Reviewerly/exHarmony huggingface dataset:
RETRACTED: Design and research on automatic recognition system of sports dance movement based on computer vision and parallel computing

Professional team sports analyst periodic analysis to obtain strategic and tactical insights into the players and the team's behavior. Target motion analysis team, including systematic determination of hostile team weaknesses, and assess trained team performance and improvement potential. Team video analysis of the current investigation is usually based on workflow. Analysts can also use information visualization techniques to draw the trajectory of the ball and the players. Video analysis is usually a time-consuming process requiring analysts to remember and comment on the scene. In contrast, typically rely on visual data generally used abstract visual mapping model abstraction. They are no longer directly linked to the observed motion context but obtained from the raw tracking data. The proposal provides low-level functions and advanced motion recognition rate by another parallel motion analysis module used as input to evaluate the dancers' performance. Application of computer vision techniques to extract the appropriate data from the track of the video input. Besides, advance besides and motion analysis technology can be used to derive and regional analysis, event analysis and correlation analysis of team players' motion analysis measurements for football analysis. Our system is a visual mode that allows video and analysts to take advantage of both research forms. Some experts on the team sports analyst survey showed the effectiveness of this integrated approach.
Conditional t-SNE: more informative t-SNE embeddings

Abstract Dimensionality reduction and manifold learning methods such as t-distributed stochastic neighbor embedding (t-SNE) are frequently used to map high-dimensional data into a two-dimensional space to visualize and explore that data. Going beyond the specifics of t-SNE, there are two substantial limitations of any such approach: (1) not all information can be captured in a single two-dimensional embedding, and (2) to well-informed users, the salient structure of such an embedding is often already known, preventing that any real new insights can be obtained. Currently, it is not known how to extract the remaining information in a similarly effective manner. We introduce conditional t-SNE (ct-SNE), a generalization of t-SNE that discounts prior information in the form of labels. This enables obtaining more informative and more relevant embeddings. To achieve this, we propose a conditioned version of the t-SNE objective, obtaining an elegant method with a single integrated objective. We show how to efficiently optimize the objective and study the effects of the extra parameter that ct-SNE has over t-SNE. Qualitative and quantitative empirical results on synthetic and real data show ct-SNE is scalable, effective, and achieves its goal: it allows complementary structure to be captured in the embedding and provided new insights into real data.
Newly Discovered Triassic Lithium Deposits in the Dahongliutan Area, NorthWest China: A Case Study for the Detection of Lithium-Bearing Pegmatite Deposits in Rugged Terrains Using Remote-Sensing Data and Images

Rare metals, such as lithium and cobalt used in rechargeable batteries, have increased in value as demands for them escalates. Concentrations of lithium-bearing minerals are found in closed-basin brines, granitic pegmatites, and associated granitic rocks containing spodumene (LiAl(SiO 3 ) 2 ) and various other economic minerals. The recently discovered Dahongliutan Li mineral occurrences are hosted by a pegmatite dyke swarm in NW China, in an area that is also prospective for Be, Rb, Nb, and Ta mineralisation. However, the high altitude and steep topography in the area make it extremely difficult to explore for mineralisation. A combination of geochemical methods, geological mapping, and high-resolution remotely sensed multispectral imagery has been used in this study to pinpoint potential locations of pegmatite-hosted Li occurrences. The exploration method developed has led to the discovery many large Li mineral occurrences in the Bayankala Fold Belt, including the 505, 507, north 509, and South Fulugou 1# and 2# mineral occurrences (documented here) with a combined resource of over 1.7 million tonnes (Mt). The laser ablation multi-receiver inductively coupled plasma mass spectrometer (LA-MC-ICP-MS) 206 Pb/ 207 Pb- 238 U/ 207 Pb isochron age of the mineralised pegmatite is 223 ± 11 Ma (N = 44, MSWD = 2.1). The 40 Ar/ 39 Ar plateau age of muscovite in the mineralised pegmatite dates between 197 ± 1 and 185 ± 1 Ma. These dates show that these granitic pegmatites (with a monzogranitic composition) were emplaced during the Late Triassic coeval with magmatism in the region. Our data show that the Li mineralisation in the Dahongliutan area has a similar age and genesis as the pegmatite-hosted deposits of the Jiajika area in the western Sichuan Province. This indicates that the Dahongliutan area is highly prospective of various pegmatite-hosted mineral deposits.
Robust line feature matching based on pair-wise geometric constraints and matching redundancy

This paper presents a novel method for matching line segments in images based on pair-wise geometric constraints and matching redundancy. In this study, pairs of line segments satisfying angle and distance constraints are used as matching primitives. To ensure that each extracted line segment is paired with another line segment, the search region of each line segment is gradually grown until it is paired. Initial pair-to-pair correspondences between two images are established using four pair-wise constraints; next, line-to-line correspondences are obtained. To effectively solve the matching conflict in the results, a method of recording the result of line pair matching based on a double-layer matrix is proposed. Based on the double-layer matrix, an effective checking method for the line matching results based on the collinearity constraint and matching redundancy is presented. It fully utilizes redundancy information and considers the collinearity of fragmented line segments. Further, it can effectively separate correct and incorrect matches from the one-to-many, many-to-one, and many-to-many matching results. The proposed method was tested on 12 image pairs from a benchmark of matched-lines, and compared with other state-of-the-art methods. The results demonstrate the superiority of the proposed method due to its higher accuracy and greater recall in challenging cases.
Privacy-Preserving Production Process Parameter Exchange

Nowadays, collaborations between industrial companies always go hand in hand with trust issues, i.e., exchanging valuable production data entails the risk of improper use of potentially sensitive information. Therefore, companies hesitate to offer their production data, e.g., process parameters that would allow other companies to establish new production lines faster, against a quid pro quo. Nevertheless, the expected benefits of industrial collaboration, data exchanges, and the utilization of external knowledge are significant.
Towards Enabling Deletion in Append-Only Blockchains to Support Data Growth Management and GDPR Compliance

Conventional blockchain implementations with append-only semantics do not support deleting or overwriting data in confirmed blocks. However, many industry-relevant use cases require the ability to delete data, especially when personally identifiable information is stored or when data growth has to be constrained. Existing attempts to reconcile these contradictions compromise on core qualities of the blockchain paradigm, as they include backdoor-like approaches such as central authorities with elevated rights or usage of specialized chameleon hash algorithms in chaining of the blocks. The contribution of this paper is a novel architecture for the blockchain ledger and consensus, which uses a tree of context chains with simultaneous validity. A context chain captures the transactions of a closed group of entities and persons, thus structuring blocks in a precisely defined way. The resulting context isolation enables consensus-steered deletion of an entire context without side effects to other contexts. We show how this architecture supports truncation, data rollover and separation of concerns, how the GDPR regulations can be fulfilled by this architecture and how it differs from sidechains and state channels.
Part-based Structured Representation Learning for Person Re-identification

Person re-identification aims to match person of interest under non-overlapping camera views. Therefore, how to generate a robust and discriminative representation is crucial for person re-identification. Mining local clues from human body parts to describe pedestrians has been extensively studied in existing methods. However, existing methods locate human body parts coarsely and do not consider the relations among different local parts. To address the above problem, we propose a Part-based Structured Representation Learning (PSRL) for better exploiting local clues to improve the person representation. There are two important modules in our architecture: Local Semantic Feature Extraction and Structured Person Representation Learning. The Local Semantic Feature Extraction module is designed to extract local features from human body semantic regions. After obtaining the local features, the Structured Person Representation Learning is proposed to fuse the local features by considering the person structure. To model the underlying person structure, a graph convolutional network is employed to capture the relations of different semantic regions. The generated structured feature encodes underlying person structure information, and local semantic feature can solve the misalignment problem caused by pose variations in feature matching. By combining them together, we can improve the descriptive ability of the generated representation. Extensive evaluations on four standard benchmarks show that our proposed method achieves competitive performance against state-of-the-art methods.
The City as a License. Implications of Blockchain and Distributed Ledgers for Urban Governance

Distributed ledger technologies (DLTs) such as blockchain have in recent years been presented as a new general-purpose technology that could underlie many aspects of social and economic life, including civics and urban governance. In an urban context, over the past few years, a number of actors have started to explore the application of distributed ledgers in amongst others smart city services as well as in blockchain for good and urban commons-projects. DLTs could become the administrative backbones of such projects, as the technology can be set-up as an administration, management and allocation tool for urban resources. With the addition of smart contracts, DLTs can further automate the processing of data and execution of decisions in urban resource management through algorithmic governance. This means that the technological set-up and design of such DLT based systems could have large implications for the ways urban resources are governed. Positive contributions are expected to be made toward (local) democracy, transparent governance, decentralization, and citizen empowerment. We argue that to fully scrutinize the implications for urban governance, a critical analysis of distributed ledger technologies is necessary. In this contribution, we explore the lens of “the city as a license” for such a critical analysis. Through this lens, the city is framed as a “rights-management-system,” operated through DLT technology. Building upon Lefebvrian a right to the city-discourses, such an approach allows to ask important questions about the implications of DLTs for the democratic governance of cities in an open, inclusive urban culture. Through a technological exploration combined with a speculative approach, and guided by our interest in the rights management and agency that blockchains have been claimed to provide to their users, we trace six important issues: quantification; blockchain as a normative apparatus; the complicated relationship between transparency and accountability; the centralizing forces that act on blockchains; the degrees to which algorithmic rules can embed democratic law-making and enforcing; and finally, the limits of blockchain's trustlessness.
Performance of a language identification system using hybrid features and ANN learning algorithms

This paper discusses the use of hybrid features and artificial neural network (ANN) for spoken language identification (LID). At the feature extraction stage, we have used RASTA-PLP features and later hybrid features by combining the state-of-the-art MFCC features with RASTA-PLP features. The classifier used at the back-end of the LID system is Feed-forward Back Propagation Neural Network (FFBPNN). Performance comparison is done for all the thirteen learning algorithms available in FFBPNN. Results indicate better performance with MFCC + RASTA-PLP features as compared to RASTA-PLP features when used individually. The classification accuracy of 94.6 percent is obtained with 'trainlm' network training function and a test error rate of 0.10 with MFCC + RASTA-PLP hybrid features. On the other hand, RASTA-PLP features provide the best classification accuracy of 89.6%. Also, in this paper two new training functions are proposed. Experimental results show that for the MFCC + RASTA-PLP features, the proposed training function 1 offers 95.1 percent accuracy and 0.0993 mean square error. Similarly, the proposed training function 2 offers 95.3 per cent classification accuracy and 0.093 minimum mean square error for MFCC + RASTA-PLP features. The feasibility of the proposed solution is estimated by simulating multiple experiments on a language database of four languages i.e. Hindi, Tamil, Malayalam and English in the working Platform of MATLAB.
Fog-based Secure Service Discovery for Internet of Multimedia Things

The Internet of Multimedia Things (IoMT) has become the backbone of innumerable multimedia applications in various fields. The wide application of IoMT not only makes our life convenient but also brings challenges to service discovery. Service discovery aims to leverage location information and trust evidence scattered in a variety of multimedia applications to find trusted IoMT devices that can provide specific service in target areas. However, the eavesdropping and tampering to these sensitive IoMT data during the trust propagation process invalidate the service discovery process. To address these challenges, we propose Secure Service Discovery (SSD) for IoMT using cross-blockchain-enabled fog computing. To resist the tampering and eavesdropping during the trust propagation process, a scalable cross-blockchain structure consisting of multiple parallel blockchains is first proposed based on fog, in which different parallel blockchains can be orchestrated to propagate encrypted location information and trust evidence of different applications. Moreover, to enable a cross-blockchain structure to leverage encrypted location information and trust evidence to find trusted IoMT devices in preset areas, a novel privacy-preserving range query is proposed to query and aggregate trust evidence. Security analysis and simulations are carried out to demonstrate the effectiveness and security of the proposed SSD.
Advance research in agricultural text-to-speech: the word segmentation of analytic language and the deep learning-based end-to-end system

Agricultural Text-to-Speech (TTS) has attracted increasingly more attention. The application of agricultural TTS and its problems are analyzed in this paper, and the traditional framework of the TTS system and its key technologies, i.e., text analysis, rhythm generation and speech synthesis are discussed. Furthermore, two advancements in agricultural TTS, the word segmentation of analytic language and the deep learning-based end-to-end TTS system, are detailed summarized. Based on the characteristics of agriculture, some appealing research directions are pointed out: how to improve the training speed and synthesis speed of the deep learning models is still the focus; the study on the approaches of weakly-supervised learning in TTS is in fancy; and research on the real-time and high-quality speech synthesis that can be deployed in mobile devices is a key point of agricultural TTS research.
CommonRoad Drivability Checker: Simplifying the Development and Validation of Motion Planning Algorithms

Collision avoidance, kinematic feasibility, and road-compliance must be validated to ensure the drivability of planned motions for autonomous vehicles. Although these tasks are highly repetitive, computationally efficient toolboxes are still unavailable. The CommonRoad Drivability Checker-an open-source toolbox-unifies these mentioned checks. It is compatible with the CommonRoad benchmark suite, which additionally facilitates the development of motion planners. Our toolbox drastically reduces the effort of developing and validating motion planning algorithms. Numerical experiments show that our toolbox is real-time capable and can be used in real test vehicles.
Dual-path attention network for single image super-resolution

Deep convolutional neural networks (CNNs) have recently made remarkable advances in single image super-resolution (SISR). The CNN structures of most existing SISR methods are just based on residual structures, dense structures, or their variants. However, these methods almost all adopt single-path structures, which makes them difficult to make full use of the complementary contextual information of the different ways of feature extraction (e.g., residual and dense connections). In this paper, we develop a novel dual-path attention network, which includes the dual-path attention groups (DPAGs) with dual skip connections (DSCs), in order to combine the advantages of both residual and dense connections for better SR performance. Each DPAG has several dual-path blocks (DPBs) and a path attention fusion (PAF). The DPBs realize the structure of the dual-path topology, while the PAF can further improve the discriminative representation ability by a channel attention (CA) mechanism, adaptively fuse the complementary contextual information produced by the two paths, and stabilize the network. Our DPAN can well pay attention to high-frequency information because each DSC contains a local skip connection and an adaptively weighted global skip connection (AWGSC), which can further adaptively bypass low-frequency features. Extensive experimental results demonstrate the superiority of the proposed DPAN in terms of both quantitative metrics and visual quality, compared with the current state-of-the-art SISR methods. For instance, compared with recent typical methods, for Bicubic (BI) degradation on the difficult dataset Urban100, our DPAN achieved the best PSNR of 33.22 dB for scale ×2 , 29.20 dB for scale ×3, and 26.99 dB for scale ×4, respectively.
Triplet online instance matching loss for person re-identification

Mining the shared features of the same identity in different scenes and the unique features of different identities in the same scene are the most significant challenges in the field of person re-identification (ReID). The Online Instance Matching (OIM) loss function and triplet loss function are the main methods for person ReID. Unfortunately, both of them have drawbacks. The OIM loss treats all samples equally and puts no emphasis on hard samples. The triplet loss processes batch construction in a complicated and fussy way and converges slowly. For these problems, we propose a Triplet Online Instance Matching (TOIM) loss function, which emphasizes hard samples and improves the person ReID accuracy effectively. It combines the advantages of the OIM loss and triplet loss and simplifies the batch construction process, which leads to quicker convergence. It can be trained on-line when handling the joint detection and identification task. To validate our loss function, we collect and annotate a large-scale benchmark dataset (UESTC-PR), which contains 499 identities and 60,437 images taken from surveillance cameras. We evaluated our proposed loss function on the Duke, Marker-1501 and UESTC-PR datasets using ResNet-50, and the results show that our proposed loss function outperforms the baseline methods, including Softmax loss, OIM loss and triplet loss.
Randomized outlier detection with trees

Abstract Isolation forest (IF) is a popular outlier detection algorithm that isolates outlier observations from regular observations by building multiple random isolation trees. The average number of comparisons required to isolate a given observation can then be used as a measure of its outlierness. Multiple extensions of this approach have been proposed in the literature including the extended isolation forest (EIF) as well as the SCiForest. However, we find a lack of theoretical explanation on why IF, EIF, and SCiForest offer such good practical performance. In this paper, we present a theoretical framework that views these approaches from a distributional viewpoint. Using this viewpoint, we show that isolation-based approaches first accurately approximate the data distribution and then secondly approximate the coefficients of mixture components using the average path length. Using this framework, we derive the generalized isolation forest (GIF) that also trains random isolation trees, but combining them moves beyond using the average path length. That is, GIF splits the data into multiple sub-spaces by sampling random splits as do the original IF variants do and directly estimates the mixture coefficients of a mixture distribution to score the outlierness on entire regions of data. In an extensive evaluation, we compare GIF with 18 state-of-the-art outlier detection methods on 14 different datasets. We show that GIF outperforms three competing tree-based methods and has a competitive performance to other nearest-neighbor approaches while having a lower runtime. Last, we highlight a use-case study that uses GIF to detect transaction fraud in financial data.
RandChain: Practical Scalable Decentralized Randomness Attested by Blockchain

Reliable and verifiable public randomness is not only an essential building block in various cryptographic primitives, but also is a critical component in many distributed and decentralized protocols, e.g., blockchain sharding. A ‘good’ randomness generator should preserve several distinctive properties, such as public-verifiability, bias-resistance, unpredictability, and availability. However, it is a challenging task to generate such good randomness. For instance, a dishonest party may behave deceptively to bias the final randomness, which is toward his preferences. And this challenge is more serious in a distributed and decentralized system. Blockchain technology provides several promising features, such as decentralization, immutability, and trustworthiness. Due to extremely high overheads on both communication and computation, most existing solutions face an additional scalability issue. We propose a sharding-based scheme, RandChain, to obtain a practical scalable distributed and decentralized randomness attested by blockchain in large-scale applications. In RandChain, we eliminate the use of computation-heavy cryptographic operations, e.g., Publicly Verifiable Secret Sharing (PVSS), in prevalent approaches. We build a sub-routine, RandGene, which utilizes a commit-then-reveal strategy to establish a local randomness, enforced by efficient Verifiable Random Function (VRF). RandGene generates the randomness based on statistical approaches, instead of cryptographic operations, to eliminate computational operations. RandChain maintains a two-layer hierarchical chain structure via a sharding scheme. The first level chain is maintained by RandGene within each shard to provide a verifiable randomness source by blockchain. The second level chain uses the randomnesses from each shard to build a randomness chain.