Reviewerly/exHarmony huggingface dataset:
Trace the Untraceable: Online Image Search Tools for Researching Late Antique Art

In the context of digital humanities and access to cultural heritage online, this paper explores the discoverability of Late Antique material in some searchable museum collections and in some major archaeological and art historical image and object databases. It follows an exploratory approach by using simple keyword searches, such as ‘late antique’ or ‘byzantine’, and comparing the results with chronological searches when a date or period filter is available. Although Late Antique material often comprises a smaller number of objects compared to more popular periods like the Roman and the Renaissance, these are difficult to research due to inconsistent labelling practices and the frequent lack of a customizable date range filter. The ongoing debates on proper periodization and nomenclature also need to be taken into consideration.
Two-Stage Recognition Algorithm for Untrimmed Converter Steelmaking Flame Video

In the process of converter steelmaking, identification of converter status is the basis of subsequent steelmaking control, directly affecting the cost and quality of steelmaking. Usually, the status of converter can be identified according to the flame of furnace port. In this paper, we propose a two-stage recognition algorithm to identify converter status using furnace flame video. In the first stage, we design a 2D feature extractor based on the shift module, aiming to capture temporal information in real time. In the second stage, an attention-based network is designed to get a more discriminative temporal attention, in which we model the distribution of temporal attention using conditional Variational Auto-Encoder (VAE) and a generative attention loss. We collect the video data and construct the corresponding data set from real steelmaking scene. Experimental results show that our algorithm can meet the accuracy and speed requirements.
Deep Demosaicking with Luminance and Chrominance Estimations

A digital camera acquires images using a single electronic sensor with a color filter array (CFA). The raw image contains luminance, defined as a spatial map of intensity, and chrominance, defined as a spatial map of each color information. Since the luminance and chrominance components have different demosaicking complexities, they should be modeled separately. In this paper, we propose a novel convolutional neural network (CNN)-based demosaicking method that separately estimates the luminance and chrominance components. Specifically, we apply two-stage CNNs consisting of a luminance component estimation network and a chrominance component estimation network. The proposed method suppresses artifacts such as false colors and reduces the computational complexity. Experimental results on several benchmark datasets demonstrate that the proposed method provides results that are better or competitive with conventional demosaicking algorithms while reducing the computational complexity.
Performance of Reinforcement Learning Simulation: x86 v/s ARM

In this paper, we develop a method for deep reinforcement of training simulation for Quadcopter and perform a comparison in the training of the neural network on x86 based machines and also on ARM- based machines. We consider the workload of training a quadcopter neural which is run on both the machines. We use a desktop for x86 machine, whereas, we consider a Raspberry pi based computer for simulation of RL algorithm. Both the machines are operating on Linux based systems. We observe that x86 based machine has a better performance than its ARM counterpart.
Quantum cryptographic resource distillation and entanglement

We look into multipartite quantum states on which quantum cryptographic protocols including quantum key distribution and quantum secret sharing can be perfectly performed, and define the quantum cryptographic resource distillable rate as the asymptotic rate at which such multipartite state can be distilled from a given multipartite state. Investigating several relations between entanglement and the rate, we show that there exists a multipartite bound entangled state whose quantum cryptographic resource distillable rate is strictly positive, that is, there exists a multipartite entangled state which is not distillable, but can be useful for quantum cryptography such as quantum key distribution and quantum secret sharing.
Quantum Representations and Scaling Up Algorithms of Adaptive Sampled-Data in Log-Polar Coordinates

In log-polar coordinates, the conventional data sampling method is to sample uniformly in the log-polar radius and polar angle directions, which makes the sample at the fovea of the data denser than that of the peripheral. The central oversampling phenomenon of the conventional sampling method gives no more efficient information and results in computational waste. Fortunately, the adaptive sampling method is a powerful tool to solve this problem in practice, so the paper introduces it to quantum data processing. In the paper, the quantum representation model of adaptive sampled data is proposed first, in which the upper limit of the sampling number of the polar angles is related to the log-polar radius. Owing to this characteristic, its preparation process has become relatively complicated. Then, in order to demonstrate the practicality of the model given in the paper, the scaling up algorithm with an integer scaling ratio based on biarcuate interpolation and its circuit implementation of quantum adaptive sampled data is given. However, due to the special properties of the adaptive sampling method in log-polar coordinates, the interpolation process of adaptive sampled data becomes quite complicated as well. At the end of this paper, the feasibility of the algorithm is verified by a numerical example.
Design and Research of Network Database System Based on Deep Learning Technology

With the continuous development of Internet technology, the traditional database (TD) has been unable to meet the requirements of distributed, diverse nodes and complex devices. The TD can only collect and store data within a fixed range of nodes, which is slow in data acquisition and limited in background operation and processing capacity. Network database (ND) is the product of the combination of Internet technology and database technology. Relying on the background database, it can use the Internet for data collection and processing, and realize network distributed computing. This paper mainly studies the design of ND system based on deep learning technology (DLT). This paper expounds the technical characteristics of the ND under the DLT, which can automatically identify the target according to the processed information, and make full use of the user’s original data resources and applications. In this paper, the ND design and implementation, design and establish a simple security database system model, complete the ND system architecture design. This paper also carries on the simulation test experiment to test the performance of the ND, and analyzes its data acquisition time and data acquisition volume. The experimental results show that when the number of data acquisition groups increases, the data acquisition time of TD also increases. It takes 10 s to collect one group of data, and 26.4 s to collect 30 groups of data, it takes 8 s to collect one set of data and 6 s to collect 30 sets of data.
Feature Removal on Software Platforms – Discontinued Core Features on Browser Platforms – A Case Study on Mozilla Firefox

Software platforms allow for the extension of features by third-party contributors. Thereby, platform innovation is an important aspects of platforms attractiveness for users and complementors. While previous research focused the introduction of new features, the aspect of feature removal and discontinued features on software platforms has been disregarded. To explore the phenomenon and motivations for feature removal on software platforms, a review of recent literature is provided. To illustrate the existence of and motivations for feature removal, a case study of the browser platform Mozilla Firefox is presented. The results reveal feature removal to regularly occur on browser platforms for user- and developer-related features. Frequent reasons for feature removal involve unused features, security concerns, and bugs. Related motivations for feature removal are discussed from the platform owner&#x0027;s perspective. Implications for complementors and users are highlighted.
Privacy in the COVID-19 Era

The sudden outbreak of COVID-19 at the late 2019 has brought enormous hurdles globally to our everyday lives and to our society. In order to mitigate the impact of the pandemic and to control the dissemination of the coronavirus, governments worldwide have taken extreme surveillance measures which most of the times invade to individuals' privacy and contradict data protection principles. In this chapter, we investigate the interplay between privacy and COVID-19 pandemic and we explore the invasive digital technologies such as the mobile contact tracing apps and health immunity passports that have been utilized to monitor people's lives.
Rewritable Data Embedding in Image based on Improved Coefficient Recovery

Nowadays, most images are stored and transmitted in certain compressed forms based on some coding standards. Usually, the image is transformed, e.g., by discrete cosine transformation, and hence coefficient makes up a large proportion of the compressed bit stream. However, these coefficients might be corrupted or completely lost due to transmission errors or damages incurred on the storage device. Therefore, in this work, we aim to improve a conventional coefficient recovery method. Specifically, instead of using the Otsu’s method adopted in the conventional method, an adaptive segmentation method is utilized to split the image into background and foreground regions, forming non-overlapping patches. Missing coefficients in these non-overlapping patches are recovered independently. In addition, a rewritable data embedding method is put forward by judiciously selecting patches to embed data. Experiments are carried to verify the basic performance of the proposed methods. In the best-case scenario, an improvement of 31.32% in terms of CPU time is observed, while up to 7149 bits of external data can be embedded into the image.
Text-Independent Speaker Identification Using Formants and Convolutional Neural Networks

Text-Independent Speaker Identification consists in finding out the identity of an individual using his/her voice independently of the content of the speech signal, that is, regardless the words uttered by the speaker. This problem is harder than Text-dependent speaker recognition where the speaker has to utter some specific word or phrase so he/she can be recognized. However, Text-Independent Speaker Identification is what we have to solve when the speaker has to be recognized without his/her collaboration as is frequently the case in many practical situations. Our proposal consists in searching within the speech signal for voiced speech content, which is the kind of speech produced when the vocal cords are vibrating. Once these segments of speech are identified, the formants are determined, formants are the resonance frequencies of the vocal tract. We use these formants to produce images which we believe should be different from one speaker to another, the way such images are built is original. Each image represent a specific speaker and so the problem of identifying speakers is turned into a problem of image recognition and we know how useful convolutional neural networks are for that purpose. For our experiments we used a collection of recordings from 21 individuals and achieved an accuracy of 92% outperforming the best results for text-independent identification published in recent works that used the same collection for testing.
Portuguese Concerns and Impact on Behaviour About Cybersecurity: A Comparison with the European Average

The advancement of the Internet has great potential for the well-being of citizens and business growth, but this new paradigm also entails cybersecurity challenges that can have high economic impacts. The growth of Internet use is also leading to a general increase in cybercrime concerns as well as the number of users’ victims of this permanent threat. This reality is also very present in Portugal as well as in other European countries. In this context, it is important to know some indicators associated with cybersecurity, to take the best measures/actions in a reasoned way, and to combat this phenomenon. This article presents the results of a study carried out in 2019 by the European Commission, whose analysis helps to understand the Portuguese positioning in the concerns and attitudes about cybercrime, to the average of the European Community citizens. Analysing this study, it was possible to perceive that the Portuguese’s concerns about cybercrime are in line with the average of the European citizens, although the percentage of Portuguese that takes attitudes and actions to combat cyber-threats is a little bit above the average recorded by citizens of European countries.
Clustering Public Opinions Related to Quarantine during Covid-19 on Twitter Using K-DENCLUE Algorithms

Twitter is a microblog-based social media platform that can upload text, images, and videos with a limit of 280 characters per tweet. There are various information and opinions obtained on this platform; one of the topics discussed in quarantine during the Covid-19 pandemic. Based on the information and opinion, there are various kinds of responses. The kind of responses could be either pros or cons toward the topic. Sentiment analysis is one way to determine the sentiment response, whether positive, negative, or neutral. There are various ways to apply sentiment analysis, one of which is using unsupervised or clustering techniques. The processes carried out in this study included preprocessing, feature vector extraction, hyper-rectangle, DENCLUE, and K-Means. Preprocessing, feature vector extraction and hyper-rectangle are used for adjusting input values in the clustering process, such as converting data into structured, normalizing, and reducing dimensions. The DENCLUE method is used to remove noise, which can complement the K-Means method because the clustering process does not have noise detection. In addition, there is a sentiment expression dictionary that is used to determine the initial value in clustering calculations. In this study, we conducted tests with the two conditions of sentiment expression (using neutral sentiment expressions and without using neutral sentiment expressions) and various group divisions. Based on the analysis and testing results, the optimal value was obtained in the conditions using a neutral sentiment expression dictionary with a value of k = 2. The value of the resulting silhouette coefficient is 0.6218 with a proper or appropriate cluster interpretation (medium structure).
Neural-Guided, Bidirectional Program Search for Abstraction and Reasoning

One of the challenges facing artificial intelligence research today is designing systems capable of utilizing systematic reasoning to generalize to new tasks. The Abstraction and Reasoning Corpus (ARC) measures such a capability through a set of visual reasoning tasks. In this paper we report incremental progress on ARC and lay the foundations for two approaches to abstraction and reasoning not based in brute-force search. We first apply an existing program synthesis system called DreamCoder to create symbolic abstractions out of tasks solved so far, and show how it enables solving of progressively more challenging ARC tasks. Second, we design a reasoning algorithm motivated by the way humans approach ARC. Our algorithm constructs a search graph and reasons over this graph structure to discover task solutions. More specifically, we extend existing execution-guided program synthesis approaches with deductive reasoning based on function inverse semantics to enable a neural-guided bidirectional search algorithm. We demonstrate the effectiveness of the algorithm on three domains: ARC, 24-Game tasks, and a ‘double-and-add’ arithmetic puzzle.
A Systematic Review of Adoption of Blockchain and Machine Learning Technology and Its Application

This research work provides a systematic review on blockchain-based applications present across multiple domains. Also, a comprehensive classification of technology applications across diverse sectors such as healthcare, supply chain, education, automobile industry, etc. has been presented. The twenty-first century has introduced significant innovations and discoveries that have completely changed our lives. In this way, Computers were first introduced followed by the internet, internet of things, and this has recently resulted in the introduction of "Blockchain" technology. This technology is considered as the most important innovation since the internet, and it is also known as the future internet since it creates a decentralized network chain. It has huge potential to impact, disrupt, and change human activities. Also, blockchain becomes an ideal tool for creating a trust-based solution. Nowadays several private, public sector firms, and government organizations are initiating new research work in the blockchain domain. This paper discusses innovative ideas, technologies, and its real-time applications by considering different industries, where blockchain can be applied to create new business opportunities.
R-Calculi for Description Logics

R-calculus for description logics is between the one for propositional logic and the one for first-order logic, because the complexity of the deduction relation of description logics is between the ones of the deduction relation of the former and of the latter. Even though the quantifier \(\forall \) occurs in the concept constructor \(\forall R,\) its equivalent form in first-order logic is a guarded first-order formula, and the deduction relation in the guarded first-order logic is decidable [, ]. Therefore, for R-calculi of description logics, we can decompose a statement C(a) into an atomic statement A(a), where A is an atomic concept, and two sets of deduction rules: one for C(a) consistent with \(\Delta ,\) and another for C(a) inconsistent with \(\Delta .\)
Notice of Retraction: ODNASSD: An End-to-end Object Detection Neural Architecture Search Space Design

With the development of deep learning, object detection methods based on the convolutional neural network have achieved superior performance compared with traditional methods. However, most detection networks are human-designed, and manually designing a well-performing detection network is inefficient and it heavily relies on expert knowledge. To address above problems, in this paper, we propose an end-to-end object detection neural architecture search space design (ODNASSD) framework, which explores a highly hardware-efficient framework with multiple techniques to save GPU memory and improve search efficiency. Besides, in order to solve the problem of the lack of backbone pre-training weights, we adopt a weights-sharing strategy. We conduct search experiments and our method achieves superior performance in precision and search efficiency.
Experimental demonstration of confidential communication with quantum security monitoring

Abstract Security issues and attack management of optical communication have come increasingly important. Quantum techniques are explored to secure or protect classical communication. In this paper, we present a method for in-service optical physical layer security monitoring that has vacuum-noise level sensitivity without classical security loopholes. This quantum-based method of eavesdropping detection, similar to that used in conventional pilot tone systems, is achieved by sending quantum signals, here comprised of continuous variable quantum states, i.e. weak coherent states modulated at the quantum level. An experimental demonstration of attack detection using the technique was presented for an ideal fibre tapping attack that taps 1% of the ongoing light in a 10 dB channel, and also an ideal correlated jamming attack in the same channel that maintains the light power with excess noise increased by 0.5 shot noise unit. The quantum monitoring system monitors suspicious changes in the quantum signal with the help of advanced data processing algorithms. In addition, unlike the CV-QKD system which is very sensitive to channel excess noise and receiver system noise, the quantum monitoring is potentially more compatible with current optical infrastructure, as it lowers the system requirements and potentially allows much higher classical data rate communication with links length up to 100 s km.
Combined Use of Equivalent and Non-Equivalent Transformations of FPGA Program Code to Embedding Additional Security Data

The paper is devoted to a stenographic approach to storing data in the space of the FPGA chips program code. In this approach, data is covertly embedded in the program code and forms a single whole with it. As a result, the presence of embedded data is hidden from the outside observer. The data hidden in this way can only be retrieved with a stego key. Traditionally for stego embedding of additional data into FPGA program code equivalent transformations of this program code are used. To do this, the program codes of the FPGA LUT units are transformed so that the FPGA functionality does not change. In this case, additional data is embedded in the target bits of the program code. This paper proposes a method that combines traditional equivalent embedding with non-equivalent embedding. The method uses an additional stego resource - inessential LUT units. This is a subset of LUT units that perform arithmetic calculations for approximate data. Inessential LUT units participate only in the calculation of the bits to be discarded as a result of rounding. Therefore, distortion for the program code of these units does not affect the correct operation of the device. The proposed method allows to increase the volume of data steganographically embedded in the FPGA program code. This increase is achieved through the use of an additional nonequivalent stego resource. An experiment was carried out in the paper, which confirmed the effectiveness of the proposed method. The experiment showed an increase in the volume of embedded data compared to using only an equivalent stego resource.
Hyperbolic Tangent Polynomial Parity Cyclic Learning Rate for Deep Neural Network

With the development of artificial intelligence technology, optimizing the performance of deep neural network model has become a hot issue in the field of scientific research. Learning rate is one of the most important hyper-parameters for model optimization. In recent years, some learning rate algorithms with cycle mechanism have been proposed. Most of them adopt warm restart and cycle mechanism to make the learning rate value cyclically change between two boundary values and prove their effectiveness by practicing in image classification task. In order to further improve the performance of neural network model and prove the effectiveness in different training task, the paper proposes a novel learning rate schedule called hyperbolic tangent polynomial parity cyclic learning rate (HTPPC), which adopts cycle mechanism and combines the advantages of warm restart and polynomial decay. In addition, the performance of HTPPC is demonstrated on image classification and object detection tasks.