Reviewerly/exHarmony huggingface dataset:
Emerging advances of blockchain technology in finance: a content analysis

Abstract Blockchain has become a widely used information system technology recently because of its effectiveness as an intermediary-free platform. While the use of blockchain in various fields, such as finance, supply chains, healthcare, education, and energy consumption, is increasingly enabling the development of Internet-enabled “distributed databases,” there are not many exploratory studies available to provide an understanding of how the field is progressing. Therefore, it is imperative to explore the status quo of blockchain technology in the finance sector, particularly highlighting how blockchain architectures can aid the finance sector to gain competitive advantage. This systematic literature review analyzes the content of the 50 most relevant articles and professional industry reports through peer-reviewed relevant academic literature in the finance sector from 2008 to 2022 to identify several possible features of blockchain research in the financial sector. This study highlighted the dimensions of blockchain technology, blockchain in finance, its competitive advantages, the current status of finance, and various challenges that keep the implementation of blockchain-based financial information systems at the initial stage. We identified three main areas that require research attention in order for blockchain technology to become the “next-generation networks” that will revolutionize the financial sector.
Research on the Teaching Method of Programming Course by Using Computational Thinking

The programming course is an important public course for undergraduate, which mainly cultivates students' ability to solve and analysis professional problems by using computational thinking.Taking C language programming course as example, this paper proposes the teaching method of programming course which is oriented by computational thinking, and carries out research from four aspects: classroom teaching, experimental teaching, course assessment and stratified teaching.The teaching practice shows that compared with the traditional teaching mode, the teaching method incorporating computational thinking can effectively improve students' learning effect.
Efficient S-box construction based on quantum-inspired quantum walks with PSO algorithm and its application to image cryptosystem

Abstract Amidst the growth of the internet and communication technologies, the requirements for the security of data transmitted via these technologies are increasing. Visual data, like images and videos, are commonly utilized for representing the majority of data due to its having more detailed information. Until now, the physical implementation of quantum computers does not have enough capability for hacking any traditional image cryptosystem, but amidst the growth of quantum resources, enough capability may be available in the near future. Most data represented by images has a long lifetime, like personal, medical, military, etc. Therefore, new quantum-inspired-based designs for image cryptosystems are required to be performed on digital resources and have the capability of defying the potential attacks from digital and quantum resources. In this study, a new substitution box (S-box) mechanism is proposed, which is based on quantum-inspired quantum walks, Hénon map, and a customized particle swarm optimization algorithm. Performance analysis of the suggested S-box proves its effectiveness and its reliability in designing various cryptosystems. Based on the effectiveness of the presented S-box, a new image cryptosystem is proposed, in which its experiential outcomes prove its efficacy and security against various attacks. The average outcome of entropy is 7.99977, UACI is 33.484%, NPCR is 99.618%, and Chi-square is 249.481 for the constructed cipher images.
Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods.
OpticalBERT and OpticalTable-SQA: Text- and Table-Based Language Models for the Optical-Materials Domain

Text mining in the optical-materials domain is becoming increasingly important as the number of scientific publications in this area grows rapidly. Language models such as Bidirectional Encoder Representations from Transformers (BERT) have opened up a new era and brought a significant boost to state-of-the-art natural-language-processing (NLP) tasks. In this paper, we present two "materials-aware" text-based language models for optical research, OpticalBERT and OpticalPureBERT, which are trained on a large corpus of scientific literature in the optical-materials domain. These two models outperform BERT and previous state-of-the-art models in a variety of text-mining tasks about optical materials. We also release the first "materials-aware" table-based language model, OpticalTable-SQA. This is a querying facility that solicits answers to questions about optical materials using tabular information that pertains to this scientific domain. The OpticalTable-SQA model was realized by fine-tuning the Tapas-SQA model using a manually annotated OpticalTableQA data set which was curated specifically for this work. While preserving its sequential question-answering performance on general tables, the OpticalTable-SQA model significantly outperforms Tapas-SQA on optical-materials-related tables. All models and data sets are available to the optical-materials-science community.
Towards an Adaptable Systems Architecture for Memory Tiering at Warehouse-Scale

Fast DRAM increasingly dominates infrastructure spend in large scale computing environments and this trend will likely worsen without an architectural shift. The cost of deployed memory can be reduced by replacing part of the conventional DRAM with lower cost albeit slower memory media, thus creating a tiered memory system where both tiers are directly addressable and cached. But, this poses numerous challenges in a highly multi-tenant warehouse-scale computing setting. The diversity and scale of its applications motivates an application-transparent solution in the general case, adaptable to specific workload demands.
Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited

Recommendation models that utilize unique identities (IDs for short) to represent distinct users and items have been state-of-the-art (SOTA) and dominated the recommender systems (RS) literature for over a decade. Meanwhile, the pre-trained modality encoders, such as BERT [9] and Vision Transformer [11], have become increasingly powerful in modeling the raw modality features of an item, such as text and images. Given this, a natural question arises: can a purely modality-based recommendation model (MoRec) outperforms or matches a pure ID-based model (IDRec) by replacing the itemID embedding with a SOTA modality encoder? In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency.
LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation

Medical image segmentation plays an essential role in developing computer-assisted diagnosis and treatment systems, yet it still faces numerous challenges. In the past few years, Convolutional Neural Networks (CNNs) have been successfully applied to the task of medical image segmentation. Regrettably, due to the locality of convolution operations, these CNN-based architectures have their limitations in learning global context information in images, which might be crucial to the success of medical image segmentation. Meanwhile, the vision Transformer (ViT) architectures own the remarkable ability to extract long-range semantic features with the shortcoming of their computation complexity. To make medical image segmentation more efficient and accurate, we present a novel light-weight architecture named LeViT-UNet, which integrates multi-stage Transformer blocks in the encoder via LeViT, aiming to explore the effectiveness of fusion between local and global features together. Our experiments on two challenging segmentation benchmarks indicate that the proposed LeViT-UNet achieved competitive performance compared with various state-of-the-art methods in terms of efficiency and accuracy, suggesting that LeViT can be a faster feature encoder for medical images segmentation. LeViT-UNet-384, for instance, achieves Dice similarity coefficient (DSC) of 78.53% and 90.32% with a segmentation speed of 85 frames per second (FPS) in the Synapse and ACDC datasets, respectively. Therefore, the proposed architecture could be beneficial for prospective clinic trials conducted by the radiologists. Our source codes are publicly available at https://github.com/apple1986/LeViT_UNet .
Iterative Learning of Weighted Rule Sets for Greedy Search

Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider learning sets of weighted action-selection rules for guiding greedy search with application to automated planning. We make two primary contributions over prior work on learning for greedy search. First, we introduce weighted sets of action-selection rules as a new form of control knowledge for greedy search. Prior work has shown the utility of action-selection rules for greedy search, but has treated the rules as hard constraints, resulting in brittleness. Our weighted rule sets allow multiple rules to vote, helping to improve robustness to noisy rules. Second, we give a new iterative learning algorithm for learning weighted rule sets based on RankBoost, an efficient boosting algorithm for ranking. Each iteration considers the actual performance of the current rule set and directs learning based on the observed search errors. This is in contrast to most prior approaches, which learn control knowledge independently of the search process. Our empirical results have shown significant promise for this approach in a number of domains.
Generalised information systems capture L-domains

A generalisation of Scott's information systems \cite{sco82} is presented that captures exactly all L-domains. The global consistency predicate in Scott's definition is relativised in such a way that there is a consistency predicate for each atomic proposition (token) saying which finite sets of such statements express information that is consistent with the given statement. It is shown that the states of such generalised information systems form an L-domain, and that each L-domain can be generated in this way, up to isomorphism. Moreover, the equivalence of the category of generalised information systems with the category of L-domains is derived. In addition, it will be seen that from every generalised information system capturing an algebraic bounded-complete domain a corresponding Scott information system can be obtained in an easy and natural way, and vice versa; similarly for Hoofman's continuous information systems \cite{ho93} and the continuous bounded-complete domains captured by them; for Chen and Jung's disjunctive propositional logic \cite{cj06} and algebraic L-domains (as well as for Wang and Li's \cite{wll20} finitary version and Lawson-compact algebraic L-domains); and for Wang and Li's conjunctive sequent calculi \cite{wlbc20} and proper continuous bounded-complete domains. The proofs always contain syntactic translations between the logical calculi involved.
A Projection Pursuit Forest Algorithm for Supervised Classification

This paper presents a new ensemble learning method for classification problems called projection pursuit random forest (PPF). PPF uses the PPtree algorithm introduced in Lee et al. (2013). In PPF, trees are constructed by splitting on linear combinations of randomly chosen variables. Projection pursuit is used to choose a projection of the variables that best separates the classes. Utilizing linear combinations of variables to separate classes takes the correlation between variables into account which allows PPF to outperform a traditional random forest when separations between groups occurs in combinations of variables. The method presented here can be used in multi-class problems and is implemented into an R (R Core Team, 2018) package, PPforest, which is available on CRAN, with development versions at https://github.com/natydasilva/PPforest.
Optimizing distributed data stream processing by tracing

Heterogeneous mobile, sensor, IoT, smart environment, and social networking applications have recently started to produce unbounded, fast, and massive-scale streams of data that have to be processed “on the fly”. Systems that process such data have to be enhanced with detection for operational exceptions and with triggers for both automated and manual operator actions. In this paper, we illustrate how tracing in distributed data processing systems can be applied to detecting changes in data and operational environment to maintain the efficiency of heterogeneous data stream processing systems under potentially changing data quality and distribution. By the tracing of individual input records, we can (1) identify outliers in a web crawling and document processing system and use the insights to define URL filtering rules; (2) identify heavy keys, such as NULL, that should be filtered before processing; (3) give hints to improve the key-based partitioning mechanisms; and (4) measure the limits of overpartitioning if heavy thread-unsafe libraries are imported.By using Apache Spark as illustration, we show how various data stream processing efficiency issues can be mitigated or optimized by our distributed tracing engine. We describe and qualitatively compare two different designs, one based on reporting to a distributed database and another based on trace piggybacking. Our prototype implementation consists of wrappers suitable for JVM environments in general, with minimal impact on the source code of the core system. Our tracing framework is the first to solve tracing in multiple systems across boundaries and to provide detailed performance measurements suitable for automated optimization, not just debugging.
Selective Distillation of Weakly Annotated GTD for Vision-Based Slab Identification System

This paper proposes an algorithm for recognizing slab identification numbers in factory scenes. In the development of a deep-learning based system, manual labeling to make ground truth data (GTD) is an important but expensive task. Furthermore, the quality of GTD is closely related to the performance of a supervised learning algorithm. To reduce manual work in the labeling process, we generated weakly annotated GTD by marking only character centroids. Whereas bounding-boxes for characters require at least a drag-and-drop operation or two clicks to annotate a character location, the weakly annotated GTD requires a single click to record a character location. The main contribution of this paper is on selective distillation to improve the quality of the weakly annotated GTD. Because manual GTD are usually generated by many people, it may contain personal bias or human error. To address this problem, the information in manual GTD is integrated and refined by selective distillation. In the process of selective distillation, a fully convolutional network is trained using the weakly annotated GTD, and its prediction maps are selectively used to revise locations and boundaries of semantic regions of characters in the initial GTD. The modified GTD are used in the main training stage, and a post-processing is conducted to retrieve text information. Experiments were thoroughly conducted on actual industry data collected at a steelmaking factory to demonstrate the effectiveness of the proposed method.
Computational Solutions for Bayesian Inference in Mixture Models

This chapter surveys the most standard Monte Carlo methods available for simulating from a posterior distribution associated with a mixture and conducts some experiments about the robustness of the Gibbs sampler in high dimensional Gaussian settings. This is a chapter prepared for the forthcoming 'Handbook of Mixture Analysis'.
Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. In this paper, a QPSO with weighted mean personal best position and adaptive local attractor (ALA-QPSO) is proposed to simultaneously enhance the search performance of QPSO and acquire good global optimal ability. In ALA-QPSO, the weighted mean personal best position is obtained by distinguishing the difference of the effect of the particles with different fitness, and the adaptive local attractor is calculated using the sum of squares of deviations of the particles’ fitness values as the coefficient of the linear combination of the particle best known position and the entire swarm’s best known position. The proposed ALA-QPSO algorithm is tested on twelve benchmark functions, and compared with the basic Artificial Bee Colony and the other four QPSO variants. Experimental results show that ALA-QPSO performs better than those compared method in all of the benchmark functions in terms of better global search capability and faster convergence rate.
Towards to Secure an IoT Adaptive Environment System

This paper, deals with the actual problem of secure an IoT adaptive system, namely using secure techniques to secure a Smart Environment System, and the privacy of their users. On a new era of interaction between persons and physical spaces, users want that those spaces smartly adapt to their preferences in a transparent way. This work wants to promote a balanced solution between the need of personal information and the user's privacy expectations. We propose a solution based on requiring the minimal information possible, together with techniques to anonymize and disassociate the preferences from the users.
Exploiting automated planning for efficient centralized vehicle routing and mitigating congestion in urban road networks

One of the pivotal challenges presented to urban traffic controllers is the effective utilization of the transport infrastructure, as a result of the growing urbanization and of the increasing number of vehicles.
Rendering Realistic Subject-Dependent Expression Images by Learning 3DMM Deformation Coefficients

Automatic analysis of facial expressions is now attracting an increasing interest, thanks to the many potential applications it can enable. However, collecting images with labeled expression for large sets of images or videos is a quite complicated operation that, in most of the cases, requires substantial human intervention. In this paper, we propose a solution that, starting from a neutral image of a subject, is capable of producing a realistic expressive face image of the same subject. This is possible thanks to the use of a particular 3D morphable model (3DMM) that can effectively and efficiently fit to 2D images, and then deform itself under the action of deformation parameters learned expression-by-expression in a subject-independent manner. Ultimately, the application of such deformation parameters to the neutral model of a subject allows the rendering of realistic expressive images of the subject. Experiments demonstrate that such deformation parameters can be learned from a small set of training data using simple statistical tools; despite this simplicity, very realistic subject-dependent expression renderings can be obtained. Furthermore, robustness to cross dataset tests is also evidenced.
Human–Robot Shared Control for Path Generation and Execution

A telerobotic system consists of integrating an operator into the control loop of a remote robot. Due to safety considerations, it is very important sometimes that the operator be able to remotely take control of the task and the robot. In this paper, a shared control mode for mobile robots is defined according to the operator ability to control the robot and the autonomy level of the robot itself. In this mode, the task execution is simultaneously accomplished by the operator and the robot, according to the percentage of the task-share coefficient. This depends on several factors such as robot autonomy, user ability, environment accessibility and task difficulty. An experimental application of the developed shared control for path generation task execution is planned for a non-holonomic mobile robot evolving inside a workspace cluttered with static obstacles. In this case, Non-Uniform Rational B-Splines curves are used to generate the robot path linking the Starting point I and the Target point F. The operator can control the robot even by selecting or inserting new control points on the initial feasible path, or by directly moving the robot via the developed Human/Robot Interface or the joystick.
Pointwise SAR image change detection using stereo-graph cuts with spatio-temporal information

In this letter, a pointwise approach using stereo-graph cuts (SGC) with spatio-temporal information is proposed for synthetic aperture radar (SAR) image change detection. The SGC is based on stereo-graph, which is designed to connect the local maximum pixels on two SAR images and can be used to capture the spatio-temporal information. With the support of stereo-graph, a novel SGC energy function is presented to quantify the spatio-temporal information and implicitly measure the difference information between the SAR images. Therefore, the change detection results can be obtained by minimizing the energy function with graph cuts technology. Experimental results on real SAR datasets confirm the validity of the proposed approach in which SGC and spatio-temporal information offer great contributions on improving the robustness and accuracy of detection.