Reviewerly/exHarmony huggingface dataset:
Materialising Memories

Everyday remembering practices have transformed tremendously over the past decades, for example from flicking through a paper photo album not too long ago to scrolling folders full of digital photos on a mobile phone today [2]. Most of the tools and media to support remembering have digitised, which comes with advantages as well as disadvantages. Materialising Memories [3] is a research program which uses design research and qualitative research methods to study people when they use their autobiographical memory in everyday life to find innovative ways to support them [1]. One specific aim is to integrate the digital with the material. This keynote address will explain what that means in practice, it will cover topics such as memory cuing and personal memory media and it will present example studies of where this digital transformation of remembering practices might or should take us in the future.
Transfer Learning for Improving Lifelog Image Retrieval

With lifelogging devices; such as wearable camera, smart watches, audio recorder or standalone smartphone applications; capturing daily moments becomes easier. In recent years, many workshops and panels have emerged and proposed benchmarks to face challenges in organizing, analyzing, managing, indexing and retrieving specific moments in the huge amount of multi-modal lifelog dataset. Recent advances in deep neural networks have given rise to new approaches to deep learning-based image retrieval. However, using deep neural networks in lifelog context systems is continuously rising challenges: relying on a convolutional neural network which is trained on images not related to the retrieval dataset reduced the performance to extract features. In this paper, we propose a novel fine-tuned Convolutional Neural Network approach based on a Long Short Term Memory processing for improving lifelog image retrieval. The experimental results show the feasibility and effectiveness of our approach with encouraging performance by reaching third place in the ImageCLEF Lifelog Moment Retrieval Task 2018.
Finding Diachronic Objects of Drifting Descriptions by Similar Mentions

In this paper, we propose that document sets consist of two types, drift descriptions that record actions on diachronic objects that could be regarded as the same over time and diversity descriptions that record actions on different objects. This research finds diachronic objects to extract a document subset of drift descriptions. We assumed that a diachronic object would be mentioned similarly and have different time-distribution appearances. Consequently, we proposed a method to find words that represent diachronic objects by similar mentions and applied it to three different document sets. The results show that it is possible to extract document objects for drift descriptions.
Identity‐and‐data privacy forward security public auditing protocol in the standard model

Summary To ensure the intactness of the stored data in cloud, numerous data public auditing mechanisms have been presented. However, most of these existing solutions suffer from several flaws: (a) identity privacy and data privacy of data owner are inevitably revealed to the auditor in the auditing process; (b) the existing public auditing mechanisms with resisting key exposure are only proved in the random oracle model. To address the problems above, in this paper, we propose an achieving identity‐and‐data privacy public auditing protocol with forward security in the standard model by incorporating knowledge proof signature, ring signature, and forward security technique. And then, we formalize the security model of forward security and anonymity of identity, in which the adversary is allowed to query private keys of some ring members. It can provide stronger security. Thus, our proposed scheme can not only achieve data owner's identity privacy and data privacy but also provide forward security for data owner's secret key. To the best of our knowledge, it is the first preserving privacy of identity‐and‐data public auditing scheme with forward security that is provably secure in the standard model. The security of the scheme is related to the computational Diffie–Hellman (CDH) problem and the subgroup decision problem. Finally, our scheme is simulatively tested; experimental results demonstrate that our mechanism is very efficient in terms of overall performance.
High-speed barcode recognition system based on OpenCV and Zbar

In recent years, the logistics industry has developed rapidly and many goods arriving at warehouse are clued for unloading .In order to reduce the occurrence of this phenomenon, it is an effective way to speed up the circulation speed of express delivery at freight stations and reduce goods information acquisition time. This paper proposes a high speed barcode recognition system and the choice of double gradient direction gradient difference and the adaptive threshold according to light intensity are added to this system. The overall development environment is under the Ubuntu system, mainly using OpenCV and Zbar. This system enables barcodes to be recognized accurately at a high speed. In the experiments, we use barcodes provided from SF Express and find that barcodes can be accurately recognized at 3m/s regardless of barcodes placement at any angle. This verifies the effectiveness of the system.
統計的不確定性を考慮したシングルイベント発生率推定法の提案

This study proposes the efficient error rate estimation method for Single Event Effects (SEE) considering statistical uncertainty. SEE is known to make significant damage to a semiconductor device used for a spacecraft on orbit by exposed to cosmic rays or high energy protons. Semiconductor devices for the spacecrafts must conduct various ground experiments to evaluate the error rate using several environment simulators. Since the ground experiment takes much cost and time, the number of experimental samples is limited. That makes it difficult to estimate the error rate with high accuracy. That is, effect of statistical uncertainty due to the small number of samples on the error rate estimation should be considered. Therefore, this study proposes the highly accurate estimation method by introducing a prior distribution update into Hamiltonian Monte Carlo method (HMC) that is one of Malkov Chain Monte Carlo (MCMC) methods considering effects of statistical uncertainty. Through numerical calculations, the validity of the proposed method is illustrated.
Body discomfort perceived by handloom weavers

Handloom weavers constitute the largest workforce, next to the agricultural community in India in its ability to provide employment to a large number of persons with far less average capital employed per worker compared to the other segments of the textile industry.Women from lower economic status dominate these sectors and their health status is found to be poor.In Manipur, handloom is a cottage industry with decentralised set-up and loom was an integral part of their furniture.One of the special features of the industry is that women are the only weavers in Manipur.Keeping the above issues in mind, the present study was planned to find out the rate of body discomfort perceived by the women handloom weavers.Imphal-East and Imphal -West districts of Manipur were selected for the study.A sample of 42 women (14 from each group using the three type of looms) were chosen adopting purposive sampling.Body part discomfort Scale (Corlett and Bishop, 1976) technique for measuring postural discomfort was used for the study.The result revealed that the mean cumulative score for body discomfort while weaving on the three looms enabled identifying the body parts involved in weaving operations and the stress inflicted on them.Neck, shoulder, upper and lower arm were the most affected body parts while weaving.Computing work time against mean cumulative body discomfort score highlighted loin loom weavers to suffer the most.
GENERATING GRAPHS IN VIRTUAL REALITY

Recent advances in Head-mounted Display (HMD) for Virtual Reality have been phenomenal.The quality of the latest HMDs such as Oculus Rift, HTC Vive and Sony PlayStation VR is high.These highly immersive devices create educational opportunities as well as challenges.Specifically, we can create or view graphs in a virtual environment.This innovative technology of generating graphs opens up a new horizon.We can plot 3D graphs so that the user can view the graphs from any angles and examine the graphs virtually.Furthermore, dynamic data can be summarized into diagrams and graphs.If such data are fed into the virtual system, we can view the change of the graphs virtually.In this paper, we will demonstrate the presentation of graphs using HMDs in Virtual Reality.We will illustrate hand-drawings and system-generated graphs in virtual spaces.Students' views on creating graphs in Virtual Reality will be examined.An overview of VR devices and software to produce graphs will also be discussed.
Using Experts’ Perceptual Skill for Dermatological Image Segmentation

There is a growing reliance on imaging equipment in medical domain, hence medical experts’ specialized visual perceptual capability becomes the key of their superior performance. In this paper, we propose a principled generative model to detect and segment out dermatological lesions by exploiting the experts’ perceptual expertise represented by their patterned eye movement behaviors during examining and diagnosing dermatological images. The image superpixels’ diagnostic significance levels are inferred based on the correlations between their appearances and the spatial structures of the experts’ signature eye movement patterns. In this process, the global relationships between the superpixels are also manifested by the spans of the signature eye movement patterns. Our model takes into account these dependencies between experts’ perceptual skill and image properties to generate a holistic understanding of cluttered dermatological images. A Gibbs sampler is derived to use the generative model’s structure to estimate the diagnostic significance and lesion spatial distributions from superpixel-based representation of dermatological images and experts’ signature eye movement patterns. We demonstrate the effectiveness of our approach on a set of dermatological images on which dermatologists’ eye movements are recorded. It suggests that the integration of experts’ perceptual skill and dermatological images is able to greatly improve medical image understanding and retrieval.
A Memory-Based Decision-Making Model for Multilingual Alternatives: The Role of Memory, Emotion and Language

In the Day-to-day lives, the human brain is making a decision, constantly, every day about things, people and thoughts. Making a decision is considered as a higher function of the human information processing system, it is a complex mechanism strongly based on the concept of “Perception”, the perception on which human reasoning is based on. Our brains are analyzing every day an immense number of choices in order to make the best decision every-time, But what if we are facing a huge number of multilingual alternatives? Do we need to translate the whole choice set? Are perceptions, memory, and emotion participating in making such decisions? and how strong are their influences? This work provides several contributions in this context in order to uncover the role of memory, emotion and their antecedents in decision-making process based on multilingual alternatives, this study develops a holistic memory-based decision model to describe the multilingual decision-making process. The research is considered a novel as long as we will combine the power of both decision-making domain and that of management, taking the led from the neuroscience’s results in order to propose a new approach for the decision-making process emphasizing the specific case of making a decision based on multilingual alternatives.
SPARK-Based Partitioning Algorithm for k-Anonymization of Large RDFs

Privacy protection for resource description framework data is very important because RDF (i.e., linked data) is widely used in published data format in many areas, including government open data, health-care for individuals, and social relationships. As data can include private information belonging to individuals or companies and can make private information available to third parties, there are several anonymization models provided for preserving privacy in practice. k-anonymity has thus gained attention in research. Recently, several RDF anonymization models have been proposed. However, current approaches focus on a model and a metric for measuring information loss but do not consider large-scale RDF data. In this paper, we propose an efficient anonymizing method for large-scale RDF data. We develop a greedy partitioning algorithm (i.e., SPARK) for RDF anonymization. SPARK is a leading platform for big data processing. The results of experiments on synthetic datasets demonstrate that our proposed method requires less running time than previous methods.
Semi-automated Quality Assurance for Domain-Expert-Driven Data Exploration – An Application to Principal Component Analysis

Processing and exploring large quantities of electronic data is often a particularly interesting but yet challenging task. Both the lack of statistical and mathematical skills and the missing know-how of handling masses of (health) data constitute high barriers for profound data exploration – especially when performed by domain experts. This paper presents guided visual pattern discovery, by taking the well-established data mining method Principal Component Analysis as an example. Without guidance, the user has to be conscious about the reliability of computed results at any point during the analysis (GIGO-principle). In the course of the integration of principal component analysis into an ontology-guided research infrastructure, we include a guidance system supporting the user through the separate analysis steps and we introduce a quality measure, which is essential for profound research results.
Further results on permutation polynomials via linear translators

In this paper, by using linear translators, we characterize a new class of permutation polynomials of the form [Formula: see text], which has a more general form than [Formula: see text]. Then, we present the compositional inverses of such permutation polynomials. Furthermore, by specifying the functions [Formula: see text] and [Formula: see text], we can get some new permutation polynomials of the forms [Formula: see text] and [Formula: see text] where [Formula: see text] is the trace function.
High Level Video Event Modeling, Recognition and Reasoning via Petri Net

A Petri net based framework is proposed for automatic high level video event description, recognition and reasoning purposes. In comparison with the existing approaches reported in the literature, our work is characterized with a number of novel features: (i) the high level video event modeling and recognition based on Petri net are fully automatic, which are not only capable of covering single video events but also multiple ones without limit; (ii) more variations of event paths can be found and modeled using the proposed algorithms; (iii) the recognition results are more accurate based on automatic built high level event models. Experimental results show that the proposed method outperforms the existing benchmark in terms of recognition precision and recall. Additional advantages can be achieved such that hidden variations of events hardly identified by humans can also be recognized.
STRENGHTENING AND DEVELOPING LOCAL LANGUAGE LITERATION THROUGH LOCAL CONTENT CURRICULUM OF PRIMARY SCHOOL: SYSTEMIC- FUNCTIONAL LINGUISTIC PERSPECTIVE

Local language, which is maintained and respected by its speakers, is protected by the government as local language is a part of Indonesian culture stated in the national constitutions of UUD 1945, the TAP MPR No. II/MPR/1988, the National Language Politic and Policy of National Language, and constitution No. 24 dated in 2009 about languages. The Implementation of those regulations is written on Formal Education Curriculum that is in local content curriculum. One of the local content curriculum aspects is the lesson plan which consists of material component: texts or handouts, especially for primary school students. The opportunity for strengthening and developing the local language on Curriculum 2013 is integrated within the material of art and culture. Referring to the percentage of hours for learning local language in Art-Culture, it lack of time for local language materials. Consequently, the local language materials should be developed and strengthened, so the coming up generation of local language speakers will have optimal vocabulary for maintaining and developing mother tongue. This problem is discussed by applying Systemic-Functional Linguistics.
Fast Landmark Recognition in Photo Albums

The performance of landmark recognition models has significantly improved following the introduction of dedicated CNN-based methods. Most existing algorithms rely on instance-level image retrieval techniques and this induces a scalability problem. Here, we tackle this landmark recognition as a classification task to achieve real-time processing. Our focus is on building an industrial architecture which provides an optimal trade-off between the execution time and performance. We first use a DELF model, which achieves state-of-the-art accuracy, to extract features. Second, we add VLAD layer to create fixed size features from the initial variable length DELF features. Third, the VLAD features are fed to a classifier which provides the final results. Finally, we include a light but powerful distractor handling post-processing step to avoid false positives. It is needed to cope with real datasets which include both landmark and non-landmark images. As an alternate pipeline, we test fine-tuning of different deep learning models to propose a simpler and faster model without the use of attention layers. The experiments show that our approaches have competitive performances while reducing the complexity of the recognition process.
Accurate features point extraction for omnidirectional camera calibration

The distorted checkerboard image affects the precision calibration in omnidirectional camera calibration due to inaccurate localization of features points. To solve this problem, an iterative refinement method is presented. Firstly, the initial-parameters are obtained from the traditional calibration method and the distorted checkerboard images are corrected to world coordinate system. Then, the features points are located in those undistorted images. The calibration parameters are recomputed in an iterative refinement until convergence. This iterative refinement method improves localization accuracy of feature points and consequently of camera calibration. The correctness and effectiveness of the method is proved by simulation experiments and physical experiments. The experiments show that the rep rojection error is reduced by 38% compared to traditional methods.
Entity Linking Based on Graph Model and Semantic Representation

A large number of applications which bridge web data with knowledge bases have led to an increase in the entity linking research. Candidate entity disambiguation plays an important role in the typical entity linking systems. Generally, graph-based candidate disambiguation approaches applied document-level topical coherence of candidate entities. Nevertheless, they do not make full use of abundant unambiguous entities to enrich semantic information during the disambiguation. To solve this problem, we propose a graph-based model combining semantic representation learning for entity linking. Specifically, we construct a referent graph based on semantic vectors trained from RDF data, in which we introduce the dynamic PageRank algorithm with unambiguous entities to enhance the performance of entity linking. Primary experiments show that this model outperforms state-of-the-art on benchmark datasets.
A Personalized Recommendation Algorithm Based on Time Factor and Reading Factor

With the rapid development of the Internet, the data in news, information, education and other application platforms are exploding, which has brought serious information overloading problems to Internet users. The recommendation algorithm is an effective solution to help Internet users select the valuable information from high volume data. Traditional recommendation algorithms ignore the change of users’ interest over time and cannot provide users with effective and reasonable recommendation lists. To solve the above problems, this paper proposes a personalized recommendation method that time factor and reading factor into consideration. This method retrieves and compute time factor and reading factor in the process of user tag selection and then produce customized information recommendation list tailored for users. The experiment shows that the accuracy of our algorithm is higher than the traditional recommendation algorithm by considering time factor and reading factor.
From simulation to reality: ground vehicle detection in aerial imagery based on deep learning

Collecting aerial data from physical world is usually time-consuming. Image simulation is a significant data source for various ground target detection systems. Unfortunately, the reality gap between simulated and real data makes the model trained on simulated image not workable on real image. A translation method is proposed for tackling the simulation-toreality problem in this paper. First, image simulation system is employed for data preparation. Then, the simulated data is converted into a more similar one to the real image. The segmentation map is the bridge between simulated and real data. At last, the target detection model is used as the utility evaluation method for the simulated data. The simulated and synthesized data is used to train a vehicle detection model. Experiments show that results trained by synthesized data are really close to the real results. The proposed translation method can assist real image for target detection task, which is an effective data augmentation method for aerial data.
Fibroblast Segmentation in Microscopic Brightfield Images with Convolutional Neural Network

The aim of this study is the comparison of the various deep convolutional neural networks for segmentation of fibroblast in brightfield microscopic images. This investigation compares two main architectures: Unet and Linknet. Every main architecture is equipped with various ‘backbone’ network creating specific bundle. The experimental dataset consisting of 16 sequences of images of monitored cells’ culture have been split into training and validation set. Then it was analysed and used for validation of the networks to establish the best bundle (net architecture and ‘backbone’). This study proved that trained deep convolutional neural networks could be used as a segmentation tool in this task.
Vehicle detection in aerial images

Abstract The vehicle detection in the aerial images is widely used in many applications. Comparing with the object detection in the ground view images, vehicle detection in the aerial images remains a challenging problem due to the small size of vehicles, monotone appearance and complex background. In this paper, we propose the solution of this issue using the convolutional neural networks. We further introduce the large-scale vehicle detection dataset with ground truth annotations for all the vehicles in the scene that considers the scene complexity due to the environmental conditions. We show the performance of the trained model with other popular neural work architectures.