Reviewerly/exHarmony huggingface dataset:
A New Journal Ranking Method: The Reputation Analysis of Citation Behavior Model

The authority of journals is usually a key indicator in guiding people to choose important journals, and the journal ranking is a common way to distinguish the journals' authority. The common ranking methods or models are based on citation data, such as the impact factor (IF), the PageRank algorithm, and the hyperlink-induced topic search algorithm. In this paper, we present a new model, named the reputation analysis of citation behavior (RACB) model, which not only considers the number of citations but also considers the reputation of inter-citation behavior. First, the model fits the function relation of the citation desire index (CDI) based on the citation data of the most reputable journal (named the top journal in the remainder of this paper). Then, the CDI values of the target journals are calculated by using the function relation of the top journal. An improved gray correlation analysis is used to describe the deviation between the CDI and the actual citation rate, named the random citation rate, indicating the inter-citation reputation value of the target journals. Finally, a case study showed that the ranking result of the RACB model has a high similarity with the IF values of 2017, which indicates that the ranking results of the RACB model that considered journal reputation evaluations are more reasonable than those of the traditional PageRank algorithm. This paper proposes a new method of journal ranking from the perspective of reputation evaluation, which can rank the journals more reasonably. It encourages researchers to cite articles more fairly in order to avoid a situation in which the low reputation of the citation behavior affects the quality of the journal that published the article.
BVD - A Blockchain Based Vehicle Database System

Blockchain, is a platform which aims to bring a new perspective in the field of security. This technology provides a secure and an immutable platform for any kind of transaction in terms of goods, services and many more. This technology serves as a secure and transparent platform for the users who wants to change their environment from a centralized to a decentralized platform. Any relevant application which benefits from a decentralized platform can be migrated to Blockchain easily. In this paper, an implementation of vehicle registration and tracking of traffic violators with the use of Blockchain technology is discussed. Migrating this application to Blockchain improves the transparency, security, immutability to the users as well as to the governmental organization.
Speckle Noise Suppression in Ultrasound Images by Using an Improved Non-local Mean Filter

In medical field, ultrasound imaging technique is a major medium for the doctors and radiologists to diagnose the patient’s problem. The presence of speckle noise degrades the visuals of the ultrasound image and making it difficult for the doctors to diagnose properly. Hence, denoising and restoration of these images came up as a challenge to enhance the quality of the image. In this paper, an improved non-local mean filter is proposed for suppression of speckle noise in ultrasound image. This improved filter is the combination of two conventional filters, i.e., non-local mean filter and bilateral filter. The newly modified method which is emerged by combining the functions of both the conventional filters results in the improvement of quality of image. The three image quality assessment parameters are being used to analyze the performance of improved non-local mean filter, i.e., peak signal-to-noise-ratio (PSNR) and structural similarity (SSIM).
Privacy-Preserving Crowd-Sourcing of Web Searches with Private Data Donor

Search engines play an important role on the Web, helping users find relevant resources and answers to their questions. At the same time, search logs can also be of great utility to researchers. For instance, a number of recent research efforts have relied on them to build prediction and inference models, for applications ranging from economics and marketing to public health surveillance. However, companies rarely release search logs, also due to the related privacy issues that ensue, as they are inherently hard to anonymize. As a result, it is very difficult for researchers to have access to search data, and even if they do, they are fully dependent on the company providing them. Aiming to overcome these issues, this paper presents Private Data Donor (PDD), a decentralized and private-by-design platform providing crowd-sourced Web searches to researchers. We build on a cryptographic protocol for privacy-preserving data aggregation, and address a few practical challenges to add reliability into the system with regards to users disconnecting or stopping using the platform. We discuss how PDD can be used to build a flu monitoring model, and evaluate the impact of the privacy-preserving layer on the quality of the results. Finally, we present the implementation of our platform, as a browser extension and a server, and report on a pilot deployment with real users.
Deformation and mineralisation in the Scotty Creek Basin, Agnew district, Eastern Goldfields, Western Australia: evidence for D<sub>1</sub>- and D<sub>3</sub>-related gold mineralisation

Gold deposits in the Agnew district display markedly different structural styles. The Waroonga and Songvang deposits are hosted in layer-parallel extensional shears formed under highly ductile conditions. In contrast, the New Holland–Genesis deposits are shallow-dipping quartz-filled brittle fractures and breccia zones that cut across the tightly folded bedding and formed during east–west compression. It is difficult to attribute their formation to a single compressive event. The Waroonga and Songvang deposits formed during D1 extension, uplift and exhumation of the Agnew granitic complex and formation of the Scotty Creek Basin at ca 2670–2660 Ma. The New Holland–Genesis deposits formed during east–west D3 compression at about ca 2650–2630 Ma. An S1 foliation wraps around the Agnew granitic complex and L1 stretching lineations form a radial pattern around the granite, consistent with formation during D1 uplift of the composite granite body. Uplift and erosion of granite bodies in the surrounding area provide a source for the granite clasts in the upper parts of the Scotty Creek Basin. As clasts in the basin are undeformed, no significant deformation occurred prior to the uplift and erosion of the source granites in this area. Syn-tectonic emplacement of the Lawlers Tonalite during formation of the Scotty Creek Basin at ca 2665 Ma may have provided a good heat/fluid source for the mineralising systems during the first gold event. The distribution of the large deposits along the western edge of the Agnew granitic complex indicates that the extensional shear along the granite contact is a first-order control on gold deposition by providing a conduit for rising hydrothermal fluids. The northerly trend of high-grade shoots in the Waroonga deposit coincides with early north-trending growth faults, which are also likely fluid conduits.
Construction of Diverse Image Datasets From Web Collections With Limited Labeling

Image datasets play a pivotal role in advancing computer vision and multimedia research. However, most of the datasets are created by extensive human effort and are extremely expensive to scale-up. To address these issues, several automatic and semi-automatic approaches have been proposed for creating datasets by refining web images. However, these approaches either include significant redundant images in the dataset or fail to provide a diverse enough set to train a robust classifier. Ideally, a representative subset should be both semantically and visually diverse so as to provide the maximum amount of information under the current budget. Most current approaches are entirely based on the analysis of visual features, which may not correlate well with image semantics, and hence, collected images may not be sufficient to give a detailed understanding of a category. In this paper, we propose a system for creating diverse image dataset collections from the web with limited manual labeling effort. It is based upon a semi-supervised sparse coding framework that employs a joint visual-semantic space to simultaneously utilize both the images and associated textual information from the web for dataset construction. In addition, the proposed system is online and is capable of collecting more discriminative images continuously as new data becomes available, which is also suitable for enriching the existing datasets. The experiments demonstrate that our system can create and enrich datasets with limited manual labeling, with better cross-dataset generalization capability and diversity compared to the state-of-the-art datasets.
Learning pairwise patterns in Community Question Answering

In recent years, forums offering community Question Answering (cQA) services gained popularity on the web, as they offer a new opportunity for users to search and share knowledge. In fact, forums allow users to freely ask questions and expect answers from the community. Although the idea of receivi ng a direct, targeted response from other users is very attractive, it is not rare to see long threads of comments, where only a small portion of them are actually valid answers. In many cases users start conversations, ask for other information, and discuss about things, which are not central to the original topic. Therefore, finding the desired information in a long list of answers might be very time-consuming. Designing automatic systems to select good answers is not an easy task. In many cases the question and the answer do not share a large textual content, and approaches based on measuring the question-answer similarity will often fail. A more intriguing and promising approach would be trying to define valid question-answer templates and use a system to understand whether any of these templates is satisfied for a given question-answer pair. Unfortunately, the manual definition of these templates is extremely complex and requires a domain-expert. In this paper, we propose a supervised kernel-based framework that automatically learns from training question-answer pairs the syntactic/semantic patterns useful to recognize good answers. We carry out a detailed experimental evaluation, where we demonstrate that the proposed framework achieves state-of-the-art results on the Qatar Living datasets released in three different editions of the Community Question Answering Challenge of SemEval.
An Innovative Approach to Involve Students with Learning Disabilities in Intelligent Learning Systems

Innovations in healthcare education have fostered the development of assistive technologies which have contributed to making the learning environment and information more accessible to learners with learning difficulties. Especially, Intelligent Tutoring Systems (ITS) have proven the benefits of personalized learning. One of the distinctive features of the intelligent system is its Learner Model (LM). In this paper, we propose a LM that takes into consideration individual differences and tends to adapt system parameters for learners with Specific Learning Disabilities (SLDs) before the session begins. The purpose of this research is the development of conceptual bases and a constructional approach of a cognitive LM founded on differentiation. However, developing ITS requires many resources and information about learners as well as it demands a long time to build. To facilitate the design process, we propose a LM, called UPCLEE that takes into account several dimensions of learner's profile. This research demonstrates how the proposed model's levels are taken into account in the design of ITS and how it can be used to provide personalization in computer-based educational systems. In this study, we collected observations, interviews, and surveys and we used a grounded theory approach to develop our LM, then we conducted a user study with 12 students (4 with SLDs) to validate our proposed model.
A Systematic Method to Describe and Identify Security Threats Based on Functional Requirements

Scenarios in which the security of software-based systems is harmed become more and more frequent. Such scenarios can lead to substantial damage, not only financially, but also in terms of loss of reputation. Hence, it is important to consider those threats to security already in the early stages of software development. However, it is non-trivial to identify all of them in a systematic manner. In particular, the knowledge about threats is not documented in a consistent manner. The Common Vulnerability Scoring System is a well known way to characterize vulnerabilities in a structured way. Our idea is to document threats in a similar way, using a template. A distinguishing feature of our approach is that we relate the threats to the envisaged functionality of the software. Our contribution is two-fold: first, we propose a general template to describe security threats that can be used in the early stages of software development. Second, we define a systematic and semi-automatic procedure to identify relevant threats for a software development project, taking the functionality of the software-to-be into account.
A conceptual framework for quantum accelerated automated design optimization

The development of practical quantum computers that can be used to solve real-world problems is in full swing driven by the ambitious expectation that quantum supremacy will be able to outperform classical super-computers. Like with any emerging compute technology, it needs early adopters in the scientific computing community to identify problems of practical interest that are suitable as proof-of-concept applications and to revise existing solution strategies and develop new ones that exploit the capabilities of the novel compute hardware. In this article we describe a conceptual framework for reducing the computational complexity of simulation-driven automated design optimization processes, which are nowadays widely used in computer-aided product development, by exploiting quantum supremacy. Our approach is based on the assumption that quantum computers will become part of hybrid high-performance computing platforms and can then be used as application-specific accelerator devices.
Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on Drones

Moving object detection is an imperative task in computer vision, where it is primarily used for surveillance applications. With the increasing availability of low-altitude aerial vehicles, new challenges for moving object detection have surfaced, both for academia and industry. In this paper, we propose a new approach that can detect moving objects efficiently and handle parallax cases. By introducing sparse flow based parallax handling and downscale processing, we push the boundaries of real-time performance with 16 FPS on limited embedded resources (a five-fold improvement over existing baselines), while managing to perform comparably or even improve the state-of-the-art in two different datasets. We also present a roadmap for extending our approach to exploit multi-modal data in order to mitigate the need for parameter tuning.
Using Convolutional Neural Networks in the Problem of Cell Nuclei Segmentation on Histological Images

Computer-aided diagnostics of cancer pathologies based on histological image segmentation is a promising area in the field of computer vision and machine learning. To date, the successes of neural networks in image segmentation in a number of tasks are comparable to human results and can even exceed them. The paper presents a fast algorithm of histological image segmentation based on the convolutional neural network U-Net. Using this approach allows to get better results in the tasks of medical image segmentation. The developed algorithm based on neural network AlexNet was used for the creation of the automatic markup of the histological image database. The neural network algorithms were trained and tested on the NVIDIA DGX-1 supercomputer using histological images. The results of the research show that the fast algorithm based on neural network U-Net can be successfully used for the histological image segmentation in real medical practice, which is confirmed by the high level of similarity of the obtained markup with the expert one.
Automated summary evaluation with inbuilt rubric method: An alternative to constructed responses and multiple-choice tests assessments

Automated summary evaluation is proposed as an alternative to rubrics and multiple-choice tests in knowledge assessment. Inbuilt rubric is a recent Latent Semantic Analysis (LSA) method that implements rubrics in an artificially-generated semantic space. It was compared with classical LSA's cosine-based methods assessing knowledge in a within-subjects design regarding two validation sources: a comparison with the results of rubric scores and multiple-choice tests, and the sensitivity of predicting the academic level of the test-taker. Results showed a higher reliability for inbuilt rubric (from Pearson correlation coefficient .81 to .49) over the classical LSA method (from .61 to .34), and a higher sensitivity using binary logistic regressions and effect sizes to predict academic level. It is concluded that inbuilt rubric has a qualitatively higher reliability and validity than classical LSA methods in a way that is complementary to models based on semantic networks. Thus, it is concluded that new automated summary evaluation approaches such as the inbuilt rubric method can be practical in terms of reliability and efficiency, and, thus, they can offer an affordable and valuable form of knowledge assessment in different educational levels.
Emilia: a speech corpus for Argentine Spanish text to speech synthesis

This paper introduces Emilia, a speech corpus created to build a female voice in Spanish spoken in Buenos Aires for the Aromo text-to-speech system. Aromo is a unit selection text-to-speech system, which employs diphones as units of synthesis. The key requirements and design criteria for Emilia were: to synthesize any text in Spanish into high-quality speech with a minimum corpus size. The text corpus was designed to guarantee the phonetic and prosodic coverage. A three-stage strategy was used: in the first stage, 741 sentences were designed with all of the syllables of Spanish spoken in Argentina, with and without stress, and in all positions within the word; in the second stage, 852 sentences were added to balance out the distribution of the diphones; and after a perceptual evaluation of the quality of synthesized speech, in the third and final stage, 625 sentences were added to achieve the specified unit coverage, and to introduce sentences with more complex syntactic and prosodic structures. Issues from all three corpus building stages are reported. The paper also presents the results from the quality perceptual evaluations of the synthesized voice. Emilia has a duration of three hours and 15 minutes; its speech quality synthesized with Aromo system is similar to the level obtained with commercial systems, with a real-time ratio less than one.
Gesture height reflects common ground status even in patients with amnesia

When we communicate, we alter our language and gesture based on the mutually shared knowledge - common ground - that we have with our listener. How memory supports these alterations remain unclear. We asked healthy adults and patients with hippocampal amnesia to engage in a referential communication task. Previous work suggests that common ground can be encoded by distinct memory systems; Amnesic patients show normal learning and referential label use as common ground increases, but inconsistently mark these labels with definite determiners (e.g., the vs. a windmill). Which memory systems support the ability to mark common ground via hand gesture? We found that gestures of both healthy participants and amnesic patients reflected common ground status. Both groups produced high gestures when common ground was lacking, and were less likely to do so as common ground increased. These findings suggest that gesture can reflect common ground status during conversation, potentially via non-declarative memory.
A Neural Bag-of-Words Modelling Framework for Link Prediction in Knowledge Bases with Sparse Connectivity

Knowledge graphs such as DBPedia and Freebase contain sparse linkage connectivity, which poses severe challenge to link prediction between entities. In addressing this sparsity problem, our studies indicate that one needs to leverage model with low complexity to avoid overfitting the weak structural information in the graphs, requiring the simple models which can efficiently encode the entities and their description information and then effectively decode their relationships. In this paper, we present a simple and efficient model that can attain these two goals. Specifically, we use a bag-of-words model, where relevant words are aggregated using average pooling or a basic Graph Convolutional Network to encode entities into distributed embeddings. A factorization machine is then used to score the relationships between those embeddings to generate linkage predictions. Empirical studies on two real datasets confirms the efficiency of our proposed model and shows superior predictive performance over state-of-the-art approaches.
Generating Causal Relations in Scientific Texts: The Long-Term Advantages of Successful Generation

A high level of text comprehension can be achieved by engaging learners in processes of organization and integration while reading a cohesive text. In the present study, we investigated the impact of an innovative generative technique on learning with scientific texts. The cohesion generation was implemented by means of explicit cohesion gaps. High school students (n = 199) were randomly assigned to either receive a fully cohesive scientific text (control condition) or a scientific text that required the selection of causal connectives, such as because, although, therefore, or however (generation condition). Learners in the generation condition were required to reflect on causal relations to complete the text. All students were tested immediately (T1) and 2 weeks after the learning phase (T2). Cognitive load was measured by a dual task and self-report measure. Contrary to our expectations, no differences were found in performance on inference questions (situation model). Learners in the generation condition performed worse on text-based questions at T1 but showed less forgetting from T1 to T2. The impact of condition on the situation model was moderated by reading skills. Remarkably, the generation success was highly predictive for learning outcomes even when controlling for learners' proficiencies. Consequently, learners who succeeded to employ effortful processes to overcome the difficulty showed a superior performance on both the text-base and situation-model questions compared to students reading the cohesive text. Moreover, in these learners, generative activity led to a sustainable learning performance 2 weeks later. Poor readers especially took advantage of generative activity, despite struggling to perform the cohesion task as indicated by the cognitive load measures. The results suggest that the activity of generating causal relations can augment inferential processing in learners who are not involved in inferential processing spontaneously. To successfully apply this generative learning technique, students require considerable instructional support.