Reviewerly/exHarmony huggingface dataset:
Performance-Aware Mutual Knowledge Distillation for Improving Neural Architecture Search

Knowledge distillation has shown great effectiveness for improving neural architecture search (NAS). Mutual knowledge distillation (MKD), where a group of models mutually generate knowledge to train each other, has achieved promising results in many applications. In existing MKD methods, mutual knowledge distillation is performed between models without scrutiny: a worse-performing model is allowed to generate knowledge to train a better-performing model, which may lead to collective failures. To address this problem, we propose a performance-aware MKD (PAMKD) approach for NAS, where knowledge generated by model <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> is allowed to train model <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B$</tex> only if the performance of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> is better than B. We propose a three-level optimization framework to formulate PAMKD, where three learning stages are performed end-to-end: 1) each model trains an initial model independently; 2) the initial models are evaluated on a validation set and better-performing models generate knowledge to train worse-performing models; 3) architectures are updated by minimizing a validation loss. Experimental results on a variety of datasets demonstrate that our method is effective.
Basic Technology

Blockchain technology and distributed ledger technology have developed rapidly since Bitcoin, the first Blockchain concept, was proposed in 2008. In this chapter, we will introduce the development history and basic concept of the Blockchain. The reader can have a general understanding of the Blockchain so that the reader will comprehend other chapters’ content more easily. We will first look back on the development history of the Blockchain and then look to the future of Blockchain technology. Next, we will discuss fundamental technologies for the Blockchain systems. These technologies ensure the efficiency and security of the Blockchain and expand the blockchain's practical scenarios. After introducing fundamental technologies, we will exhibit three classes of blockchain: public chain, private chain, and consortium chain. Finally, we will illustrate the architecture of Blockchain systems.
Factor analysis of functioning and development of higher education market in the Orel Region

Higher education market is currently dynamically developing under the influence of the processes of digitalization, globalization, as well as the formation of an innovative economy. But this development is actively hindered by a number of deep structural problems that have worsened during the economic and demographic crises. Regional universities face a serious challenge in the form of a steady decline in the number of students, as well as the ongoing state policy of optimization. The modern higher education market does not have time to adapt to the requirements of the labor market, which causes a personnel imbalance. This has determined the relevance of the research, which purpose is to determine the factors of development of the higher education market. The study on issue in the Orel Region demonstrates that such factors as the population size, age groups distribution, income level of the population, the regulatory and legal provision of higher education, the level of employment and migration processes in the region have a significant impact on the demand and consumption of educational services, as well as the number of university students. The conducted research has shown that in the Orel Region there is no qualitatively established connection between the regional labor market and the higher education market, which leads to a shortage of certain personnel in the region and, as a result, to an imbalance in the labor market. The revealed negative trends indicate the need for regional universities to mobilize internal forces aimed at activating the strategy of attracting applicants. To attract the maximum number of applicants, the marketing strategy should focus mainly on the positioning of the university in all media. Conducting professional tests at the faculties will attract talented high school graduates to the university. The inflow of additional funds to regional funds can ensure the introduction of a system of a lesser qualification in the course of training and the creation of endowment funds. These measures can be considered as a system of anti-crisis measures aimed at developing the market of educational services in the field of higher education.
Computer Science Named Entity Recognition in the Open Research Knowledge Graph

Domain-specific named entity recognition (NER) on Computer Science (CS) scholarly articles is an information extraction task that is arguably more challenging for the various annotation aims that can hamper the task and has been less studied than NER in the general domain. Given that significant progress has been made on NER, we anticipate that scholarly domain-specific NER will receive increasing attention in the years to come. Currently, progress on CS NER – the focus of this work – is hampered in part by its recency and the lack of a standardized annotation aims for scientific entities/terms. Directly addressing these issues, this work proposes a standardized task by defining a set of seven contribution-centric scholarly entities for CS NER viz., research problem, solution, resource, language, tool, method, and dataset.
Guidelines for developing recommendations on changing ‘multimedia technologies’ course content according to data analysis of job regional open positions on hh.ru site

The paper is devoted to a series of studies aimed at processing the data on job searches in Vladivostok, published on the open specialized Internet site hh.ru, followed by the development of tips for improving ‘Multimedia Technologies’ training course. An open-source analytics platform Knime.com was used to review the data. The article deals with the findings of the analysis, which show a certain level of salaries for occupations that require professional skills in ‘Multimedia Technologies’ course content. The survey of the 1st -4th-year students of 09.03.01 IT and Computer Science major was carried out at Maritime State University named after Admiral G.I. Nevelskoy to test a hypothesis about the necessity of making adjustments in the subject syllabus. 74 people participated in the survey. Finally, practical hints for improving Multimedia Technology training course content are given to perfect the course to meet the needs of both students and potential employers.
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations

Few-shot learning (FSL) aims to recognize novel queries with only a few support samples through leveraging prior knowledge from a base dataset. In this paper, we consider the domain shift problem in FSL and aim to address the domain gap between the support set and the query set. Different from previous cross-domain FSL work (CD-FSL) that considers the domain shift between base and novel classes, the new problem, termed cross-domain cross-set FSL (CDSC-FSL), requires few-shot learners not only to adapt to the new domain, but also to be consistent between different domains within each novel class. To this end, we propose a novel approach, namely stabPA, to learn prototypical compact and cross-domain aligned representations, so that the domain shift and few-shot learning can be addressed simultaneously. We evaluate our approach on two new CDCS-FSL benchmarks built from the DomainNet and Office-Home datasets respectively. Remarkably, our approach outperforms multiple elaborated baselines by a large margin, e.g., improving 5-shot accuracy by 6.0 points on average on DomainNet. Code is available at https://github.com/WentaoChen0813/CDCS-FSL .
IoT Device Friendly Leveled Homomorphic Encryption Protocols

Leveled homomorphic encryption (LHE) is important because it enables calculations on encrypted data. Are there any weaknesses of LHEƒ The answer is yes. The LHE process is too heavy. If analyzers attempt to collect data from IoT devices and use them safely; this is a significant challenge because IoT devices have low performance. First, the load should be clarified in this study when using LHE with smartphones, which are high-performance IoT devices, and Raspberry Pi, an IoT device with lower performance than smartphones. Next, we tackle the system design of an LHE that does not burden IoT devices. Changing the LHE algorithm or performing hardware optimization in the proposed method is needless. We implemented three LHE systems, including the proposed method, and evaluated the most suitable for IoT devices. We demonstrated that the proposed method is 31 - 91 times faster than the existing methods for plaintext encryption on smartphones.
Autonomous Vehicle for Physically Impaired People

The concept of automation has synonymously been associated with the luxury factor for many years, however seldom has its potential been profoundly deployed in unlocking the welfare avenues for the physically impaired people. The term ‘physically impaired' not only includes the medical patients who, unfortunately, have lost their complete ability for locomotion but also those in the upper sector of the age bracket or those with degenerative diseases like muscle atrophy, who do not possess the necessary strength to travel via means of a mechanical wheelchair and are bound to an assistant or helper to facilitate most basic of their necessities.Our solution shall also be suitable for elderly patients who are visually impaired. With this problem statement, acting as the major impetus for our motivation, we have designed an approach to design a prototype of an autonomous ground vehicle, possessing autonomic movement capabilities, suitable enough for navigation in an inner home environment. It describes a fundamental approach involving aspects like ground mapping of the necessary locations in an open space, self-navigation capabilities to the path entered and obstacle avoidance capabilities in the hardware aspect followed by a small software model that works on voice feature to map the path to be traced by the vehicle. This model can further be implemented in a real-time aforementioned hardware system to make the system capable enough of working on voice command features for inner home navigation.
Imitating Playstyle with Dynamic Time Warping Imitation

Imitation learning has been demonstrated as a useful technique in automatic game testing and the development of believable Non-Player Characters (NPCs). However, imitation learning methods typically focus on learning a policy to complete a task without consideration about the playstyle used. In this work we consider the case where the task is to imitate a given playstyle. We defined a player’s playstyle based on the strategies they use in order to complete the overall task. This has been achieved by rewarding a learning agent based on the similarity of the agent and demonstration trajectories, within a learnt representation space. This allows the playstyle to be learnt in levels that differ to the one the demonstrations were collected in.
AttentionHCare: Advances on computer-aided medical prognosis using attention-based neural networks

With the increasing number of institutions adhering to Electronic Health Records (EHRs) and with the evolution of Artificial Neural Networks, the research on computer-aided medical systems has gained attention. The development of automated prognosis systems is a topic of intense research; nevertheless, despite significant advances, such systems have not yet benefited from techniques of attention and encoding-decoding. We investigate this field by designing a neural network architecture, named AttentionHCare, that innovates by putting together the latest methods for patient trajectory prediction (longitudinal prediction); that is, given a history of hospital admissions, what is the most probable clinical event to take place next? Answering that question might ease the work of healthcare professionals, who must deal with massive amounts of data when working on preventive prognosis. We proposed an architecture composed of Long Short-Term Memory (LSTM), attention mechanisms, and an encoder-decoder scheme in a single ensemble. Over datasets MIMIC-III and MIMIC-IV-ED, our experiments demonstrated results that surpassed baselines and state-of-the-art proposals. Our contribution is to elucidate the use and efficacy of the most recent techniques on neural networks in the medical domain, raising design decisions for current and future works.
Fully Succinct Batch Arguments for $$\textsf{NP}$$ from Indistinguishability Obfuscation

Non-interactive batch arguments for $$\textsf{NP} $$ provide a way to amortize the cost of $$\textsf{NP} $$ verification across multiple instances. In particular, they allow a prover to convince a verifier of multiple $$\textsf{NP} $$ statements with communication that scales sublinearly in the number of instances. In this work, we study fully succinct batch arguments for $$\textsf{NP} $$ in the common reference string (CRS) model where the length of the proof scales not only sublinearly in the number of instances T, but also sublinearly with the size of the $$\textsf{NP} $$ relation. Batch arguments with these properties are special cases of succinct non-interactive arguments (SNARGs); however, existing constructions of SNARGs either rely on idealized models or strong non-falsifiable assumptions. The one exception is the Sahai-Waters SNARG based on indistinguishability obfuscation. However, when applied to the setting of batch arguments, we must impose an a priori bound on the number of instances. Moreover, the size of the common reference string scales linearly with the number of instances. In this work, we give a direct construction of a fully succinct batch argument for $$\textsf{NP} $$ that supports an unbounded number of statements from indistinguishability obfuscation and one-way functions. Then, by additionally relying on a somewhere statistically-binding (SSB) hash function, we show how to extend our construction to obtain a fully succinct and updatable batch argument. In the updatable setting, a prover can take a proof $$\pi $$ on T statements $$(x_1, \ldots , x_T)$$ and “update” it to obtain a proof $$\pi '$$ on $$(x_1, \ldots , x_T, x_{T + 1})$$ . Notably, the update procedure only requires knowledge of a (short) proof for $$(x_1, \ldots , x_T)$$ along with a single witness $$w_{T + 1}$$ for the new instance $$x_{T + 1}$$ . Importantly, the update does not require knowledge of witnesses for $$x_1, \ldots , x_T$$ .
Consensus on Demand

Digital money can be implemented efficiently by avoiding consensus. However, no-consensus designs are fundamentally limited, as they cannot support general smart contracts, and similarly they cannot deal with conflicting transactions. We present a novel protocol that combines the benefits of an asynchronous, broadcast-based digital currency, with the capacity to perform consensus. This is achieved by selectively performing consensus a posteriori, i.e., only when absolutely necessary. Our on-demand consensus comes at the price of restricting the Byzantine participants to be less than a one-fifth minority in the system, which is the optimal threshold. We formally prove the correctness of our system and present an open-source implementation, which inherits many features from the Ethereum ecosystem.
Toward an Ontological Cyberattack Framework to Secure Smart Cities with Machine Learning Support

With the emergence and the movement toward the Internet of Things (IoT), one of the most significant applications that have gained a great deal of concern is smart cities.In smart cities, IoT is leveraged to manage life and services within a minimal, or even no, human intervention.IoT paradigm has created opportunities for a wide variety of cyberattacks to threaten systems and users.Many challenges have been faced in order to encounter IoT cyberattacks, such as the diversity of attacks and the frequent appearance of new attacks.This raises the need for a general and uniform representation of cyberattacks.Ontology proposed in this paper can be used to develop a generalized framework, and to provide a comprehensive study of potential cyberattacks in a smart city system.Ontology can serve in building this intended general framework by developing a description and a knowledge base for cyberattacks as a set of concepts and relation between them.In this article we have proposed an ontology to describe cyberattacks, we have identified the benefits of such ontology, and discussed a case study to show how we can we utilize the proposed ontology to implement a simple intrusion detection system with the assistance of Machine Learning (ML).The ontology is implemented using protégé ontology editor and framework, WEKA is utilized as well to construct the inference rules of the proposed ontology.Results show that intrusion detection system developed using the ontology has shown a good performance in revealing the occurrence of different cyberattacks, accuracy has reached 97% in detecting cyber-attacks in a smart city system.
The Analysis of Blockchain Digital Currency Product Innovation Based on Artificial Immune Algorithm

In recent years, with the rapid growth of the digital currency industry, blockchain technology has attracted wide attention, and its research mostly focuses on the protection of economic security and privacy. In order to study the application and development mechanism of currency products, this work investigates the practical application of digital currency products under blockchain technology at the present stage, and analyzes the potential security risks. The Artificial Immune Algorithm (AIA) is an intelligent method to imitate the function of the biological immune system, which provides a new solution for solving complex distributed problems. Firstly, a blockchain digital currency product is proposed based on AIA. Secondly, taking the credit risk of innovation risk in financial currency products as an example, the Diffie-Hellman (DH) with blockchain technology is used. Finally, the generation and optimization of the plan are realized, and an innovative risk plan is proposed for blockchain digital currency products on the basis of AIA. The results show that: <xref ref-type="disp-formula" rid="deqn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1)</xref> The improved AIA implements a guided AIA with adaptive parameter update. <xref ref-type="disp-formula" rid="deqn2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</xref> The digital signature technology using the DH algorithm has a stronger anti-attack ability than the traditional blockchain digital signature technology. It improves the security of blockchain digital currency. This work has successfully established the risk-immune system of digital currency product innovation, laying a theoretical foundation for the innovation and development of blockchain digital currency products in the future.
Text Augmentation based Imbalance Learning for Unstructured Text Data

A balanced dataset is a key requirement in all data-driven approaches. However, in the real world, it is often difficult to come up with such a dataset. Generally, instances of a class of interest are much lower than the number of instances of other classes (imbalance problem). Numerous attempts have been made for solving the imbalance problem in structure data. However, very few attempts have been made for solving this problem in unstructured text data. In this paper, we explore text augmentation-based imbalance learning for text data. We proposed an oversampling-based methodology for class imbalance problems. The experimental results show significant improvement (20% to 42%) in text classification on various imbalanced datasets.
Designing a Topic-Based Literature Exploration Tool in AR - An exploratory study for neuroscience

Figure 1: Exploring neuroscience literature to find brain regions corresponding to a specific disease.A user investigates brain regions co-occuring with Depression, left, and adjusts the filters for co-occurrences of brain regions with Anxiety, right.
Rubber Tyred Gantry Automatic Alignment System based on Computer Vision

The existing rubber tyred gantry (RTG) mainly adopts methods such as traditional laser, magnetic nail or other technologies to orient. In view of the shortcomings of the technology, such as single use scenario, limited information and high construction cost, a visual automatic alignment system based on Mask-RCNN is proposed to accomplish the integration of perception and decision-making. The system based on ROS (Robot Operating System) is mainly composed of target detection and segmentation module, motion decision module and visual human- machine interaction interface. The target detection and segmentation module is mainly used to perceive the target, including its foreground segmentation module based on Mask-RCNN and the target orientation module based on traditional image algorithm; The motion decision module is mainly used to control the movement of the tire crane, including motion modeling module and logic control module; The visual human-computer interaction interface is mainly used to adjust system parameters and monitor system operation status, including the back-end used for providing data and the front-end used for data interaction. Finally, the designed visual automatic alignment system has been tested on the tire crane, and the orientation accuracy and operation efficiency have been improved enormously.
Learned Progressive Image Compression With Dead-Zone Quantizers

Progressive coding is essential to the practical deployment of learned image compression over heterogeneous networks and clients. Existing methods for learned progressive image compression require complex and empirical design to achieve near-optimal rate-distortion performance over a wide range of bit-rates. However, these methods are limited by the implicit learned mechanism based on neural networks and introduction of uniform quantizers. In this paper, we propose generalized learned progressive image compression with analytic rate-distortion optimization using dead-zone quantizers on the latent representation. Specifically, we reveal that dead-zone quantizers, as a general case of uniform quantizers, are equivalent to uniform quantizers in fixed-rate nonlinear transform coding and can prevent extra redundancy in embedded quantization for progressive coding. Consequently, we propose rate-distortion optimized learned progressive coding by approximating the optimal quantizer in the source spaces using dead-zone quantizers in an analytic manner on the Laplacian source. To our best knowledge, this paper is the first to achieve general learned progressive coding from the perspective of optimal quantizers. The proposed method achieves theoretically sound and practically efficient embedded quantization and learned progressive coding of latent representations with improved rate-distortion performance. It can also enable embedded quantization with diverse assignments of truncation points and support flexible configuration of quality layers of varying numbers and at varying target bit-rates. Furthermore, we successfully incorporate the proposed method into existing pre-trained fixed-rate models to realize progressive learned image compression without re-training. Experimental results demonstrate that the proposed method achieves state-of-the-art rate-distortion performance in learned progressive image compression compared with traditional codecs and recent learned methods.