Reviewerly/exHarmony huggingface dataset:
Research on Scheme and Technology for Mobile User’s Trajectory Privacy Protection

With the development of positioning technologies and wireless communication equipment, the application of location based services is becoming more and more extensive, and a large amount of user mobile trajectory data has been generated. While people enjoy the convenient services brought by these technologies, their own trajectory privacy is also facing great security threats. The article describes and summarizes the system structure, protection scheme, and protection technology in track privacy protection, analyzes and compares it, and points out the future research direction of trajectory privacy protection.
Multi-View Network Representation Learning Algorithm Research

Network representation learning is a key research field in network data mining. In this paper, we propose a novel multi-view network representation algorithm (MVNR), which embeds multi-scale relations of network vertices into the low dimensional representation space. In contrast to existing approaches, MVNR explicitly encodes higher order information using k-step networks. In addition, we introduce the matrix forest index as a kind of network feature, which can be applied to balance the representation weights of different network views. We also research the relevance amongst MVNR and several excellent research achievements, including DeepWalk, node2vec and GraRep and so forth. We conduct our experiment on several real-world citation datasets and demonstrate that MVNR outperforms some new approaches using neural matrix factorization. Specifically, we demonstrate the efficiency of MVNR on network classification, visualization and link prediction tasks.
A knowledge-based question answering system to provide cognitive assistance to radiologists

With the advent of computers and natural language processing, it is not surprising to see that humans are trying to use computers to answer questions. By the 1960s, there were systems implemented on the two major models of question answering, IR-based and knowledge-based, to answer questions about sport statistics and scientific facts. This paper reports on the development of a knowledge-based question answering system that is aimed at providing cognitive assistance to radiologists. Our system represents the question as a semantic query to a medical knowledge base. Evidence obtained from textual and imaging data associated with the question is then combined to arrive at an answer. This question answering system has 3 stages: i) question text and answer choices processing, ii) image processing, and iii) reasoning. Currently, the system can answer differential diagnosis and patient management questions, however, we can tackle a wider variety of question types by improving our medical knowledge coverage in the future.
Reasoning about Strategic Abilities

In alternating-time temporal logic ATL * , agents with perfect recall assign choices to sequences of states, i.e., to possible finite histories of the game. However, when a nested strategic modality is interpreted, the new strategy does not take into account the previous sequence of events. It is as if agents collect their observations in the nested game again from scratch, thus, effectively forgetting what they observed before. Intuitively, it does not fit the assumption of agents having perfect recall of the past. In this article, we investigate the alternative semantics for ATL * where the past is not forgotten in nested games. We show that the standard semantics of ATL * coincides with the “truly perfect recall” semantics for agents with perfect information and in case of so-called “objective” abilities under uncertainty. On the other hand, the two semantics differ significantly for the most popular (“subjective”) notion of ability under imperfect information. The same applies to the standard vs. “truly perfect recall” semantics of ATL * with persistent strategies. We compare the relevant variants of ATL * by looking at their expressive power, sets of validities, and tractability of model checking.
Use of Frog Vle in Science Learning

Frog Virtual Learning Environment (Frog VLE) is one of the supporting learning applications utilizing Information and Communication Technology (ICT) in 21st- century science learning. This paper concept tries to look at various issues in using VLE Frog to carry out science learning activities. Some previous studies were analyzed in depth for this purpose. The results of the analysis show that Frog VLE has not been widely used to support teacher science learning. In fact, the VLE Frog needs to be given a serious support to add to the process of learning science in the future, the teacher must be given continuous and consistent training in the use of the Frog VLE in the process of learning science.
Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images

We propose an algorithm to recognize breast parenchyma regions containing mass-like abnormalities in dedicated breast CT images using texture feature descriptors. From 53 patient breast CT scans (29 of which containing masses), we first isolated the parenchyma through automatic segmentation, and we obtained a total of 14,751 normal 2D image patches (negatives), and 2,100 containing a breast mass (positives). We extracted 141 texture features (10 first-order descriptors, 6 Haralick features, 20 run-length features, 45 structural and pattern descriptors, 60 Gabor features), which we then analyzed through multivariate analysis of variance (MANOVA) and linear discriminant analysis, resulting in an area under the ROC curve (AUC) of 0.9. We finally identified the most discriminant features through sequential forward selection, and used them to train and validate a neural network by dividing the data into multiple batches, with each batch always containing the whole set of positive cases, and as many different negative examples. To avoid the possible bias due to the high skewness in class proportion, the training was performed on all these batches independently, without re-initializing the network weights after each training. The network was tested using an additional independent 18 patient breast CT scans (8 normal and 10 containing a mass), on a total of 7,274 image patches (852 positives, 6,422 negatives) which were not used during the training/validation phase, resulting in 95.6% precision, 95.8% recall, and 0.99 AUC. Our results suggest that the proposed approach could be further evaluated and expanded for computer-aided detection tasks in breast CT imaging.
Usability of KITIKIT: a mobile-based app for implementing customizable online product ordering

Pugo, branded as the woodcarving capital of La Union, is well known for woodcarving products. The town has skillful tribes who practice woodcarving. One of these tribes was the Ifugaos, and when they learned that the art of woodcarving could make money for them, a wave of Ifugaos migrated along the Marcos Highway to showcase their hand-carved wood products. The widespread of woodcarving business was due to the availability of the raw materials, tools used and durability of the product. Home decors, furniture and other sculptural forms made out of wood are some of the products that are now distributed in the market. This research was then conceptualized for the benefit of the woodcarving industry stakeholders. The researchers proposed the KITIKIT, a mobile app to ease the searching for hand-carved wood products. Usability testing using USE questionnaire had been conducted to find the level of usability of the KITIKIT. The results show that the proposed application is acceptable to users in terms of usefulness, ease of use and satisfaction.
Evaluation Measures for Extended Association Rules Based on Distributed Representations

Indirect association rules and association action rules are two notable extensions of traditional association rules. Since these two extended rules consist of a pair of association rules, they share the same essential drawback of association rules: a huge number of rules will be derived if the target database to be mined is dense or the minimum threshold is set low. One practical approach for alleviating this essential drawback is to rank the rules to identify which one to be examined first in a post-processing. In this paper, as a new application of representation learning, we propose evaluation measures for indirect association rules and association action rules, respectively. The proposed measures are assessed preliminary using a dataset on Japanese video-sharing site and that on nursery.
An Empirical Study on Clone Evolution by Analyzing Clone Lifetime

The existence of code clones has several negative impacts on software maintenance which is why vast amount of research exists in the literature to characterize clone evolution. Most of those focused on clone genealogy, and clone changeness (consistent and inconsistent changes). However, analyzing clone evolution with respect to clone location and clone lifetime requires more attention to better characterize clone evolution. In this research, an empirical study has been performed on clone evolution by considering clone location (i.e., Inter-File and Intra-File) and clone lifetime. The study has been performed on four open source software covering 12 to 66 number of versions. In the study, it has been found that, (i) Intra-File clones occurred in a repository more than Inter-File clones, which infers that developers tend to clone code in the same file than different files and (ii) Intra-File clones are more volatile than Inter-File clones, which infers that developers like to refactor or change clones of the same file more than clones spanning different files. These observations help to conclude which clones should get more attention during clone maintenance tasks like refactoring.
Providing Satellite Data to Mobile Developers Using Semantic Technologies and Linked Data

Copernicus is the European program for monitoring the Earth. It consists of a set of complex systems that collect data from satellites and in-situ sensors, process it, and provide users with reliable and up-to-date information on a range of environmental and security issues. Information extracted from Copernicus data is made available to users through Copernicus services addressing six thematic areas: land, marine, atmosphere, climate, emergency and security. The data processed and disseminated puts Copernicus at the forefront of the big data paradigm and gives rise to all relevant challenges: volume, velocity, variety, veracity and value. In this paper we discuss the challenges of big Copernicus data and how the Copernicus program handled them. We also present lessons learned from our project Copernicus App Lab, which takes Copernicus services information and makes it available on the Web using semantic technologies to aid its take up by mobile developers.
ReRide

Motorbike commuting is the new frontier for exploring digital technology where designing for embodied interaction takes on a more central role. In this paper, building on previous work on embodied self-monitoring, we present our ongoing work of designing a modular platform with a particular focus on real-time estimation and presentation of posture data while riding. In particular, we present "Bike Area Network' (BANk) as a system architecture to help guide the design of such a platform. We share our ongoing work as an invitation for the community of researchers and practitioners of designing for embodied interaction to further explore this new frontier of research.
Bias Compensated Recursive Least-squares Identification for the Systems under Poor Observation Condition

For the autoregressive signal under poor observation condition, a bias compensated recursive least-square identification algorithm is presented based on the incremental observation equation. Moreover, it is easy to know that the obtained estimates of model parameters and noise variance converge to the corresponding true values with probability one, i.e. they are strongly consistent. A simulation example shows its effectiveness.
Dataset Management as a Special Collection

There are several dataset management challenges to be faced in the coming years. The incorporation of the datasets into special collections will be a challenge. As well as formats management, libraries with datasets will have to deal with issues such as right management, interoperability or the election adequacy to the end-user and findability. However, it must be recognized that incompatibilities could be solved through data auditing. The appropriate type of digital preservation strategy will have to be considered in order to maintain accessibility.This paper presents a review of the literature that discusses datasets in special collections.
Architecturally Compressed CNN: An Embedded Realtime Classifier (NXP Bluebox2.0 with RTMaps)

The convolution neural networks have revolutionized the computer vision domain. It has proven to be a dominant technology to carry out tasks such as image classification, semantic segmentation, and object detection. The convolution neural networks surpass the performance of the existing algorithms such as SIFT, HOG, etcetera. Where, instead of manually engineering the features, supervised learning help to learn the essential low-level and high-level features necessary for classifications. The convolution neural networks have become a popular tool to counter computer vision problems. However, it is computationally, and memory intensive to train and deploy the network because of the model size of a deep convolution neural networks. However, the research in the field of design space exploration (DSE) of neural networks and compression techniques to develop compact architectures, have made convolution neural networks memory and computationally efficient. These techniques have also improved the feasibility of convolution neural network for deployment on embedded targets. The paper explores the concept of compact convolution filters to reduce the number of parameters in a convolution neural network. The intuition behind the approach is that replacing convolution filters with a stack of compact convolution filters helps in developing a compact architecture with competitive accuracy. This paper explores the fire module a compact convolution filter and proposes a method of recreating a state-of-the-art architecture VGG-16 using the fire modules to develop a compact architecture, which is further trained on the CIFAR-10 dataset and deployed on a real-time embedded platform known as Bluebox 2.0 by NXP using RTMaps software framework.
Kyne

A display device, whilst seemingly static shows a lot of movement. Movement or change on such screens is perceived due to rapid, successive change in frames. Outside of screens, perceived movement on static objects may be achieved through a combination of optics and illusions. Kyne is a series of visual experiments, involving the perception of change in static distorted artifacts. It involves varied methods of obscuring or masking parts of static artifacts and taking advantage of persistence of vision to animate these artifacts. The artifacts explored use paper, refreshable static non-illuminated e-ink displays, and laser cut acrylic, and their masks include transparencies and digital projections. This paper illustrates the experiments conducted with different artifact-mask pairings and speculates on possible future pairings.
Constructing and Maintaining Corpus-Driven Annotations

A reference library can be described as a corpus of an individual composition of documents containing related work of research, documents of favorite authors, or proceedings of a conference. The documents in the corpus may change over time; new documents extend the corpus while other documents are sorted out. A subset of documents may contain meaningful annotations describing their content while other documents contain only weakly annotations. Enriching documents with meaningful annotations is beneficial for the performance of applications like semantic search, content aggregation, automated relationship discovery, query answering and information retrieval. However, enriching and maintaining a document with meaningful annotations is non-trivial. Available (semi-) automatic annotation tools ignore the individual composition of documents in corpora by annotating documents with generic named-entity related data. In this paper, we present techniques for enriching and maintaining annotations for document-specific databases considering changes in the composition of documents.
Semantics Consistent Adversarial Cross-Modal Retrieval

Cross-modal retrieval returns the relevant results from the other modalities given a query from one modality. The main challenge of cross-modal retrieval is the “heterogeneity gap” amongst modalities, because different modalities have different distributions and representations. Therefore, the similarity of different modalities can not be measured directly. In this paper, we propose a semantics consistent adversarial cross-modal retrieval approach, which learns a semantics consistent representation for different modalities with same semantic category. Specifically, we encourage the class center of different modalities with same semantic label to be as close as possible, and also minimize the distances between the samples and the class center with same semantic label from different modalities. Comprehensive experiments on Wikipedia dataset are conducted and the experimental results show the efficiency and effectiveness of our approach in cross-modal retrieval.
Renal parenchyma segmentation from abdominal CT images using multi-atlas method with intensity and shape constraints

Segmentation of the renal parenchyma consisting of the cortex and the medulla responsible for the renal function is necessary to assess contralateral renal hypertrophy and to predict renal function after renal partial nephrectomy (RPN). In this paper, we propose an automatic renal parenchyma segmentation from abdominal CT images using multi-atlas methods with intensity and shape constraints. First, atlas selection is performed to select the training images in a training set which is similar in appearance to the target image using volume-based registration and intensity similarity. Second, renal parenchyma is segmented using volume- and model-based registration and intensity-constrained locally-weighted voting to segment the cortex and medulla with different intensities. Finally, the cortex and medulla are refined with the threshold value selected by applying a Gaussian mixture model and the cortex slab accumulation map to reduce leakage to the adjacent organs with similar intensity to the medulla and under-segmented area due to lower intensity than the training set. The average dice similarity coefficient of renal parenchyma was 92.68%, showed better results of 15.84% and 2.47% compared to the segmentation method using majority voting and intensity-constrained locally-weighted voting, respectively. Our method can be used to assess the contralateral renal hypertrophy and to predict the renal function by measuring the volume change of the renal parenchyma, and can establish the basis for treatment after renal partial nephrectomy.
Towards the Next Generation of Scenario Walkthrough Tools – A Research Preview

[Context and motivation] With the rise of cyber-physical systems (CPS), smart ecosystems, and the Internet of Things (IoT), software-intensive systems have become pervasive in everyone’s daily life. The shift from software systems to ubiquitous adaptive software-intensive systems not only affects the way we use software but further has an impact on the way these systems are designed and developed. Gathering requirements for such systems can benefit from elicitation processes that are conducted in the field with domain experts. [Question/problem] More traditional elicitation approaches such as interviews or workshops exhibit limitations when it comes to gathering requirements for systems of this nature – often lacking an in-depth context analysis and understanding of contextual constraints which are easily missed in a formal elicitation setting. Furthermore, dedicated methods which focus on understanding the system context such as contextual design are not widely adopted by the industry as they are perceived to be time-consuming and cumbersome to apply. [Principal ideas/results]. In this research preview paper we argue that scenario-based RE, scenario walkthrough approaches in particular, have the potential to support requirements elicitation for ubiquitous adaptive software-intensive systems through facilitating broader stakeholder involvement and enabling contextual requirements elicitation within the workplace of future system end-users. The envisioned on-site scenario walkthroughs can either be conducted by an analyst or by future end-users of the system themselves. [Contribution] We describe a research agenda including our ongoing research and our efforts to develop a novel framework and tool support for scenario-based RE.
RETRACTED: Moving object surveillance using object proposals and background prior prediction

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. Journal of Visual Communication and Image Representation was made aware that the paper “Moving object surveillance using object proposals and background prior prediction” was previously published in Multimedia Tools and Applications, 2017, https://doi.org/10.1007/s11042-017-4820-9. The content was submitted to Journal of Visual Communication and Image Representation without disclosure of its previous publication. As such this article represents a misuse of the scientific publishing system. The authors offer their apologies to readers of the journal.
Vision based eye closeness classification for driver’s distraction and drowsiness using PERCLOS and support vector machines

Driver inattention and drowsiness are part causes of road accidents in Malaysia. Statistics shows that about 3 deaths per 10 000 registered vehicles reported by Malaysian Institute of Road Safety Research (MIROS) in 2016. Hence, an assistant system is needed to monitor driver’s condition like some car manufacturers introduced to their certain models of car. The assistant system is a part of main system known as advanced driver assistance systems (ADAS) are systems developed to enhance vehicle systems for safety and better driving. Desire to build safer vehicles and roads to reduce the number of fatalities and by legislation cause demand for ADAS. However, there are several challenges to design, implement, deploy, and operate ADAS. The system is expected to gather accurate input, be fast in processing data, accurately predict context, and react in real time. Suitable approach is needed to fulfil the system expectation. There are four types of detection including by using physiological sensors, driver performance, computer vision, and hybrid system. This paper describes the drowsiness and driver in attention detection and classification using computer vision approach. Our approach aims to classify driver drowsiness and inattention using computer vision. We proposed a technique to classify drowsiness into three different classes of eye state; open, semi close and close. The classification is done by using feature extraction method, percentage of eye closure (PERCLOS) technique and Support Vector Machine (SVM) classifier. Two types of data training and testing images of drivers’ eye condition which are eye with spectacles and eye without spectacles have been used. The results show that the proposed technique can classify the classes of distraction or drowsiness with high accuracy. Furthermore, by using only one type of eye condition data training, we are also able to classify the three different drowsiness classes regardless the eye conditions.