Reviewerly/exHarmony huggingface dataset:
A Robust Residual Dense Neural Network For Countering Antiforensic Attack on Median Filtered Images

Recently, antiforensic methods have been proposed that invalidate most of the state-of-the-art median filter digital image forensic techniques. Also, the existing counter antiforensic methods decline noticeably when evaluated on small-sized patches in JPEG compressed images. In this letter, we have developed a robust residual dense (Neural) network-based counter antiforensic median filter detection technique that exploits local dense connection and residual learning of features for improved classification of images. Experimental results demonstrate that the proposed approach achieves superior performance to state-of-the-art techniques in detecting forgeries, even in small patches, in JPEG compressed images, for both median filtered and antiforensic median filtered images.
ImageProof: Enabling Authentication for Large-Scale Image Retrieval

With the explosive growth of online images and the popularity of search engines, a great demand has arisen for small and medium-sized enterprises to build and outsource large-scale image retrieval systems to cloud platforms. While reducing storage and retrieval burdens, enterprises are at risk of facing untrusted cloud service providers. In this paper, we take the first step in studying the problem of query authentication for large-scale image retrieval. Due to the large size of image files, the main challenges are to (i) design efficient authenticated data structures (ADSs) and (ii) balance search, communication, and verification complexities. To address these challenges, we propose two novel ADSs, the Merkle randomized k-d tree and the Merkle inverted index with cuckoo filters, to ensure the integrity of query results in each step of image retrieval. For each ADS, we develop corresponding search and verification algorithms on the basis of a series of systemic design strategies. Furthermore, we put together the ADSs and algorithms to design the final authentication scheme for image retrieval, which we name ImageProof. We also propose several optimization techniques to improve the performance of the proposed ImageProof scheme. Security analysis and extensive experiments are performed to show the robustness and efficiency of ImageProof.
Hacking Difference in Indonesia

The paper offers an ethnographic account of racial and cultural difference as sites to contest dominant practices of computing and technology. Specifically, we focus on how a collective of Indonesian biohackers position the care labor of a generation of women (referred to as Nenek-nenek in Bahasa Indonesia) to retrace the origins and boundaries of their making, hacking, and citizen science practices. The paper's contribution is to bring the study of the political economy of hacking and making into conversation with themes of racial and cultural difference in postcolonial computing across HCI, STS, and design. More specifically, the paper examines how Indonesian biohackers position situated histories and expertise as properly technological. Further, we show how their articulation of Indonesian difference was in turn appropriated by foreign hackers and commentators to envision tech futures against the status quo.
Protecting Data Privacy for Permissioned Blockchains using Identity-Based Encryption

Blockchain is an emerging decentralized architecture and distributed public ledger technology underlying Bitcoin, and has recently attracted intensive attention from governments, financial institutions and high-tech enterprises. It is believed that blockchain can improve efficiency, reduce costs and enhance data security, but it is still in the face of serious privacy issues which may hinder the wide application of blockchain. In this paper, We present a practical scheme by adding the Identity-Based encryption system, which effectively improves the data privacy for non-transaction applications. Analyses show that our proposal has a high security level which can prevent both disguise and passive attacks, and is functional, effective and practical in many applications for non-transactional scenarios.
Proposal-Driven Segmentation for Videos

Effectively utilizing the common information in a set of video frames is a vital aspect in video segmentation. However, existing methods that transport the common information from a prior frame to the current frame do not make use of the common information effectively. In order to address this issue, we apply a new strategy that jointly segments object through a convolutional neural network (CNN) to build a proposal-driven framework for exploiting the common information between two video frames by processing two video frames simultaneously in this letter. Moreover, proposals from the video frames are found useful for refining the segmentation results through fusing their segmentation results with the ones of the video frames. In our framework, proposals with features are generated by a faster region-CNN, and the L2 loss function is used to establish proposal pairs among proposals from the two selected frames. A new trained ResNet then keeps proposal pairs, which contain the same content, and the PSPNet model for segmentation is utilized to generate the segmentation results belonging to the frames and proposals. Finally, the proposals' segmentation results are refined using the video frames' segmentation results. The VOT 2016 segmentation dataset, the DAVIS 2017 dataset, and the SegTrack v2 dataset were used for training and testing our framework. Experimental results show that our proposal-driven segmentation framework is able to achieve higher accuracies in video segmentation challenge compared to those of the existing video segmentation methods.
An Empirical Investigation of Efficient Spatio-Temporal Modeling in Video Restoration

We present a comprehensive empirical investigation of efficient spatio-temporal modeling in video restoration tasks. To achieve a better speed-accuracy trade-off, our investigation covers the intersection of three dimensions in deep video restoration networks: spatial-wise, channelwise and temporal-wise. We enumerate various network architectures ranging from 2D convolutional models to their 3D extensions, and discuss their gain and loss in terms of training time, model size, boundary effects, prediction accuracy and the visual quality of restored videos. Under a strictly controlled computational budget, we also specifically explore the design inside each residual building block in a video restoration network, which consists a mixture of 2D and 3D convolutional layers. Our findings are summarized as follows: (1) In 3D convolutional models, setting more computation/channels for spatial convolution leads to better performance than on temporal convolution. (2) The best variant of 3D convolutional models is better than 2D convolutional models, but the performance gap is close. (3) In a very limited range, the performance can be improved by the increase of temporal window size (5 frames for 2D model) or padding size (6 frames for 3D model). Based on these findings, we propose the wide-activated 3D convolutional network for video restoration (WDVR), which achieves state-of-the-art restoration accuracy under constrained computational budgets with low runtime latency. Our solution based on WDVR also won 2nd places in three out of four tracks of NTIRE 2019 Challenge for Video Super-Resolution and Deblurring. Code and models are released at https://github.com/ychfan/ wdvr_ntire2019.
Bidirectional LSTM: An Innovative Approach for Phishing URL Identification

Today, prevailing phishing scams have caused serious network security problems. By adopting a domain generation algorithm, the cyber attackers are able to disguise their malware in various forms, making it easier to steal personal identity and property. This paper proposed an innovative approach to detect malicious URLs by employing a Bidirectional Long Short-Term Memory (B-LSTM) network. The paper also compares the performance of the B-LSTM network with that of conventional machine learning methods and unidirectional LSTM. The results demonstrate that the B-LSTM network has superior performance than the other machine learning methods, producing a F1 score of 95.9%.
Solar irradiance modelling using an offline coupling procedure for the Weather Research and Forecasting (WRF) model

The research proposed herein seeks to improve solar irradiance magnitude and variability results produced by the Weather Research and Forecasting (WRF) model using a novel offline coupling procedure (OCP). The OCP includes simulations in clear sky conditions, where the effects from varying atmospheric composition depend on the broadband clear sky model used, on cloud attenuation and on decomposition techniques to accurately separate the global irradiance into direct and diffuse components. Furthermore, shadowing and slope effects from orographic features and other obstacles are included with much greater detail. Benefits of the offline coupling procedure were quantified by comparison against local solar radiation measurements over a period of one year. A baseline test to explore the different configuration options was implemented. It considers two aerosol databases, three broadband clear sky models, two cloud attenuation corrections based on clear sky index (either global or by components) and four decomposition models. Given the amount of results from baseline tests, the relative root-mean-square error (rRMSE) was used as the criterion to identify the most suitable OCP configuration, which was subsequently used in the performance analysis. The current baseline test comprises seven sites selected from the Baseline Radiation Network (BSRN) in different terrain complexities and atmospheric conditions. Statistical indicators associated with the annual global horizontal irradiance reveal that OCP improved WRF results by 88.4% in terms of the relative mean bias error (rMBE) and by 5.0% in the value of the Nash-Sutcliffe efficiency (NSE). A standard OCP configuration to model global horizontal irradiance only is proposed. The rMBE of annual hourly global horizontal irradiance oscillated between −3.6% and 3.9%, whilst the NSE varied between 0.608 and 0.939. The analogous quantities for diffuse horizontal irradiance were -19.3%⩽rMBE⩽4.6% and 0.371⩽NSE⩽0.717, while for direct normal irradiance -6.5%⩽rMBE⩽23.2% and 0.101⩽NSE⩽0.656.
Research on Cross Language Text Keyword Extraction Based on Information Entropy and TextRank

In order to extract keywords from cross-language documents as accurately as possible, especially for the language whose keyword extraction technology is not mature, a text keyword extraction method based on information entropy and TextRank is proposed to extract the accurate keywords from the translated Chinese documents. This method determines the basic importance of words according to the information entropy of words, and then uses the information entropy of words to vote iteratively through the TextRank algorithm. This method solves the problem that TextRank algorithm easily extracts frequent non key words as keywords. The experimental results show that the proposed method can extract keywords more accurately than TextRank in the processing of cross-lingual bilingual translated documents.
Key Competences of Information Systems Project Managers

Project managers have a main role in all kinds of projects, and information systems (IS) projects are not an exception. In order to perform their activities well, project managers need to develop several complementary competences, including technical, contextual and behavioral competences. In literature we find important references on the project managers' general competences as, for instance, the ICB from IPMA. However, very little attention has been paid to the eventually distinctive features of IS project managers. In order to identify the most important competences of these project managers, we carried out a questionnaire-based survey. The results show that the top 12 competences are: communication; engagement and motivation; project requirements and objectives; leadership; reliability; results orientation; conflict and crisis; project orientation; teamwork; interested parties; resilience (emerging as a new competence); and ethics. Furthermore, results indicated that the project management bodies of knowledge are suitable to characterize the IS project manager's competences.
Investigating the effect of source characteristics on task comparability in integrated writing tasks

The study presents an attempt to explore the impact of source characteristics on task comparability in integrated writing tasks. To this end, two read-listen-write tasks of TOEFL iBT were selected to differ in topic, structural organization, and lexical and conceptual overlap, suggested to be significant in affecting summary quality (Cho, Rijmen, & Novák, 2013; Li, 2014; Yu, 2009). The performance of a group of TOEFL candidates on the two tasks was qualitatively analyzed and quantitatively compared according to the skills integrated tasks aim to assess: 1. comprehension of the source information, 2. discourse synthesis, 3. citation of the sources, and 4. source borrowing strategies (Cumming et al., 2005). Comparisons were also made of examinees’ overall scores on the test, assigned by a group of EAP specialists in a Canadian university based on TOEFL iBT integrated writing rating scale. Significant differences were observed in test-takers’ overall scores, comprehension of source information, type and quality of source borrowing strategies, and tendency either to use their own original ideas and structures or to rely more on those of one source in their summaries. Implications are provided for test developers and test users as well as for future research to be conducted.
A concise review on sensor signal acquisition and transformation applied to human activity recognition and human–robot interaction

Human activitiy recognition deals with the integration of sensing and reasoning aiming to understand better people’s actions. Moreover, it plays an important role in human interaction, human–robot interaction, and brain–computer interaction. When these approaches have to be developed, different efforts from signal processing and artificial intelligence are considered. In that sense, this article aims to present a concise review of signal processing in human activitiy recognition systems and describe two examples and applications both in human activity recognition and robotics: human–robot interaction and socialization, and imitation learning in robotics. In addition, it presents ideas and trends in the context of human activity recognition for human–robot interaction that are important when processing signals within that systems.
Assisted pattern mining for discovering interactive behaviours on the web

When the hypotheses about users’ behaviour on interactive systems are unknown or weak, mining user interaction logs in a data-driven fashion can provide valuable insights. Yet, this process is full of challenges that prevent broader adoption of data-driven methods. We address these pitfalls by assisting user researchers in customising event sets, filtering the noisy outputs of the algorithms and providing tools for analysing such outputs in an exploratory fashion. This tooling facilitates the agile testing and refinement of the formulated hypotheses of use. A user study with twenty participants indicates that compared to the baseline approach, assisted pattern mining is perceived to be more useful and produces more actionable insights, despite being more difficult to learn.
Precision Pose Measurement of an Object With Flange Based on Shadow Distribution

This paper proposes a novel method to achieve pose measurement in 3-D space using a microscopic camera. For an object with flange on its surface, there is a shadow region around the flange in the image if the camera is equipped with a ring light source. The flange's shadow distribution caused by the ring light source is deduced based on imaging principle. An optimal model used to estimate object's pose is designed based on the flange's shadow distribution. Take the objects with circle flange, which are commonly used in precision assembly as an example, the shadow-forming model and the relation between the rotation angle and the shadow width deviation are presented. The rotation axis is determined on the basis of two particular rays on the shadow. Then the object's orientation is computed based on the rotation axis and the rotation angle. A series of simulations and experiments are conducted to verify the high precision of the proposed pose measurement method. The root-mean-square (rms) errors of position and orientation measurement are 6.26 $\mu \text{m}$ and 0.19°, respectively.
Personalized Diet Recommendation Based on K-means and Collaborative Filtering Algorithm

Abstract With the improvement of people’s living standards, people pay more and more attention to the health of diet, and traditional dietary recommendations are difficult to meet the user’s dietary preferences and nutritional balance. This paper first uses the k-means clustering algorithm to divide the food set into multiple disjoint subsets, then uses the user-based collaborative filtering algorithm to recommend the food that the user may like. The recommended food and food in standard recipes set by user’s own situation are in the same cluster, which meets the user’s nutritional balance. The experimental results show that the recommended effect of this method is effective, and the recommended accuracy rate is over 70%.
Intelligent Vehicle Path Planning Based on Improved Artificial Potential Field Algorithm

Goal unreachable problem and local minimum point problem appear when the traditional artificial potential field (APF) method is used for intelligent vehicle path planning. Aiming at the problem of goal unreachable with obstacle nearby, a modified repulsion potential field function is proposed in this paper to realize the regulation of potential field. The modified repulsion potential field introduces the relative distance between the intelligent vehicle and the target point, and at the same time increases the regulation factor to ensure that the minimum potential point of the whole situation field is always at the target point. In order to solve the local minimum problem caused by the traditional artificial potential field method in path planning, this paper introduces the fuzzy control (FC) algorithm based on the improved artificial potential field algorithm, proposes the APF-FC fusion algorithm, and uses the fuzzy control of fuzzy control algorithm to solve the local minimum problem of APF. Finally, the APF-FC path planning algorithm is simulated. The verification results show that the algorithm combines the characteristics of artificial potential field and fuzzy algorithm well, and effectively solves the related defects of traditional artificial potential field algorithm. The effectiveness and applicability of the path planning of the algorithm are significantly improved.
Can a Machine Learn Through Customer Sentiment?: A Cost-Aware Approach to Predict Support Ticket Escalations

Given the connection between customer happiness and support ticket escalation, we describe an approach that 1) analyzes the emotions in conversations between a customer and a support analyst and 2) provides organizations with a cost-based mechanism to evaluate machine-learning algorithms trained on emotion-related features to predict support ticket escalations.
Toward Application of Blockchain for Improved Health Records Management and Patient Care

Technological advancements have proven to be indispensable for improving patient care, yet they continue to present a host of problems. One of the most pressing concerns is how to improve quality of care while controlling costs. Beyond clinical care, one plausible solution is to share patient information freely and efficiently. Hospitals and clinics may share data internally, but external information sharing remains an issue. Despite the digitization of medical records, there remains a lack of adequate computing infrastructure or unwillingness to share data among providers. Care quality often suffers as a result. Implementing a type of peer-to-peer distributed digital technology, known as a blockchain, to record and transmit transactional data could be a solution to these concerns. Originally, blockchain was developed to record cryptocurrency transactions. However, as blockchain technologies have matured and adopted across dissimilar industries, the feasibility of possible applications of blockchain technology in healthcare is getting more attention. This article explores possible opportunities of adoption of blockchain technology to improve patient data security, privacy, and care while outlining the challenges that practitioners may encounter.
Evaluating the Performance of Virtual Reality Navigation Techniques for Large Environments

We present results from two studies comparing the performance of four different navigation techniques (flight, teleportation, world-in-miniature, and 3D cone-drag) and their combinations in large virtual reality map environments. While prior work has individually examined each of these techniques in other settings, our study presents the first direct comparison between them in large open environments, as well as one of the first comparisons in the context of current-generation virtual reality hardware. Our first study compared common techniques (flight, teleportation, and world-in-miniature) for search and navigation tasks. A follow-up study compared these techniques against 3D cone drag, a direct-manipulation navigation technique used in contemporary tools like Google Earth VR. Our results show the strength of flight as a stand-alone navigation technique, but also highlight five specific ways in which viewers can combine teleportation, world-in-miniature, and 3D cone drag with flight, drawing on the relative strengths of each technique to compensate for the weaknesses of others.
IVLG: Interactive Visualization of Large Graphs

There has been significant effort in recent years to explore and navigate very large linked datasets, due to the increase of their availability. Many techniques have been developed that extract the information from such datasets and present it to the user as diagrams, while others take advantage of the hierarchies of the datasets to filter and aggregate them, allowing the users to access specific information. In order to overcome the limitations regarding the volume of the presented information, we have developed a novel technique that enables the interactive visualization as one continuous graph of datasets with millions of elements. IVLG is a fully fledged prototype system that implements this technique based on a client-server architecture, enabling many users to have concurrently access to the information through a user-friendly interface. It allows the user to navigate the dataset through different levels of abstraction and locate information using innovative exploration techniques. A carefully designed storage schema along with an API that takes advantage of the appropriate indexing handles datasets with millions of elements without raising any performance issues, even when accessed from devices with limited computational resources. The proposed demonstration showcases the advantages and technical features of IVLG. A series of demonstration scenarios will show how IVLG can adapt accordingly and handle diverse real and synthetic datasets that vary on size and average node degree and how the system functionalities support the user experience and the exploration of the information.