of-the-art person ReID approaches. Additionally, it achieves rank-1 accuracy of 92.8% on Market1501 and 86.2% on DukeMTMCReID with ResNet50. The code and models have been released2.
Make scientific data FAIR

All disciplines should follow the geosciences and demand best practice for publishing and sharing data, argue Shelley Stall and colleagues. All disciplines should follow the geosciences and demand best practice for publishing and sharing data, argue Shelley Stall and colleagues.
Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm

Since different features may require different costs, the cost-sensitive feature selection problem become more and more important in real-world applications. Generally, it includes two main conflicting objectives, i.e., maximizing the classification performance and minimizing the feature cost. However, most existing approaches treat this task as a single-objective optimization problem. To satisfy various requirements of decision-makers, this paper studies a multi-objective feature selection approach, called two-archive multi-objective artificial bee colony algorithm (TMABC-FS). Two new operators, i.e., convergence-guiding search for employed bees and diversity-guiding search for onlooker bees, are proposed for obtaining a group of non-dominated feature subsets with good distribution and convergence. And two archives, i.e., the leader archive and the external archive are employed to enhance the search capability of different kinds of bees. The proposed TMABC-FS is validated on several datasets from UCI, and is compared with two traditional algorithms and three multi-objective methods. Results have shown that TMABC-FS is an efficient and robust optimization method for solving cost-sensitive feature selection problems.
Spatiotemporal Anomaly Detection Using Deep Learning for Real-Time Video Surveillance

Rapid developments in urbanization and autonomous industrial environments have augmented and expedited the need for intelligent real-time video surveillance. Recent developments in artificial intelligence for anomaly detection in video surveillance only address some of the challenges, largely overlooking the evolving nature of anomalous behaviors over time. Tightly coupled dependence on a known normality training dataset and sparse evaluation based on reconstruction error are further limitations. In this article, we propose the incremental spatiotemporal learner (ISTL) to address challenges and limitations of anomaly detection and localization for real-time video surveillance. ISTL is an unsupervised deep-learning approach that utilizes active learning with fuzzy aggregation, to continuously update and distinguish between new anomalies and normality that evolve over time. ISTL is demonstrated and evaluated on accuracy, robustness, computational overhead as well as contextual indicators, using three benchmark datasets. Results of these experiments validate our contribution and confirm its suitability for real-time video surveillance.
Human Activity Recognition: A Survey

Human Activity Recognition (HAR) has been a challenging problem yet it needs to be solved. It will mainly be used for eldercare and healthcare as an assistive technology when ensemble with other technologies like Internet of Things(IoT). HAR can be done with the help of sensors, smartphones or images. In this paper, we present various state-of-the-art methods and describe each of them by literature survey. Different datasets are used for each of the methods wherein the data are collected by different means such as sensors, images, accelerometer, gyroscopes, etc. and the placement of these devices at various locations. The results obtained by each technique and the type of dataset are then compared. Machine learning techniques like decision trees, K-nearest neighbours, support vector machines, hidden markov models are reviewed for HAR and later the survey for deep neural network techniques like artificial neural networks, convolutional neural networks and recurrent neural networks is also presented.
Self-Supervised Monocular Depth Hints

Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser-scans or other ground-truth data. The disadvantage, however, is that the photometric reprojection losses used with self-supervised learning typically have multiple local minima. These plausible-looking alternatives to ground-truth can restrict what a regression network learns, causing it to predict depth maps of limited quality. As one prominent example, depth discontinuities around thin structures are often incorrectly estimated by current state-of-the-art methods. Here, we study the problem of ambiguous reprojections in depth-prediction from stereo-based self-supervision, and introduce Depth Hints to alleviate their effects. Depth Hints are complementary depth-suggestions obtained from simple off-the-shelf stereo algorithms. These hints enhance an existing photometric loss function, and are used to guide a network to learn better weights. They require no additional data, and are assumed to be right only sometimes. We show that using our Depth Hints gives a substantial boost when training several leading self-supervised-from-stereo models, not just our own. Further, combined with other good practices, we produce state-of-the-art depth predictions on the KITTI benchmark.
Integral Object Mining via Online Attention Accumulation

Object attention maps generated by image classifiers are usually used as priors for weakly-supervised segmentation approaches. However, normal image classifiers produce attention only at the most discriminative object parts, which limits the performance of weakly-supervised segmentation task. Therefore, how to effectively identify entire object regions in a weakly-supervised manner has always been a challenging and meaningful problem. We observe that the attention maps produced by a classification network continuously focus on different object parts during training. In order to accumulate the discovered different object parts, we propose an online attention accumulation (OAA) strategy which maintains a cumulative attention map for each target category in each training image so that the integral object regions can be gradually promoted as the training goes. These cumulative attention maps, in turn, serve as the pixel-level supervision, which can further assist the network in discovering more integral object regions. Our method (OAA) can be plugged into any classification network and progressively accumulate the discriminative regions into integral objects as the training process goes. Despite its simplicity, when applying the resulting attention maps to the weakly-supervised semantic segmentation task, our approach improves the existing state-of-the-art methods on the PASCAL VOC 2012 segmentation benchmark, achieving a mIoU score of 66.4% on the test set. Code is available at https://mmcheng.net/oaa/.
Global-Local Temporal Representations for Video Person Re-Identification

This paper proposes the Global-Local Temporal Representation (GLTR) to exploit the multi-scale temporal cues in video sequences for video person Re-Identification (ReID). GLTR is constructed by first modeling the short-term temporal cues among adjacent frames, then capturing the long-term relations among inconsecutive frames. Specifically, the short-term temporal cues are modeled by parallel dilated convolutions with different temporal dilation rates to represent the motion and appearance of pedestrian. The long-term relations are captured by a temporal self-attention model to alleviate the occlusions and noises in video sequences. The short and long-term temporal cues are aggregated as the final GLTR by a simple single-stream CNN. GLTR shows substantial superiority to existing features learned with body part cues or metric learning on four widely-used video ReID datasets. For instance, it achieves Rank-1 Accuracy of 87.02% on MARS dataset without re-ranking, better than current state-of-the art.
LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT

The data generated by millions of sensors in the industrial Internet of Things (IIoT) are extremely dynamic, heterogeneous, and large scale and pose great challenges on the real-time analysis and decision making for anomaly detection in the IIoT. In this article, we propose a long short-term memory (LSTM)-Gauss-NBayes method, which is a synergy of the long short-term memory neural network (LSTM-NN) and the Gaussian Bayes model for outlier detection in the IIoT. In a nutshell, the LSTM-NN builds a model on normal time series. It detects outliers by utilizing the predictive error for the Gaussian Naive Bayes model. Our method exploits advantages of both LSTM and Gaussian Naive Bayes models, which not only has strong prediction capability of LSTM for future time point data, but also achieves an excellent classification performance of the Gaussian Naive Bayes model through the predictive error. We evaluate our approaches on three real-life datasets that involve both long-term and short-term time dependence. Empirical studies demonstrate that our proposed techniques outperform the best-known competitors, which is a preferable choice for detecting anomalies.
Implementation of Principal Component Analysis on Masked and Non-masked Face Recognition

This paper represents an implementation of Principal Component Analysis (PCA) on masked and non-masked face recognition. Security is an essential term in our today's life. In various Biometric technology, face recognition is widely used to secure any system because it is better than any other traditional techniques like PIN, password, fingerprint etc. and most reliable to identify or verify a person efficiently. In recent years, face recognition is a very challenging task because of different occlusion or masks like the existence of sunglasses, scarves, hats and different types of make-up or disguise ingredients. The accuracy rate of face recognition is influenced by these types of masks. Many algorithms have been developed recently for non-masked face recognition which are widely used and give better performance. Still in the field of masked face recognition, few contributions has been done. Therefore, in this work a statistical procedure has been selected which is applied in non-masked face recognition and also apply in the masked face recognition technique. PCA is more effective and successful statistical technique and widely used. For this reason in this work, PCA algorithm has been chosen. Finally, a comparative study also done here for a better understanding.
IoT Wearable Sensors and Devices in Elderly Care: A Literature Review

The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer’s disease, frailty, Parkinson’s, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology.
Salience-Guided Cascaded Suppression Network for Person Re-Identification

Employing attention mechanisms to model both global and local features as a final pedestrian representation has become a trend for person re-identification (Re-ID) algorithms. A potential limitation of these methods is that they focus on the most salient features, but the re-identification of a person may rely on diverse clues masked by the most salient features in different situations, e.g., body, clothes or even shoes. To handle this limitation, we propose a novel Salience-guided Cascaded Suppression Network (SCSN) which enables the model to mine diverse salient features and integrate these features into the final representation by a cascaded manner. Our work makes the following contributions: (i) We observe that the previously learned salient features may hinder the network from learning other important information. To tackle this limitation, we introduce a cascaded suppression strategy, which enables the network to mine diverse potential useful features that be masked by the other salient features stage-by-stage and each stage integrates different feature embedding for the last discriminative pedestrian representation. (ii) We propose a Salient Feature Extraction (SFE) unit, which can suppress the salient features learned in the previous cascaded stage and then adaptively extracts other potential salient feature to obtain different clues of pedestrians. (iii) We develop an efficient feature aggregation strategy that fully increases the network’s capacity for all potential salience features. Finally, experimental results demonstrate that our proposed method outperforms the state-of-the-art methods on four large-scale datasets. Especially, our approach exceeds the current best method by over 7% on the CUHK03 dataset.
Blockchain as a confidence machine: The problem of trust &amp; challenges of governance

Blockchain technology was created as a response to the trust crisis that swept the world in the wake of the 2008 financial crisis. Bitcoin and other blockchain-based systems were presented as a “trustless” alternative to existing financial institutions and even governments. Yet, while the trustless nature of blockchain technology has been heavily questioned, little research has been done as to what blockchain technologies actually bring to the table in place of trust. This article draws from the extensive academic discussion on the concepts of “trust” and “confidence” to argue that blockchain technology is not a ‘trustless technology’ but rather a ‘confidence machine’. First, the article provides a review of the multifaceted conceptualisations of trust and confidence, and the relationship between these two concepts. Second, the claim is made that blockchain technology relies on cryptographic rules, mathematics, and game-theoretical incentives in order to increase confidence in the operations of a computational system. Yet, such an increase in confidence ultimately relies on the proper operation and governance of the underlying blockchain-based network, which requires trusting a variety of actors. Third, the article turns to legal, constitutional and polycentric governance theory to explore the governance challenges of blockchain-based systems, in light of the tension between procedural confidence and trust.
Cross-modality deep feature learning for brain tumor segmentation

Recent advances in machine learning and prevalence of digital medical images have opened up an opportunity to address the challenging brain tumor segmentation (BTS) task by using deep convolutional neural networks. However, different from the RGB image data that are very widespread, the medical image data used in brain tumor segmentation are relatively scarce in terms of the data scale but contain the richer information in terms of the modality property. To this end, this paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. The proposed cross-modality deep feature learning framework consists of two learning processes: the cross-modality feature transition (CMFT) process and the cross-modality feature fusion (CMFF) process, which aims at learning rich feature representations by transiting knowledge across different modality data and fusing knowledge from different modality data, respectively. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance when compared with the baseline methods and state-of-the-art methods.