Reviewerly/exHarmony huggingface dataset:
Region-based convolutional neural network using group sparse regularization for image sentiment classification

As an information carrier with rich semantics, images contain more sentiment than texts and audios. So, images are increasingly used by people to express their opinions and sentiments in social network. The sentiments of the images are overall and should come from different regions. So, the recognition of the sentiment regions will help to concentrate on important factors the affect the sentiments. Meanwhile, deep learning method for image sentiment classification needs simple and efficient approach for simultaneously carrying out pruning and feature selection whilst optimizing the weights. Motivated by these observations, we design a region-based convolutional neural network using group sparse regularization for image sentiment classification: R-CNNGSR. The method obtains the initial sentiment prediction model through CNN using group sparse regularization to get compact neural network, and then automatically detect the sentiment regions by combining the underlying features and sentimental features. Finally, the whole image and the sentiment region are fused to predict the overall sentiment of the images. Experiment results demonstrate that our proposed R-CNNGSR significantly outperforms the state-of-the-art methods in image sentiment classification.
Interacting with Digital Documents

The aim of this study was to find out what is historians' task-based information interaction like in digital environments. The study was based on TBII framework [20]. The research was conducted in real-life setting using interview and shadowing. The following task process types were identified: (i) searching secondary sources, (ii) reading and making notes, (iii) collecting and processing data, (iv) analyzing and (v) writing. We found that IR interfaces were not always optimal for historians' use, and that the interfaces should support the expression of information needs better. The data processing consumes a lot of historians' time and they could benefit from designing better tools. We also found out that task process types have an effect on how digital tools or data are being used. Therefore, the context in which the tool or data is being used, should be taken into account when designing tools for historians.
Ground-state energy estimation of the water molecule on a trapped ion quantum computer

Quantum computing leverages the quantum resources of superposition and entanglement to efficiently solve computational problems considered intractable for classical computers. Examples include calculating molecular and nuclear structure, simulating strongly-interacting electron systems, and modeling aspects of material function. While substantial theoretical advances have been made in mapping these problems to quantum algorithms, there remains a large gap between the resource requirements for solving such problems and the capabilities of currently available quantum hardware. Bridging this gap will require a co-design approach, where the expression of algorithms is developed in conjunction with the hardware itself to optimize execution. Here, we describe a scalable co-design framework for solving chemistry problems on a trapped ion quantum computer, and apply it to compute the ground-state energy of the water molecule. The robust operation of the trapped ion quantum computer yields energy estimates with errors approaching the chemical accuracy, which is the target threshold necessary for predicting the rates of chemical reaction dynamics.
Funny Accents: Exploring Genuine Interest in Internationalized Domain Names

International Domain Names (IDNs) were introduced to support non-ASCII characters in domain names. In this paper, we explore IDNs that hold genuine interest, i.e. that owners of brands with diacritical marks may want to register and use. We generate 15 276 candidate IDNs from the page titles of popular domains, and see that 43% are readily available for registration, allowing for spoofing or phishing attacks. Meanwhile, 9% are not allowed by the respective registry to be registered, preventing brand owners from owning the IDN. Based on WHOIS records, DNS records and a web crawl, we estimate that at least 50% of the 3 189 registered IDNs have the same owner as the original domain, but that 35% are owned by a different entity, mainly domain squatters; malicious activity was not observed. Finally, we see that application behavior toward these IDNs remains inconsistent, hindering user experience and therefore widespread uptake of IDNs, and even uncover a phishing vulnerability in iOS Mail.
Robust authentication for paper-based text documents based on text watermarking technology

Aiming at the problem of easy tampering and difficult integrity authentication of paper text documents, this paper proposes a robust content authentication method for printed documents based on text watermarking scheme resisting print-and-scan attack. Firstly, an authentication watermark signal sequence related to content of text document is generated based on the Logistic chaotic map model; then, the authentication watermark signal sequence is embedded into printed paper document by using a robust text watermarking scheme; finally, the watermark information is extracted from scanned image of paper document, and compared with the authentication watermark information calculated in real time by the text document content obtained by OCR technology, thereby performing content integrity authentication of the paper text documents. Experimental results show that our method can achieve the robust content integrity authentication of paper text documents, and can also accurately locate the tampering position. In addition, the document after embedding the watermark information has a good visual effect, and the text watermarking scheme has a large information capacity.
Device-independent quantum secret sharing in arbitrary even dimensions

We present a device-independent quantum secret sharing scheme in arbitrary even dimension. We propose a $d$-dimensional $N$-partite linear game, utilizing a generic multipartite higher-dimensional Bell inequality, a generalization of Mermin's inequality in a higher dimension. The probability of winning this linear game defines the device-independence test in the proposed scheme. The security is proved under a causal independence assumption on measurement devices and it is based on the polygamy property of entanglement. By defining ${\ensuremath{\epsilon}}_{\mathrm{cor}}$ correctness and ${\ensuremath{\epsilon}}^{c}$ completeness for a quantum secret sharing scheme, we also show that the proposed scheme is ${\ensuremath{\epsilon}}_{\mathrm{cor}}$ correct and ${\ensuremath{\epsilon}}^{c}$ complete.
Speciality Grand Challenges: Blockchain

SPECIALTY GRAND CHALLENGE article Front. Blockchain, 15 March 2019Sec. Blockchain Technologies Volume 2 - 2019 | https://doi.org/10.3389/fbloc.2019.00001
On the Constructions of New Symmetric Ciphers Based on Nonbijective Multivariate Maps of Prescribed Degree

The main purpose of this paper is to introduce stream ciphers with the nonbijective encryption function of multivariate nature constructed in terms of algebraic graph theory. More precisely, we describe the two main symmetric algorithms for creation of multivariate encryption transformations based on three families of bipartite graphs with partition sets isomorphic to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant="double-struck">K</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi mathvariant="double-struck">K</mml:mi></mml:mrow></mml:math> is selected as the finite commutative ring. The plainspace of the algorithm is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi mathvariant="normal">Ω</mml:mi><mml:mo>=</mml:mo><mml:mo stretchy="false">{</mml:mo><mml:mi>x</mml:mi><mml:mrow><mml:mo>∣</mml:mo></mml:mrow><mml:mrow><mml:mo stretchy="false">∑</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>∈</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant="double-struck">K</mml:mi></mml:mrow><mml:mrow><mml:mo>⁎</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:mrow><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mo> </mml:mo><mml:mi>x</mml:mi><mml:mo>∈</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant="double-struck">K</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup><mml:mo stretchy="false">}</mml:mo><mml:mo>⊂</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant="double-struck">K</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup><mml:mo>,</mml:mo></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mi mathvariant="normal">Ω</mml:mi><mml:mo>≅</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant="double-struck">K</mml:mi></mml:mrow><mml:mrow><mml:mo>⁎</mml:mo></mml:mrow></mml:msup><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant="double-struck">K</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>.</mml:mo></mml:math> The second algorithm is a generalization of the first one with using the jump operator, where generalized encryption map has an essentially higher degree in comparison with the previous version. Moreover, the degree of this generalized map is not bounded by some constant. This property guarantees resistance of the cipher to linearization attacks.
Object Counting and Instance Segmentation with Image-level Supervision

Common object counting in a natural scene is a challenging problem in computer vision with numerous real-world applications. Existing image-level supervised common object counting approaches only predict the global object count and rely on additional instance-level supervision to also determine object locations. We propose an image-level supervised approach that provides both the global object count and the spatial distribution of object instances by constructing an object category density map. Motivated by psychological studies, we further reduce image-level supervision using a limited object count information (up to four). To the best of our knowledge, we are the first to propose image-level supervised density map estimation for common object counting and demonstrate its effectiveness in image-level supervised instance segmentation. Comprehensive experiments are performed on the PASCAL VOC and COCO datasets. Our approach outperforms existing methods, including those using instance-level supervision, on both datasets for common object counting. Moreover, our approach improves state-of-the-art image-level supervised instance segmentation with a relative gain of 17.8% in terms of average best overlap, on the PASCAL VOC 2012 dataset. Code link: https://github.com/GuoleiSun/CountSeg
Rotation Independent Digit Recognition in Sign Language

Usually deaf people use sign language to communicate with others. A large number of people including children have disabling hearing loss. In sign language, to indicate signs, hand gestures are used, however, hands can be rotated in any angle. There are few published research work to recognize digits in sign language. In this paper, we have proposed a 10 layers convolutional neural network model using residual learning to detect digits in sign language of any angle. We have collected 700 sign digit images from which we have prepared 7000 rotated images, rotating each image in arbitrary rotation 10 times. Using our proposed model, we have got 97.28% accuracy for 7000 rotated images. Our Proposed method improves accuracy by 10.57% than the latest research work.
Deep Image Similarity Measurement Based on the Improved Triplet Network with Spatial Pyramid Pooling

Image similarity measurement is a fundamental problem in the field of computer vision. It is widely used in image classification, object detection, image retrieval, and other fields, mostly through Siamese or triplet networks. These networks consist of two or three identical branches of convolutional neural network (CNN) and share their weights to obtain the high-level image feature representations so that similar images are mapped close to each other in the feature space, and dissimilar image pairs are mapped far from each other. Especially, the triplet network is known as the state-of-the-art method on image similarity measurement. However, the basic CNN can only handle fixed-size images. If we obtain a fixed size image via cutting or scaling, the information of the image will be lost and the recognition accuracy will be reduced. To solve the problem, this paper has proposed the triplet spatial pyramid pooling network (TSPP-Net) through combing the triplet convolution neural network with the spatial pyramid pooling. Additionally, we propose an improved triplet loss function, so that the network model can realize twice distance learning by only inputting three samples at one time. Through the theoretical analysis and experiments, it is proved that the TSPP-Net model and the improved triple loss function can improve the generalization ability and the accuracy of image similarity measurement algorithm.
Photomontage detection using steganography technique based on a neural network

This article presents a steganographic method StegoNN based on neural networks. The method is able to identify a photomontage from presented signed images. Unlike other academic approaches using neural networks primarily as classifiers, the StegoNN method uses the characteristics of neural networks to create suitable attributes which are then necessary for subsequent detection of modified photographs. This also results in a fact that if an image is signed by this technique, the detection of modifications does not need any external data (database of non-modified originals) and the quality of the signature in various parts of the image also serves to identify modified (corrupted) parts of the image. The experimental study was performed on photographs from CoMoFoD Database and its results were compared with other approaches using this database based on standard metrics. The performed study showed the ability of the StegoNN method to detect corrupted parts of an image and to mark places which have been most probably image-manipulated. The usage of this method is suitable for reportage photography, but in general, for all cases where verification (provability) of authenticity and veracity of the presented image are required.
Towards privacy preserving unstructured big data publishing

Various sources and sophisticated tools are used to gather and process the comparatively large volume of data or big data that sometimes leads to privacy disclosure (at broader or finer level) for the data owner. Privacy preserving data publishing approaches such as k-anonymity, l-diversity, and t -closeness are very well used to de-identify data, however, chances of re-identification of attributes always exist as data is collected from multiple sources such as public web, social media, Internet whereabouts, and sensors that are highly prone to data linkages. In literature, k-anonymity stands out amongst the most popular mainstream data anonymization approaches that can also be used for large sized data. However, applying k-anonymization for variety of data (especially unstructured data) is difficult in the traditional way, due to the fact that it requires the given data to be classified into the personal data, the quasi identifiers, and the sensitive data. We identify existing approaches from the literature of Natural Language Processing(NLP) to convert the unstructured data to structured form in order to apply k-anonymization over the generated structured records. We adopt a two phase Conditional Random Field (CRF) based Named Entity Recognition (NER) approach to represent unstructured data into the structured form. Further, we propose an Improved Scalable k-Anonymization (ImSKA) to anonymize the well represented unstructured data that achieves privacy preserving unstructured big data publishing. We compare both of the propose approaches namely NER and ImSKA with existing approaches and the results show that our proposed solutions outperform the existing approaches in terms of F1 score and Normalized Cardinality Penalty (NCP), respectively. Since, NER approaches are widely used for bio-medical datasets, we have also used a well-known Bio-NER dataset called GENIA corpus for measuring the performance.
The Utilization of Mobile Devices for Improving Access to Online Learning for Distance Education’s Students

Recent advances in mobile devices have opened up opportunities for every individual to be able to communicate and obtain information quicker. Users of mobile devices in Asia now account for 49% of mobile device users in the world. This growth is partly supported by the rapid development of the Internet use. Indonesia has a potentially huge market in the use of mobile devices and Internet. The extent of utilization of mobile devices by the students pursuing higher education, especially at Universitas Terbuka (UT), is an important component of this development. This study seeks to develop an understanding of the following issues: (1) the types of the mobile devices that are used by students, (2) the types of learning and information accessed through mobile devices, (3) the time used to access learning through mobile devices, (4) the degree of students’ satisfaction in using the mobile devices for learning, and (5) the effectiveness of using mobile devices for learning. The data of this research were collected by using an online questionnaire containing 26 questions. The questionnaires were distributed to UT students from four faculties and the Graduate Programs from all UT’s regional offices at 38 locations spread across Indonesia, including UT’s regional office for overseas student. Overall, there were 767 respondents. The results of this research show that the utilization of mobile devices, especially in the use of smartphones to access UT’s online learning, seems to have great potential.
Library and information scientific journals in the citation indices

The authors analyze the presence of the Russian journals in library and information sciences in the leading databases with the citation analysis function, i. e. Web of Science Core Collection (WoS CC), including Emerging Sources Citation Index, Scopus and Russian Index of Science Citation, as well as Russian Science Citation Index, EBSCO, LISA. About fifty Russian journals in these disciplines are revealed. The authors emphasize their insufficient coverage by the science citation databases. They suggest the following reasons for this situation: the limited number of journals in the library and information studies, non-compliance with formal requirements of citation databases, low quality of publications. The authors formulate the measures to improve the situation: to comply with international standards, to improve their quality so they could compete to foreign journals, as the international database generators and experts use international standards to select science journals for indexing.
Composite pulses with errant phases

Composite pulses --- sequences of pulses with well defined relative phases --- are an efficient, robust and flexible technique for coherent control of quantum systems. Composite sequences can compensate a variety of experimental errors in the driving field (e.g. in the pulse amplitude, duration, detuning, chirp, etc.) or in the quantum system and its environment (e.g. inhomogeneous broadening, stray electric or magnetic fields, unwanted couplings, etc.). The control parameters are the relative phases between the constituent pulses in the composite sequence, an accurate control over which is required in all composite sequences reported hitherto. In this paper, we introduce two types of composite pulse sequences which, in addition to error compensation in the basic experimental parameters, compensate systematic errors in the composite phases. In the first type of such composite sequences, which compensate pulse area errors, relative phase errors of up to 10% can be tolerated with reasonably short sequences while maintaining the fidelity above the 99.99% quantum computing benchmark. In the second type of composite sequences, which compensate pulse area and detuning errors, relative phase errors of up to 5% can be compensated.
Factors That Influence Productivity: A Checklist

In all areas of professional work, there is are a lot of different factors that influence productivity. Especially in knowledge work, where we do not have easily and clearly measurable work products, it is difficult to capture these factors. Software development is a type of knowledge work that comes with even more specific difficulties, as software developers deal nowadays with incredibly large and complex systems. We provide a list of factors that empirically have been shown to impact productivity as checklist that a developer or software manager can use to improve productivity. We will discuss technical factors related to the product, the process and the development environment and soft factors related to the corporate culture, the team culture, individual skills and experiences, the work environment and the individual project.
PAPP

CPU memory prefetchers can substantially interfere with prime and probe cache side-channel attacks, especially on in-order CPUs which use aggressive prefetching. This interference is not accounted for in previous attacks. In this paper, we propose PAPP, a Prefetcher-Aware Prime Probe attack that can operate even in the presence of aggressive prefetchers. Specifically, we reverse engineer the prefetcher and replacement policy on several CPUs and use these insights to design a prime and probe attack that minimizes the impact of the prefetcher. We evaluate PAPP using Cache Side-channel Vulnerability (CSV) metric and demonstrate the substantial improvements in the quality of the channel under different conditions.
Staircase Detection Using a Lightweight Look-Behind Fully Convolutional Neural Network

Staircase detection in natural images has several applications in the context of robotics and visually impaired navigation. Previous works are mainly based on handcrafted feature extraction and supervised learning using fully annotated images. In this work we address the problem of staircase detection in weakly labeled natural images, using a novel Fully Convolutional neural Network (FCN), named LB-FCN light. The proposed network is an enhanced version of our recent Look-Behind FCN (LB-FCN), suitable for deployment on mobile and embedded devices. Its architecture features multi-scale feature extraction, depthwise separable convolutions and residual learning. To evaluate its computational and classification performance, we have created a weakly-labeled benchmark dataset from publicly available images. The results from the experimental evaluation of LB-FCN light indicate its advantageous performance over the relevant state-of-the-art architectures.