Reviewerly/exHarmony huggingface dataset:
Video Crowd Counting via Dynamic Temporal Modeling

Crowd counting aims to count the number of instantaneous people in a crowded space, which plays an increasingly important role in the field of public safety. More and more researchers have already proposed many promising solutions to the crowd counting task on the image. With the continuous extension of the application of crowd counting, how to apply the technique to video content has become an urgent problem. At present, although researchers have collected and labeled some video clips, less attention has been drawn to the spatiotemporal characteristics of videos. In order to solve this problem, this paper proposes a novel framework based on dynamic temporal modeling of the relationship between video frames. We model the relationship between adjacent features by constructing a set of dilated residual blocks for crowd counting task, with each phase having an expanded set of time convolutions to generate an initial prediction which is then improved by the next prediction. We extract features from the density map as we find the adjacent density maps share more similar information than original video frames. We also propose a smaller basic network structure to balance the computational cost with a good feature representation. We conduct experiments using the proposed framework on five crowd counting datasets and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.
Model-based approach for agile requirements engineering using SysML and Papyrus

Abstract This paper presents a model for the application of agile requirements engineering. The literature review made it possible to identify the requirements elicitation process based on standards such as capability maturity model integration, software engineering body of Knowledge and how their practices can complement those of agile methodologies, to reduce risk factors and guarantee the success of software projects. This process gives importance to the continuous communication with stakeholders in an iterative process, which is maintained throughout the project and begins with the identification of the problem domain, and then gradually refining characteristics and objectives in user stories and it ends with the development of the product backlog, which will serve as input to the application agile development process. The results propose a model, indicating the different stages for requirements engineering using the agile systems modeling language and the Papyrus tool.
On Tradeoffs Between Document Signature Methods for a Legal Due Diligence Corpus

While document signatures are a well established tool in IR, they have primarily been investigated in the context of web documents. Legal due diligence documents, by their nature, have more similar structure and language than we may expect out of standard web collections. Moreover, many due diligence systems strive to facilitate real-time interactions and so time from document ingestion to availability should be minimal. Such constraints further limit the possible solution space when identifying near duplicate documents. We present an examination of the tradeoffs that document signature methods face in the due diligence domain. In particular, we quantify the trade-off between signature length, time to compute, number of hash collisions, and number of nearest neighbours for a 90,000 document due diligence corpus.
Developing forensic authorship profiling

Current research into the task of determining the characteristics of an anonymous writer, authorship proling, does not meet the demands of the forensic context, because of the lack of transparency of certain computational techniques, their requirements for large data sets, and, most importantly, since the strength of register variation does not guarantee that findings obtained in other registers will apply to forensic registers such as, for example, a threatening letter. The present article demonstrates how previously established findings related to stylistic variation in English for gender, age, and social class also apply to the kinds of texts often analysed by forensic linguists through an experiment involving 96 participants. These results constitute an example of linguistically-motivated profiling research and it is argued that the agenda to move from authorship profiling to forensic authorship profiling should be led by previously established knowledge of language variation.
SONGS FROM YOUTUBE AS AN ALTERNATIVE MEDIA INNOVATION  FOR TEACHING AND LEARNING ENGLISH

Since English is not our language, some EFL students often face difficulties in learning it. Therefore, the teachers try to create or adapt several techniques and try to find an alternative media for teaching English that can be used to solve the problem. Anyway, songs from YouTube are one of the alternative media in learning English. Through songs you can make the boredom away, can also have some fun while you are studying. Qualitative method was employed in this research. The data needed in this research were collected by using open-ended questionnaire and interview related to the songs from YouTube as a media innovation in learning English. The method used in this research is random sampling and chosen them as the representative of English Department of Lambung Mangkurat University academic year 2015 to 2017 which 40 students had involved during the research. The data analyzed based on the questionnaire and interview, all of the students mentioned that they love studying English through songs with a variation, not only seriously focused on grammar. They learnt many new words either the meaning or the way of pronouncing the words in the songs.  From the selected songs on YouTube application, they just realized that songs can be used to learn grammar as well. In addition, the importance of through knowledge of English Language is becoming more and more obvious in modern world in which communication increasingly takes place in English. Keywords: media, songs, youtube, learning, English
A Multivariate Skew-Normal Mean-Variance Mixture Distribution and Its Application to Environmental Data with Outlying Observations

The presence of outliers, skewness, kurtosis, and dependency are well-known challenges while fitting distributions to many data sets.Developing multivariate distributions that can properly accomodate all these aspects has been the aim of several researchers.In this regard, we introduce here a new multivariate skew-normal mean-variance mixture based on Birnbaum-Saunders distribution.The resulting model is a good alternative to some skewed distributions, especially the skew-t model.The proposed model is quite flexible in terms of tail behavior and skewness, and also displays good performance in the presence of outliers.For the determination of maximum likelihood estimates, a computationally efficient Expectation-Conditional-Maximization (ECM) algorithm is developed.The performance of the proposed estimation methodology is illustrated through Monte Carlo simulation studies as well as with some real life examples.
Dense Surface Models from Airborne and Spaceborne (Multi-)Stereo Images

Digital surface models representing the heights of an area can be derived from two or more (multi-)stereo images of airborne or spaceborne sensors. A satellite stereo image of a current very-high-resolution satellite like WorldView or GeoEye with ground pixel sizes of about half a meter allows the derivation of surface models in the range of the same resolution. Such surface models are the basis of many applications like the three-dimensional representation of the area, 3D change detection, calculation of volumes, detection of sight lines, or water flow and flooding. Satellite imagery covers large areas of about 400 square kilometers with ground resolution of about 1 meter, while airborne images from planes or drones usually cover only small areas but with higher resolution. In this chapter the basics of digital surface models are shown, and the actually best method for deriving dense digital surface models from airborne and spaceborne images is described. Some examples finally show the possible results.
Clustering and stranding in Dutch

Abstract This paper has both a descriptive and a theoretical aim. The descriptive one is to demonstrate that the phenomenon of clustering is not limited to verbs, but that it also affects adpositions. The theoretical one is to develop a formal analysis that captures the common properties of verb clustering and adposition clustering. For that purpose we employ the framework of Head-driven Phrase Structure Grammar. Both the descriptive and the theoretical part are backed up with quantitative data about the use of adposition clusters in a sample consisting of one million words of spoken Dutch and one million words of written Dutch.
A Priori Membership for Data Representation: Case Study of SPECT Heart Data Set

The image is important source for analytics. However, global reductions and local features are hard to solve. This paper proposes an innovative membership for data representation thus providing an easier and simpler way for both. Technically, it adopts relevant preference for inference. In this paper, its operations include reducing variables and identifying sparse features by taking advantage of evidence. In illustration, an image study of proton emission in UCI SPECT is presented. It discloses key variables of abnormal samples buried in normal range. The contribution of this paper lies in providing priori data to enhance representation learning.
A Survey of Group Intelligence Optimization Algorithms

The group intelligent optimization algorithm provides some new ideas for solving many practical problems. These algorithms have stronger robustness and stronger search ability, and are easy to implement in parallel. It is easy to combine with other algorithms to improve the performance of the algorithm and solve complex practical problems. The effect of the experiment is more obvious. In this paper, the improvement and application of particle swarm optimization algorithm, fireworks algorithm and artificial bee colony algorithm in intelligent algorithm are reviewed, and the advantages and disadvantages are analyzed. The future development of intelligent algorithm is prospected.
Use of new technologies in physics studying

Modern education is impossible to imagine without information and communication technologies, which are developing at an enormous pace. Currently, the perception of students can be improved through the use of new technologies. The results of the development of the software application "Electronic laboratory (E-lab)" are presented. The lab is implemented as an application with a set of practical tasks, laboratory works, animations and theoretical tasks to study physics using augmented and virtual reality technologies. Unity 3D cross-platform environment was chosen as the development platform. The main functionality was written in C#. Graphic models were created using Substance Painter. The article describes the development process of the presented application, its functionality, as well as the user interface.
Introduction to the Special Issue on Cryptographic Engineering for Internet of Things: Security Foundations, Lightweight Solutions, and Attacks

editorial Free Access Share on Introduction to the Special Issue on Cryptographic Engineering for Internet of Things: Security Foundations, Lightweight Solutions, and Attacks Editors: Lejla Batina Radboud University Radboud UniversityView Profile , Sherman S. M. Chow Chinese University of Hong Kong Chinese University of Hong KongView Profile , Gerhard Hancke City University of Hong Kong City University of Hong KongView Profile , Zhe Liu University of Luxembourg 8 Nanjing University of Aeronautics and Astronautics University of Luxembourg 8 Nanjing University of Aeronautics and AstronauticsView Profile Authors Info & Claims ACM Transactions on Embedded Computing SystemsVolume 18Issue 3May 2019 Article No.: 22pp 1–3https://doi.org/10.1145/3322641Published:05 June 2019Publication History 0citation315DownloadsMetricsTotal Citations0Total Downloads315Last 12 Months68Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Bridging Scientific Visualization and Unreal VR

This chapter presents a practical solution integrating consumer virtual reality (VR) hardware into a scientific visualization pipeline. It shows the benefits of such hardware, but especially the challenges of using it with existing scientific visualization tools. The ParaView-Unreal bridge demonstrates an automatic process to send arbitrary geometry to an Unreal VR scene from within ParaView. In the future, it would be beneficial for the area of scientific visualization if any arbitrary scientific visualization tool or pipeline could send its geometry over to an Unreal VR scene. The chapter also shows how the Unreal VR plugin is able to maintain a 90 Hz rendering loop while transmitting geometry data from ParaView and updating graphics resources within Unreal. A fundamental goal of the bridge is for the user to be able to efficiently perform certain actions in the VR environment that they are familiar with in ParaView as well.
A Prototype System for Saving and Representing Personal Moments

This study is the first stage of the ongoing research called Moments which is divided into three stages: Reproduction, Enhancement and Evolution. The first stage Reproduction is designed to create a way for user to save a special moment and to experience that moment in a user-friendly way in the future. Firstly, a multi-sensor equipment is designed to collect environment data in user’s daily life. Secondly, the data integration system can collect relevant information based on user data from open data platform. At last, an immersive virtual reality environment is provided for user to explore his/her past experiences.
Human Rights Weekend: Artificial Intelligence, Big Data &amp; Human Rights: Progress or Setback?

Amsterdam Law Forum (ALF) is the student-run 'International Law Journal' of VU University. Every year ALF publishes a winter, spring, and summer issue. The journal consists of three sections; scientific articles, opinion articles, and commentaries. As of this year, ALF also creates a section for inaugural speeches. In addition, ALF hosts a conference in spring with a relevant legal theme, where renowned speakers are invited to share their perspectives. Overall, ALF is a topical journal that provides a platform for established scholars and young academics to share knowledge, opinions and experiences and to make contributions to the international law discourse. Staff, PhD students and master students who have written a very good thesis are invited to submit an article to ALF. What is learned in the cradle is carried to the tomb: we are looking forward to sharing your articles on our website!
Digital Audio Watermarking by Amplitude Embedding System

This paper proposed an amplitude embedding system for digital audio watermarking in the wavelet domain. In this paper, the relationship between the watermarked coefficients and the original coefficients is first presented and then amplitude-based embedding rules are combined into an amplitude-based embedding system that incorporates a system state. For the embedding in this system, the information includes synchronization codes and the watermarks are embedded into the DWT lowest frequency sub-band coefficients which can be described by a matrix form. Moreover, the SNR is rewritten as a performance index which is a quadratic form. Then, a nonlinear function connecting the performance index and the embedding system is derived. Accordingly, an optimization-based embedding system is proposed. Finally, the Karush–Kuhn–Tucker (KKT) Theorem is used to obtain the optimal solution, which is an important formula for watermarking. In addition, the hidden information can be extracted without knowledge of the original audio. In the experiments, the performance of the proposed system is tested and the results verify the high SNR and strong robustness against multimedia signal processing or malicious attacks.
Application of technology of frequency-resonant processing of satellite images and photographs on area of hydrogen production and hydrogen degasation of the Earth

Summary The results of experimental studies carried out with using the technology of frequency-resonance processing and interpretation of Earth remote sensing data (satellite images) and photographs in order to study the possibility of using it for hydrogen accumulations searching and locating are analyzed. The experiments were carried out at the site of hydrogen production in Mali, at the site of exploratory well drilling in the USA, and also at the sites of visible hydrogen degassing in various regions of the world. When processing an image of a hydrogen production site in Mali, signals were recorded at the resonant frequencies of hydrogen and deuterium. By scanning the cross-section near one of the wells, the responses at the hydrogen frequencies were recorded in the following depth intervals: 1) 25–245 m; 2) 635–750 m; 3) 950–970 m. The results of studies at the well drilling site in the United States allow us to conclude that there are no hydrogen accumulations in commercial volumes within it. However, this area is promising for the search for oil, gas and gas condensate. At the surveyed areas of visible hydrogen degassing in the USA, Russia, Turkey, Ukraine, and Oman almost always recorded responses at the resonant frequencies of hydrogen and deuterium. Within these areas, vertical channels of deep fluids, mineral matter, and chemical elements migration were found. In many parts of the survey areas, responses were also recorded at the resonant frequencies of oil, gas and condensate. The results of experimental studies of a demonstration character allow us to state that the mobile technology of frequency-resonance processing and decoding of remote sensing data (satellite images) and photographs can be successfully used for the operative detection and mapping of possible accumulations (large) of hydrogen in areas (sites) of intensive hydrogen degassing.
Evolutionarily-tuned support vector machines

Support vector machine (SVM) classifiers can cope with many different classification tasks but improperly selected hyperparameters may deteriorate their performance. Moreover, datasets are getting bigger in terms of their size and the number of features. This is often coupled with low training data quality and presence of redundant features, which can adversely affect classification accuracy and time performance. Furthermore, high memory and computational complexity of SVM training can be a limiting factor for its application over huge datasets. We address these issues with evolutionarily-tuned SVM, where we utilize evolutionary algorithms for optimizing hyperparameters, along with selecting features and training instances. The performance of our method is compared on several benchmark datasets to other methods for optimizing SVMs, as well as to other classifiers. The results show that our algorithm gives high performance in both accuracy and classification time when compared with the state-of-the-art methods for SVM optimization.
Cost-Driven Cloud Service Recommendation for Building E-Commerce Websites

With the evolution of software engineering technology, using cloud services to replace self-built information systems has been proven an economical and reliable way. However, how to help e-commerce service system builders to choose suitable compositions of cloud services that meet their needs is still a challenge. In the past decade, a number of academic studies have explored the selection strategies and algorithms for cloud services, however, most efforts are not able to consider multiple types of cloud services simultaneously to provide composite cloud service solutions. To address the above issue, this study proposes a cost-driven recommendation method, called ECSSR (E-Commerce Service Suite Recommendation). ECSSR takes the user budget as the core factor and simultaneously considers the user’s preferences for the service types. A prototype system, referred to as ECClouder, is also designed and implemented to realize the features of ECSSR. ECClouder is able to collect the user’s requirements, convert application-level requirements into infrastructure-level requirements, and produce appropriate cloud service solutions. The case study show that ECClouder can effectively help users to find cloud service solutions that are reasonably priced and meet their needs.
Regulating LIBRA: The Transformative Potential of Facebook’s Cryptocurrency and Possible Regulatory Responses

Libra is the first private cryptocurrency with the potential to change the worldwide payment and monetary system landscape. Due to the scale and reach provided by its affiliation with Facebook, the question will be not whether, but how, to regulate it. This short paper introduces the Libra project and analyses the potential responses open to regulators worldwide.
Emo-View: Convey the Emotion of the Back-Seat Passenger with an Emoji in Rear-View Mirror to the Driver

With the development of the self-driving technology, more diverse in-car interaction design has become an essential tendency for the future. In order to enhance the interaction experience between driver and back-seat passenger, we propose the concept of “AEIC” (Augmented Emoji in Car). By modifying the existing in-car equipment with information augment, we can offer multi-modal ways of interaction inside the car for the driver and back-seat passenger, so that they can have a better mutual understanding of each other under the premise of safe drive regulations. By optimizing the current central rear-view mirror, the mood of back-seat passenger can be detected and judged by means of facial recognition. Though the sound, light and Emoji, the passenger’s current emotional state will be fed back to the driver. In order to test the designed scheme, we constructed a vehicle driving simulator experimental platform with highly free scalability to obtain interactive data to conduct the user research. We invited 20 participants to participate in our prototype test, by observing them simulating the driving scenes we designed in the platform, their interactive feedback information can be precisely collected. Then we conducted depth interviews with the users about the earlier experience of interactions in the simulated scenario to obtain the reference information for further design iterations. On the basis of these results we concluded that: a. With the conversion of light source color and the intervention of Emoji (symbolic expression), the driver can have a better understanding of the back-seat passenger’s emotional state. b. The information interface, which is designed based on the attention span and short-term memory, can optimizing information output in the car without increasing the cognitive load. However, we found that there are significant differences in interactive feedbacks of different user combinations in scenario simulation. Accordingly, we should design according to different users. At the same time, we believe that the experiments based on the vehicle driving simulator platform is conducive to the preliminary basic research on in-car interaction. By amplifying interaction feedback in the driving scenes, the designers can identify the potential design needs more conveniently. Therefore, we will continue to use the platform to conduct the simulation experiments on the design prototype in the follow-up research to gradually address the gap in the interaction between the front-seat driver and back-seat passenger.