Reviewerly/exHarmony huggingface dataset:
A Strong Baseline for Multiple Object Tracking on VidOR Dataset

This paper explores a simple and efficient baseline for multi-class and multiple objects tracking on VidOR dataset. The task is to build a robust object tracker that not only localize objects with bounding boxes in every video frame but also link the bounding boxes that indicate the same object entity into a trajectory. The task's challenges are the low resolution and imbalance of data and the disappearance of the object for a long time. According to the above characteristics, we design a robust detection model, proposed a new deep metric learning method, and explored some useful tracking algorithms to help complete the video object detection task.
Low-cost fitness and activity trackers for biometric authentication

Abstract Fitness and activity tracking devices acquire, process and store rich behavioural data that are consumed by the end-user to learn health insights. This rich data source also enables a secondary use of being part of a biometric authentication system. However, there are many open research challenges with the use of data generated by fitness and activity trackers as a biometric source. In this article, the challenge of using data acquired from low-cost devices is tackled. This includes investigating how to best partition the data to deduce repeatable behavioural traits, while maximizing the uniqueness between participant datasets. In this exploratory research, 3 months’ worth of data (heart rate, step count and sleep) for five participants is acquired and utilized in its raw form from low-cost devices. It is established that dividing the data into 14-h segments is deemed the most suitable based on measuring coefficients of variance. Several supervised machine learning algorithms are then applied where the performance is evaluated by six metrics to demonstrate the potential of employing this data source in biometric-based security systems.
Learning in Virtual Environments

This chapter addresses the questions of why, when, and how to use virtual reality to support learning processes for human beings. It focuses therefore on what can and cannot be done in a real environment versus what can and cannot be done in a virtual environment, as well as on how using virtual reality can make some types of learning easier as long as certain conditions are fulfilled. These conditions include the shifting of some inner beliefs and the choice of an accurate paradigm. The paradigm of enaction will be presented as an example of an accurate paradigm for virtual reality. Some conceptual keys and landmarks for design will be proposed in the context of the Trinologic metamodel developed by the author. Such metamodels should facilitate the connection between human actions, learning, and the characteristics of the outer world, whether this world is real or virtual.
HOW CORRECTLY WE TELL PUPILS ABOUT THE NUMBER OF NUMERALS IN DIFFERENT NOTATIONS?

The article proposes to take a fresh look at the traditional for any informatics course material, which describes the fundamental principles of number representation. It considers in details the ideas how different number notations are formed; special attention is paid to the problem related to the amount of numerals used for presenting values in every digit. It is shown that frequent in school informatics definition of radix as an amount of numerals that employed in a system becomes incorrect under careful review. For those who want to discuss this problem at a lesson, two ways are possible: one (stricter) is founded on mathematical formulas, and another is based on a generalization of various notation examples; both methods are presented in the article. Besides for demonstrative narrating of the material to pupils the authors designed their own analogy, which facilitates understanding of the topic. Practical application of notations to computer technique is also discussed.
Using Machine Learning with PySpark and MLib for Solving a Binary Classification Problem: Case of Searching for Exotic Particles

Searching for exotic particles in high-energy represents a major challenge for physicists. In this paper, we propose to solve the binary classification problem in the area of exotic particles using the Apache Spark environment with the Mlib library. Then, We compare the performance of four methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT). In this work, we use “SUSY” dataset, collected from UCI machine learning repository, for the experimentation phase.
ACM Multimedia BioMedia 2020 Grand Challenge Overview

The BioMedia 2020 ACM Multimedia Grand Challenge is the second in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year's challenge, participants are asked to develop algorithms that automatically predict the quality of a given human semen sample using a combination of visual, patient-related, and laboratory-analysis-related data. Compared to last year's challenge, participants are provided with a fully multimodal dataset (videos, analysis data, study participant data) from the field of assisted human reproduction. The tasks encourage the use of the different modalities contained within the dataset and finding smart ways of how they may be combined to further improve prediction accuracy. For example, using only video data or combining video data and patient-related data. The ground truth was developed through a preliminary analysis done by medical experts following the World Health Organization's standard for semen quality assessment. The task lays the basis for automatic, real-time support systems for artificial reproduction. We hope that this challenge motivates multimedia researchers to explore more medical-related applications and use their vast knowledge to make a real impact on people's lives.
Risk curves: A methodology to evaluate the risk of fraud by stock price manipulation based on game theory and detection software

Historically, fraudulent episodes result in high losses for investors, layoff of executives, and the erosion of confidence in the stock market, among other negative consequences. In this paper, we established a framework that connects Game Theory and Detection Software to estimate the probability of defrauding by manipulation of stock prices in terms of the effort-damage ratio of the audit team. Furthermore, the method allows to define optimum thresholds values for the score random variable in the detection software's alarm structure. The proposed approach is illustrated by analyzing the financial episode called the plier bubble that occurred in Brazil in 2011. The results reveal a suitable way to quantify the fraud risk probability, thus being a valuable tool to risk management in the stock market.
Towards better contact-tracing in the UK

As a second pandemic wave breaks over much of the world, the need for sustainable and effective test, trace, and isolate programmes is more pressing than ever. The study by Michelle Kendall and colleagues1 in The Lancet Digital Health is thus welcome and timely. This assessment of the effect of the pilot UK Test and Trace programme on the Isle of Wight provides indirect, yet convincing, evidence of success in reducing the effective reproduction number, and thereby the size of the first wave. However, it is unclear how the programme will fare when expanded to settings that are less ideal than the Isle of Wight, given its small population size and distinct natural boundaries.
Text-to-Image Synthesis via Aesthetic Layout

In this work, we introduce a practical system which synthesizes an appealing image from natural language descriptions such that the generated image should maintain the aesthetic level of photographs. Our proposed method takes the text from the end-users via a user-friendly interface and produces a set of different label maps via the primary generator PG. Then, choosing a subset from the label maps set is performed through the primary aesthetic appreciation PAA. Next, our subset of label maps is fed into the accessory generator AG, which is the state-of-the-art image-to-image translation. Last but not least, our subset of generated images is ranked via the accessory aesthetic appreciation AAA, and the most appealing image is produced.
Fuzzy polychromatic sets constraint model for route planning problem

Unmanned aerial vehicle route planning is a complex multiconstrained multiobjective optimization problem. Due to the complexity of various constraints and the mutual coupling between them, the expression of constraint conditions is not universal and normative. The development, maintenance, and upgrading of an existing route planning system are very difficult. In this article, by establishing the polychromatic sets of aircraft, aircraft equipment, and flight actions, creating the fuzzy relational matrix between equipment and actions and between actions and actions, this article realizes the standardized and generalized expression of the constraint condition of the route planning problem. Then the analysis and inspection of the constraint conditions are realized by the polychromatic sets operation rules.
Fast Hyper-walk Gridded Convolution on Graph

The existing graph convolution methods usually suffer high computation burden, large memory requirement and intractable batch-process. In this paper, we propose a high-efficient hyper-walk gridded convolution (hyper-WGC) method to encode non-regular graph data, which overcomes all these aforementioned problems. To high-efficient capture graph topology structures, we propose random hyper-walk by taking advantages of random-walks as well as node/edge encapsulation. The random hyper-walk could greatly mitigate the problem of exponentially explosive sampling times occurred in the original random walk, while well preserving graph structures to some extent. To efficiently encode local hyper-walks around one reference node, we project hyper-walks into an order space to form image-like grid data, which more favors those conventional convolution networks. We experimentally validate the efficiency and effectiveness of our proposed hyper-WGC, which has high-efficient computation speed, and comparable or even better performance when compared with those baseline GCNs.
A Study on the Crime Prevention Effectiveness of CCTV

연구목적은 방범용 CCTV의 감시 범위과 운용기간을 중심으로 절도와 폭력범죄예방효과를 실증적으로 검증하여, CCTV 설치와 관리의 효율성 및 범죄예방효과를 증진시키고자 한다. 연구방법은 첫째, 최근 CCTV의 범죄예방효과성에 대한 국내·외 선행연구들을 수집하여 정리하고, 둘째, 연구대상지역 방범용 CCTV 운용기간별(1년~3년), 감시범위별로 절도와 폭력의 설치 전·후 발생건수를 실험구역과 통제구역 간 비교를 통해 Odds Ratio값을 산출하여 범죄예방효과여부를 검증하였다. 연구결과는 첫째, 대상지역의 방범용 CCTV 절도예방효과는 감시범위가 좁을수록 절도 예방효과가 높고, 운용기간으로는 1년차보다 2년, 3년이 경과한 후에 예방효과가 높은 것으로 확인되었다. 둘째, 연구대상지역별 CCTV 절도예방효과 비교·분석결과, 공통 결과는 100m와 125m 감시범위에서 운용기간 2년차에 가장 예방효과가 높았으며, 상이한 결과는 두 지역 간 감시범위별, 운용기간별, 전체 CCTV 통합효과에서 K구는 효과가 없었으나, G구는 효과가 있었다는 점에서 지역별 특성이 절도폭력예방효과에 영향을 미친다는 사실을 추정할 수 있다. 셋째, CCTV 폭력예방효과는 감시구간별로는 예방효과가 있으나 높지 않다. 운용기간별로는 설치 1년과 2년차에서 유사한 수준의 예방효과가 있으나, 3년차에는 예방효과가 없거나 대폭 낮아졌다. 넷째, K구와 G구는 공통적으로 모든 감시범위에서 폭력예방효과가 있고, 감시범위가 좁을수록 예방효과가 높다. 또한 운용기간별로는 1년과 2년차에 폭력예방효과가 있다. 상이한 결과는 G구의 감시범위별 예방효과의 수준이 K구보다 전반적으로 높으며, 감시범위별 폭력예방효과에서 상반된 결과를 보인다. 역시 폭력예방효과에서도 지역별 특성이 폭력예방효과에 영향을 미친다고 추정할 수 있다.The purpose of the research is to empirically verify the effectiveness of theft and violent crime prevention based on the scope of surveillance and operation period of CCTVs for crime prevention. The research method first collected and organized prior domestic and foreign studies on the effectiveness of crime prevention of CCTVs, and secondly, Odds Ratio value was verified by comparing the number of cases before and after theft and violence between the experimental and control zones by the period of operation of CCTVs (1 year to 3 years) and the scope of surveillance in the target areas. The results of the study showed that the narrower the surveillance range, the higher the prevention effect of theft, and the higher the prevention effect after two and three years of operation. Second, as a result of comparing and analyzing the effects of prevention of theft of CCTVs in each studied area, it can be estimated that the common results were the most preventative effect in the second year of operation, and the different results were that K-gu did not work in the integrated effects of surveillance range, period of operation, and overall CCTV between the two regions, but the characteristics of G-gu affected the prevention of theft. Third, CCTV violence prevention effect has a preventive effect by surveillance section, but it is not high. By period of operation, there is a similar level of prevention effect in the first and second years of installation, but there is no or has been significantly reduced in the third year. Fourth, K-gu and G-gu have violence prevention effects in all surveillance ranges in common, and the narrower the range, the higher the prevention effect. In addition, there is an effect of preventing violence in the first and second years of operation. Different results show that the level of preventive effects by monitoring range of G district is generally higher than that of K district, and that the opposite results are shown in the prevention of violence by surveillance range. In addition, it can be estimated that regional characteristics affect the prevention of violence.
Nonlinear Dynamics of a Cavity Containing a Two-Mode Coherent Field Interacting with Two-Level Atomic Systems

This study analytically explored two coupled two-level atomic systems (TLAS) as two qubits interacting with two modes of an electromagnetic field (EMF) cavity via two-photon transitions in the presence of dipole–dipole interactions between the atoms and intrinsic damping. Using special unitary su(1,1) Lie algebra, the general solution of an intrinsic noise model is obtained when an EMF is initially in a generalized coherent state. We investigated the population inversion of two TLAS and the generated quantum coherence of some partitions (including the EMF, two TLAS, and TLAS–EMF). It is possible to generate quantum coherence (mixedness and entanglement) from the initial pure state. The robustness of the quantum coherence produced and the sudden appearance and disappearance of coherence depended not only on dipole–dipole coupling but also on the intrinsic noise rate. The growth of mixedness and entanglement may be enhanced by increasing dipole–dipole coupling, leading to more robustness against intrinsic noise.
Strong Normalization for the Simply-Typed Lambda Calculus in Constructive Type Theory Using Agda

We consider a pre-existing formalization in Constructive Type Theory of the pure Lambda Calculus in its presentation in first order syntax with only one sort of names and alpha-conversion based upon multiple substitution, as well as of the system of assignment of simple types to terms. On top of it, we formalize a slick proof of strong normalization given by Joachimski and Matthes whose main lemma proceeds by complete induction on types and subordinate induction on a characterization of the strongly normalizing terms which is in turn proven sound with respect to their direct definition as the accessible part of the relation of one-step beta reduction. The proof of strong normalization itself is thereby allowed to consist just of a direct induction on the type system. The whole development has been machine-checked using the system Agda. We assess merits and drawbacks of the approach.
Cooperative Smartphone Tracking System: Software Design and Implementation

Mobile applications have been increasingly adopted in monitoring and tracking systems such as health tracking systems for elderly and chronic patients. Due to the ubiquitous nature of smartphones, using smartphones in such tracking systems would be time and cost-effective. However, the limited battery capacity in smartphones could cause an interruption in reporting the tracking information, especially when users spend a long time without recharging batteries. Continuous tracking could be critically important, especially for the elderly and patients with serious illnesses. In this paper, we develop a mobile application for Android smartphones that report the location and health information cooperatively using the CEET clustering protocol. On the server-side, we exploited the Google Firebase real database that receives the users’ tracking information and can notify the data changes within milliseconds. We present a performance evaluation of the system through real experiments using the developed application. The results show the outstanding performance of the proposed approach in reducing the tracking energy (by around 55%). The developed app does not require any modification neither to the manufacturing specifications of the smartphones nor to the established wireless environment. The user just needs to install the app to his/her smartphones using apk file or from the Google play.
Commonsense Learning

Learning commonsense knowledge and conducting commonsense reasoning are basic human ability to make presumptions about the type and essence of ordinary situation in daily life, which serve as very important goals in human-centric Artificial Intelligence (AI). With the increasing number of media types and quantities provided by various Internet services, commonsense learning and reasoning with no doubt are playing key roles in making progresses for human-centric multimedia analysis. Therefore, this paper first introduces the basic concept of commonsense knowledge and commonsense reasoning, then summarizes commonsense resources and benchmarks, gives an overview on recent commonsense learning and reasoning methods, and discusses several popular applications of commonsense knowledge in real-world scenarios. This work distinguishes itself from existing literature that merely pays attention to natural language processing in focusing more on multimedia which include both natural language processing and computer vision. Furthermore, we also present our insights and thinking on future research directions for commonsense.
The effect of word class on speaker-dependent information in the Standard Dutch vowel /aː/

Linguistic structure co-determines how a speech sound is produced. This study therefore investigated whether the speaker-dependent information in the vowel [aː] varies when uttered in different word classes. From two spontaneous speech corpora, [aː] tokens were sampled and annotated for word class (content, function word). This was done for 50 male adult speakers of Standard Dutch in face-to-face speech (N = 3128 tokens), and another 50 male adult speakers in telephone speech (N = 3136 tokens). First, the effect of word class on various acoustic variables in spontaneous speech was tested. Results showed that [aː]'s were shorter and more centralized in function than content words. Next, tokens were used to assess their speaker-dependent information as a function of word class, by using acoustic-phonetic variables to (a) build speaker classification models and (b) compute the strength-of-evidence, a technique from forensic phonetics. Speaker-classification performance was somewhat better for content than function words, whereas forensic strength-of-evidence was comparable between the word classes. This seems explained by how these methods weigh between- and within-speaker variation. Because these two sources of variation co-varied in size with word class, acoustic word-class variation is not expected to affect the sampling of tokens in forensic speaker comparisons.
Cross-Granularity Learning for Multi-Domain Image-to-Image Translation

Image translation across diverse domains has attracted more and more attention. Existing multi-domain image-to-image translation algorithms only learn the features of the complete image without considering specific features of local instances. To ensure the important instance to be more realistically translated, we propose a cross-granularity learning model for multi-domain image-to-image translation. We provide detailed procedures to capture the features of instances during the learning process, and specifically learn the relationship between style of the global image and the style of an instance on the image through the enforcing of the cross-granularity consistency. In our design, we only need one generator to perform the instance-aware multi-domain image translation. Our extensive experiments on several multi-domain image-to-image translation datasets show that our proposed method can achieve superior performance compared with the state-of-the-art approaches.
From text to graph: a general transition-based AMR parsing using neural network

Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing, which tells the inner meaning of language form. Recently, Abstract Meaning Representation (AMR) is attracted by many researchers for its semantic representation ability on an entire sentence. However, due to the non-projectivity and reentrancy properties of AMR graphs, they lose some important semantic information in parsing from sentences. In this paper, we propose a general AMR parsing model which utilizes a two-stack-based transition algorithm for both Chinese and English datasets. It can incrementally parse sentences to AMR graphs in linear time. Experimental results demonstrate that it is superior in recovering reentrancy and handling arcs while is competitive with other transition-based neural network models on both English and Chinese datasets.
Diagnostic Value of Determination Blast Cell Population Lineage Using WPC Scattergram Hematology Analyzer

The diagnosis of hematology malignancies requires examination that includes morphology, immunophenotyping, and cytogenetics. Immunophenotyping is the most trusted examination in determining hematology malignancies lineage, but it is only available in large hospitals and the costs are relatively expensive, so the determination of lineage depends on bone marrow aspiration examination. Therefore it is necessary to have an easier and more reliable alternative to assist BMA morphology. White Precursor Cell (WPC) scattergram Sysmex XN-1000 has the capability to differentiate malignancy lineage. The purpose of this study was to determine the diagnostic value of determining lineage generated by WPC scattergram compared to the lineage from BMA examination. BMA blood samples were simultaneously examined by BMA morphology interpretation using microscope and WPC scattergram Sysmex XN-1000 examination. The hematology malignancies lineage resulting from BMA and WPC scattergram examination was then analyzed statistically to determine the suitability, sensitivity, and specificity. The results of determining the lineage of blast cell population based on WPC scattergram resulted in a suitability with a sensitivity of 93.75% and specificity of 94.74% for determining the hematological malignancy of myeloid lineage and 94.74% and 93.75% for lymphoid lineage, with a diagnostic accuracy of 94.91%. Based on this study it can be concluded that the WPC scattergram can determine the lineage of hematological malignancies with a suitability and high diagnostic value of lineage based on BMA morphology.