Reviewerly/exHarmony huggingface dataset:
A Graph Representation of Semi-structured Data for Web Question Answering

The abundant semi-structured data on the Web, such as HTML-based tables and lists, provide commercial search engines a rich information source for question answering (QA).Different from plain text passages in Web documents, Web tables and lists have inherent structures, which carry semantic correlations among various elements in tables and lists.Many existing studies treat tables and lists as flat documents with pieces of text and do not make good use of semantic information hidden in structures.In this paper, we propose a novel graph representation of Web tables and lists based on a systematic categorization of the components in semi-structured data as well as their relations.We also develop pre-training and reasoning techniques on the graph model for the QA task.Extensive experiments on several real datasets collected from a commercial engine verify the effectiveness of our approach.Our method improves F1 score by 3.90 points over the state-of-the-art baselines.
E-Government, Corruption Reduction and the Role of Culture

Using a panel dataset gathered from 57 countries over the period 2003 to 2014, this paper examines the impact of cultural factors on the relationship between e-government development and corruption. The analysis reveals that e-government development have a weak and positive impact on the corruption levels across all countries but varied according to the different cultural factors. Based on the cultural typology of the GLOBE project, the authors found that e-government development was more effective in reducing corruption in countries with certain cultural characteristics. Cultures that put less emphasis on controlling uncertainty shared power more equally among members, valued individualism, and focused more on future development were more favorable to e-government development than others. Finally, they discussed the cultural implications on e-planning.
JUST at SemEval-2020 Task 11: Detecting Propaganda Techniques Using BERT Pre-trained Model

This paper presents the JUST team submission to semeval-2020 task 11, Detection of Propaganda Techniques in News Articles.Knowing that there are two subtasks in this competition, we have participated in the Technique Classification subtask (TC), which aims to identify the propaganda techniques used in specific propaganda fragments.We have used and implemented various models to detect propaganda.Our proposed model is based on BERT uncased pre-trained language model as it has achieved state-of-the-art performance on multiple NLP benchmarks.The performance result of our proposed model has scored 0.55307 F1-Score, which outperforms the baseline model provided by the organizers with 0.2519 F1-Score, and our model is 0.07 away from the best performing team.Compared to other participating systems, our submission is ranked 15 th out of 31 participants.
A Secure Chaotic Block Image Encryption Algorithm Using Generative Adversarial Networks and DNA Sequence Coding

This paper proposes a block encryption algorithm based on a new chaotic system that combines generative adversarial networks (GANs) and DNA sequence coding. First, the new one-dimensional chaotic system that combines GANs with DNA sequence coding generates two more complex key stream sequences. Then, the two different random sequences are combined with an improved Feistel network by utilizing the product of the block matrix to encrypt the image to scramble and diffuse the image. Finally, the security performance of this algorithm is quantitatively analysed. The simulation results show that the proposed chaotic system has a large key space, and the new algorithm yields adequate security and can resist exhaustive attacks and chosen-plaintext attacks. Therefore, this approach provides a new algorithm for secure transmission and protection of image information.
The effect of knowledge management process on the employee commitment: Evidence from digital marketing industry

Knowledge management is a process of systematic and transparent creation, development and application of knowledge to maximize knowledge-related performance and business value from knowledge and available wisdom assets. Knowledge management is vitally important with business and one of the competitive advantages. The aim of the study is to find out the relationship between the knowledge management process and employees’ commitment in the digital marketing companies. Through the mix-method research, the research result pointed out that four dimensions of knowledge management process; i.e., Knowledge Acquisition, Knowledge Conversion, Knowledge Application, and Knowledge Protection, positively impact on the employee's commitment. Some managerial implications for the digital marketing businesses were proposed to improve the employee commitment based the knowledge management process.
Quantitative analysis of robot gesticulation behavior

Social robot capabilities, such as talking gestures, are best produced using data driven approaches to avoid being repetitive and to show trustworthiness. However, there is a lack of robust quantitative methods that allow to compare such methods beyond visual evaluation. In this paper a quantitative analysis is performed that compares two Generative Adversarial Networks based gesture generation approaches. The aim is to measure characteristics such as fidelity to the original training data, but at the same time keep track of the degree of originality of the produced gestures. Principal Coordinate Analysis and procrustes statistics are performed and a new Fréchet Gesture Distance is proposed by adapting the Fréchet Inception Distance to gestures. These three techniques are taken together to asses the fidelity/originality of the generated gestures.
Risk-Aware Individual Trajectory Data Publishing With Differential Privacy

Large-scale spatiotemporal data mining has created valuable insights into managing key areas of society and the economy. It has encouraged data owners to release/publish trajectory datasets. However, the ill-informed publication of such valuable datasets may lead to serious privacy implications for individuals. Moreover, as a major goal of data protection, balancing privacy and utility remains a challenging problem due to the diversity of spatiotemporal data. However, the user dimension was not considered for traditional frameworks, which limits the application at the global level as opposed to the user level. Many researchers overcome this issue by assuming that a user in the dataset generates only one trajectory. Actually, a user always generates multiple and repetitive trajectories during observation. Only considering one trajectory for one user may cause insufficient privacy protection at the trajectory level alone, as a user's privacy can be manifested in many trajectories collectively. In addition, it demonstrates strong user correlation when using multiple and repetitive trajectories. If not considered, additional information will be lost, and the utility will be decreased. In this article, we propose a novel privacy-preserved trajectory data publishing method, i.e., IDF-OPT, which can reduce global least-information loss and guarantee strong individual privacy. Comprehensive experiments based on an actual trajectory publishing benchmark demonstrate that the proposed method maintains high practicability in trajectory data mining.
Online Child Sexual Exploitation: A New MIS Challenge

This paper deals with the difficult yet increasingly important MIS phenomenon of online child sexual exploitation (online CSE). Through the use of secondary and publicly available data fromthe Federal Bureau of Investigation (FBI), as well as primary data from a cybercrime police unit in the United Kingdom, this study takes a grounded theory approach and organises the role that technologies and social actors play in shaping online CSE.The paper contributes to IS theory by providing a consolidated model for online CSE, which we label as the Technology and Imagery Dimensions Model (TIDM).This combines the staging of the phenomenon and the key dimensions that depict how the use of technology and imagery both fuels and defuses the phenomenon.In informing the construction of the model, the paper extracts, organises, and generalises the affordances of technology and discusses the role of information systems in detecting online CSE.
CAPTCHA Recognition Method Based on CNN with Focal Loss

In order to distinguish between computers and humans, CAPTCHA is widely used in links such as website login and registration. The traditional CAPTCHA recognition method has poor recognition ability and robustness to different types of verification codes. For this reason, the paper proposes a CAPTCHA recognition method based on convolutional neural network with focal loss function. This method improves the traditional VGG network structure and introduces the focal loss function to generate a new CAPTCHA recognition model. First, we perform preprocessing such as grayscale, binarization, denoising, segmentation, and annotation and then use the Keras library to build a simple neural network model. In addition, we build a terminal end-to-end neural network model for recognition for complex CAPTCHA with high adhesion and more interference pixel. By testing the CNKI CAPTCHA, Zhengfang CAPTCHA, and randomly generated CAPTCHA, the experimental results show that the proposed method has a better recognition effect and robustness for three different datasets, and it has certain advantages compared with traditional deep learning methods. The recognition rate is 99%, 98.5%, and 97.84%, respectively.
Reddit entity linking dataset

We introduce and make publicly available an entity linking dataset from Reddit that contains 17,316 linked entities, each annotated by three human annotators and then grouped into Gold, Silver, and Bronze to indicate inter-annotator agreement. We analyze the different errors and disagreements made by annotators and suggest three types of corrections to the raw data. Finally, we tested existing entity linking models that are trained and tuned on text from non-social media datasets. We find that, although these existing entity linking models perform very well on their original datasets, they perform poorly on this social media dataset. We also show that the majority of these errors can be attributed to poor performance on the mention detection subtask. These results indicate the need for better entity linking models that can be applied to the enormous amount of social media text.
A Face Detection Method Based on Skin Color Model and Improved AdaBoost Algorithm

This paper integrates skin color model and improved AdaBoost into a face detection method for high-resolution images with complex backgrounds. Firstly, the skin color areas were detected in a multi-color space. Each image was subject to adaptive brightness compensation, and converted into the YCbCr space, and a skin color model was established to solve face similarity. After eliminating the background interference by morphological method, the skin color areas were segmented to obtain the candidate face areas. Next, the inertia weight control factors and random search factor were introduced to optimize the global search ability of particle swarm optimization (PSO). The improved PSO was adopted to optimize the initial connection weights and output thresholds of the neural network. After that, a strong AdaBoost classifier was designed based on optimized weak BPNN classifiers, and the weight distribution strategy of AdaBoost was further improved. Finally, the improved AdaBoost was employed to detect the final face areas among the candidate areas. Simulation results show that our face detection method achieved high detection rate at a fast speed, and lowered false detection rate and missed detection rate.
WGI-Net: A weighted group integration network for RGB-D salient object detection

Abstract Salient object detection is used as a pre-process in many computer vision tasks (such as salient object segmentation, video salient object detection, etc.). When performing salient object detection, depth information can provide clues to the location of target objects, so effective fusion of RGB and depth feature information is important. In this paper, we propose a new feature information aggregation approach, weighted group integration (WGI), to effectively integrate RGB and depth feature information. We use a dual-branch structure to slice the input RGB image and depth map separately and then merge the results separately by concatenation. As grouped features may lose global information about the target object, we also make use of the idea of residual learning, taking the features captured by the original fusion method as supplementary information to ensure both accuracy and completeness of the fused information. Experiments on five datasets show that our model performs better than typical existing approaches for four evaluation metrics.
Energy and Cost Efficient Resource Allocation for Blockchain-Enabled NFV

Network function virtualization (NFV) is a promising technology to make 5G networks flexible and agile. NFV decreases operators' OPEX and CAPEX by decoupling the physical hardware from the functions they perform. In NFV, users' service request can be viewed as a service function chain (SFC) consisting of several virtual network functions (VNFs) which are connected through virtual links. Resource allocation in NFV is done through a centralized authority called NFV Orchestrator (NFVO). This centralized authority suffers from some drawbacks such as single point of failure and security. Blockchain (BC) technology is able to address these problems by decentralizing resource allocation. The drawbacks of NFVO in NFV architecture and the exceptional BC characteristics to address these problems motivate us to focus on NFV resource allocation to users' SFCs without the need for an NFVO based on BC technology. To this end, we assume there are two types of users: users who send SFC requests (SFC requesting users) and users who perform mining process (miner users). For SFC requesting users, we formulate NFV resource allocation (NFV-RA) problem as a multi-objective problem to minimize the energy consumption and utilized resource cost, simultaneously.
Target Detection and Segmentation in Circular-Scan Synthetic Aperture Sonar Images Using Semisupervised Convolutional Encoder–Decoders

We propose a framework for saliency-based, multi-target detection and segmentation of circular-scan, synthetic-aperture-sonar (CSAS) imagery. Our framework relies on a multi-branch, convolutional encoder-decoder network (MB-CEDN). The encoder portion of the MB-CEDN extracts visual contrast features from CSAS images. These features are fed into dual decoders that perform pixel-level segmentation to mask targets. Each decoder provides different perspectives as to what constitutes a salient target. These opinions are aggregated and cascaded into a deep-parsing network to refine the segmentation. We evaluate our framework using real-world CSAS imagery consisting of five broad target classes. We compare against existing approaches from the computer-vision literature. We show that our framework outperforms supervised, deep-saliency networks designed for natural imagery. It greatly outperforms unsupervised saliency approaches developed for natural imagery. This illustrates that natural-image-based models may need to be altered to be effective for this imaging-sonar modality.
Joint Image Encryption and Screen-Cam Robust Two Watermarking Scheme

This paper proposes a joint encryption and screen-cam robust watermarking scheme. This method combines the advantages of smartphone, encryption and watermarking technologies, thereby achieving watermark extraction with a smartphone, partial decryption and tracking leakage from sneak shots. We design a dual watermarking algorithm to achieve watermark detection from both encrypted and decrypted images. First, a watermark is embedded in the discrete Fourier transform (DFT) domain to enable leakage tracking. Then, a second watermark is generated based on QR (Quick response) code encoding and inverse DFT to achieve high watermark capacity and error correction ability, where the secret key for decryption is included in the watermark message. By hiding this message carrying the watermark for the encrypted image in the changes caused by embedding the first watermark, we can improve imperceptibility and will not affect the effectiveness of the proposed scheme. Finally, to enhance the robustness of watermark after encryption, a chaotic mapping-based segment encryption algorithm is proposed. In the process of watermark detection, to cope with perspective correction, a frame locating based algorithm is employed to achieve watermark synchronization from a recaptured picture of the encrypted image. Considering the severe quality degradation, we use a noise component and local statistic feature-based method to extract the message bits. The experimental results show that the proposed scheme is secure, and highly robust, to screen-cam the process for both before and after decryption. Additionally, after decryption, the proposed scheme also has high robustness against common image processing attacks.
Possibilities for the Utilization of an Automatized, Electronic Blockchain-based, Students’ Attendance Register, using a Universities’ Modern Security Cameras

This article introduces a solution for a security camera system, based on artificial intelligence, capable of an automatized composition of a students' attendance register, which helps in managing the in-school administrative burdens, and lessens the required paperwork, which has encumbered teachers to this day.So far, such a solution has not yet been implemented at any University.Furthermore, to keep the data storage process secure, blockchain technology will be implemented.This way, data will be stored after encryption on various nodes, increasing data security compared to the traditional, cloud-based data storage.The camera system is capable of monitoring the students' attendance of lectures.To be eligible for a so-called digital subject approval, each time a student attends a lecture, a record of facial features must include a timestamp, with the classroom number, the name of the lecturer, and the designation of the subject in question.Last, but not least, the system could be utilized in fire protection too, as, in the case of an eventual evacuation, the camera system would be able to indicate the exact position of each student within the premises, so it would provide a great asset to the rescuers and/or firemen.
Building Interpretable Interaction Trees for Deep NLP Models

This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing. We construct a tree to encode salient interactions extracted by the DNN. Six metrics are proposed to analyze properties of interactions between constituents in a sentence. The interaction is defined based on Shapley values of words, which are considered as an unbiased estimation of word contributions to the network prediction. Our method is used to quantify word interactions encoded inside the BERT, ELMo, LSTM, CNN, and Transformer networks. Experimental results have provided a new perspective to understand these DNNs, and have demonstrated the effectiveness of our method.
Web Based Recognition and Translation of American Sign Language with CNN and RNN

Individuals with hearing hindrance utilize gesture based communication to exchange their thoughts. Generally hand movements are used by them to communicate among themselves. But there are certain limitations when they communicate with other people who cannot understand these hand movements. There is a need to have a mechanism that can act as a translator between these people to communicate. It would be easier for these people to interact if there exists direct infrastructure that is able to convert signs to text and voice messages. As of late, numerous such frameworks for gesture based communication acknowledgment have been developed. But most of them are made either for static gesture recognition or dynamic gesture recognition. As sentences are generated using combinations of static and dynamic gestures, it would be simpler for hearing debilitated individuals if such computerized frameworks can detect both the static and dynamic motions together. We have proposed a design and architecture of American Sign Language (ASL) recognition with convolutional neural networks (CNN). This paper utilizes a pretrained VGG-16 architecture for static gesture recognition and for dynamic gesture recognition, spatiotemporal features were learnt with the complex architecture, called deep learning. It contains a bidirectional convolutional Long Short Term Memory network (ConvLSTM) and 3D convolutional neural network (3DCNN) and this architecture is responsible to extract 2D spatio temporal features.
Capacity of Quantum Symmetric Private Information Retrieval With Collusion of All But One of Servers

Quantum private information retrieval (QPIR) is a protocol in which a user retrieves one of multiple classical files by downloading quantum systems from non-communicating n servers each of which contains a copy of all files, while the identity of the retrieved file is unknown to each server. Symmetric QPIR (QSPIR) is QPIR in which the user only obtains the queried file but no other information of the other files. In this article, we consider the (n-1)-private QSPIR in which the identity of the retrieved file is secret even if any n-1 servers collude, and derive the QSPIR capacity for this problem which is defined as the maximum ratio of the retrieved file size to the total size of the downloaded quantum systems. For any even number n of servers, we show that the capacity of the (n-1)-private QSPIR is 2/n, when we assume that there are prior entanglements among the servers. We construct an (n-1)-private QSPIR protocol of rate ⌈n/2⌉ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> and prove that the capacity is upper bounded by 2/n even if any error probability is allowed. The (n-1)-private QSPIR capacity is strictly greater than the classical counterpart.
Pengembangan Bahan Ajar Menyimak Berbasis Multimedia Interaktif pada Pelajaran Tematik dengan Tema “Indahnya Kebersamaan” untuk Siswa Kelas IV Sekolah Dasar

This study aims to develop thematic teaching materials based on interactive multimedia to improve students' listening skills. This study used the research and development model of Borg and Gall (1983). The multimedia-based thematic teaching materials developed were validated by linguists, media experts and graphic experts. The stages of testing teaching materials consist of small group trials and field trials. The validation of material experts was 87.5%; the results of the validation of linguists were 82.5%; the results of the validation of graphic experts were 87.5%. Based on data and student response questionnaires, learning multimedia-based teaching materials can attract students' interest. Student learning outcomes show a positive status. Thus, the product that can be developed is useful in the learning of fourth-grade elementary school students.
A High-Performance and Low-Cost Montgomery Modular Multiplication Based on Redundant Binary Representation

In this brief, Redundant Binary Representation (RBR) is applied in Montgomery modular multiplication (MMM) to eliminate the long carry chain and realize parallel computation. A novel MMM algorithm based on RBR is proposed. Based on the proposed algorithm, different sizes of high-performance and low-cost Montgomery multipliers are implemented in TSMC CMOS process technology. The experimental results demonstrate that our design has significant advantages in terms of performance, area and Area-Time-Product over previous researches. It&#x2019;s worth mentioning that our 8192-bit Montgomery multiplier (TMSC 65nm) with 603MHz working frequency and 878.1K equivalent gates can complete the MMM in only 3403ns.