Reviewerly/exHarmony huggingface dataset:
Medical Symptom Detection in Intelligent Pre-Consultation Using Bi-directional Hard-Negative Noise Contrastive Estimation

Leveraging artificial intelligence (AI) techniques in medical applications is helping our world to deal with the shortage of healthcare workers and improve the efficiency and productivity of healthcare delivery. Intelligent pre-consultation (IPC) is a relatively new application deployed on mobile terminals for collecting patient's information before a face-to-face consultation. It takes advantages of state-of-the-art machine learning techniques to assist doctors on clinical decision-making. One of key functions of IPC is to detect medical symptoms from patient queries. By extracting symptoms from patient queries, IPC is able to collect more information on patient's health status by asking symptom-related questions. All collected information will be summarized as a medical record for doctors to make clinical decision. This saves a great deal of time for both doctors and patients. Detecting symptoms from patient's query is challenging, as most patients lack medical background and often tend to use colloquial language to describe their symptoms. In this work, we formulate symptom detection as a retrieval problem and propose a bi-directional hard-negative enforced noise contrastive estimation method (Bi-hardNCE) to tackle the symptom detection problem. Bi-hardNCE has both forward contrastive estimation and backward contrastive estimation, which forces model to distinguish the true symptom from negative symptoms and meanwhile distinguish true query from negative queries. To include more informative negatives, our Bi-hardNCE adopts a hard-negative mining strategy and a false-negative eliminating strategy, which achieved a significant improvement on performance. Our proposed model outperforms commonly used retrieval models by a large margin.
Hierarchical generator of tracking global hypotheses

The presence of crowds, crossing people, occlusions, and individuals entering and leaving the monitored scenario turns the automatization of Multi-Object Tracking into a demanding task. Due to the difficulties in dealing with those situations, the data association between the incoming observations and their corresponding identities could produce split, merged, and even missed tracks. This article proposes a Hierarchical Generator of Tracking Global Hypotheses (HGTGH) to prevent those errors. In this method, the data association process is divided into hierarchical levels according to multiple factors, such as the duration of tracking on the individuals or the number of frames in a row where they have been missed. A dedicated formulation of the association cost at each level properly combines various affinity metrics. Instead of generating hypotheses for each individual and analyzing them through a batch of future frames, the proposed method immediately generates a global hypothesis that describes the assignment of a whole set of identities on every incoming frame. The generated hypothesis is also able to render new people entering the scene. Thanks to this advantage, the proposed method simultaneously addresses the reduction of identity switches and the problem of starting new tracks. This novel data association method constitutes the core of an online tracking algorithm, which has been evaluated over the MOT17 dataset to demonstrate its effectiveness.
Objective evaluation of mechanical expressiveness in android and human faces

Extensive research has been conducted to realize highly expressive android faces. In contrast, not much is being discussed which android faces are mechanically better and how advanced each android is because a fair and meaningful standard index of expressiveness does not exist. Therefore, in this study, we propose a numerical index and three types of visualization methods for facial deformation expressiveness to investigate the mechanical performance of android faces compared to humans. We used a data set of three-dimensional displacement vectors of over 100 facial points on the faces of two androids and three human males to calculate the expressiveness that reflects how diversely each facial point could move. As a result, we found that the expressiveness of the androids was significantly lower than humans, especially in the lower faces. The expressiveness of the androids was approximately only 20% that of humans even in the most expressive axis and two orders of magnitude lower than humans in three-dimensional evaluation. Thus, our method revealed the lackness of mechanical performance in recently developed androids to move facial skin widely. This method enabled us to reasonably discuss future directions to realize next generation androids with expressiveness similar to that in humans.
Challenges in implementing a digital pathology workflow in surgical pathology

The rate of digital pathology adoption had been slow until the COVID-19 pandemic served as catalyst, demonstrating the possibilities of saving time and money by converting glass slides into whole slide images (WSI) and thus making them available to all staff pathologist immediately after, regardless of where the pathologists are located, in their office, at home or travel location. Regardless of the cataclysmic proportions of this event in health care and in pathology specifically speaking, the transition is still slow. After the COVID 19 pandemic, there was an increase in interest among laboratories and pathology groups. We recognize two periods, pre COVID 19 with low interest and low conversion rates; and during COVID 19 where the interest grew exponentially but not much so the adoption. Right now, we are on this high interest, but low adoption period. Since it is not for lack of interest from pathologists and administrators, now the issue is that all these interested parties have found out that the transition process is intrinsically slow and full of challenges. In this paper we are reviewing the main challenges that laboratories are facing in the adoption process, some are obvious, other are not so much. Those challenges can be divided in financial, technical, and perhaps the most difficult to overcome, changing management. All these challenges are not insurmountable, and the key is not to tackle them when they appear, but far in advance, this is accomplished by a thorough investigation of the variables and the particular use cases of the institution and to start educating medical and non-medical stakeholders about the benefits of a digital workflow, how these benefits will definitively pay off the investment and will render the laboratory not only prepared for other disruptive event like COVID 19, but it will also make them ready for the fast growing trend of utilizing machine learning and artificial intelligence to analyze images and finetune the diagnosis.
A Blockchain-based Material Donation Platform

The COVID-19 pandemic has led to a worldwide surge in demand for masks, protective clothing, and other epidemic prevention materials. The lack of epidemic prevention materials has put the lives of frontline health care workers at serious risk. However, epidemic prevention materials are not being distributed fairly and efficiently. This, coupled with the occasional scramble for scarce materials, makes epidemic prevention materials scarcer. The traditional centralized donation model makes it difficult to obtain the demand for materials in a timely manner, and the existing blockchain-based donation systems have not improved the efficiency of material donation. Moreover, most of the donation systems do not consider privacy and security issues. In this paper, we propose a blockchain-based material donation platform designed and implemented through the Ethereum platform. We solve the difficulty of demand acquisition and improve the transparency of the donation process through blockchain; reduce the possibility of a second disaster and improve the efficiency of material distribution through smart contracts; and protect the privacy and security of the donation process through zero-knowledge proof. We validate the security and efficiency of the proposed epidemic donation platform.
Blockchains’ federation for enabling actor-centered data integration

The pervasive presence of the Internet results in an increasing number of people and systems interacting with each other. However, since each system only captures its own representation of reality, the information of the involved actors is scattered and even duplicated. This scattering generates problems by itself since only a small fragmented vision of the actors’ reality is available for each system. At the same time, the duplication of information in different systems can lead to inconsistencies that impede the correct integration of different systems. In this paper, we present an architecture based on the use of an upper blockchain in charge of federating a set of lower-level blockchains. This way, actor-centered data integration is achieved, offering a single, global vision of the dispersed information of each actor. This solution, called FedBlocks, relies on the blockchains’ federation concept where all interconnected systems are communicated and can share authorized data of their actors, providing a benefit for these systems and for the actors themselves. The proposed implementation has been successfully tested integrating 5689 records from 50 different institutions, belonging to 1156 actors.
Image Retrieval Based on Blockchain

There are two problems in the traditional picture retrieval process: (1) It is difficult to find a specific picture accurately. (2) The index has the risk of being attacked and lost. To solve these two problems, we introduced the blockchain for secure indexing. In the face of traditional text indexing methods, we utilize the blockchain to guarantee the preservation of that index. When facing that precise indexing of pictures, we find the precise pictures by hashing the pictures.
An Adaptive High-Fidelity Image Compression Framework for Internet of Vehicles

This paper proposes a new adaptive high-fidelity image compression solution to achieve a high compression ratio with the least distortion using a generative adversarial network. This work focuses on preserving the details by compressing the salient regions in the image with a high bit rate to guarantee the generation of high-quality outputs that sustain most of its characteristics. The image background is compressed with a lower bit rate. This work is tested against the Kodak, CLIC, MOTS, and UADTV datasets based on the bit-per-pixel rate where the results prove that our work achieves the highest quality with the lowest rate. To achieve a lower bit rate, the arithmetic coding algorithm is applied to the compression sequence which reduces the rate by 35%. With the achieved low bit rate, our work boosts the rate of image transmission by a factor of more than 2.
Car security system with face recognition using Convolution Neural Network

Different automobile security systems have emerged over time as a replacement to the normal locks for safety. But security and privacy flaws have been observed over time. This is a serious fault, as normal locks are not very safe. Also, people have always struggled to find their keys. This project is focused upon creating a more secure and simple security system using face recognition system. The main objective of the project is to form a more secure and simpler system for automobile using Siamese Neural Network, which would allow the user to not carry keys to unlock or start the car all the time. There have been many apt uses for this algorithm which involves systems using various datasets to perform comparisons and identifications. This would allow the user to hold a very distinctive access to the car while attempting to nullify the possibility of replicating the key. Basically, only a registered identity of the user with respect to his facial observational values would be matched to grant access to the system. So therefore, with a larger dataset, thorough accuracy could be achieved relentlessly.
Construction of Model to Enhance College Students’ Core Competitiveness from Perspective of Employment Competition through IoT Apps

The lack of college students’ core competitive advantage in employment is the main reason for the difficulty of college students’ employment. Therefore, in the perspective of employment competition, the paper designs the method of building the model to improve college students’ core competitiveness.
Research on educational information technology system under the background of “Internet+”

The popularization of Internet and new media technology has promoted the upgrading of educational software and hardware, and promoted the continuous improvement of educational technology. This paper studies and analyzes the educational informatization technology system from the concept of the educational informatization technology system, the defects of the educational informatization technology system, and the development trend of the educational informatization technology system, and summarizes the characteristics of the educational informatization technology system. The direction and measures for improvement are set out, so that the educational information technology system can be continuously improved and developed.
Image thresholding segmentation based on weighted Parzen-window and linear programming techniques

Image segmentation by thresholding is an important and fundamental task in image processing and computer vision. In this paper, a new bi-level thresholding approach based on weighted Parzen-window and linear programming techniques is proposed to use in image thresholding segmentation. First, by proposing a weighted Parzen-window to describe the gray level distribution status, we obtain the boundaries for the foreground and background of the image. Then the image thresholding problem can be transformed into the problem of solving a linear programming problem for computing the coefficient values of weighted Parzen-window. The results of testing on synthetic, NDT and a set of benchmark images indicate that the proposed method can achieve a higher segmentation accuracy and robustness in comparison to some classical thresholding methods, such as inter class variance method (OTSU), Kapur's entropy-based method (KSW), and some state-of-art methods that consider spatial information, such as CHPSO, GLLV histogram method and GABOR histogram method.
BCadvisor: Enabling Green Blockchain Systems Through Resource-Oriented Benchmarking

As an emerging technique, blockchain becomes widely-adopted in numerous fields, including supply chain, cloud computing, smart healthcare, etc. Nonetheless, some drawbacks, especially the high resource cost, also expose with the deepening of blockchain applications. Moreover, we lack the standard tools for benchmarking blockchain’s resource efficiency. In this case, even researchers keep on proposing lightweight blockchains, we have no idea about their actual validity. Generally speaking, two daunting challenges are yet to be addressed, i.e., the compatible framework and the resource-oriented benchmarking metrics. Motivated by such facts, this paper presents a resource-oriented blockchain benchmarking tool, named BCadvisor. Specifically, we develop a modular architecture, with three collaborating models. Such framework not only realizes a pipeline for data parsing, metric storage, and result visualization but also can easily support any new blockchain. Furthermore, we design a novel process named Three-step Resource-oriented Benchmarking (TsRoB), which divides the blockchain into 4 layers and evaluates the resource efficiency of each layer. Finally, we conduct comprehensive benchmarking on four representative blockchains and discuss the results. Our experiments can serve as a guideline for researchers in pursuing green blockchain running.
Achieving Privacy-Preserving Weighted Similarity Range Query over Outsourced eHealthcare Data

Similarity queries have been widely employed to offer more effective medical care to patients in eHealthcare. As a special query, similarity query with user-defined weights, which allows users (i.e., doctors in eHealthcare) to define the weight for the distance metric, has received particular interest recently. In order to make the weighted similarity query service more flexible and reliable, healthcare centers tend to outsource the healthcare data and the corresponding similarity query service to a powerful cloud. However, due to privacy concerns, healthcare centers usually demand to encrypt the data before outsourcing them to the cloud. Although some existing privacy-preserving similarity query schemes can be adapted to handle weighted similarity range queries, they may face issues in either the practicality or the accuracy of query results. Aiming at addressing these issues, in this paper, we design an efficient privacy-preserving weighted similarity range query (EPW-Sim) scheme, which is practical and can return accurate query results. Specifically, we first discover a lower bound for the distance metric, i.e., weighted Euclidean distance, and further leverage the lower bound as a filtration condition to design an efficient weighted similarity range query algorithm. Second, we apply a modified asymmetric-scalar-product encryption (MASPE) scheme to preserve the privacy of the designed algorithm and propose our EPW-Sim scheme. Finally, we analyze the security of our scheme and conduct experiments to validate its efficiency, and the results demonstrate that our scheme is privacy-preserving and efficient.
Жанрові особливості та творчий потенціал сучасної української літератури (на прикладі українського цифрового самвидаву)

The article is devoted to the study of genre features and creative potential of modern Ukrainian literature, which has the form of digital self-publishing. The study used hermeneutic, comparative, system-structural and structural-functional methods, as well as conceptual, plot-image, problem-thematic analysis, text-analytical method of "careful reading" to read the archetypal and code content of texts and others. Currently, there are more than 10 literary digital self-published resources inUkraine. However, the prose resources "Booknet" and "Arkush" have the real influence on the literary process. Both allow authors to communicate with readers through digital self-publishing. Five factors influence the work of modern Ukrainian authors: 1) the influence of classical Ukrainian and world canons; 2) the influence of postmodernism both in the literature and at the social and ideological levels; 3) the influence of mass literature of the Russian Federation; 4) the influence of translations and modern European literature; 5) the influence of the Internet and communication with the reader. In the context of digital self-publishing, each of these factors has its own specifics. The influence of cinema on mass literature is especially powerful. This can be manifested as a replication of the template (simplification), and in the form of systemic plot and psychological complication of textual structures. Today, digital self-publishing is represented by a whole range of genres: fiction (science fiction, fantasy, mystical prose, etc.), realism (historical, romantic, social, psychological novel, detective, thriller, etc.), and literature for children and youth. In each genre we see original novels that are not inferior in quality to those published through publishing houses. The latter makes it possible to talk about the emergence of writers inUkrainewho can be "indie authors". That corresponds to the global changes in book publishing.
A Lightweight Advertisement Ecosystem Simulation Platform for Security Analysis

Based on the statistics, advertisements (ads) generate more than 80% of companies' revenues. However, the complexity of the ads ecosystem blurs the boundaries of responsibility between companies. It can not analyze privacy and security issues in such an ecosystem. For example, collecting user information without user consent for data analysis and displaying ads will cause privacy leakage. But we can not explain which types of the company need to take measures to protect privacy. In our paper, to clarify the responsibility of companies in the advertisement ecosystem, we divide them into six types of entities according to their needs and functions. Then, we design a lightweight simulation platform to illustrate the advertisement ecosystem and support security and privacy analysis. Finally, we take personal ads recommendations based on federated learning as an example to verify the feasibility for privacy and security analysis in this platform.
RippleSign: Isogeny-Based Threshold Ring Signatures with Combinatorial Methods

Threshold ring signature schemes are widely used in blockchains and cryptocurrencies. Isogeny-based signature schemes benefit from short public key and signature sizes. In this paper, by using trapdoor commitments in ring signatures, we propose a decentralized post-quantum threshold ring signature scheme, RippleSign, based on isogenies between supersingular elliptic curves. Our scheme enjoys perfect anonymity and is un-forgeable under a chosen-message attack. In terms of practicality, our threshold scheme has signature size of 187 KB with 100 participants at the 128-bit security level. In addition, our scheme takes about 2 seconds to produce (2,3)-threshold ring signature.
Research on Knowledge Graph Construction for Python Programming Language

As the core and foundation of artificial intelligence, knowledge graph provides new power to smart education. With the popularization and low age of learning programming languages, building knowledge graphs for them is especially crucial for learning this programming language. Therefore, the paper takes Python programming language as an example, designs the schema layer and data layer of this knowledge graph, and constructs the knowledge graph of Python programming language through five steps: ontology construction, data analysis, entity extraction, relationship extraction, and knowledge storage. Finally, the application of this knowledge graph is explained, which helps learners understand the global knowledge, detect the knowledge level and recommend learning paths, effectively build a cognitive framework and promote personalized learning.
The Smart Education Progress Measurement: Can Field Experts’ Opinions Help?

The results of a study on the schools’ digital transformation process evaluation in the Russian context through the prism of experts’ opinions are presented. A smart school is considered a result of the school’s digital transformation process. While we use classical quantitative methods for evaluation of the process of transformational changes, these methods fail to capture implicit characteristics of it. The research uses data from expert evaluation collected in the monitoring of the digital transformation of Russian general educational organizations in 2021 linked to ICT (information and communication technology) coordinators’ survey indicators of the same schools. We display insignificant correlations between expert evaluation scale scores and indicators of the digital environment. The study shows that the idea of a smart education being a result of a school’s digital transformation has not yet received wide recognition among educators. In order to promote the idea of smart education widely, it is necessary to present educators with real examples of a successful transition from a traditional school to a smart school.