Reviewerly/exHarmony huggingface dataset:
Experimental quantum key distribution with uncharacterized sources and projective measurements

In theory, quantum key distribution (QKD) can offer information-theoretic secure communication based on the laws of quantum mechanics. However, the vast majority of practical QKD implementations assume the perfect state preparation to ensure security, which is a demanding requirement with current technology. Here, by incorporating the mismatched-basis data, we report an experimental decoy-state QKD demonstration with uncharacterized encoding sources, which only requires that the encoding states are two-dimensional. Furthermore, the measurement operation of the receiver is loosened to be projective measurements. With a rigorous statistical fluctuation analysis, we can distribute secret keys when the transmission distances of the standard fiber link are 101 and 202&#x00A0;km. Our experimental demonstration represents a significant step toward realizing long-distance quantum communication, even with uncharacterized sources and projective measurements.
Translation inference through multi-lingual word embedding similarity

This paper describes our contribution to the Shared Task on Translation Inference across Dictionaries (TIAD-2019). In our approach, we construct a multi-lingual word embedding space by projecting new languages in the feature space of a language for which a pretrained em- bedding model exists. We use the similarity of the word embeddings to predict candidate translations. Even if our projection methodology is rather simplistic, our system outperforms the other participating sys- tems with respect to the F1 measure for the language pairs which we predicted.
Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotation

We present a method to automatically label pathologies in volumetric medical data. Our solution makes use of a healthy statistical shape model to label pathologies in novel targets during model fitting. We achieve this using an EM algorithm: the E-step classifies surface points into pathological or healthy classes based on outliers in predicted correspondences, while the M-step performs probabilistic fitting of the statistical shape model to the healthy region. Our method is independent of pathology type or target anatomy, and can therefore be used for labeling different types of data. The method is able to detect pathologies with higher accuracy than standard robust detection algorithms, which we show using true positive rate and F1 scores. Furthermore, the method provides an estimate of the uncertainty of the synthesized label. The detection also directly improves surface reconstruction results, as shown by a decrease in the average and Hausdorff distances to ground truth. The method can be used for automated diagnosis or as a pre-processing step to accurately label large amounts of images.
Serverless Computing and Cloud Function-based Applications

Serverless computing is a growing industry trend with corresponding rise in interest by scholars and tinkerers. Increasingly, open source and academic system prototypes are being proposed especially in relation with cloud, edge and fog computing among other distributed computing specialisations. Due to the strict separation between elastically scalable stateless microservices bound to stateful backend services prevalent in this computing paradigm, the resulting applications are inherently distributed with favourable characteristics such as elastic scalability and disposability. Still, software application developers are confronted with a multitude of different methods and tools to build, test and deploy their function-based applications in today's serverless ecosystems. The logical next step is therefore a methodical development approach with key enablers based on a classification of languages, tools, systems, system behaviours, patterns, pitfalls, application architectures, compositions and cloud services around the serverless application development process.
Coarse-to-Fine 3D Human Pose Estimation

Leveraging powerful deep convolutional networks, 2d human pose estimation has achieved great success. On the other hand, 3d human pose estimation is still a challenging task that attracts great attention. Due to the inherent depth ambiguity in 2d to 3d mapping, conventional methods are typically not able to predict 3d locations precisely, especially for the joints far from the torso. In this paper, we propose a coarse-to-fine model to predict 3d joint locations progressively. We observe that some joints like shoulders and hips are relatively easy to get precise 3d locations, which can be utilized to facilitate the prediction of hard joints that are far from the torso. To make this happen, a set of constraints based on human limb length ratio prior is proposed to guide the model to generate reasonable predictions. We conduct experiments on the Human3.6M dataset. Comparison of experimental results on the benchmark dataset turns out that our approach outperforms the baseline method.
Augmented Reality Interface: Guidelines for the Design of Contrast Ratios

Abstract As part of the quality assurance process in industrial production, employees make hundreds of decisions every day based on their experience and the given inspection specifications. The detection of quality deviations currently depends on the individual competence of the persons involved in the production process. With the automatic recognition of textured surfaces in camera images, valuable information can be given to support these persons in their decisions. For the design of augmented reality systems, the cognitive decision-making processes are first described in a model-based way and the requirements for design of the decision-making process are defined. The different lighting environments in the industrial process represent one of the important requirements of the augmented reality system, to function independently of the lighting. The good perceptibility of the information is the basic requirement for the recognition of contents. When implementing an augmented reality system, it is problematic to consider the different illuminance levels in a room. Depending on the task at hand, usually, the light at a machine is weaker and brighter in the quality control area. It is important that user-centered implementation is observed. Manual intervention by the end user in the augmented reality system is necessary to compensate for differences in brightness. This paper examines different lighting environments and the degree to which the user can clearly see content. An empirical evaluation is conducted to examine the clarity of content in AR. The aim is to find out how the difference in contrast at different lighting situations influences the differences in brightness of colors. The colors are chosen to display content but also to emphasize content. As a result, a usage recommendation can be made for grey values as well as for different color values. The consequence of this investigation is then used for the further development of sample solutions for the design of an augmented reality system. The focus lies on the user-centered development process and thus the integration of user requirements into the technical implementation should be ensured. The perceptibility of the information in an augmented reality system should not only play a role technically, but also physically. The perception of information is omnipresent, so one can perceive our environment. With the help of the study, a natural and easy perception of information should take place, which supports the recognition of important information in the everyday working environment.
Comparison of Random Forest and Support Vector Machine for Indonesian Tweet Complaint Classification

In digital business, the managerial commonly need to process text so that it can be used to support decision-making. The number of text documents contained ideas and opinions is progressing and challenging to understand one by one. Whereas if the data are processed and correctly rendered using machine learning, it can present a general overview of a particular case, organization, or object quickly. Numerous researches have been accomplished in this research area, nevertheless, most of the studies concentrated on English text classification. Every language has various techniques or methods to classify text depending on the characteristics of its grammar. The result of classification among languages may be different even though it used the same algorithm. Given the greatness of text classification, text classification algorithms that can be implemented is the support vector machine (SVM) and Random Forest (RF). Based on the background above, this research is aimed to find out the performance of support vector machine algorithm and random forest in classification of Indonesian text. 1. Result of SVM classifier with cross validation k-10 is derived the best accuracy with value 0.9648, however, it spends computational time as long as 40.118 second. Then, result of RF classifier with values, i.e. 'bootstrap': False, 'min_samples_leaf': 1, 'n_estimators': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None is achieved accuracy is 0.9561 and computational time 109.399 second.
A Genetic Algorithm with Best Combination Operator for the Traveling Salesman Problem

In this research, we propose a genetic algorithm with best combination operator (BC(x,y)O) for the traveling salesman problem. The idea of best combination operator is to find the best combination of some disjoint sub-solutions (also the reverse of sub-solutions) from some known solutions. We use BC(2,1)O together with a genetic algorithm. The proposed genetic algorithm uses the swap mutation operator and elitism replacement with filtration for faster computational time. We compare the performances of GA (genetic algorithm without BC(2,1)O), IABC(2,1)O (iterative approach of BC(2,1)O), and GABC(2,1)O (genetic algorithm with BC(2,1)O). We have tested GA, IABC(2,1)O, and GABC(2,1)O three times and pick the best solution on 50 problems from TSPLIB. From those 50 problems, the average of the accuracy from GA, IABC(2,1)O, and GABC(2,1)O are 65.12%, 94.21%, and 99.82% respectively.
Information-Theoretic Multi-task Learning Framework for Bayesian Optimisation

Bayesian optimisation is a widely used technique for finding the optima of black-box functions in a sample efficient way. When there are concurrent optimisation tasks/functions then it may be possible to transfer knowledge across each other in a multi-task setting and improve the efficiency further. Transferring knowledge requires estimation of task similarity, which in turn requires good knowledge about the objective functions. However, in a multi-task Bayesian optimisation setting the number of observations for all functions can be small, especially at the beginning, making reliable computation of task similarities difficult. In this paper, we propose a novel multi-task Bayesian optimisation method that uses information theory based approach to transfer knowledge across tasks and handle the uncertainty of similarity measurements in an unified framework. Each optimisation task uses contribution from other optimisation task via a mixture model on the location of optima by appropriately combining distribution over optimal locations for each individual task. The probability distribution of the optimal location for individual tasks can be obtained because the objective functions are modeled using Gaussian processes. The weights of the mixture distributions are computed based on the similarities (measured via KL divergence) between two distributions and then appropriately weighting down by the uncertainty in the knowledge. That is, we encourage transfer of knowledge only when two tasks are confident about their high similarity measure and discourage if they are not confident, even if the similarity is high. We evaluate and demonstrate the effectiveness of our proposed method on both synthetic and a set of hyperparameter tuning tests compared to state-of-the-art algorithms.
Secured Key Generation and Transmission, using Intel-SGX and Optical Communications

Key distribution is a growing concern for symmetric cryptography and PKI certificate management. Most of the current key-distribution mechanisms assume the use of the Internet and LAN/WAN networks, which are exposed to security hazards and other weaknesses. To overcome them, the use of comprehensive and robust cryptographic mechanisms, such as RSA are proposed. Another solution is the use of PKI/CA, which generates certificates and distributes them directly to the sender and receiver. This paper introduces a strict security approach covering the entire process, from key/certificate generation to the use of the received key by the other party. The approach is based on the following two elements: a. generation of a key/certificate in a highly secured environment provided by Intel-SGX technology. b. Key distribution using undetected high-speed optical camera communication (OCC), based on the rolling shutter effect of modern smart cameras. The Intel SGX processor enables the creation and protection of random keys and supports secure encryption and decoding of information. The key/certificate transfer is done by blinking LED lights in a specific sequence and frequency, following a coding system. The receiver decodes the received blinks to a bit string using a corresponding image processing application. This optical media ensures secure transfer without the ability to quote it. The experiment results show that this method is feasible, efficient, and implementable.
Collaboration skills-based multimedia-based integrated instruction (CS–MBI<sub>2</sub>): a development study on refraction concept

Abstract Development of physics interactive multimedia is still rarely found, especially regarding to collaboration skills enhancement. This study is having a purpose to develop a Multimedia-Based Integrated Instruction (MBI 2 ) that expected to define student’s collaboration skills profile. Researcher uses Sadiman’s model of development research method which consists of identifying students need, formulating instructional objective, formulating learning materials, developing evaluation instrument, producing, and revising. As a research sample, thirty-three eight grader students in Bandung are being involved in this study. Result shows each profile of student’s collaboration skills respectively 3.30; 3.06; 3.82; and 2.70 (by maximum student’s achievement score of 4). It can be concluded that development of Multimedia-Based Integrated Instruction (MBI 2 ) research is capable for defining junior high school student’s collaboration skills profile on refraction concept.
Learning Spatial-Aware Cross-View Embeddings for Ground-to-Aerial Geolocalization

Image-based geolocalization is an important alternative to GPS-based localization in GPS-denied situations. Among them, ground-to-aerial geolocalization is particularly promising but also difficult due to drastic viewpoint and appearance differences between ground and aerial images. In this paper, we propose a novel spatial-aware Siamese-like network to address the issue by exploiting the spatial transformer layer to effectively alleviate the large view variation and learn location discriminative embeddings from the cross-view images. Furthermore, we propose to combine the triplet ranking loss with a simple and effective location identity loss to further enhance the performances. We test our method on a publicly available dataset and the results show that the proposed method outperforms state-of-the-art by a large margin.
Mini-Batch VLAD for Visual Place Retrieval

This study investigates the visual place retrieval of an image query using a geotagged image dataset. Vector of Locally Aggregated Descriptors (VLAD) is one of the local features that can be used for image place recognition. VLAD describes an image by the difference of its local feature descriptors from an already computed codebook. Generally, a visual codebook is generated from k-means clustering of the descriptors. However, the dimensionality of visual features is not trivial and the computational load of sample distances in a large image dataset is challenging. In order to design an accurate image retrieval method with affordable computation expenses, we propose to use the mini-batch k-means clustering to compute VLAD descriptor(MB-VLAD). The proposed MB-VLAD technique shows advantage in retrieval accuracy in comparison with the state of the art techniques.
In Their Own Words: A Companion Robot for Detecting the Emotional State of Persons with Parkinson’s Disease

In typical human interactions emotional states are communicated via a variety of modalities such as auditory (through speech), visual (through facial expressions) and kinesthetic (through gestures). However, one or more modalities might be compromised in some situations, as in the case of facial masking in Parkinson’s disease (PD). In these cases, we need to focus the communication and detection of emotions on the reliable modalities, by inferring emotions from what is being said, and compensate for the modalities that are problematic, by having another agent (e.g., a robot) provide the missing facial expressions. We describe the initial development stage of a robot companion that can assist the communication and detection of emotions in interactions where some modalities are totally or partially compromised. Such is the case for people living with Parkinson’s disease. Our approach is based on a Latent Dirichlet Allocation topic model as a principled way to extract features from speech based on a trained classifier that can be linked to measures of emotion. The trained model is integrated into a robotic cognitive architecture to perform real-time, continuous speech detection of positive, negative, or neutral emotional valence that is expressed through the facial features of a humanoid robot. To evaluate the integrated system, we conducted a human-robot interaction experiment in which the robot credibly detected and displayed emotions as it listened to utterances spoken by a confederate. The utterance were directly extracted from interviews with people with Parkinson’s Disease. The encouraging results will form the basis for further developments of finer prediction models to be employed in a companion robot for persons with PD.
Aplikasi Game Edukasi Matematika Berbasis Android

One technology that is developing very rapidly now is information technology and mobile communication (mobile). Mathematics is a science that is widely used in everyday life such as used in trade transactions, carpentry, and so forth. But unfortunately mathematics is considered a difficult subject to understand and is considered terrible by some students who do not like the subject. Like what happened at 004 Batu Aji State Elementary School in Batam. In addition, learning techniques are a bit rigid, because they only come from textbooks. Therefore an educational game was made by incorporating elements of mathematics learning in it to make it look interesting and increase the interest of elementary school students. Indicator variables used are addition, subtraction, multiplication, and division. The purpose of this study is to apply and develop mathematics learning media in the form of an android-based educational game in the hope that it can improve the ability to count quickly, add insight and memory to students.
Comparing Deep Learning Strategies and Attention Mechanisms of Discrete Registration for Multimodal Image-Guided Interventions

In medical imaging, deep learning has been applied to segmentation and classification tasks successfully, whereas its use for image registration tasks is still limited. The use of discrete registration can alleviate the problems limiting the use of CNN based registration for large displacements by helping to capture more complex deformations. We evaluate different building blocks of learning based discrete registration for the CuRIOUS multimodal image registration challenge. We also propose a new attention module, which estimates information contents of a grid point, compare different loss functions and evaluate the influence of self-supervised pre-training of feature extraction step.
Intelligent Human Action Recognition: A Framework of Optimal Features Selection based on Euclidean Distance and Strong Correlation

Extracting salient and most prominent features from a given video sequence is one of the most critical steps in human action recognition.  In this article, proposed a novel method for human action recognition, which efficiently addresses the problem of selection of most prominent and robust features in the feature selection step. Three types of features are fused based on their highest values and later most optimal features are selected with an implementation of a novel Euclidean distance (ED) and strong correlation (SC) method. In the final phase, selected features are classified using multi-class support vector machine (M-SVM). Four publically available datasets are utilized including Weizmann, KTH, UCF YouTube, and HMDB51 with an improved classification accuracy of average more than 94%. Experimental results authenticate our claim that the proposed method outperforms compared to several existing methods.
Durkheim and the Internet: On Sociolinguistics and the Sociological Imagination. JanBlommaert. London, New York: Bloomsbury Academic, 2018. xii + 123pp.

Journal of Linguistic AnthropologyVolume 30, Issue 1 p. 145-147 Book Review Durkheim and the Internet: On Sociolinguistics and the Sociological Imagination. Blommaert, Jan. London, New York: Bloomsbury Academic, 2018. xii + 123pp. Michael M. Prentice, Corresponding Author Michael M. Prentice michael.prentice@manchester.ac.uk University of ManchesterSearch for more papers by this author Michael M. Prentice, Corresponding Author Michael M. Prentice michael.prentice@manchester.ac.uk University of ManchesterSearch for more papers by this author First published: 06 December 2019 https://doi.org/10.1111/jola.12259Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume30, Issue1May 2020Pages 145-147 RelatedInformation
The Arrangement of Model Instrument (Tentative) for Campus Evaluation with Earthquake and Tsunami Disaster Mitigation Insight

Abstract Padang is one of the big cities on the West Coast of Sumatra which is at risk of earthquakes and tsunamis. Meanwhile, campus should be a model that is able to play an important role in disaster mitigation. The study aims to identify and develop tentative model instrument for the evaluation of campuses with the earthquake and tsunami disaster mitigation insight. The method used in this research is literature study such as decisions or policies, results of studies, technical guidelines, books, and publications, both covering earthquakes and tsunamis. The structured instrument model is expected to be a tentative model that will continue to be tested and developed through expert validation and testing both limited and extensive trials. In this study, only tentative model instruments will be presented. This instrument is useful for assessing and recommending earthquake and tsunami disaster mitigation actions for campuses, especially in the city of Padang.