Reviewerly/exHarmony huggingface dataset:
Adaptive Feature Calculation and Diagonal Mapping for Successive Recovery of Tampered Regions

This article proposes an adaptive scheme for image tampered region localization and content recovery. To generate the watermark information comprised of the authentication data and recovery data, we firstly propose the Adaptive Authentication Feature Calculation algorithm to obtain the authentication data, which includes the information of block location and block feature. The DWT-based Block Feature Calculation method is then proposed to calculate the block feature, and the quantization method is employed to calculate the block location. The recovery data is composed of self-recovery bits and mapped-recovery bits. The self-recovery bits are obtained by the Set Partitioning in Hierarchical Trees encoding algorithm. For retrieving the damaged codes caused by tampering, we propose the Diagonal Mapping algorithm and apply it to the self-recovery bits, thus generating the mapped-recovery bits, to provide a guarantee of recovery data. Experimental results show the superior performance of the proposed scheme in terms of tamper detection and image recovery, by comparing with the state-of-the-art works. The results demonstrate that the proposed method shows efficiency in the adaptiveness, well localization, strong capability for image recovery, and the effectiveness of attack resistance.
RUTUT: Roman Urdu to Urdu Translator Based on Character Substitution Rules and Unicode Mapping

Urdu language written in English alphabets for communication is known as Roman Urdu. In pronunciation, both are the same but different in spelling and have different shapes of the alphabet. A survey acknowledges that 300 million people are speaking Urdu and about 11 million speakers in Pakistan from which maximum users prefer Roman Urdu for the textual communication. Today most of the modern technologies like computers and mobile phones using English script, due to this local Urdu user has to use English letters to type Urdu script that is Roman Urdu. In this research, Roman Urdu to Urdu Translator (RUTUT) is proposed that consists of preprocessing methods, rule-based character substitution and Unicode based character mapping techniques. It can transliterate the messages or descriptions from the Roman Urdu script to Urdu script which may help the Urdu speaker to elaborate their message in efficient manners. The focus of this research is to analyze the issues related to the Roman Urdu script to Urdu script transliteration and develop a translator based on the concepts of transliteration. This research analyzed Roman Urdu data and identified different rules-based character substitution techniques that transform the Roman Urdu into Urdu script at fundamental levels. This research is carried out using a python programming language in programming tool Anaconda in Jupiter notebook and user-friendly Graphical User Interface (GUI) created by using Tkinter library. To evaluate the RUTUT, different translational tests are performed and compare those results with famous Google online translator and ijunoon online transliteration. The analyses of results show that the proposed RUTUT approach translates accurately than Google online translator and ijunoon online transliteration.
Enhanced Story Representation by ConceptNet for Predicting Story Endings

Predicting endings for narrative stories is a grand challenge for machine commonsense reasoning. The task requires ac- curate representation of the story semantics and structured logic knowledge. Pre-trained language models, such as BERT, made progress recently in this task by exploiting spurious statistical patterns in the test dataset, instead of 'understanding' the stories per se. In this paper, we propose to improve the representation of stories by first simplifying the sentences to some key concepts and second modeling the latent relation- ship between the key ideas within the story. Such enhanced sentence representation, when used with pre-trained language models, makes substantial gains in prediction accuracy on the popular Story Cloze Test without utilizing the biased validation data.
Locally-Centralized Certificate Validation and its Application in Desktop Virtualization Systems

To validate a certificate, a user needs to install the certificate of the root certification authority (CA) and download the certificate revocation information (CRI). Although operating systems and browsers manage the certificate trust list (CTL) of publicly-trusted root CAs for global users, locally-trusted root CAs still play an important role and it is difficult for a user to manage its CTL properly by itself. Meanwhile, the CRI access is inefficient, sometimes even unavailable, and causes privacy leakage. We revisit these problems by analyzing the TLS sessions within an organization. To the best of our knowledge, we are the first to analyze CTL management and CRI access on the scale of medium-sized organizations. Based on the analysis, a locally-centralized design is proposed to manage the CTLs of all users by IT administrators and access the CRI services for all users, within an organization. We apply this design to desktop virtualization systems to demonstrate its applicability, and build vCertGuard with oVirt and KVM-QEMU. In vCertGuard, the CTLs of all virtual machines (VMs) are managed in the VM monitors (VMMs). In the CTL, the self-signed certificates of publicly-trusted root CAs are properly configured, while each locally-trusted certificate chain is specified one by one. vCertGuard accesses the CRI services for all VMs, and the downloaded CRI is cached and shared among VMs. Because most TLS servers are visited by multiple users of an organization, it reduces the cost of CRI access. Experimental results of the prototype system show that vCertGuard maintains the CTLs with a negligible overhead, and significantly improves the performance of CRI access.
Bit Independence Criterion Extended to Stream Ciphers

The bit independence criterion was proposed to evaluate the security of the S-boxes used in block ciphers. This paper proposes an algorithm that extends this criterion to evaluate the degree of independence between the bits of inputs and outputs of the stream ciphers. The effectiveness of the algorithm is experimentally confirmed in two scenarios: random outputs independent of the input, in which it does not detect dependence, and in the RC4 ciphers, where it detects significant dependencies related to some known weaknesses. The complexity of the algorithm is estimated based on the number of inputs l, and the dimensions, n and m, of the inputs and outputs, respectively.
Detection of Ki67 Hot-Spots of Invasive Breast Cancer Based on Convolutional Neural Networks Applied to Mutual Information of H&amp;E and Ki67 Whole Slide Images

Ki67 hot-spot detection and its evaluation in invasive breast cancer regions play a significant role in routine medical practice. The quantification of cellular proliferation assessed by Ki67 immunohistochemistry is an established prognostic and predictive biomarker that determines the choice of therapeutic protocols. In this paper, we present three deep learning-based approaches to automatically detect and quantify Ki67 hot-spot areas by means of the Ki67 labeling index. To this end, a dataset composed of 100 whole slide images (WSIs) belonging to 50 breast cancer cases (Ki67 and H&amp;E WSI pairs) was used. Three methods based on CNN classification were proposed and compared to create the tumor proliferation map. The best results were obtained by applying the CNN to the mutual information acquired from the color deconvolution of both the Ki67 marker and the H&amp;E WSIs. The overall accuracy of this approach was 95%. The agreement between the automatic Ki67 scoring and the manual analysis is promising with a Spearman’s ρ correlation of 0.92. The results illustrate the suitability of this CNN-based approach for detecting hot-spots areas of invasive breast cancer in WSI.
A Model of Threats to the Confidentiality of Information Processed in Cyberspace Based on the Information Flows Model

This article covers one of the fundamental problems of information security—building a threat model. The article discusses a new method for identifying typical threats to information confidentiality based on the information flow model. The threat model is based on the description of the system. An incorrect description of the system leads to the formation of an incorrect threat model. A review of the subject area revealed several approaches used to describe the system in terms of circulating information flows. Each of these approaches has its own pros and cons. The model of information flows proposed in this work reduces the description of any information system to an eight-digit alphabet. Analysis of the structure of the elementary information flow identified four typical threats to confidentiality, the Cartesian product of a set of threats and a set of streams is a complete model of typical threats to the confidentiality of information processed in cyberspace.
Four Types of Toxic People: Characterizing Online Users’ Toxicity over Time

Identifying types of online users' toxic behavior reveals important insights from social media interactions, including whether a user becomes "radicalized" (more toxic) or "pacified" (less toxic) over time. In this research, we design two metrics to identify toxic user types: F score that captures the changes in a user's toxicity, and G score that captures the direction of the shift taking place in the user's toxicity pattern. We apply these metrics to a dataset of 4M user comments from Reddit by defining four toxic user types based on the toxicity scores of a user's comments: (a) Steady Users whose toxicity scores are steady over time, (b) Fickle-Minded Users that switch between toxic and non-toxic commenting, (c) Pacified Users whose commenting becomes less toxic in time, and (d) Radicalized Users that become gradually toxic. Findings from the Reddit dataset indicate that fickle-minded users form the largest group (31.2%), followed by pacified (25.8%), radicalized (25.4%), and steadily toxic users (17.6%). The results suggest that the most typical behavior type of toxicity is switching between toxic and non-toxic commenting. This research has implications for preserving the user-friendliness of online communities by identifying continuously toxic users and users in danger of becoming radicalized (in terms of their toxic behavior), and designing interventions to mitigate these behavior types. Using the metrics we have defined, identifying these user types becomes possible. More research is needed to understand why these patterns take place and how they could be mitigated.
DCA: Diversified Co-attention Towards Informative Live Video Commenting

We focus on the task of Automatic Live Video Commenting (ALVC), which aims to generate real-time video comments with both video frames and other viewers’ comments as inputs. A major challenge in this task is how to properly leverage the rich and diverse information carried by video and text. In this paper, we aim to collect diversified information from video and text for informative comment generation. To achieve this, we propose a Diversified Co-Attention (DCA) model for this task. Our model builds bidirectional interactions between video frames and surrounding comments from multiple perspectives via metric learning, to collect a diversified and informative context for comment generation. We also propose an effective parameter orthogonalization technique to avoid excessive overlap of information learned from different perspectives. Results show that our approach outperforms existing methods in the ALVC task, achieving new state-of-the-art results.
Min-entropy as a resource for one-shot private state transfer, quantum masking, and state transition

We give an operational meaning to the min-entropy of a quantum state as a resource measure for various interconnected tasks. In particular, we show that the min-entropy without smoothing measures the amount of quantum information that can be hidden or encoded perfectly in the one-shot setting when the quantum state is used as a randomness/correlation source. First, we show that the min-entropy of entanglement of a pure bipartite state is the maximum number of qubits privately transferable when the state is used as quantum one-time pad. Then, through the equivalence of quantum secret sharing(QSS)-like protocols, it is also shown that the min-entropy of a quantum state is the maximum number of qubits that can be masked when the state is used as a randomness source for a quantum masking process. Consequently we show that the min-entropy of a quantum state is the half of the size of quantum state it can catalytically dephase.This gives a necessary and sufficient condition for catalysts for state transition processes.
Obstacle Avoidance and Multitarget Tracking of a Super Redundant Modular Manipulator Based on Bezier Curve and Particle Swarm Optimization

Abstract A super redundant serpentine manipulator has slender structure and multiple degrees of freedom. It can travel through narrow spaces and move in complex spaces. This manipulator is composed of many modules that can form different lengths of robot arms for different application sites. The increase in degrees of freedom causes the inverse kinematics of redundant manipulator to be typical and immensely increases the calculation load in the joint space. This paper presents an integrated optimization method to solve the path planning for obstacle avoidance and discrete trajectory tracking of a super redundant manipulator. In this integrated optimization, path planning is established on a Bezier curve, and particle swarm optimization is adopted to adjust the control points of the Bezier curve with the kinematic constraints of manipulator. A feasible obstacle avoidance path is obtained along with a discrete trajectory tracking by using a follow-the-leader strategy. The relative distance between each two discrete path points is limited to reduce the fitting error of the connecting rigid links to the smooth curve. Simulation results show that this integrated optimization method can rapidly search for the appropriate trajectory to guide the manipulator in obtaining the target while achieving obstacle avoidance and meeting joint constraints. The proposed algorithm is suitable for 3D space obstacle avoidance and multitarget path tracking.
Ridge Regression Based on Gradient Descent Method with Memory Dependent Derivative

Ridge regression is a supervised biased estimation regression method in machine learning. In fact, its principle is similar to that of ordinary least squares(OLS) estimation, except that OLS is improved by giving up the unbiasedness of OLS plus a penalty term, which solves the problem of multicollinearity (approximate linear correlation of independent variables), between data. Ridge regression algorithms are the same in optimizing the loss function of linear regression, and usually use gradient descent or stochastic gradient descent. However, when dealing with complex nonlinear problems, these methods can easily fall into the trap of local minima, and global consciousness and convergence are not strong. In order to retain the gradient information well and enhance the global convergence, the gradient descent method of memory-dependent derivative is introduced into the optimized loss function part of the ridge regression algorithm.
Children as Designers - Recognising divergent creative modes in Participatory Design

Children and designers have divergent, sometimes contradictory notions of what "design" means. Unrestricted by the lens of adulthood and the shackles of formal creative education, children ask unexpected questions, draw unlikely conclusions, and have a different experience of creation. How do we recognise, address and adapt to these differences of creativity, and how can we successfully draw from them in our design process? In this paper, we describe tensions in participatory design (PD) sessions we have conducted, focusing on the needs and strengths of the children in their role as designers. We inspect the creative experience of diverse children through the creative modes of the Storyteller, the Scientist, the Actor and the Explorer. With these modes, we offer a perspective that enables PD practitioners to facilitate engagements that better support participant's creative experiences.
GripNet: Graph information propagation on supergraph for heterogeneous graphs

• A novel supergraph data structure to segregate a heterogeneous graph into interconnected, semantically-coherent subgraphs for efficient learning. • A new graph representation learning framework based on supergraph for heterogeneous graphs and graph-based data integration. • Extensive experiments on seven large-scale datasets for link prediction and node classification tasks. Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks. Existing popular methods either capture semantic relationships but indirectly leverage node/edge attributes in a complex way, or leverage node/edge attributes directly without taking semantic relationships into account. When involving multiple convolution operations, they also have poor scalability. To overcome these limitations, this paper proposes a ible and efficient Gr aph i nformation p ropagation Net work (GripNet) framework. Specifically, we introduce a new supergraph data structure consisting of supervertices and superedges. A supervertex is a semantically-coherent subgraph. A superedge defines an information propagation path between two supervertices. GripNet learns new representations for the supervertex of interest by propagating information along the defined path using multiple layers. We construct multiple large-scale graphs and evaluate GripNet against competing methods to show its superiority in link prediction, node classification, and data integration. The code is available at https://github.com/nyxflower/GripNet .
Health insurance risk assessment using cognitive mapping and multiple‐criteria decision analysis

Abstract Health insurance risk analysis is crucial to the development of the insurance industry, allowing insurance companies to anticipate potential losses in health insurance's inverted product life cycle. The health insurance risk evaluation process is, however, carried out in various ways, which limits the application of transparency, fairness, and justice principles in the calculation of risk rewards. By combining cognitive mapping and measuring attractiveness by a categorical‐based evaluation technique (i.e., MACBETH, a multiple‐criteria decision analysis technique), this study sought to create a nonparametric and distinctive decision support system for individual private health insurance risk analysis. The proposed system facilitates greater transparency in the calculation of health insurance risk rewards. The results of a real‐world application of this system were analyzed and discussed in face‐to‐face group meetings with independent experts who work for two of the largest insurance companies in Portugal, thereby incorporating realism into the proposed evaluation mechanism. The advantages and limitations of this assessment framework are also discussed.
Kaya: A Testing Framework for Blockchain-based Decentralized Applications

In recent years, many decentralized applications based on blockchain (DApp) have been developed. Some development tools provide testing functions, but only for developers to write unit tests for smart contracts rather than test DApp as a whole. Moreover, due to the difficulty for testers to understand the implementation details of smart contracts, insufficient functional testing causes some DApps not to meet functional design expectations. The inherent complexity of DApp, inconvenient pre-state setting, and not-so-readable logs make DApp testing challenging. In this paper, we propose Kaya, a testing framework for DApps to bridge these gaps. Firstly, Kaya formulate automatically executed test cases that cover both front-end behaviors and back-end logics with simple setting. Secondly, Kaya provides a flexible and convenient way for test engineers to set the blockchain pre-states. Thirdly, Kaya transforms incomprehensible addresses into readable variables for easier comprehension. Besides, to fit the various application environments, we provide both GUI and CLI for test engineers to use Kaya. Our case study and preliminary human study demonstrates the potential of Kaya in helping test engineers to test DApps more easily. A demo video is at https://youtu.be/7DyI_EpVZFw.
The effectiveness of machine translation to improve the system of translating language on cultural context

Abstract The objective of this study is to present the effectiveness of machine translation in translating language provided by the technology as well as to point out some disadvantages and advantages in using Google Translate (GT) on the cultural context in Indonesia. This research results that the written translation is better than the translation of images using GT. Consider the cultural context as expressive in society to be spread throughout the world, and GT as one of the technological tools available in communication must be used well to overcome language translation systems and cultural barriers.
A Survey on Vulnerability Detection Tools of Smart Contract Bytecode

As the core of present blockchain applications, smart contracts are designed to help multiple parties reach an agreement. Along with the promotion of smart contract applications, a large number of economic losses caused by attacks on smart contract vulnerabilities have emerged. Since most smart contracts only disclose bytecode, in recent years, there have been numerous researches on the vulnerability detection of smart contract bytecode, mainly for Ethereum smart contracts, achieving considerable results. My survey summarizes the methods and supported vulnerability types of these tools, aimed at Ethereum or EOSIO, over the years. The problems reflected in it shed light on the future work of smart contract bytecode vulnerability detection.
Exploring Temporal Information for Dynamic Network Embedding

Representing nodes in a network as low-dimensional dense vectors can facilitate the analysis of complex networks, which is a challenging task and has attracted increasing attention. However, in the real world, networks are changing over time, such as cooperation in citation networks and communication in email networks. Most of the recent embedding methods only focus on static networks. Thus they ignore the critical temporal information, which serves as a supplement to structure information and has been proved to improve the quality of node embedding. In this work, we propose an unsupervised deep learning model called DTINE, which explores temporal information for further enhancing the robustness of node representations in dynamic networks. To preserve network topology, we pertinently design a temporal weight and sampling strategy to extract features from the neighborhoods. An attention mechanism will be applied on the recurrent neural network to measure the contributions of historical information and capture the evolution of the networks. Experimental results on four real-world networks demonstrate that the proposed method achieves better performance than state-of-the-art methods.