Reviewerly/exHarmony huggingface dataset:
Privacy-aware and Efficient Student Clustering for Sport Training with Hash in Cloud Environment

With the wide adoption of health and sport concepts in human society, how to effectively analyze the personalized sports preferences of students based on past sports training records has become a crucial and emergent task with positive research significance. However, the past sports training records of students are often accumulated with time and stored in a central cloud platform and therefore, the data volume is too large to be processed with quick response. In addition, the past sports training records of students often contain certain sensitive information, which probably discloses partial user privacy if we cannot protect the data well. Considering these two challenges, a privacy-aware and efficient student clustering approach, named PESC is proposed, which is based on a hash technique and deployed on a central cloud platform connecting multiple local servers. Concretely, in the cloud platform, each student is firstly assigned an index based on the past sports training records stored in a local server, through a uniform hash mapping operation. Then similar students are clustered and registered in the cloud platform based on the students' respective sport indexes. At last, we infer the personalized sport preferences of each student based on their belonged clusters. To prove the feasibility of PESC, we provide a case study and a set of experiments deployed on a time-aware dataset.
Utilizing Off-Chain Storage Protocol for Solving the Trilemma Issue of Blockchain

Reno, SahaHaque, Md. MokammelToday, numerous apps are being deployed on the blockchain to extend security and transparency. In order to cope with most of the real-world applications, blockchain is needed to be in its perfect state. For this purpose, the trilemma concept is addressed to emphasize the complete cooperation of decentralization, security and scalability without compromising any of these attributes. However, after years of revolution in the digital transaction sector, miners face serious scalability issues with Bitcoin’s peak limit of only 2–3 TPS. Furthermore, the ledger size of Bitcoin is on the way to exceeding 350 GB and growing exponentially. Among all other cryptocurrencies, Bitcoin is far ahead in terms of security and decentralization. However, it lacks proper scalability. Instead of being secure and decentralized enough, the Bitcoin blockchain cannot sustain any longer without scalability. In this paper, this trilemma problem is focused on making the Bitcoin scalable enough, so that a Bitcoin-like secure and decentralized blockchain can be adopted in real-life applications. A theoretical strategy for increasing throughput and reducing storage dependency is introduced in this paper. A solution for Bitcoin’s scalability issue in terms of throughput and storage, which uses IPFS and a dual-blockchain technique, is presented.
Approximation of Digital Surfaces by a Hierarchical Set of Planar Patches

We show that the plane-probing algorithms introduced in Lachaud et al. (J. Math. Imaging Vis., 59, 1, 23–39, 2017), which compute the normal vector of a digital plane from a starting point and a set-membership predicate, are closely related to a three-dimensional generalization of the Euclidean algorithm. In addition, we show how to associate with the steps of these algorithms generalized substitutions, i.e., rules that replace square faces by unions of square faces, to build finite sets of elements that periodically generate digital planes. This work is a first step towards the incremental computation of a hierarchy of pieces of digital plane that locally fit a digital surface.
Online learning for the Dirichlet process mixture model via weakly conjugate approximation

The Dirichlet process (DP) mixture model is widely used for clustering and density estimation. The use of the DP mixture model has become computationally feasible because of the development of various Markov chain Monte Carlo algorithms. However, when analyzing large data, DP mixture models are impractical owing to their high computational costs. An online learning algorithm that processes data sequentially as they arrive is an attractive way to analyze large data. Existing online learning algorithms based on variational inference are very fast; however, their performance is unsatisfactory owing to the crude approximation of the posterior distribution. We propose a novel mini-batch online learning algorithm based on assumed density filtering, which takes full advantage of available computing resources to improve performance and achieves better performances relative to existing online algorithms based on variational inference.
Dynamic quantum-group authentication protocol based on starlike cluster states

<p indent="0mm">Unlike the traditional one-to-one identity authentication, group identity authentication is group oriented and can authenticate all users belonging to the same group. In this paper, we propose a dynamic quantum-group authentication protocol. Using a special entangled state, i.e., the starlike cluster state, the quantum identity authentication center in a group can authenticate all users belonging to the same group. In addition, the protocol has the following characteristics. (1) The number of users participating in group authentication can change dynamically, i.e., new users are allowed to join group authentication, and the old users are allowed to exit group authentication. (2) When new users join or old users exit group authentication, the authentication keys of all the old users do not need to be redistributed. Compared with the existing quantum-group authentication protocols, the proposed protocol is more flexible and suitable in practical scenarios.
Exploring Effective Knowledge Transfer for Few-shot Object Detection

Recently, few-shot object detection~(FSOD) has received much attention from the community, and many methods are proposed to address this problem from a knowledge transfer perspective. Though promising results have been achieved, these methods fail to achieve shot-stable:~methods that excel in low-shot regimes are likely to struggle in high-shot regimes, and vice versa. We believe this is because the primary challenge of FSOD changes when the number of shots varies. In the low-shot regime, the primary challenge is the lack of inner-class variation. In the high-shot regime, as the variance approaches the real one, the main hindrance to the performance comes from misalignment between learned and true distributions. However, these two distinct issues remain unsolved in most existing FSOD methods. In this paper, we propose to overcome these challenges by exploiting rich knowledge the model has learned and effectively transferring them to the novel classes. For the low-shot regime, we propose a distribution calibration method to deal with the lack of inner-class variation problem. Meanwhile, a shift compensation method is proposed to compensate for possible distribution shift during fine-tuning. For the high-shot regime, we propose to use the knowledge learned from ImageNet as guidance for the feature learning in the fine-tuning stage, which will implicitly align the distributions of the novel classes. Although targeted toward different regimes, these two strategies can work together to further improve the FSOD performance. Experiments on both the VOC and COCO benchmarks show that our proposed method can significantly outperform the baseline method and produce competitive results in both low-shot settings (shot<5) and high-shot settings (shot>=5). Code is available at https://github.com/JulioZhao97/EffTrans_Fsdet.git.
Blockchain of Resource-Efficient Anonymity Protection with Watermarking for IoT Big Data Market

According to the ever-growing supply and demand of IoT content, IoT big data in diversified applications are deemed a valuable asset by private and public sectors. Their privacy protection has been a hot research topic. Inspired by previous work on bounded-error-pruned IoT content market, we observe that the anonymity protection with robust watermarking can be developed by further pruning data for better resource-efficient IoT big data without violating the required quality of sensor service or quality of decision-making. In this paper, resource-efficient anonymity protection with watermarking is thus proposed for data consumers and owners of IoT big data market via blockchain. Our proposed scheme can provide the IoT data with privacy protections of both anonymity and ownership in IoT big data market with resource efficiency. The experiments of four different-type IoT datasets with different settings included bounded-errors, sub-stream sizes, watermark lengths, and ratios of data tampering. The performance results demonstrated that our proposed scheme can provide data owners and consumers with ownership and anonymity via watermarking the IoT big data streams for lossless compressibility. Meanwhile, the developed DApp with our proposed scheme on the Ethereum blockchain can help data owners freely share and trade with consumers in convenience with availability, reliability, and security without mutual trust.
Webly Supervised Image Hashing with Lightweight Semantic Transfer Network

Recent studies have verified the success of deep hashing for efficient image retrieval. However, most existing methods require abundant human labeling data to optimize the large number of involved network parameters, which consequently restricts the scalability of deep image hashing. Alternatively, learning from freely available web images that inherently include rich semantics is a promising strategy. Nevertheless, the domain distribution gap will prevent transferring the semantics involved in the source web images to the target images. Besides, most existing deep image hashing methods suffer from excessive training time to achieve satisfactory performance without explicit supervision. How to efficiently train the deep image hashing network is another important problem that needs to be seriously considered. In this paper, we propose a Webly Supervised Image Hashing (WSIH) with a well-designed lightweight network. Our model enhances the semantics of unsupervised image hashing with the weak supervision from freely available web images, and simultaneously avoids involving over-abundant parameters in the deep network architecture. Particularly, we train a concept prototype learning network on the web images, learning well-trained network parameters and the prototype codes that hold the discriminative semantics of the potential visual concepts in target images. Further, we meticulously design a lightweight siamese network architecture and a dual-level transfer mechanism to efficiently translate the semantics learned from source web images to the target images. Experiments on two widely-tested image datasets show the superiority of the proposed method in both retrieval accuracy and training efficiency compared to state-of-the-art image hashing methods.The source codes of our method are available at: https://github.com/christinecui/WSIH.
Modeling Adaptivity in a Dynamic Task

Adaptivity is examined within a complex task environment: the Kanfer-Ackerman Air Traffic Controller Task©. A computational model is developed in ACT-R to account for such adaptivity using an implicit learning mechanism.
Multiscale mineralogical investigations for mineral potentiality mapping of Ras El-Kharit-Wadi Khashir district, Southern Eastern Desert, Egypt

Through various scales of observation, ranging from remote sensing data, field investigations, hand specimens, microscopic petrographic examinations, XRD, to SEM, indicators of various mineralization types are highlighted in Ras El-kharit-wadi Khashir (Eastern Desert, Egypt). Systematic remote sensing exploration of the mineralized zones is performed through integrating Sentinel 2 and ASTER datasets. False-color combinations, informative band ratios, relative absorption band depth, and CEM techniques were applied to discriminate rock units and various types of hydrothermal alterations. Moreover, ALOS PALSAR DEM was utilized to decipher the structural lineaments. Intensive field investigations confirmed hydrothermally altered zones that were picked out through remote sensing analysis and revealed that the study area is affected by cataclastic metamorphism to some extent. Magmatic and metamorphic rock types are represented by propylitic, phyllic, argillic, and silicification zones. Sericitization, chloritization, epidotization, kaolinitization, carbonatization, and silicification are recorded utilizing petrographic and remote sensing investigations. Moreover, the current study reveals that the detected alteration is the main reason for the apparent wide range of petrographic characteristics of each rock type and bearing several opaque minerals, such as pyrite, magnetite, titanomagnetite, chalcopyrite, arsenopyrite, covellite, galena, goethite, and hematite. Most of these opaques were identified using ore microscopy, XRD, and SEM. The distribution of hydrothermal alterations, representative samples bearing mineralization, structurally dissected zones are integrated to build a mineral potentiality map of the study area. The resultant MPM was confirmed via field survey and emphasized the usefulness of the current integrated approach besides highlighting about 125 km2 as potential mineralized zones.
PreciseEdge raster RGB image segmentation algorithm reduces user input for livestock digital body measurements highly correlated to real-world measurements

Computer vision is a tool that could provide livestock producers with digital body measures and records that are important for animal health and production, namely body height and length, and chest girth. However, to build these tools, the scarcity of labeled training data sets with uniform images (pose, lighting) that also represent real-world livestock can be a challenge. Collecting images in a standard way, with manual image labeling is the gold standard to create such training data, but the time and cost can be prohibitive. We introduce the PreciseEdge image segmentation algorithm to address these issues by employing a standard image collection protocol with a semi-automated image labeling method, and a highly precise image segmentation for automated body measurement extraction directly from each image. These elements, from image collection to extraction are designed to work together to yield values highly correlated to real-world body measurements. PreciseEdge adds a brief preprocessing step inspired by chromakey to a modified GrabCut procedure to generate image masks for data extraction (body measurements) directly from the images. Three hundred RGB (red, green, blue) image samples were collected uniformly per the African Goat Improvement Network Image Collection Protocol (AGIN-ICP), which prescribes camera distance, poses, a blue backdrop, and a custom AGIN-ICP calibration sign. Images were taken in natural settings outdoors and in barns under high and low light, using a Ricoh digital camera producing JPG images (converted to PNG prior to processing). The rear and side AGIN-ICP poses were used for this study. PreciseEdge and GrabCut image segmentation methods were compared for differences in user input required to segment the images. The initial bounding box image output was captured for visual comparison. Automated digital body measurements extracted were compared to manual measures for each method. Both methods allow additional optional refinement (mouse strokes) to aid the segmentation algorithm. These optional mouse strokes were captured automatically and compared. Stroke count distributions for both methods were not normally distributed per Kolmogorov-Smirnov tests. Non-parametric Wilcoxon tests showed the distributions were different (p&lt; 0.001) and the GrabCut stroke count was significantly higher (p = 5.115 e -49 ), with a mean of 577.08 (std 248.45) versus 221.57 (std 149.45) with PreciseEdge. Digital body measures were highly correlated to manual height, length, and girth measures, (0.931, 0.943, 0.893) for PreciseEdge and (0.936, 0. 944, 0.869) for GrabCut (Pearson correlation coefficient). PreciseEdge image segmentation allowed for masks yielding accurate digital body measurements highly correlated to manual, real-world measurements with over 38% less user input for an efficient, reliable, non-invasive alternative to livestock hand-held direct measuring tools.
Quantifying multiqubit magic channels with completely stabilizer-preserving operations

In this paper we extend the resource theory of magic states to the channel domain by considering completely stabilizer-preserving operations (CSPOs) as free. We introduce and characterize the set of CSPO-preserving and completely CSPO-preserving superchannels. We quantify magic channels by extending the generalized robustness and the min-relative entropy defined for magic states to channels and show that they bound the single-shot dynamical magic cost and distillation. We also provide analytical conditions for qubit interconversion under CSPOs and show that it is a linear programming feasibility problem and hence can be efficiently solved. Lastly, we give a classical simulation algorithm whose runtime is related to the generalized robustness of magic channels. Our algorithm depends on some predefined precision, and if there is no bound on the desired precision then it achieves a constant runtime.
Trajectory Control of An Articulated Robot Based on Direct Reinforcement Learning

Reinforcement Learning (RL) is gaining much research attention because it allows the system to learn from interacting with the environment. Yet, with all these successful applications, the application of RL in direct joint torque control without the help of an underlining dynamic model is not reported in the literature. This study presents a split network structure that enables successful training of RL to learn the direct torque control for trajectory following a six-axis articulated robot without prior knowledge of the dynamic robot model. The training took a very long time to converge. However, we were able to show the successful control of four different trajectories without needing an accurate dynamics model and complex inverse kinematics computation. To show the RL-based control’s effectiveness, we also compare the RL control with the Model Predictive Control (MPC), another popular trajectory control method. Our results show that while the MPC achieves smoother and more accurate control, it does not automatically treat the singularity. In addition, it requires complex inverse dynamics calculations. On the other hand, the RL controller instinctively avoided the violent action around the singularities.
Leveraging Balanced Semantic Embedding for Generative Zero-Shot Learning

Generative (generalized) zero-shot learning [(G)ZSL] models aim to synthesize unseen class features by using only seen class feature and attribute pairs as training data. However, the generated fake unseen features tend to be dominated by the seen class features and thus classified as seen classes, which can lead to inferior performances under zero-shot learning (ZSL), and unbalanced results under generalized ZSL (GZSL). To address this challenge, we tailor a novel balanced semantic embedding generative network (BSeGN), which incorporates balanced semantic embedding learning into generative learning scenarios in the pursuit of unbiased GZSL. Specifically, we first design a feature-to-semantic embedding module (FEM) to distinguish real seen and fake unseen features collaboratively with the generator in an online manner. We introduce the bidirectional contrastive and balance losses for the FEM learning, which can guarantee a balanced prediction for the interdomain features. In turn, the updated FEM can boost the learning of the generator. Next, we propose a multilevel feature integration module (mFIM) from the cycle-consistency branch of BSeGN, which can mitigate the domain bias through feature enhancement. To the best of our knowledge, this is the first work to explore embedding and generative learning jointly within the field of ZSL. Extensive evaluations on four benchmarks demonstrate the superiority of BSeGN over its state-of-the-art counterparts.
The Database of Byzantine Book Epigrams Project: Principles, Challenges, Opportunities

This paper presents an overview of the history, conceptualization, and development of the Database of Byzantine Book Epigrams, an ongoing research project hosted at Ghent University. It also offers a glimpse into current and future research threads carried out within the project, with an eye on long-term sustainability. The first part of the paper pinpoints the position of DBBE within the broad field of Digital Humanities and addresses the question of how and why Byzantine metrical paratexts have been collected in an open-access online database. In the second part of the article, we describe the main features of the relational database currently available, both from the perspective of its users and from a technical point of view. The third section of the paper includes the description of four subprojects connected to DBBE, which at present involve the development of a graph database complementary to the relational one, the implementation of natural language pre-processing applied to the DBBE corpus, the linguistic analysis of formulaicity in book epigrams, and the exploration of the broad implications of the study of book epigrams for a better understanding of Byzantine book culture.
User Modelling to support Behavioural Modelling in Smart Environments

Behavioural modelling in smart environments has the capability to allow the adequate provision of services to users of smart environments with inputs from user modelling. Although promising behavioural models developed for users in smart and normal environments already exist, the effectiveness of these behavioural models in terms of user-specific preferences is unclear. This study aims to investigate ways in which user models can be constructed for behavioural modelling purposes in smart environments. To achieve this aim, the Generic User Model was developed to cater for the limitations of existing user models by ensuring that behavioural modelling and monitoring are conducted by using inputs from the user model. The Generic User Model comprises five sub models, namely, static, dynamic, internal activity and external context models.