Reviewerly/exHarmony huggingface dataset:
Data Anonymization for Data Protection on Publicly Recorded Data

Data protection in Germany has a long tradition ( https://www.goethe.de/en/kul/med/20446236.html ). For a long time, the German Federal Data Protection Act or Bundesdatenschutzgesetz (BDSG) was considered as one of the strictest. Since May 2017 the EU General Data Protection Regulation (GDPR) regulates data protection all over Europe and it strongly influenced by the German law. When recording data in public areas, the recordings may contain personal data, such as license plates or persons. According to the GDPR this processing of personal data has to fulfill certain requirements to be considered lawful. In this paper, we address recording visual data in public while abiding by the applicable laws. Towards this end, a formal data protection concept is developed for a mobile sensor platform. The core part of this data protection concept is the anonymization of personal data, which is implemented with state-of-the-art deep learning based methods achieving almost human-level performance. The methods are evaluated quantitatively and qualitatively on example data recorded with a real mobile sensor platform in an urban environment.
Multi Sense Embeddings from Topic Models

Distributed word embeddings have yielded state-of-the-art performance in many NLP tasks, mainly due to their success in capturing useful semantic information. These representations assign only a single vector to each word whereas a large number of words are polysemous (i.e., have multiple meanings). In this work, we approach this critical problem in lexical semantics, namely that of representing various senses of polysemous words in vector spaces. We propose a topic modeling based skip-gram approach for learning multi-prototype word embeddings. We also introduce a method to prune the embeddings determined by the probabilistic representation of the word in each topic. We use our embeddings to show that they can capture the context and word similarity strongly and outperform various state-of-the-art implementations.
Texture-Based Fuzzy Connectedness Algorithm for Fetal Ultrasound Image Segmentation for Biometric Measurements

Fuzzy connectedness segmentation approach guided by texture properties of the image is proposed for segmenting fetal organs such as femur, cranial bones, and abdomen from ultrasound images. This semiautomatic segmentation technique is proposed for fetal biometric measurements of biparietal diameter, head circumference, occipital diameter, femur length, and abdominal circumference. The texture information in the ultrasound images guides the fuzzy connectedness algorithm for efficient segmentation of fetal structures and thereby accurate biometric measurements. The proposed algorithm is compared with the manual segmentation of an expert and evaluation is performed with respect to region-based and distance-based metrics. The performance evaluation indicates that the proposed technique is comparable to manual segmentation results across all gestational ages.
The singularity attack to the multivariate signature scheme HIMQ-3

<p style='text-indent:20px;'>We present a cryptanalysis of a signature scheme HIMQ-3 due to Kyung-Ah Shim et al [<xref ref-type="bibr" rid="b10">10</xref>], which is a submission to National Institute of Standards and Technology (NIST) standardization process of post-quantum cryptosystems in 2017. We will show that inherent to the signing process is a leakage of information of the private key. Using this information one can forge a signature.
A Full-Reference Image Quality Assessment Model Based on Quadratic Gradient Magnitude and LOG Signal

Image quality assessment aims at estimating the subject quality of images and builds models to high efficiently evaluate the perceptual quality of the image for many applications. Because the human visual system (HVS) is highly sensitive to structural information, various image features have been studied and widely applied in IQA metrics design. Previous work has validated that the image gradient magnitude and the Laplacian of Gaussian (LOG) operator are efficient structural features in IQA tasks. Most of the IQA metrics work capably only when the distorted image is totally registered with the reference image, and perform poorly on images even with small translations. In this paper, we suggested an FR-IQA method with a simple combination of the gradient magnitude and the LOG signals, which obtains satisfied performance in evaluating image quality while considering the shift-invariance property for not well-registered reference and distortion image pair. Experimental results show that the proposed model works robustly on three large scale subjective IQA databases which contain a variety of distortion types and levels, stays in the state-of-the-art FR-IQA models and achieves the best performance in terms of weighted average score over the three databases. Furthermore, we proved that the proposed model performs better in translation-invariance test compared with the competitors.
Study of Human–Computer Interaction in Augmented Reality

In the present day scenario, it can be observed that in majority of the projects coming up, the focus on specific product requirement and their immediate solutions is the approach to the existing and upcoming augmented reality problem statements. Exploring more into the functionalities and the roadmap to it is not taken much interest in. This paper desires to explore a few of such augmented reality applications when overlapped with human and computer interaction concepts for them to be a little more user-centric giving them an advantage of not only being more robust with respect to usability but also be popular in the markets for the same reasons. It is a small-scale attempt to bridge the gap between user convenience and upcoming technology of augmented reality, by studying various augmented reality projects in different fields including medical instruments, augmented dance, augmented architectural study, etc., and assessing them on the basis of the most common human–computer interaction parameters that were identified by literature surveys and further inferring the most important parameter, the problem areas, advantages, and challenges of the system by the results obtained.
Convolutional Neural Networks Tracker with Deterministic Sampling for Sudden Fall Detection

Fall-down event detection has increasingly become more important for surveillance systems, which has been catalyzed by widespread availability of surveillance cameras in public places. This event detection plays a vital role in ensuring one’s safety, especially in public health applications such as monitoring system for toddlers, patients, and elderly peoples. Fall-down event detection has been extensively researched because of price decrements in video-based sensor and high-speed computer networks. This paper has adopted a deterministic sampling approach to train the object appearance model using a multiple-model fully convolutional neural network (MMFCNN) tracker. The CNNs tracker comprises of three convolutional layers and three fully connected layers to model the object appearance with minimal user intervention except during the initialization stage. The ground truth location of the object in the first frame is the sole user input information that will be fed to the MMFCNN tracker. The positive training samples are generated by translating the object ground truth location in eight different directions with a step size, k=1. On the other hand, the negative training samples are generated from eight-neighborhood regions of equal size that surround the object in a clockwise rotation direction. The experimental results show that MMFCNN tracker has better performance in term of robustness and reliability, which are crucial for fall down event detection as compared to TCNN and MDNET.
Inter-Rater Agreement and Usability: A Comparative Evaluation of Annotation Tools for Sentiment Annotation

We present the results of a comparative evaluation study of five annotation tools with 50 participants in the context of sentiment and emotion annotation of literary texts. Ten participants per tool annotated 50 speeches of the play Emilia Galotti by G. E. Lessing. We evaluate the tools via standard usability and user experience questionnaires, by measuring the time needed for the annotation, and via semi-structured interviews. Based on the results we formulate a recommendation. In addition, we discuss and compare the usability metrics and methods to develop best practices for tool selection in similar contexts. Furthermore, we also highlight the relationship between inter-rater agreement and usability metrics as well as the effect of the chosen tool on annotation behavior.
A document representation framework with interpretable features using pre-trained word embeddings

We propose an improved framework for document representation using word embeddings. The existing models represent the document as a position vector in the same word embedding space. As a result, they are unable to capture the multiple aspects as well as the broad context in the document. Also, due to their low representational power, existing approaches perform poorly at document classification. Furthermore, the document vectors obtained using such methods have uninterpretable features. In this paper, we propose an improved document representation framework which captures multiple aspects of the document with interpretable features. In this framework, a document is represented in a different feature space by representing each dimension with a potential feature word with relatively high discriminating power. A given document is modeled as the distances between the feature words and the document. To represent a document, we have proposed two criteria for the selection of potential feature words and a distance function to measure the distance between the feature word and the document. Experimental results on multiple datasets show that the proposed model consistently performs better at document classification over the baseline methods. The proposed approach is simple and represents the document with interpretable word features. Overall, the proposed model provides an alternative framework to represent the larger text units with word embeddings and provides the scope to develop new approaches to improve the performance of document representation and its applications.
Towards Improved Steganalysis: When Cover Selection is Used in Steganography

This paper proposes an improved steganalytic method when cover selection is used in steganography. We observed that the covers selected by existing cover selection methods normally have different characteristics from normal ones, and propose a steganalytic method to capture such differences. As a result, the detection accuracy of steganalysis is increased. In our method, we consider a number of images collected from one or more target (suspected but not known) users, and use an unsupervised learning algorithm such as k-means to adapt the performance of a pre-trained classifier towards the cover selection operation of the target user(s). The adaptation is done via pseudo-labels from the suspected images themselves, thus allowing the re-trained classifier more aligned with the cover selection operation of the target user(s). We give experimental results to show that our method can indeed help increase the detection accuracy, especially when the percentage of stego images is between 0.3 and 0.7.
Exploring the Potential of Blockchain as an Enabler for Three Types of Energy Communities

We focus on exploring the opportunities related to blockchain in regard to three different types of energy communities i.e., industrial microgrid, apartment building and virtual community, as seen by a group of Finnish energy sector experts. To address the research problem, we first summarize results from literature for blockchain as an enabler for energy communities, discuss the technical aspects of blockchain and summarize the status of ongoing pilots and demonstrations around blockchain. Our results from empirical study among energy industry experts show that there clearly is potential for blockchain as an enabler especially of energy efficient use of resources, trading without intermediaries, load balancing, new business, automated functions and long-range in system valuable resources. Despite this potential, multiple gaps exist and future research on blockchain is still called for.
A Method of FPGA-Based Extraction of High-Precision Time-Difference Information and Implementation of Its Hardware Circuit

Abstract: The positioning technology to find shallow underground vibration sources based on a wireless sensor network is receiving great interest in the field of underground position measurements. The slow peaking and strong multi-waveform aliasing typical of the underground vibration signal result in a low extraction accuracy of the time difference and a poor source-positioning accuracy. At the same time, the transmission of large amounts of sensor data and the host computer's slow data processing speed make locating a source a slow process. To address the above problems, this paper proposes a method for high-precision time-difference measurements in near-field blasting and a method for its hardware implementation. First, based on the broadband that is typical of blast waves, the peak frequency of the P-wave was obtained in the time-frequency domain, taking advantage of the difference in the propagation speed of the P-wave, S-wave, and the surface wave. Second, the phase difference between two sensor nodes was found by means of a spectral decomposition and a correlation measurement. Third, the phase ambiguity was eliminated using the time interval of the first break and the dynamic characteristics of the sensors. Finally, following a top-down design idea, the hardware system was designed using Field Programmable Gate Array(FPGA). Verification, using both numerical simulations and experiments, suggested that compared with generalized cross-correlation-based time-difference measurement methods, the proposed method produced a higher time-difference resolution and accuracy. Compared with the traditional host computer post-position positioning method, the proposed method was significantly quicker. It can be seen that the proposed method provides a new solution for solving high-precision and quick source-location problems, and affords a technical means for developing high-speed, real-time source-location instruments.
TEINet: Towards an Efficient Architecture for Video Recognition

Efficiency is an important issue in designing video architectures for action recognition. 3D CNNs have witnessed remarkable progress in action recognition from videos. However, compared with their 2D counterparts, 3D convolutions often introduce a large amount of parameters and cause high computational cost. To relieve this problem, we propose an efficient temporal module, termed as Temporal Enhancement-and-Interaction (TEI Module), which could be plugged into the existing 2D CNNs (denoted by TEINet). The TEI module presents a different paradigm to learn temporal features by decoupling the modeling of channel correlation and temporal interaction. First, it contains a Motion Enhanced Module (MEM) which is to enhance the motion-related features while suppress irrelevant information (e.g., background). Then, it introduces a Temporal Interaction Module (TIM) which supplements the temporal contextual information in a channel-wise manner. This two-stage modeling scheme is not only able to capture temporal structure flexibly and effectively, but also efficient for model inference. We conduct extensive experiments to verify the effectiveness of TEINet on several benchmarks (e.g., Something-Something V1&V2, Kinetics, UCF101 and HMDB51). Our proposed TEINet can achieve a good recognition accuracy on these datasets but still preserve a high efficiency.
Legal Creation of Smart Contracts and the Legal Effects

Abstract Smart contracts are software codes based on the Blockchain technology which are involved in the field of information technology. The Blockchain smart contract is still in the contractual scope in nature, although its origin and automatic performance functions are different from existing contracts, but it can be identified as a typical unnamed contract. Therefore, at the legal level, a smart contract is a new form of contract that can automatically and automatically perform all or part of the contract. While discussing the technical aspects of smart contracts, we cannot ignore the relevant issues at the legal level. Smart contracts are developments and innovations in the form of traditional contracts. Smart contracts exist in electronic form and automatically fulfill contracts through software that is accurately encoded on the Blockchain. How to create smart contracts according to law and evaluate the legal effect of smart contracts are the main research issue of this paper.
Collaborative Topic Regression-Based Recommendation Systems: A Comparative Study

The collaborative filtering is a very popular and powerful approach prominently being used in many research areas of computer science like recommendation systems, information retrieval, data mining, etc. When used for making recommendations, the traditional collaborative filtering methods suffer from certain problems, where the data sparsity is the significant one that causes the deterioration of the recommendation quality. In order to alleviate this issue, the research fraternity has started proposing the use of some additional domain information in formulating recommendations. In literature, different models have been proposed that make use of such kind of add-on information extensively and have also shown the promising performance than the other state-of-the-art approaches. Hence, the increasing use of add-on information is creating an overwhelming impact on the recommendation field. The piety of this article is to present a meticulous comparative study of various such recommendation models especially those which belong to the family of collaborative topic regression recommendation models in the light of several parameters and this study further leads to propose a novel recommendation prototype based on the fusion of different kinds of auxiliary domain knowledge.
Brands and Caps Labeling Recognition in Images Using Deep Learning

In this paper, we investigate and analyze applying several well-known models to the task of brands detection in images. Besides this we explore applying these models for solution of industrial task of detection objects on running production line. In first parts of paper we compare and analyze most effective and widely used architectures as Faster R-CNN (based on Inception v2 and ResNet-50/101), SSD (based on Inception v2 and MobileNet). We implement such comparison using dataset of beer brands. The obtained results confirm the effectiveness of applying Faster R-CNN. But such models are resource intensive. In contrast SSD models perform processing faster than Faster R-CNN and can be considered as basic models for detection and segmentation of objects in images and video in real time. Based on getting results of comparing models in the last parts of paper intelligent system for detection and recognition labeling of bottles in real time is given.
Surveillance System for Abnormal Driving Behavior Detection

The objective of this research is to detect abnormal car driving behaviors that may result in accidents. The behaviors include exhaustion from driving for a long time, taking eyes off the road, and the abnormal eye-blink from the sleepiness. The researchers are interested to develop a mobile application that analyzes car driving behaviors using a face detection algorithm. Our application is easy to install and to use on any Android based smartphone. Concretely, our mobile application considers three factors: (1) time of driving; (2) abnormal eye-blinking; and (3) taking eyes off the road. We integrate these factors with the acceleration of the driving vehicle to analyze and calculate abnormality level. Therefore, we send a voice notification to the driver when the calculated abnormality level exceeds 10. We perform experiments and experimental results show that our mobile application is feasible in realistic situations.
PENGGUNAAN EDMODO SEBAGAI MEDIA PEMBELAJARAN MATEMATIKA

Along with the rapid development of the times, all aspects of life also changed. This is a challenge for educators to be able to use technology optimally and can facilitate interaction with students in the learning process, one of which is Edmodo. Edmodo is a social learning platform for teachers / lecturers and students / students that provides several features to support e-learning such as assignments, quizzes, assessments, etc. Through edmodo teachers and students can share notes and documents and can share discussions online. This study aims to describe the use of edmodo in analytic geometry learning in 5th semester students of the Mathematics Education study program at Quality University, Medan. This research is a qualitative research. Qualitative research is said because it is descriptive and tends to use analysis of research subjects that are highlighted. Analysis techniques used are reduction, presentation, and verification and conclusion. Validation using triangulation (combined). Reduction namely that has been obtained is reduced so that it gives a clearer picture and makes it easier for researchers to collect. Presentation is done in the form of brief description. Verification and drawing conclusions are new findings that have never before existed.
Comparison of Track to Track fusion methods for nonlinear process and measurement models

Automotive sensors play a vital role in the environment perception for vehicles in advanced driver assistance systems (ADAS). Sensors have their own distinctive advantages and drawbacks, which makes it imperative to fuse information from disparate sources. The fusion can be performed either at the sensor or the track level. Track to track fusion (T2TF) offers a big advantage as individual sensor blocks can be treated as grey or even black boxes i.e. a very limited knowledge of their characteristics might be required. In this paper, we study T2TF for a single target vehicle, tracked by two generic sensors, differing in kinematic tracking accuracy. A challenging reference trajectory is simulated consisting of both linear and nonlinear motion segments. The main objective is to compare the performance of different nonlinear sensor fusion algorithms, comprising of several combinations of prediction and measurement update methods. We show that the covariance intersection based update methods outperform the Kalman filter derivatives, as they tend not to produce overly optimistic estimates.
Liquidation of the uranium mine Rožná I in Dolní Rožínka

The liquidation of the Rožna I uranium mine in Dolni Rožinka is a current problem, as the uranium ore mining was terminated in 2017 and the deposit will have to be liquidated after all mining activities in this area are finished. As soon as the mine areas cease to be used, the mine will be liquidated. The liquidation will start with the stopping of the mine water pumping, which will cause gradual flooding of the mine. The uranium mine, as a non-gassing mine, will be liquidated by a complete, non-strengthened backfill of the initial mine workings opening to the surface. After filling up the initial mine workings, a closure sinking platform will be built at their mouth. The next part is devoted to the problems of mine water during gradual flooding. During the flooding process, the geochemistry of the waters changes and their stratification occurs as a result of the dissolution of the secondary minerals from the old mine workings. Once the deposit is flooded, the mine water will be pumped and cleaned for a certain time horizon. As soon as the water contamination no longer exceeds the permitted limits, the water will be discharged freely to the local watercourse without prior cleaning.