Reviewerly/exHarmony huggingface dataset:
Clipping the Page – Automatic Article Detection and Marking Software in Production of Newspaper Clippings of a Digitized Historical Journalistic Collection

This paper describes utilization of article detection and extraction on the Finnish Digi ( https://digi.kansalliskirjasto.fi/etusivu?set_language=en ) newspaper material of the National Library of Finland (NLF) using data of one newspaper, Uusi Suometar 1869–1918. We use PIVAJ software [1] for detection and marking of articles in our collection. Out of the separated articles we can produce automatic clippings for the user. The user can collect clippings for own use both as images and as OCRed text. Together these functionalities improve usability of the digitized journalistic collection by providing a structured access to the contents of a page.
Multi-scale Feature Fusion Single Shot Object Detector Based on DenseNet

SSD (Single Shot Multibox Detector) is one of advanced object detection methods and apparently can detect objects with high accuracy and fast speed. However, detecting small objects accurately remains a problem full of challenges for SSD. To handle this troublesome problem, our paper introduce a multi-scale feature fusion single shot object detector based on DenseNet (MFSOD), which combine the dense convolutional network (DenseNet) with SSD framework. Firstly, we add additional convolutional layers after backbone network to realize multi-scale feature detection. In addition, the feature fusion module is designed to fuse the multi-scale features from different layers, introducing the contextual information in object detection. Finally, we evaluate the proposed method on PASCAL VOC2007 and MS COCO benchmark datasets. The results indicate that our proposed method achieves 78.9% mAP on PASCAL VOC2007 test and 27.1% mAP on MS COCO test-dev2015 at the speed of 23 FPS. MFSOD algorithm outperforms the conventional SSD in aspects of accuracy, especially for small objects, and satisfies the demand of real-time application.
An algorithm for DNA read alignment on quantum accelerators

With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors allow us to efficiently compute important algorithms in various fields. In this paper, we propose a quantum algorithm to address the challenging field of big data processing for genome sequence reconstruction. This research describes an architecture-aware implementation of a quantum algorithm for sub-sequence alignment. A new algorithm named QiBAM (quantum indexed bidirectional associative memory) is proposed, that uses approximate pattern-matching based on Hamming distances. QiBAM extends the Grover's search algorithm in two ways to allow for: (1) approximate matches needed for read errors in genomics, and (2) a distributed search for multiple solutions over the quantum encoding of DNA sequences. This approach gives a quadratic speedup over the classical algorithm. A full implementation of the algorithm is provided and verified using the OpenQL compiler and QX simulator framework. This represents a first exploration towards a full-stack quantum accelerated genome sequencing pipeline design. The open-source implementation can be found on https://github.com/prince-ph0en1x/QAGS.
Differential phase shift quantum key distribution with variable loss revealing blinding and control side-channel attacks

Realistic quantum key distribution (QKD) systems suffer from side-channel attacks, which manipulate single-photon detectors. Although measurement-device-independent QKD schemes were proposed to free QKD parties (Alice and Bob) from such measurement devices, these schemes are not easy to be implemented in practice because they require precise synchronization between signals from distant parties. On the other hand, differential phase shift (DPS) QKD is a simple system for practical implementation with current optical equipment. In this study, we propose a simple modification in DPS QKD to prevent side-channel attacks (control blinding and controlling attacks) such that Bob randomly attenuates the incoming signal. This modification allows Bob to utilize photon statistics during attenuated time slots in DPS-QKD systems, using which the side-channel attacks are revealed.
The effect of Grit on Career Decision Making Self-Efficacy and Career Preparation Behaviors of University Students in Physical Education

The purpose of this study is to investigate the effect of Grit on career decision making self-efficacy and career preparation behaviors of university students in physical education. The purpose of this study is to examine the effect of Grit on university students in physical education in Seoul and Gyeonggido. The data were analyzed by frequency, factor analysis, correlation analysis, regression analysis, and structural equation modeling using SPSS 21.0 program and AMOS 21.0 program. Based on the results and discussions, the following conclusions were obtained. First, all variables such as steady effort and continuous interest, which are sub-factors of Grit, had a positive influence on all of the sub-factors of career decision making self-efficacy, such as goal selection, future planning and job information. These results indicate that Grit is a very important variable to enhance career decision making self-efficacy. Second, all variables such as steady effort and continuous interest, which are sub-factors of Grit, had a positive influence on career exploration behavior, information gathering behavior, and job experience behavior. These results indicate that Grit is a very important variable for enhancing career preparation behavior. Third, all variables such as goal selection, future plan, and job information, which are sub-factors of career decision making self-efficacy, have positive significance in career exploration behavior, information gathering behavior, It affected. These results indicate that career decision making self-efficacy of university students is a very important variable to enhance career preparation behavior. Fourth, the relationship among Grit, career decision making self-efficacy and career preparation behavior was confirmed, and it was confirmed that there was a positive causality between each variables.
Convolutional Neural Networks for Profiled Side-channel Analysis

Recent studies have shown that deep learning algorithms are very effective for evaluating the security of embedded systems.The deep learning technique represented by Convolutional Neural Networks (CNNs) has proven to be a promising paradigm in the profiled side-channel analysis attacks.In this paper, we first proposed a novel CNNs architecture called DeepSCA.Considering that this work may be reproduced by other researchers, we conduct all experiments on the public ASCAD dataset, which provides electromagnetic traces of a masked 128-bit AES implementation.Our work confirms that DeepSCA significantly reduces the number of side-channel traces required to perform successful attacks on highly desynchronized datasets, which even outperforms the published optimized CNNs model.Additionally, we find that DeepSCA pre-trained from the synchronous traces works well in presence of desynchronization or jittering after a slight fine-tuning.
Automatic Parking Controller with a Twin Artificial Neural Network Architecture

We propose an artificial deep neural network- (ANN-) based automatic parking controller that overcomes a stubborn restriction prevalent in traditional approaches. The proposed ANN learns human-like control laws for automatic parking through supervised learning from a training database generated by computer-aided optimizations or real experiments. By learning the relationships between the instantaneous vehicle states and the corresponding maneuver parameters, the proposed twin controller yields lateral and longitudinal maneuvering parameters for executing automatic parking tasks in confined spaces. The proposed automatic parking controller exhibits a twin architecture comprising a main agent and its cloned agent. Before the main agent assumes a maneuvering action, the cloned agent predicts the consequences of the maneuvering action through a Collision Checking and Adjustment (CCA) system. The proposed parking agent operates like a human driver in a manner that is characterized by an unplanned trajectory. In addition, the kinematics of the subject vehicle is not exactly modelled for parking control. The simulation results demonstrate that the proposed twin agent emulates the attributes of a human driver such as adaptive control and determines the consequences of the tentative maneuvering action under varying kinematic models of the subject vehicle. We validate the proposed parking controller by simulating the software-in-the-loop architecture using a PreScan simulator in which the dynamics of the virtual vehicle’s behavior resemble a real vehicle.
Application interactive methods and technologies of teaching chemistry

Abstract Currently, chemistry education solves problems related to the definition of new values in order to improve the quality of education, personal development of the pupils, and problems of the teaching strategies that will stimulate the interest and commitment of the students for the natural sciences. The emphasis is on interactive teaching, using the subject matter in practice with ICT. Therefore, our goal is to create and verify an interactive chemistry course for lower secondary schools based on the characteristics of interactive, ICT-connected education and more interesting presentation of the subject matter. The first stage of our work consisted of an analysis of previous studies to establish the theoretical foundations on which to the base the use of ICT. Next, we have presented some of the developed methodological options, which illustrate the ability to use interactive methods. In the second stage, according to the different interactive chemistry programs and analysis of curriculum of the chemistry subject in the Czech Republic and Kazakhstan, we developed interactive teaching materials for chemistry lessons with interesting embedded content for lower secondary schools. In the next stage, all prepared materials will be verified and eventually adapted into the final form for use in chemistry teaching.
Possibilities to Use a Virtual Opponent for Enhancements of Reactions and Perception of Young Karate Athletes

Abstract We conducted a virtual reality (VR) training with ten sessions, performed by fifteen young karate athletes, who responded to attacks of a virtual opponent to improve their response behavior and their decision-making. The control groups continued with their normal training. Results of the Friedman tests with subsequent Dunn-Bonferroni post-hoc-tests and estimation of effect sizes showed that the karate specific response behavior (measured by a movement analysis) improved significantly due to the training. The parameters time for response (as the time for the attack initiation) and response quality improved with large effect sizes for the intervention groups, whereas the control groups demonstrated improvements with only small effect sizes. The unspecific response behavior (analyzed by two forms of the reaction test of the Vienna test system) did not show any significant changes. Paired t-tests revealed an improvement in attack recognition. While in the pretests, the intervention groups responded to late movement stages of the attack (execution of the main phase), they responded to early movement stages (reduction of distance and preparing steps) in the posttests. Furthermore, Friedman-tests and bivariate correlation analysis showed that the intervention groups were highly motivated to perform the VR training because of the new and safe learning conditions.
Double Anchor R-CNN for Human Detection in a Crowd

Detecting human in a crowd is a challenging problem due to the uncertainties of occlusion patterns. In this paper, we propose to handle the crowd occlusion problem in human detection by leveraging the head part. Double Anchor RPN is developed to capture body and head parts in pairs. A proposal crossover strategy is introduced to generate high-quality proposals for both parts as a training augmentation. Features of coupled proposals are then aggregated efficiently to exploit the inherent relationship. Finally, a Joint NMS module is developed for robust post-processing. The proposed framework, called Double Anchor R-CNN, is able to detect the body and head for each person simultaneously in crowded scenarios. State-of-the-art results are reported on challenging human detection datasets. Our model yields log-average miss rates (MR) of 51.79pp on CrowdHuman, 55.01pp on COCOPersons~(crowded sub-dataset) and 40.02pp on CrowdPose~(crowded sub-dataset), which outperforms previous baseline detectors by 3.57pp, 3.82pp, and 4.24pp, respectively. We hope our simple and effective approach will serve as a solid baseline and help ease future research in crowded human detection.
StakeCube: Combining Sharding and Proof-of-Stake to Build Fork-Free Secure Permissionless Distributed Ledgers

Our work focuses on the design of a scalable permissionless blockchain in the proof-of-stake setting. In particular, we use a distributed hash table as a building block to set up randomized shards, and then leverage the sharded architecture to validate blocks in an efficient manner. We combine verifiable Byzantine agreements run by shards of stakeholders and a block validation protocol to guarantee that forks occur with negligible probability. We impose induced churn to make shards robust to eclipse attacks, and we rely on the UTXO coin model to guarantee that any stakeholder action is securely verifiable by anyone. Our protocol works against adaptive adversary, and makes no synchrony assumption beyond what is required for the byzantine agreement.
Dynamic Graph Attention for Referring Expression Comprehension

Referring expression comprehension aims to locate the object instance described by a natural language referring expression in an image. This task is compositional and inherently requires visual reasoning on top of the relationships among the objects in the image. Meanwhile, the visual reasoning process is guided by the linguistic structure of the referring expression. However, existing approaches treat the objects in isolation or only explore the first-order relationships between objects without being aligned with the potential complexity of the expression. Thus it is hard for them to adapt to the grounding of complex referring expressions. In this paper, we explore the problem of referring expression comprehension from the perspective of language-driven visual reasoning, and propose a dynamic graph attention network to perform multi-step reasoning by modeling both the relationships among the objects in the image and the linguistic structure of the expression. In particular, we construct a graph for the image with the nodes and edges corresponding to the objects and their relationships respectively, propose a differential analyzer to predict a language-guided visual reasoning process, and perform stepwise reasoning on top of the graph to update the compound object representation at every node. Experimental results demonstrate that the proposed method can not only significantly surpass all existing state-of-the-art algorithms across three common benchmark datasets, but also generate interpretable visual evidences for stepwisely locating the objects referred to in complex language descriptions.
Measurement of In-Plane Displacement in Two Orthogonal Directions by Digital Speckle Pattern Interferometry

The measurement of in-plane displacement in two orthogonal directions is of considerable significance for modern industries. This paper reports on a spatial carrier phase-shift digital speckle pattern interferometry (DSPI) for the simultaneous measurement of in-plane displacement in two orthogonal directions. The object is illuminated from a single direction and observed from four symmetrical directions simultaneously. One pair of the four observation directions is sensitive to in-plane displacement in one direction, and the other pair is sensitive to in-plane displacement in the perpendicular direction, resulting in the displacement in two directions being measured independently. The polarization property of light is used to avoid cross-interference between the two pairs of beams. Spatial carrier frequencies are generated by aperture misalignment, and the displacement in two directions is modulated onto the same interferogram. With a spatial carrier phase-shift technique, the displacement can be separated in the frequency domain and the phase can be evaluated from a single interferogram in real time. The capability of DSPI is described by theoretical discussions and experiments.
Detection and Recognition of SAR Small Ship Objects Using Deep Neural Network

Synthetic aperture radar(SAR) ship target detection plays an increasingly important role in marine monitoring. Aimed at the problems of recognizing small size of ship targets in SAR images and the inability of traditional methods to extract fine target features due to external disturbances, we propose an improved SAR small target detection model based on the deep learning technology. The proposed model mainly consists of two parts:region proposal network(RPN) and object detection network. Firstly, a CNN model is designed and trained to accurately identify small ship targets. Then, the model is used to initialize the parameters of the shared feature extraction layer. Last, we train the proposed object detection model using a self-collected Sentinel-1 SAR small target dataset. The experimental results show that the proposed target detection model has better detection and recognition performance and anti-interference ability for small ship scalable targets in SAR images, and has certain reference value for the research of small target detection in SAR images.
Traffic extreme situations detection in video sequences based on integral optical flow

Road traffic analysis is an important task in many applications and it can be used in video surveillance systems to prevent many undesirable events. In this paper, we propose a new method based on integral optical flow to analyze cars movement in video and detect flow extreme situations in real-world videos. Firstly, integral optical flow is calculated for video sequences based on optical flow, thus random background motion is eliminated; secondly, pixel-level motion maps which describe cars movement from different perspectives are created based on integral optical flow; thirdly, region-level indicators are defined and calculated; finally, threshold segmentation is used to identify different cars movements. We also define and calculate several parameters of moving car flow including direction, speed, density, and intensity without detecting and counting cars. Experimental results show that our method can identify cars directional movement, cars divergence and cars accumulation effectively.
Classification as Catachresis: Double Binds of Representing Difference with Semiotic Infrastructure

Background This article explores the results of a three-year ethnographic study of how semiotic infrastructures—or digital standards and frameworks such as taxonomies, schemas, and ontologies that encode the meaning of data—are designed. Analysis It examines debates over best practices in semiotic infrastructure design, such as how much complexity adopted languages should characterize versus how restrictive they should be. It also discusses political and pragmatic considerations that impact what and how information is represented in an information system. Conclusion and implications This article suggests that all databased representations are forms of data power, and that examining semiotic infrastructure design provides insight into how culturally informed conceptions of difference structure how we access knowledge about our social and material worlds.
Dealing with Distributional Assumptions in Preregistered Research

Virtually any inferential statistical analysis relies on distributional assumptions of some kind. The violation of distributional assumptions can result in consequences ranging from small changes to error rates through to substantially biased estimates and parameters fundamentally losing their intended interpretations. Conventionally, researchers have conducted assumption checks after collecting data, and then changed the primary analysis technique if violations of distributional assumptions are observed. An approach to dealing with distributional assumptions that requires decisions to be made contingent on observed data is problematic, however, in preregisteredresearch, where researchers attempt to specify all important analysis decisions prior to collecting data. Limited methodological advice is currently available regarding how to deal with the prospect of distributional assumption violations in preregistered research. In this article, we examine several strategies that researchers could use in preregistrations to reduce the potential impact of distributional assumption violations. We suggest that pre-emptively selecting analysis methods that are as robust as possible to assumption violations, performing planned robustness analyses, and/or supplementing preregistered confirmatory analyses with exploratory checks of distributional assumptions may all be useful strategies. On the other hand, we suggest that prespecifying “decision trees” for selecting data analysis methods based on the distributional characteristics of the data may not be practical in most situations.
Mining version history to predict the class instability

While most of the existing class stability assessors just rely on structural information retrieved from a desired source code snapshot. However, class stability is intrinsically characterized by the evolution of a number of dependencies and change propagation factors which aid to promote the ripple effect. Identification of classes prone to ripple effect (instable classes) through mining the version history of change propagation factors can aid developers to reduce the efforts needed to maintain and evolve the system. We propose Historical Information for Class Stability Prediction (HICSP), an approach to exploit change history information to predict the instable classes based on its correlation with change propagation factors. Subsequently, we performed two empirical studies. In the first study, we evaluate the HICSP on the version history of 10 open source projects. Subsequently, in the second replicated study, we evaluate the effectiveness of HICSP by tuning the parameters of its stability assessors. We observed the 4 to 16 percent improvement in term of F-measure value to predict the instable classes through HICSP as compared to existing class stability assessors. The promising results indicate that HICSP is able to identify instable classes and can aid developers in their decision making.
Gender Prediction Based on Chinese Name

Much work has been done on the problem of gender prediction about English using the idea of probability models or traditional machine learning methods. Different from English or other alphabetic languages, Chinese characters are logosyllabic. Previous approaches work quite well for Indo-European languages in general and English in particular, however, their performance deteriorate in Asian languages such as Chinese, Japanese and Korean. In our work, we focus on Simplified Chinese characters and present a novel approach incorporating phonetic information (Pinyin) to enhance Chinese word embedding trained on BERT model. We compared our method with several previous methods, namely Naive Bayes, GBDT, and Random forest with word embedding via fastText as features. Quantitative and qualitative experiments demonstrate the superior of our model. The results show that we can achieve 93.45% test accuracy using our method. In addition, we have released two large-scale gender-labeled datasets (one with over one million first names and the other with over six million full names) used as a part of this study for the community.