Reviewerly/exHarmony huggingface dataset:
The convergence of blockchain, IoT and 6G: Potential, opportunities, challenges and research roadmap

6G networks envisioned being the game changer in next generation wireless communication systems that will address the challenges of limited information speed escalated with the augmentation of billions of data applications encountered by the current fifth generation (5G) networks. Some key radical technologies in 6G together with existing 5G candidate schemes will guarantee the expected quality of experience (QoE) to attain ubiquitous wireless connectivity for the Internet of Everything (IoE) ranging from the telecom industry to digital smart industries. Blockchain technology (BCT) has gained significant attention due to undertake the decentralization, transparency, spectrum resource scarcity, inherent privacy and security, interoperability, confidentiality, and emerging smart applications domains including Industrial IoT (IIoT) and Industry 5.0 applications. The mismatch between the requirements of many data intensive disruptive IoT applications and 5G network capabilities steered the demand of decentralized BCT based 6G architecture. Inspired by these facts, this paper studies an extensive survey to draw a new direction of blockchain integration into 6G mobile networks, IoT technologies, and smart industries in terms of infrastructure sharing, computational loads, latency, bandwidth overhead, business model, sustainability goals, and edge intelligence focusing the potential merits and challenges. We highlighted the convergence of IoT in blockchain to enable intelligent distribution in future IIoT and the technical model of 6G networks to realize the successful deployment of BCT schemes. This paper pointed out the current intriguing challenges, canvassed the mitigation techniques, and plausible future research opportunities that may privilege the pursuit of this vision.
A Deep-Fusion Network for Crowd Counting in High-Density Crowded Scenes

Abstract People counting has been investigated extensively as a tool to increase the individual’s safety and to avoid crowd hazards at public places. It is a challenging task especially in high-density environment such as Hajj and Umrah, where millions of people gathered in a constrained environment to perform rituals. This is due to large variations of scales of people across different scenes. To solve scale problem, a simple and effective solution is to use an image pyramid. However, heavy computational cost is required to process multiple levels of the pyramid. To overcome this issue, we propose deep-fusion model that efficiently and effectively leverages the hierarchical features exits in various convolutional layers deep neural network. Specifically, we propose a network that combine multiscale features from shallow to deep layers of the network and map the input image to a density map. The summation of peaks in the density map provides the final crowd count. To assess the effectiveness of the proposed deep network, we perform experiments on three different benchmark datasets, namely, UCF_CC_50, ShanghaiTech, and UCF-QNRF. From experiments results, we show that the proposed framework outperforms other state-of-the-art methods by achieving low Mean Absolute Error (MAE) and Mean Square Error (MSE) values.
LF-ACO: an effective formation path planning for multi-mobile robot

Multi-robot path planning is a hot problem in the field of robotics. Compared with single-robot path planning, complex problems such as obstacle avoidance and mutual collaboration need to be considered. This paper proposes an efficient leader follower-ant colony optimization (LF-ACO) to solve the collaborative path planning problem. Firstly, a new Multi-factor heuristic functor is proposed, the distance factor heuristic function and the smoothing factor heuristic function. This improves the convergence speed of the algorithm and enhances the smoothness of the initial path. The leader-follower structure is reconstructed for the position constraint problem of multi-robots in a grid environment. Then, the pheromone of the leader ant and the follower ants are used in the pheromone update rule of the ACO to improve the search quality of the formation path. To improve the global search capability, a max-min ant strategy is used. Finally, the path is optimized by the turning point optimization algorithm and dynamic cut-point method to improve path quality further. The simulation and experimental results based on MATLAB and ROS show that the proposed method can successfully solve the path planning and formation problem.
Meta-Health Stack: A new approach for breast cancer prediction

Data analytics and machine learning have grown in importance to efficiently manage large amounts of healthcare data. Recent statistics indicate that breast cancer is the most commonly diagnosed cancer worldwide. Different tumor features are available in various datasets for breast cancer detection. Filtering those to obtain an accurate diagnosis is time-consuming and challenging. Machine learning algorithms are beneficial for finding a significant relationship between various features and malignant tumors. This research proposes a new ensemble-based framework named Meta-Health Stack to predict breast cancer more efficiently. In this framework, to extract the most relevant features, the Extra Trees classifier is used to integrate the attributes obtained from Variance Inflation Factor, Pearson’s Correlation, and Information Gain to detect the tumors’ hidden patterns. Finally, three approaches, including Boosting, Bagging, and Voting, were combined with equal weights together through the Stacking approach. The proposed method resulted in a 97% F1-score and 98% precision tested on Wisconsin Diagnosed Diagnostic Breast Cancer (WDBC) dataset. Based on the findings, we noticed that the suggested framework’s performance works perfectly due to the selection of more appropriate features by the Extra Trees algorithm. Furthermore, we recommend that this proposed framework be used to diagnose breast cancer in its early stages as it works effectively. Using this framework, breast cancer recovery and therapy will be more successful. Moreover, to evaluate the performance of the proposed framework, it has been implemented on three other medical datasets. Results show an appropriate performance in predicting other illnesses as well.
Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

Abstract Geological reports are frequently used by geologists involved in geological surveys and scientific research to record the results and outcomes of geological surveys. With such a rich data source, a substantial amount of knowledge has yet to be mined and analyzed. This paper focuses on automatically information extraction from geological reports, namely, geological named entity recognition. Geological named entity recognition has an important role in data mining, knowledge discovery and Knowledge graph construction. Existing general named entity recognition models/tools are limited in the domain of geoscience due to the various language irregularities associated with geological text, such as informal sentence structures, several domain‐geoscience words, large character lengths and multiple combinations of independent words. We present Bidirectional encoder representations from transformers (BERT)‐(Bidirectional gated recurrent unit network) BiGRU‐ (Conditional random field) CRF, which is a deep learning‐based geological named entity recognition model that is designed specifically with these linguistic irregularities in mind. Based on the pretrained language model, an integrated deep learning model incorporating BERT, BiGRU and CRF is constructed to obtain character vectors rich in semantic information through the BERT pretrained language model to alleviate for the lack of specificity of static word vectors (e.g., word2vec) and to improve the extraction capability of complex geological entities. We demonstrate our proposed model by applying it to four test datasets, including a geoscience NER data set from regional geological reports, and by comparing its performance with those of five baseline models.
Cyclic Differentiable Architecture Search

Differentiable ARchiTecture Search, i.e., DARTS, has drawn great attention in neural architecture search. It tries to find the optimal architecture in a shallow search network and then measures its performance in a deep evaluation network. The independent optimization of the search and evaluation networks, however, leaves a room for potential improvement by allowing interaction between the two networks. To address the problematic optimization issue, we propose new joint optimization objectives and a novel Cyclic Differentiable ARchiTecture Search framework, dubbed CDARTS. Considering the structure difference, CDARTS builds a cyclic feedback mechanism between the search and evaluation networks with introspective distillation. First, the search network generates an initial architecture for evaluation, and the weights of the evaluation network are optimized. Second, the architecture weights in the search network are further optimized by the label supervision in classification, as well as the regularization from the evaluation network through feature distillation. Repeating the above cycle results in a joint optimization of the search and evaluation networks and thus enables the evolution of the architecture to fit the final evaluation network. The experiments and analysis on CIFAR, ImageNet and NATS-Bench [95] demonstrate the effectiveness of the proposed approach over the state-of-the-art ones. Specifically, in the DARTS search space, we achieve 97.52% top-1 accuracy on CIFAR10 and 76.3% top-1 accuracy on ImageNet. In the chain-structured search space, we achieve 78.2% top-1 accuracy on ImageNet, which is 1.1% higher than EfficientNet-B0. Our code and models are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/microsoft/Cream</uri> .
A principal component analysis of barriers to the implementation of blockchain technology in the South African built environment

Purpose Blockchain technology is one of the emerging innovative technologies making waves globally, and it has been adjudged to have the capability to transform businesses. With the different capabilities of the technology, such as immutability of information and decentralisation of authority attributes, the built environment is slow in its adoption. This study aims to explore the barriers to the implementation of blockchain technology in the construction using a principal component analysis (PCA) approach. Design/methodology/approach This research took a post-positivist philosophical stance, which informed a quantitative research approach through a questionnaire survey. From the South African built environment and information technology sector, 79 respondents were drawn through a snowballing sampling technique. The built environment professionals include architect, construction project manager, construction manager, quantity surveyor and engineer. Retrieved data were screened and analysed by adopting the descriptive analysis and PCA while Cronbach alpha evaluated the reliability. Also, Kruskal–Wallis H non-parametric test was used to determine the differences in the opinion of the respondent groups. Findings The analysis revealed that all the identified barriers ranked above the average mean with lack of clarity, scalability risks and lack of skills or knowledge ranking top three. PCA clustered the identified barriers into three components: organisational barriers, social barriers and technological barriers. Research limitations/implications This study was carried out in the Gauteng province of South Africa, leaving out other provinces due to accessibility, cost and time constraints. Practical implications Built environment organisations need to be kept abreast of the capabilities of blockchain technology as the major barrier observed was the lack of clarity of blockchain technology. Also, the technological barriers identified from this study need to be addressed by information technology experts to give consumers the desired value for money in implementing blockchain technology for the built environment. Originality/value The blockchain technology capabilities are incomparable to any other invention thus far. Therefore, it is very important that the numerous stakeholders in the built environment be made aware of the blockchain technology capabilities while formulating a solution to the identified barriers. This will aid its implementation in the built environment and help the industry measure up with its counterparts.
Exploring Motion and Appearance Information for Temporal Sentence Grounding

This paper addresses temporal sentence grounding. Previous works typically solve this task by learning frame-level video features and align them with the textual information. A major limitation of these works is that they fail to distinguish ambiguous video frames with subtle appearance differences due to frame-level feature extraction. Recently, a few methods adopt Faster R-CNN to extract detailed object features in each frame to differentiate the fine-grained appearance similarities. However, the object-level features extracted by Faster R-CNN suffer from missing motion analysis since the object detection model lacks temporal modeling. To solve this issue, we propose a novel Motion-Appearance Reasoning Network (MARN), which incorporates both motion-aware and appearance-aware object features to better reason object relations for modeling the activity among successive frames. Specifically, we first introduce two individual video encoders to embed the video into corresponding motion-oriented and appearance-aspect object representations. Then, we develop separate motion and appearance branches to learn motion-guided and appearance-guided object relations, respectively. At last, both motion and appearance information from two branches are associated to generate more representative features for final grounding. Extensive experiments on two challenging datasets (Charades-STA and TACoS) show that our proposed MARN significantly outperforms previous state-of-the-art methods by a large margin.
Finding real bugs in big programs with incorrectness logic

Incorrectness Logic (IL) has recently been advanced as a logical theory for compositionally proving the presence of bugs—dual to Hoare Logic, which is used to compositionally prove their absence. Though IL was motivated in large part by the aim of providing a logical foundation for bug-catching program analyses, it has remained an open question: is IL useful only retrospectively (to explain existing analyses), or can it actually be useful in developing new analyses which can catch real bugs in big programs? In this work, we develop Pulse-X, a new, automatic program analysis for catching memory errors, based on ISL, a recent synthesis of IL and separation logic. Using Pulse-X, we have found 15 new real bugs in OpenSSL, which we have reported to OpenSSL maintainers and have since been fixed. In order not to be overwhelmed with potential but false error reports, we develop a compositional bug-reporting criterion based on a distinction between latent and manifest errors, which references the under-approximate ISL abstractions computed by Pulse-X, and we investigate the fix rate resulting from application of this criterion. Finally, to probe the potential practicality of our bug-finding method, we conduct a comparison to Infer, a widely used analyzer which has proven useful in industrial engineering practice.
A Quadrotor With a Passively Reconfigurable Airframe for Hybrid Terrestrial Locomotion

Despite efforts to circumvent and alleviate the impact of a mid-flight collision, it remains extremely challenging to safeguard a multirotor vehicle when it operates in cluttered environments. In this work, we introduce a flying robot with the ability to roll through a gap narrower than its diameter to prevent a possible aerial collision entirely. The novelty of the proposed design lies in a simple passive mechanism that redirects the propelling thrust for the terrestrial operation without the need for extra actuators. As a result, the robot remains compact and lightweight. Furthermore, to overcome the underactuation associated with the passive structure, the transitions between flight and rolling are accomplished with a highly dynamic maneuver. In the experimental demonstration, the robot seamlessly switched between the aerial and terrestrial locomotion to safely negotiate a 10-cm opening.
Knowledge graph embedding for data mining vs. knowledge graph embedding for link prediction – two sides of the same coin?

Knowledge Graph Embeddings, i.e., projections of entities and relations to lower dimensional spaces, have been proposed for two purposes: (1) providing an encoding for data mining tasks, and (2) predicting links in a knowledge graph. Both lines of research have been pursued rather in isolation from each other so far, each with their own benchmarks and evaluation methodologies. In this paper, we argue that both tasks are actually related, and we show that the first family of approaches can also be used for the second task and vice versa. In two series of experiments, we provide a comparison of both families of approaches on both tasks, which, to the best of our knowledge, has not been done so far. Furthermore, we discuss the differences in the similarity functions evoked by the different embedding approaches.
Automated Generation of Accurate &amp; Fluent Medical X-ray Reports

Our paper aims to automate the generation of medical reports from chest X-ray image inputs, a critical yet time-consuming task for radiologists.Existing medical report generation efforts emphasize producing human-readable reports, yet the generated text may not be well aligned to the clinical facts.Our generated medical reports, on the other hand, are fluent and, more importantly, clinically accurate.This is achieved by our fully differentiable and end-to-end paradigm that contains three complementary modules: taking the chest X-ray images and clinical history document of patients as inputs, our classification module produces an internal checklist of disease-related topics, referred to as enriched disease embedding; the embedding representation is then passed to our transformer-based generator, to produce the medical report; meanwhile, our generator also creates a weighted embedding representation, which is fed to our interpreter to ensure consistency with respect to diseaserelated topics.Empirical evaluations demonstrate very promising results achieved by our approach on commonly-used metrics concerning language fluency and clinical accuracy.Moreover, noticeable performance gains are consistently observed when additional input information is available, such as the clinical document and extra scans from different views.
Deep Reinforcement Learning for Intelligent Dual-UAV Reconnaissance Mission Planning

The reconnaissance of high-value targets is prerequisite for effective operations. The recent appreciation of deep reinforcement learning (DRL) arises from its success in navigation problems, but due to the competitiveness and complexity of the military field, the applications of DRL in the military field are still unsatisfactory. In this paper, an end-to-end DRL-based intelligent reconnaissance mission planning is proposed for dual unmanned aerial vehicle (dual UAV) cooperative reconnaissance missions under high-threat and dense situations. Comprehensive consideration is given to specific mission properties and parameter requirements through the whole modelling. Firstly, the reconnaissance mission is described as a Markov decision process (MDP), and the mission planning model based on DRL is established. Secondly, the environment and UAV motion parameters are standardized to input the neural network, aiming to deduce the difficulty of algorithm convergence. According to the concrete requirements of non-reconnaissance by radars, dual-UAV cooperation and wandering reconnaissance in the mission, four reward functions with weights are designed to enhance agent understanding to the mission. To avoid sparse reward, the clip function is used to control the reward value range. Finally, considering the continuous action space of reconnaissance mission planning, the widely applicable proximal policy optimization (PPO) algorithm is used in this paper. The simulation is carried out by combining offline training and online planning. By changing the location and number of ground detection areas, from 1 to 4, the model with PPO can maintain 20% of reconnaissance proportion and a 90% mission complete rate and help the reconnaissance UAV to complete efficient path planning. It can adapt to unknown continuous high-dimensional environmental changes, is generalizable, and reflects strong intelligent planning performance.
One-shot Scene Graph Generation

As a structured representation of the image content, the visual scene graph (visual relationship) acts as a bridge between computer vision and natural language processing. Existing models on the scene graph generation task notoriously require tens or hundreds of labeled samples. By contrast, human beings can learn visual relationships from a few or even one example. Inspired by this, we design a task named One-Shot Scene Graph Generation, where each relationship triplet (e.g., "dog-has-head'') comes from only one labeled example. The key insight is that rather than learning from scratch, one can utilize rich prior knowledge. In this paper, we propose Multiple Structured Knowledge (Relational Knowledge and Commonsense Knowledge) for the one-shot scene graph generation task. Specifically, the Relational Knowledge represents the prior knowledge of relationships between entities extracted from the visual content, e.g., the visual relationships "standing in'', "sitting in'', and "lying in'' may exist between "dog'' and "yard'', while the Commonsense Knowledge encodes "sense-making'' knowledge like "dog can guard yard''. By organizing these two kinds of knowledge in a graph structure, Graph Convolution Networks (GCNs) are used to extract knowledge-embedded semantic features of the entities. Besides, instead of extracting isolated visual features from each entity generated by Faster R-CNN, we utilize an Instance Relation Transformer encoder to fully explore their context information. Based on a constructed one-shot dataset, the experimental results show that our method significantly outperforms existing state-of-the-art methods by a large margin. Ablation studies also verify the effectiveness of the Instance Relation Transformer encoder and the Multiple Structured Knowledge.
Memristor-Based Neural Network Circuit of Operant Conditioning Accorded With Biological Feature

Most memristor-based associative memory neural networks are focused on classical conditioning and ignored operant conditioning. In this paper, a memristor-based neural network of operant conditioning accorded with biological feature is designed. The designed circuit includes a voltage control module, an operant module and synapse modules. It realizes learning, forgetting, long-term memory, reinforcement and punishment functions based on variable synapse structure and double self-protection measure. Meanwhile, the four factors that affect operant conditioning such as contingency, immediacy, magnitude and deprivation are discussed and implemented. The simulation results in PSPICE show that the circuit can be used to simulate actual conditioned reflex and complicated applications. The memristor-based neural network circuit of operant conditioning provides more references for further development of neural networks.