Reviewerly/exHarmony huggingface dataset:
Robust Person Re-Identification Through the Combination of Metric Learning and Late Fusion Techniques

Fusion techniques with the aim to leverage the discriminative power of different appearance features for person representation have been widely applied in person re-identification. They are performed by concatenating all feature vectors (known as early fusion) or by combining matching scores of different classifiers (known as late fusion). Previous studies have proved that late fusion techniques achieve better results than early fusion ones. However, majority of the studies focus on determining the suitable weighting schemes that can reflect the role of each feature. The determined weights are then integrated in conventional similarity functions, such as Cosine [L. Zheng, S. Wang, L. Tian, F. He, Z. Liu and Q. Tian, Queryadaptive late fusion for image search and person reidentification, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2015, pp. 1741–1750]. The contribution of this paper is two-fold. First, a robust person re-identification method by combining the metric learning with late fusion techniques is proposed. The metric learning method Cross-view Quadratic Discriminant Analysis (XQDA) is employed to learn a discriminant low dimensional subspace to minimize the intra-person distance while maximize the inter-person distance. Moreover, product rule-based and sum rule-based late fusion techniques are applied on these distances. Second, concerning feature engineering, the ResNet extraction process has been modified in order to extract local features of different stripes in person images. To show the effectiveness of the proposed method, both single-shot and multi-shot scenarios are considered. Three state-of-the-art features that are Gaussians of Gaussians (GOG), Local Maximal Occurrence (LOMO) and deep-learned features extracted through a Residual network (ResNet) are extracted from person images. The experimental results on three benchmark datasets that are iLIDS-VID, PRID-2011 and VIPeR show that the proposed method [Formula: see text]% [Formula: see text]% of improvement over the best results obtained with the single feature. The proposed method that achieves the accuracy of 85.73%, 93.82% and 50.85% at rank-1 for iLIDS-VID, PRID-2011 and VIPeR, respectively, outperforms different SOTA methods including deep learning ones. Source code is publicly available to facilitate the development of person re-ID system.
Noncrossing varying coefficient support vector quantile regression

Quantile regression fits specified percentiles of the response, such as the 90th percentile, and can potentially describe the entire conditional distribution of the response. Sometimes quantile functions estimated at different quantiles can cross each other. Varying coefficient models are a useful extension of classical linear models. We propose a new noncrossing varying coefficient support vector quantile regression method based on a location-scale model. To choose the hyper-parameters we apply the model selection method that use cross validation techniques. The proposed method provides a good solution for estimating noncrossing quantile regression functions when several quantiles are required. Real examples are provided to show the usefulness of the proposed method.
KESIAPAN WARGA SEKOLAH DALAM MENGHADAPI BENCANA BANJIR BANDANG DI SMA NEGERI 2 BOGOR

Disaster preparedness in schools is still on the minimum level, whereas schools necessarily are the centers of teaching and learning activities to give proper education for the nation’s better future. The purpose of this research is to analyze the preparedness of SMAN 2 Bogor to face flash flood disaster, and to analyze the impact of its occurrence. This research uses qualitative method, and the locus is Sukaresmi Village, Tanah Sareal Sub-district, Bogor City, West Java. The data is obtained from predetermined informants and analyzed by qualitative analysis technique. The parameters used in the analysis are knowledge and attitude parameters, policies and guidelines, emergency response plans, disaster warning systems, also resource mobilization. The results show that disaster preparedness in SMAN 2 Bogor is held by using various resources of school residents and supporting facilities, yet it has not been maximally done to increase the capacity of students and other elements of SMAN 2 Bogor. In general, schools only focus on the academic achievement, which ultimately leads to the lack of sensitivity toward people’s welfare needs. Awareness of disaster preparedness should not be owned only by the students, but also by educators, officials, and all elements of the school. However, this research analysis focuses more on the students. The unawareness of disaster preparedness planning is the main factor which makes the socialization and capacity improvement can not be done sustainably. Co-ordination and consultation with Provincial Government and Regional Disaster Management Agency is the necessary thing to do for the disaster prepardness planning.
Eye movements in developing readers: From basic research to classroom application.

Video stream: https://vimeo.com/362645755&#x0D; Eye-movement recording has made it possible to achieve a detailed understanding of oculomotor and cognitive behavior during reading and of changes in this behavior across the stages of reading development. Given that many students struggle to attain even basic reading skills, a logical extension of eye-movement research involves its applications in both the diagnostic and instructional areas of reading education. The focus of this symposium is on eye-movement research with potential implications for reading education.&#x0D; Christian Vorstius will review results from a large-scale longitudinal study that examined the development of spatial parameters in fixation patterns within three cohorts, ranging from elementary to early middle school, discussing an early development window and its potential influences on reading ability and orthography. Ronan Reilly and Xi Fan will present longitudinal data related to developmental changes in reading-related eye movements in Chinese. Their findings are indicative of increasing sensitivity to lexical predictability and sentence coherence. The authors suggest that delays in the emergence of these reading behaviors may signal early an increased risk of reading difficulty.&#x0D; Jochen Laubrock’s presentation will focus on perceptual span development and explore dimensions of this phenomenon with potential educational implications, such as the modulation of perceptual span in relation to cognitive load, as well as preview effects during oral and silent reading --and while reading comic books.
A Multi-User Public Key Encryption with Multi-Keyword Search out of Bilinear Pairings

Internet of Things (IoT) and cloud computing are adopted widely in daily life and industrial production. Sensors of IoT equipment gather personal, sensitive and important data, which is stored in a cloud server. The cloud helps users to save cost and collaborate. However, the privacy of data is also at risk. Public-key encryption with keyword search (PEKS) is convenient for users to use the data without leaking privacy. In this article, we give a scheme of PEKS for a multi-user to realize the multi-keyword search at once and extend it to show a rank based on keywords match. The receiver can finish the search by himself or herself. With private cloud and server cloud, most users’ computing can be outsourced. Moreover, the PEKS can be transferred to a multi-user model in which the private cloud is used to manage receivers and outsource. The store cloud and the private cloud both obtain nothing with the keyword information. Then our IoT devices can easily run these protocols. As we do not use any pairing operations, the scheme is under more general assumptions that means the devices do not need to take on the heavy task of calculating pairing.
Predicting Cryptocurrency Defaults

We examine all available 146 Proof-of-Work based cryptocurrencies that started trading prior to the end of 2014 and track their performance until December 2018. We find that about 60% of those cryptocurrencies were eventually in default. The substantial sums of money involved mean those bankruptcies will have an enormous societal impact. Employing cryptocurrency-specific data, we estimate a model based on linear discriminant analysis to predict such defaults. Our model is capable of explaining 87% of cryptocurrency bankruptcies after only one month of trading and could serve as a screening tool for investors keen to boost overall portfolio performance and avoid investing in unreliable cryptocurrencies.
Exploring Media Architecture Design in Virtual Design Environments

This case study explores how architecture students can learn to design media architecture within virtual design environments tools. The target participants of this study are advanced (i.e. 3rd year) architecture students at the University of Applied Science, Bochum, Germany. To evaluate the student-experience, students were asked to develop a media architecture structure during the semester. Once the project finished the students provided feedback via surveys and interviews. The feedback was analysed employing thematic analysis. The case study shows that students are curious about technology in the design process and that technology has a growing relevance in an architecture career. The feedback will be used to improve future teaching approach.
Black-Box Testing : Analisis Kualitas Aplikasi Source Code Bank Programming

The Source Code Bank Programming application that the researchers built uses Borland Delphi which functions to support software learning activities and as a reference for software developers, especially the Delphi programming language. In the process of developing Source Code Bank Programming is still in the prototype stage and further development is still being carried out so that it can be competitively competitive in the world. It is necessary to do various evaluations of the quality of the application in order to match the expectations of the user, one of which is to analyze the quality of the application using the Black-Box Testing method. Testing the Black-Box Source Code Bank Programming uses 3 methods, namely; 1) Graph-based testing, 2) Equivalence Partitioning, and 3) Boundary Value Analysis. The research results show that graph testing, equivalence partitioning, and boundary value analysis will be able to help the process of making test cases and make testing simpler, There are errors in testing and are included in the defect-list, the test results also show that the fulfillment of user needs for public users with a value 0.90, user registers/member with a value of 1.00, students with a value of 0.90, lecturers with a value of 0.82 and admin with a value of 0.84 are classified as good because the degree of value in each user module is greater than 0.8.
Realization of Detection Algorithms for Key Parts of Unmanned Aerial Vehicle Based on Deep Learning

Fixed-point attack on the key parts of small aerial vehicles is one of the important means of UAV (Unmanned Aerial Vehicle) countermeasure. Fixed-wing aircraft flying in the air has two characteristics: fast speed and fast attitude change of aircraft. However, the traditional detection method of key parts of fixed-wing aircraft in infrared images is not only slow in speed but also low in accuracy. This paper presents an improved target detection and motion tracking algorithm based on deep learning method. The algorithm uses a deeper neural network to enhance the feature extraction capabilities of CNN and obtain richer feature information. The detection algorithm obtains the position and motion direction of the key points of the target. Experimental results show that the detection algorithm proposed in this paper is up to 30 frame/s, with an average accuracy of 91.5%/. The method achieved good results in the experiment.
RESTful API Implementation in Making a Master Data Planogram Using the Flask Framework (Case Study: PT Sumber Alfaria Trijaya, Tbk)

One of developingÂ retail company and is one of the biggest retail companies in Indonesia, namely Alfamart which is owned by PT. Sumber Alfaria Trijaya, Tbk. Alfamart must have the best marketing strategy and increase innovation for the satisfaction of customers in order to survive in high business competition. One strategy to improve marketing is the arrangement of product displays in stores known as planograms. Planogram is a concept that is used in planning the arrangement and placement of products according to certain categories based on consumer spending habits that aim to increase sales at retail. This research was conducted to create a web-based planogram master application using the Flask framework with the python programming language. The method used in this study is the RESTful API, which is the implementation of web services that work through HTTP links. This research produces a web-based master data application that can be used by users in entering data needed in making a planogram.Keywords: RESTful API, Python Flask, Planogram
Towards Improving the Imperceptibility of Steganography Schemes: Adaptive Scaling Approach

A common practice of several steganography schemes is to replace the insignificant Discrete Cosine Transform (DCT) coefficients with the secret data. Scaling is an important step in image-based steganography schemes where secret image data is scaled down to blend closely to small high frequency DCT coefficients of the cover image. Scaling affects the stego image quality. Previous work either improved stego quality at the cost of low capacity or improved capacity at the cost of low stego quality. The proposed technique uses fixed block segmentation to create several unique homogeneous regions. The homogeneous regions on secret image are mapped on to the corresponding hiding area of high frequency DCT coefficients of cover image. Each homogeneous region of secret data has a corresponding single scale. The scaling factor value for each region is determined by finding a single value of the corresponding secret homogeneous regions and DCT coefficient in the cover using mean scale. The proposed methodology gives better stego image quality while keeping high hiding capacity. Results compared with fixed scaling shows improvement of stego quality for high fixed hiding capacity.
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning

To facilitate research in the direction of sample efficient reinforcement learning, we held the MineRL Competition on Sample Efficient Reinforcement Learning Using Human Priors at the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019). The primary goal of this competition was to promote the development of algorithms that use human demonstrations alongside reinforcement learning to reduce the number of samples needed to solve complex, hierarchical, and sparse environments. We describe the competition, outlining the primary challenge, the competition design, and the resources that we provided to the participants. We provide an overview of the top solutions, each of which use deep reinforcement learning and/or imitation learning. We also discuss the impact of our organizational decisions on the competition and future directions for improvement.
Implementation of a virtual occupancy sensor for smart building support

Advancements and wide availability of cheap sensors are fuelling the development of Internet of Things (IoT) applications. More sensors mean more data, and more relationships between data points need to be tracked in order to effectively understand, manage and control systems. Having access to either real-time presence data or patterns extracted from historical data is particularly valuable when dealing with facility design and management due to their direct correlation with energy consumption and indoor comfort. We propose a straightforward, cost effective and privacy-preserving method to extract the occupancy information. By aggregating semantic knowledge, motion sensor data and data from dwelling entrance doors, a robust virtual occupancy sensor has been developed; it is underpinned by an ontology that was developed on top of the set of standard ontologies like Building topology ontology (BOT) and Smart appliance reference ontology (Saref) that allowed describing all relevant datapoint and demo site metadata and enabled automated processing of collected data. The method is replicable to all built environment described in a similar way where motion information is collected and where there are clear boundaries of monitored space, and the occupancy information can be useful in different application cases. While predictive occupancy models or expensive sensing alternatives have been already exploited for similar purposes, our solution is simple, inexpensive, replicable and easy to implement in existing buildings.
A lovely day

Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole. --- Sheena Erete, Editor
Effective Use of Target-side Context for Neural Machine Translation

Through the progress made in a sentence-level neural machine translation (NMT), a contextaware NMT has been rapidly developed to exploit previous sentences as context.Recent work in the context-aware NMT incorporates source-or target-side contexts.In contrast to the sourceside context, the target-side context causes a gap between training that utilizes a ground truth sentence and inference using a machine-translated sentence as context.This gap leads to translation quality deteriorating because the translation model is trained with only the ground truth data that cannot be used in the inference.In this paper, we propose sampling both the ground truth and the machine-translated previous sentences of the target-side for the context-aware NMT.The proposed method can make the translation model robust against mistakes and biases made at the inference.Models using our proposed approach show improvements over models using the previous approaches in English ↔ Japanese and English ↔ German translation tasks.
BERT-Based Simplification of Japanese Sentence-Ending Predicates in Descriptive Text

Japanese sentence-ending predicates intricately combine content words and functional elements, such as aspect, modality, and honorifics; this can often hinder the understanding of language learners and children. Conventional lexical simplification methods, which replace difficult target words with simpler synonyms acquired from lexical resources in a word-by-word manner, are not always suitable for the simplification of such Japanese predicates. Given this situation, we propose a BERT-based simplification method, the core feature of which is the high ability to substitute the whole predicates with simple ones while maintaining their core meanings in the context by utilizing pre-trained masked language models. Experimental results showed that our proposed methods consistently outperformed the conventional thesaurus-based method by a wide margin. Furthermore, we investigated in detail the effectiveness of the average token embedding and dropout, and the remaining errors of our BERT-based methods.
Multi-grained Chinese Word Segmentation with Weakly Labeled Data

In contrast with the traditional single-grained word segmentation (SWS), where a sentence corresponds to a single word sequence, multi-grained Chinese word segmentation (MWS) aims to segment a sentence into multiple word sequences to preserve all words of different granularities.Due to the lack of manually annotated MWS data, previous work train and tune MWS models only on automatically generated pseudo MWS data.In this work, we further take advantage of the rich word boundary information in existing SWS data and naturally annotated data from dictionary example (DictEx) sentences, to advance the state-of-the-art MWS model based on the idea of weak supervision.Particularly, we propose to accommodate two types of weakly labeled data for MWS, i.e., SWS data and DictEx data by employing a simple yet competitive graph-based parser with local loss.Besides, we manually annotate a high-quality MWS dataset according to our newly compiled annotation guideline, consisting of over 9,000 sentences from two types of texts, i.e., canonical newswire (NEWS) and non-canonical web (BAIKE) data for better evaluation.Detailed evaluation shows that our proposed model with weakly labeled data significantly outperforms the state-of-the-art MWS model by 1.12 and 5.97 on NEWS and BAIKE data in F1.
The Ambiguity of Black Swans

The fourfold pattern of decision-making under risk, discussed in (Kahneman 2011), is described by the author as “one of the core achievements of prospect theory”. It is a 2 × 2 contingency table, where the columns are high probability, the certainty effect, and low probability, the possibility effect, and the rows are gains and losses from a given status quo.
Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis

Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences. As manual segmentation is tedious, time-consuming, and with low replicability, automatic MYO segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium. Specifically, we propose to combine conventional neural networks and recurrent neural networks to incorporate temporal information between sequences to ensure temporal consistency. We evaluated our framework on the automated cardiac diagnosis challenge (ACDC) dataset. The experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in the Dice coefficient.
Sampling from rough energy landscapes

We examine challenges to sampling from Boltzmann distributions associated with multiscale energy landscapes. The multiscale features, or roughness, corresponds to highly oscillatory, but bounded, perturbations of a smooth landscape. Through a combination of numerical experiments and analysis we demonstrate that the performance of Metropolis Adjusted Langevin Algorithm can be severely attenuated as the roughness increases. In contrast, we prove that Random Walk Metropolis is insensitive to such roughness. We also formulate two alternative sampling strategies that incorporate large scale features of the energy landscape, while resisting the impact of fine scale roughness; these also outperform Random Walk Metropolis. Numerical experiments on these landscapes are presented that confirm our predictions. Open questions and numerical challenges are also highlighted.