Reviewerly/exHarmony huggingface dataset:
A Detection Method for Plagiarism Reports of Students

In recent years, plagiarism that uses the sentences or phrases of others without permission has become a social problem. It is widely spread from very familiar student reports to novels and worldwide academic papers. In this paper, we deal with plagiarism in student reports, and explain the plagiarism patterns often found there. Then we propose a method to detect them efficiently and accurately. This method is based on the way of making two texts to be compared with into one-dimension string respectively, repeating shift and mutual comparison, and checking the matching section of words. This method can accurately detect perfect matches of any size, regardless of its placement in the text. To this basic detection method, we combine some heuristics which are estimation of detection possibility and compression of strings, to improve both detection accuracy of the plagiarism sections and the reduction of calculation time, and propose it as a plagiarism detection method. This method has only one parameter for plagiarism judgement, and it is also intuitive and easy to set. Finally, we show the effectiveness of the proposed method, especially the introduced heuristics, using real data.
Facial Expression Recognition on partial facial sections

Research by psychologists have shown that subjects had a preference for a side of a face when it was expressing emotions. This paper seeks to find what accuracies can be attained when only a segment of the face is considered. We show that using one side of the face only reduces accuracy by 0.34% but at half the computationally time required. Various other sections of the face are evaluated for similar performance. We demonstrate that using smaller portions of the face have an expected computation reduction but dont suffer the same degree of accuracy loss. For evaluating we train with a Convolutional Neural Network. To test what portions of a facial image are useful, the full face, half face, eyes, single eye, mouth and half of the mouth are chosen. These images come from the JAFFE, CK+ and KDEF datasets.
Trust in Cloud-Based Services: A Framework for Consumer Adoption of Software as a Service

ABSTRACT Guidelines originating from practice suggest the Trust Services Criteria of security, availability, processing integrity, confidentiality, and privacy should be key factors affecting the trust of cloud-based systems. In our study, we test a model of end-user adoption of cloud-based software as a service (SaaS) by integrating the five principles from the Trust Services Criteria with systems risk. Using a survey methodology to collect data and analyzing it with structural equation modeling, we test hypotheses associated with the model. Our results show statistical support for the factors of security, confidentiality, and privacy as influencing consumers' trust in cloud-based services. Additionally, our results demonstrate trust in cloud-based services negatively influences the perceived systems risk, and these two items (trust and systems risk) significantly influence the decision by end-users to adopt a cloud-based system. Overall, our findings support the use of the Trust Services Criteria for assessing consumers' trust in cloud-based services.
Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression

This paper presents our approach for the engagement intensity regression task of EmotiW 2019. The task is to predict the engagement intensity value of a student when he or she is watching an online MOOCs video in various conditions. Based on our winner solution last year, we mainly explore head features and body features with a bootstrap strategy and two novel loss functions in this paper. We maintain the framework of multi-instance learning with long short-term memory (LSTM) network, and make three contributions. First, besides of the gaze and head pose features, we explore facial landmark features in our framework. Second, inspired by the fact that engagement intensity can be ranked in values, we design a rank loss as a regularization which enforces a distance margin between the features of distant category pairs and adjacent category pairs. Third, we use the classical bootstrap aggregation method to perform model ensemble which randomly samples a certain training data by several times and then averages the model predictions. We evaluate the performance of our method and discuss the influence of each part on the validation dataset. Our methods finally win 3rd place with MSE of 0.0626 on the testing set. https://github.com/kaiwang960112/EmotiW_2019_ engagement_regression
Interactive 360-Degree Glasses-Free Tabletop 3D Display

We present an interactive 360-degree tabletop display system for collaborative work around a round table. Users are able to see 3D objects on the tabletop display anywhere around the table without 3D glasses. The system uses a visual perceptual mechanism for smooth motion parallax in the horizontal direction with fewer projectors than previous works. A 360-degree camera mounted above the table and image recognition software detects users' positions around the table and the heights of their faces (eyes) as they move around the table in real-time. Those mechanics help display correct vertical and horizontal direction motion parallax for different users simultaneously. Our system also has a user interaction function with a tablet device that manipulates 3D objects displayed on the table. These functions support collaborative work and communication between users. We implemented a prototype system and demonstrated the collaborative features of the 360-degree tabletop display system.
Design of an evolving Fuzzy-PID controller for optimal trajectory control of a 7-DOF redundant manipulator with prioritized sub-tasks

Articulated manipulators constitute a major fraction of industrial robots. With the increase in the need to perform complex tasks requiring a higher degree of accuracy, such manipulators require a more robust and efficient control. In this paper, an evolving Fuzzy-PID control design for the trajectory tracking problem of a redundant 7-DOF serial manipulator is proposed. The scaling factors of the Fuzzy controller are evolved over time by using recent meta-heuristic techniques, namely, Moth-Flame Optimisation (MFO), Novel Bat Algorithm (NBA) and Sine Cosine Algorithm (SCA). The proposed technique which utilizes Intelligent control methods combined with meta-heuristics provides a promising hybrid control design, which has been found to be more efficient in our software implementation, in terms of reduced tracking error of the manipulator for its kinematic control in the joint space as compared to other controllers of the same nature that are generally used in practise. The performance of the controller is compared in the time-domain responses under external disturbances and have been found to outperform standard controllers without the need of any additional training.
The citation impact of information behavior theories in scholarly literature

Theories of information behavior have been developed and used in a variety of disciplines to describe the needs, seeking/search/sharing behaviors, and uses of information among individuals and groups in a variety of contexts. While the development of a theory is a complex, evolutionary process, often involving many thinkers over a long period of time, there is generally a single or small group of publications that can be pointed to as the genesis of a theory. The genesis publications for information behavior theories were examined to determine the information behavior theories receiving the most direct citations in scholarly literature. The findings of this study may be beneficial to library and information science researchers in identifying influential and growing theories of information behavior for incorporation as the foundation of their own research.
Tying Together Solutions for Digital Manufacturing: Assessment of Connectivity Technologies &amp; Approaches

This paper concerns the development of low-cost solutions to address challenges in digital manufacturing (DM). Service Oriented Architectures (SOAs) are a promising approach for addressing the requirements of a low-cost DM architecture. Interaction between services in a SOA is facilitated by a connectivity technology, i.e., a framework for interoperable data exchange between heterogeneous participants. We review a variety of connectivity technologies according to their suitability for use in an SME manufacturer's production environment, and we assess how they have been integrated into past architectures. We then provide insights into an incremental and modular architecture for manufacturing SMEs.
SRPRID: Pedestrian Re-Identification Based on Super-Resolution Images

Intelligent security expects to avoid the occurrence of robbery, theft, and other undesirable situations through video surveillance. In video surveillance, images of human faces undimmed are not easily available, so pedestrian re-identification (person ReID) is an alternative technique which attracts a mount of researchers attention. Person ReID is a technique used to match pedestrian images across cameras. Due to the interference of shooting angle and camera quality, it is difficult to obtain the images of high resolution, no obstructions, simple backgrounds and similar posture, which brings great challenges to the research of person ReID. Most existing methods of pedestrian re-identification ignore the inconsistency of resolution, and they are based on the assumption that all images have similar and high enough resolution by default. In this paper, we propose a hybrid framework, Super-Recognition of Pedestrian Re-Identification (SRPRID), in order to strengthen pedestrian re-identification based on multi-resolutions images captured by disparate cameras. Particularly, residual dense block (RDB) and Integrated Attention (InnAttn) block are merged to SRPRID. It is worth mentioning that the rank_1 accuracy of our method outperforms the state-of-art method by 17.2 points (86.9% - 69.7%) on CUKH03 dataset of extremely challenging.
Simulation of Quantum Radar Cross Section for Electrically Large Targets With GPU

As an extension of traditional radar, quantum radar has the advantages of enhancing detection capability and improving resolution, which has attracted enormous attentions. However, the researches on the scattering characteristics of quantum radar are limited to two-dimensional targets, impeding the practical applications of quantum radar. In this paper, the universal expression of quantum radar cross section (QRCS) for three-dimensional targets is introduced. We have demonstrated the achievement of large-scale computing of QRCS by using GPU accelerating technique. And QRCSs of typical two- and three- dimensional targets are compared with previous work to verify the accuracy, and the improved efficiency of acceleration method is indicated simultaneously. Ultimately, the QRCS of a typical electrically large and complex target, B2 aircraft, is simulated unprecedentedly. The proposed method is convinced to be an efficient tool for analyzing quantum radar scattering properties of electrically large structures.
Definitions, formulas, and simulated examples for plagiarism detection with FAIR metrics

ABSTRACT In prior work, we proposed a family of metrics as a tool to quantify adherence to or deviation from good citation practices in scholarly research and publishing. We called this family of metrics FAIR as an acronym for Fair Attribution to Indexed Reports and Fair Acknowledgment of Information Records , and introduced definitions for these metrics with counts of instances of correct or incorrect attribution or nonattribution in primary research articles with citations for previously published references. In the present work, we extend our FAIR family of metrics by introducing a collection of ratio‐based metrics to accompany the count‐based metrics described previously. We illustrate the mathematical properties of the ratio‐based metrics with various simulated examples in order to assess their suitability as a means of identifying papers under peer review as more or less likely to be suspicious for plagiarism. These FAIR metrics would alert peer reviewers to prioritize low‐scoring manuscripts for closer scrutiny. Finally, we outline our planned strategy for future validation of the FAIR metrics with an approach using both expert human analysts and automated algorithms for computerized analysis.
Voice Assistant Strategies and Opportunities for People with Tetraplegia

To help both designers and people with tetraplegia fully realize the benefts of voice assistant technology, we conducted interviews with fve people with tetraplegia in the home to understand how this population currently uses voice-based interfaces as well as other technologies in their everyday tasks. We found that people with tetraplegia use voice assistants in specifc places, such as in their beds, or when traveling in their wheelchair. In addition, we note the inefciencies for people with tetraplegia when using voice assistance.
Identifying Stealthy Attackers in a Game Theoretic Framework Using Deception

A great deal of effort is devoted to detecting the presence of cyber attacks, so that defenders can respond to protect the network and mitigate the damage of the attack. Going beyond detection, identifying in as much detail as possible what specific type of attacker the defender is facing (e.g., what their goals, capabilities, and tactics are) can lead to even better defensive strategies and may be able to help with eventual attribution of attacks. However, attackers may wish to avoid both detection and identification, blending in or appearing to be a different type of attacker. We present a game-theoretic approach for optimizing defensive deception actions (e.g., honeypots) with the specific goal of identifying specific attackers as early as possible in an attack. We present case studies showing how this approach works, and initial simulation results from a general model that captures this problem.
WealthAdapt

In this paper, we propose a general network adaptation framework, namely WealthAdapt, to effectively adapt a large network for small data tasks, with the assistance of a wealth of related data. While many existing algorithms have proposed network adaptation techniques for resource-constrained systems, they typically implement network adaptation based on a large dataset and do not perform well when facing small data tasks. Because small data have poor feature expression ability, it may result in incorrect filter selection and overfitting during fine-tuning in the network adaptation process. In WealthAdapt, we first expand the target small data task with the wealth of big data, before we perform network adaptation, in order to enrich the features and improve the fine-tuning performance during adaptation. We formally establish network adaptation for small data tasks as an optimization problem and solve it through two main techniques:model-based fast selection andwealth-incorporated iteration adaptation. Experimental results demonstrate that our framework is applicable to both the vanilla convolutional network VGG-16 and more complex modern architecture ResNet-50, outperforming several state-of-the-art network adaptation pipelines on multiple visual classification tasks includinggeneral object recognition, fine-grained object recognition andscene recognition.
Efficient Reuse of Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: A Phenotype Embedding Approach

Background: Many efforts have been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to construct comprehensive patient profiles for delivering better health-care. Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results. Objective: The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records. Methods: We formally define and analyse the model adaptation problem in phenotype-mention identification tasks. We identify "duplicate waste" and "imbalance waste", which collectively impede efficient model reuse. We propose a phenotype embedding based approach to minimize these sources of waste without the need for labelled data from new settings. Results: We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% (duplicate waste), i.e. phenotype mentions without the need for validation and model retraining, and with very good performance (93-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% (imbalance waste), i.e. the effort required in "blind" model-adaptation approaches. Conclusions: Adapting pre-trained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse.
ComChain: A blockchain with Byzantine fault‐tolerant reconfiguration

Summary Selecting which blockchain participants can decide upon a new block is a difficult problem. Consortium blockchains need the participants to be predetermined while public blockchains incentivize all participants to waste their resources to decide every block. In this paper, we introduce the community blockchain that allows potentially all participants to decide upon “some” block while restricting the set of participants deciding upon “one” block. To this end, we propose a blockchain reconfiguration, a Byzantine consensus protocol that allows to dynamically change the set of blockchain participants deciding upon the upcoming blocks. The resulting blockchain, called ComChain , is resilience optimal and transitions through different configurations of participants recorded in dedicated blocks so that each configuration decides upon its subsequent transaction blocks. We evaluate an implementation that adds reconfiguration to the Red Belly Blockchain and demonstrates its practical performance in a distributed system.
Reliable object recognition system for cloud video data based on LDP features

Object recognition is one of the research areas with good scope in most of the applications. However, the object recognition on cloud stored data is very limited and the video based object recognition systems are minimal. Taking this into account, the videos are processed for recognizing the objects of interest by incorporating advanced image processing activities. The video frames are extracted from the videos for recognizing the objects. In order to recognize the objects, the objects have to be detected first. The objects are detected by means of SURF detector and the combination of local and global LDP features is extracted. Finally, the objects present in the videos are matched with the objects of interest. The performance of the proposed object recognition system for cloud video data is tested in three rounds. Initially, the proposed work is tested with different videos and then the proposed work is evaluated by varying the feature extractors such as Local Binary Pattern (LBP), Local LDP, Global LDP. Finally, the video processing time is calculated in terms of both CPU and GPU. All the performance evaluations are carried out in terms of accuracy, sensitivity, specificity and time consumption. The performance of the proposed approach is proven to be satisfactory.
Automatic Generation and Evaluation of Usable and Secure Audio reCAPTCHA

CAPTCHAs are challenge-response tests to differentiate humans from automated agents, with tasks that are easy for humans but difficult for computers. The most common CAPTCHAs require humans to decipher characters from an image and are unsuitable for visually impaired people. As an alternative, audio CAPTCHA was proposed, which require deciphering spoken digits/letters. However, current audio CAPTCHAs suffer from low usability and are insecure against Automatic Speech Recognition (ASR) attacks. In this work, we propose reCAPGen, a system that uses ASR for generating secure CAPTCHAs. We evaluated four audio CAPTCHA schemes with 60 sighted and 19 visually impaired participants. We found that our proposed Last Two Words scheme was the most usable with success rate of >78.2% and low response time of <14.5s. Furthermore, solving our audio CAPTCHAs can transcribe unknown words with >82% accuracy.
Atmosphere in Participatory Design

The relationship between democracy and design has been the topic of significant discussion in the design community. It is also at the core of participatory design that relies on the principle of genuine participation. According to this, users are not mere informants but legitimate participants in the design process. A great deal of participatory design, however, is driven by instrumental logics rather than participatory and democratic principles. In analysing these power relations, science and technology studies (STS) provides the starting point to introduce the concepts of ‘engineering an atmosphere’ (i.e. the process) and ‘engineered atmosphere’ (i.e. the outcome). These concepts problematise the principles and modes of participatory design, highlighting the tensions between economic and social agendas and top-down and bottom-up interactions. This problematic can be shown in the way that new teachnologies are targeted at older populations, necessitating an interrogation of the processes underpirnning the design and development of technological products and devices. It is important to reflect on who is included and who is excluded from technological design and innovation, which is always, and necessarily so, a fluid process.
Towards a Generic Unsupervised Method for Transcription of Encoded Manuscripts

Historical ciphers, a special type of manuscripts, contain encrypted information, important for the interpretation of our history. The first step towards decipherment is to transcribe the images, either manually or by automatic image processing techniques. Despite the improvements in handwritten text recognition (HTR) thanks to deep learning methodologies, the need of labelled data to train is an important limitation. Given that ciphers often use symbol sets across various alphabets and unique symbols without any transcription scheme available, these supervised HTR techniques are not suitable to transcribe ciphers. In this paper we propose an un-supervised method for transcribing encrypted manuscripts based on clustering and label propagation, which has been successfully applied to community detection in networks. We analyze the performance on ciphers with various symbol sets, and discuss the advantages and drawbacks compared to supervised HTR methods.