Reviewerly/exHarmony huggingface dataset:
Multi-layer Feature Fusion Network with Atrous Convolution for Pedestrian Detection

Abstract In this paper, we present a simple but effective framework K-AFPN that incorporates feature pyramid method for small-size pedestrian detection, fully utilizing the lower-layer detail features and higher-layer semantic features. The method not only enhances the robustness of the features, but also improves the discrimination of the feature maps, achieving competitive accuracy. In addition, atrous convolution is used to optimize the network for high-resolution feature maps, avoiding information loss caused by frequent down or up sampling. On top of the backbone network, K-means algorithm is used to obtain optimal initial anchor base sizes, which reduces computational costs and improves location accuracy. Hence, our method pays more concentration on pedestrians, especially those of relatively small size. Comprehensive experimental results on two classic pedestrian benchmarks illustrate the effectiveness of the proposed approach.
Checklist and Alert Language: Impact on ESL Pilot Performance in Airline Operations

Flight crewmembers utilize checklists during typical phases of flight, which may also encompass non-normal conditions. Written English language on checklists combined with crew alerting can be used by flight crewmembers to read and comprehend system related issues, and respond to system conditions on the flight deck. Design and integration of English language on checklists and alerting systems should provide information that can be utilized for flight decision-making purposes. English language can be challenging for English as-a-second language flight crewmembers. Review of literature suggests that ESL adults experience fundamental challenges with reading and interpreting written English language text corpora based on their background knowledge, English language proficiency, and contextual use of written English language in airline operations. This paper provides a survey of ESL flight crew performance issues when they use checklists and alerting systems during non-normal conditions. Survey results indicated that flight crewmember use of written English language checklists has an impact on their performance in airline operations. Design and integration of written English language on checklists and alerting systems were factors leading to ESL flight crewmember procedural divergence and misunderstandings. Flight crewmembers’ metacognitive strategy use, background knowledge, and their English language proficiency (reading comprehension level), were factors that impacted their performance and flight safety. Future studies should focus on ESL flight crewmember use of written English language on checklists and alerting systems and impact on flight crewmember performance in airline operations.
Studies on the Construction of Online Learning Adaptive Intervention Strategy Library

Online learning is the main implementation of distance education and adult education. A pertinence and timely online teaching intervention could determine the persistence and quality of online learning. However, the existing intervention mechanism is mainly determined by teachers and focus on offline intervention which cannot be compatible with the online learning system well. In this paper we propose a model of online intervention which based on the learning behavior features. After interveners selection, the adaptive intervention measures could be generated in the intervention strategy library and executed automated. We emphasis on the construction of the intervention strategy library which combines with historical intervention experiences in strategy forming and representation. Besides this, we take a decision-making algorithm to classify the strategies and make adaptive and accurate strategies to different kinds of interveners. Adaptive online intervention strategy library is the premise and basis of automated online intervention, which can reduce the gap between the large number of online learners and the limited number of teachers. It also can improve the awareness of learners and teachers, thus gain the better effect on online learning ultimately.
Experiencing Technology Independence

In this paper we present an embedded systems design flow, supporting variations in technology - hardware vs. software - at implementation time. The work builds on seasoned and new approaches, tools and techniques, such as software production lines, FPGA design, and high level synthesis. We define the necessary context for such a design flow to succeed, and introduce supporting tools and interfaces to enable the designer to take decisions, which are further automatically transferred into the synthesis phases. We exemplify our solutions on a motor controller design, considering several features that are potentially required to be implemented, and where all the elements are possible to be implemented either as hardware or software modules.
Extending Two-level Information Modeling to the Internet of Things

Interoperability is a major challenge for the Internet of Things (IoT). The real potential of the IoT lies in facilitating largescale sharing of high-quality context-rich information through systems-of-IoT-systems, rather than IoT systems that operate as isolated technology silos. Real large-scale interoperability requires layers of standards, and each layer addresses different interoperability challenges. The SensorThings API data model seeks to tackle data interoperability at the data and informational layers of IoT platforms. SensorThings API is aligned to the ISO/OGC O&M data standard, and like O&M it is semi-structured. Semi-structured models allow for variance within implementations for different use-cases, which is both necessary and detrimental to systems interoperability.In this paper we propose that the SensorThings API data model should be defined as a set of archetypes, used to capture extensible domain concepts using a two-level modeling IoT systems design approach. Extending two-level modeling to the IoT using the SensorThings API as a base for domain concepts definition allows for a powerful framework to manage variance within systems implementation and maintaining semantic interoperability within systems-of-IoT-systems across diverse use-cases.
ANALISIS KEBUTUHAN SINERGITAS PENELITIAN DAN PENGABDIAN PADA MASYARAKAT SERTA PENGEMBANGAN PEMBELAJARAN

There have been several challenges in facing the era of industrial revolution 4.0. namely Artificial Intelligence (AI), data and information (big data), cloud computing (cloud technology), internet, digital money (bitcoin), social media to data security are challenges that must be faced by college graduates today. Industry 4.0 holds a commitment to increase flexibility in manufacturing, in bulk, with better quality and productivity, resulting in rapid changes experienced by society, namely the rapid development of information technology that has a lot of impacts on human life both positive and negative. which is very strategic in the midst of its community, which must be able to provide enlightenment for the surrounding community, nation and state.
Self-learning Guide for Bioloid Humanoid Robot Assembly with Elements of Augmented Reality to Support Experiential Learning in Sauro Research Seeding

A self-learning guide for assembly robots BIOLOID PREMIUM in human form is presented, where elements of augmented reality are included in order to facilitate the process of assembly and manipulation of prototypes. This guide was evaluated with students belonging to the research seeding SAURO (research seeding in Automation and Robotics) of the Institución Universitaria Pascual Bravo. For the design of the guide was necessary the 3D model of the humanoid robot and the creation of augmented reality markers, all with the aim of promoting the interaction of students with technological objects and robotic platforms; for this the software Google sketchup and Build AR Pro were used. It was achieved that the process of assembling the BIOLOID PREMIUM humanoid robot at the time of execution decree. The students using the guide manifested who had fun learning how to put together the robot, who had flexibility in learning and could manage their own time. Likewise, the use of this guide facilitated the understanding of the parts of the robot and the function that each of them fulfills and even the students declared the control of the self-learning process. On the other hand, students externalized that their interpretation skills of 3D models and their spatial location got better with the use of the guide with elements of augmented reality.
The “Five W” of MS and EE

Before starting with the technical parts we would like to answer five simple questions regarding the problem of MS and EE:.
Learning compact graph representations via an encoder-decoder network

Feature representation learning for classification of multiple graphs is a problem with practical applications in many domains. For instance, in chemoinformatics, the learned feature representations of molecular graphs can be used to classify molecules which exhibit anti-cancer properties. In many previous work, including discriminative subgraph mining and graphlet-based approaches, a graph representation is derived by counting the occurrence of various graph sub-structures. However, these representations fail to capture the co-occurrence patterns that are inherently present among different sub-structures. Recently, various methods (e.g., DeepWalk, node2vec) have been proposed to learn representations for nodes in a graph. These methods use node co-occurrence to learn node embeddings. However, these methods fail to capture the co-occurrence relationship between more complex sub-structures in the graph since they were designed primarily for node representation learning. In this work, we study the problem of learning graph representations that can capture the structural and functional similarity of sub-structures (as evidenced by their co-occurrence relationship) in graphs. This is particularly useful when classifying graphs that have very few sub-structures in common. The proposed method uses an encoder-decoder model to predict the random walk sequence along neighboring regions (or sub-structures) in a graph given a random walk along a particular region. The method is unsupervised and can be used to obtain generic feature representations of graphs making it applicable with various types of graphs. We evaluate the learned representations using several real-world datasets on the binary graph classification task. The proposed model is able to achieve superior results against multiple state-of-the-art techniques.
Graph Learning for Spatiotemporal Dynamic Signal

We address the problem of learning hidden graph structure from spatiotemporal signals which are prevalent in distributed sensor networks. Based on a space-time representation model that takes into account correlated properties in dynamic evolution, we formulate the graph learning problem as a regularized multi-convex optimization problem. A correlation-aware and differential smoothness-based graph learning method (CADS) is proposed, which simultaneously estimates the time correlation of each vertex and refines the graph under the differential smoothness prior. The proposed method promotes such smoothness property in each learning step, leading to an improvement of learning accuracy. Experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed CADS which outperforms the state-of-the-art graph learning methods in classification and prediction tasks.
Ensemble Of Neural Networks For High Endothelial Venules Detection In Meca-79 Immunohistochemistry Images

The presence of high endothelial venules (HEVs) in the tumor micro-environment is associated with favorable clinical out-come in breast cancer and other human solid tumors. HEVs can be highlighted in tissue samples by MECA-79 immunohistochemistry. HEVs are heterogeneously scattered in the tumor micro-environment, preferentially in areas infiltrated by lymphocytes, and their density must be assessed at high resolution on whole slide in the tumor area. We present a frame-work for HEV detection through classification of MECA-79 immunostaining in whole slide images. A classifier is provided, as well as a whole slide image metric for general performance evaluation of detectors. An original ensemble strategy for convolutional neural networks has been developed and its relevance in MECA-79 and general immunostaining classification tasks is discussed.
Multimodal genetic programming using program similarity measurement and its application to wall-following problem

In this paper, we examine the effectiveness of multimodal genetic programming (MMGP) on the wall-following problem, which is a well-known benchmark problem of genetic programming (GP). MMGP aims to obtain multiple local optimal programs, including global optimal programs, that is, programs that achieve the same goal with different program structures. In this paper, we apply MMGP to the wall-following problem. The purpose of the wall-following problem is to find a program to control a robot having twelve distance sensors and four movements to follow irregular walls. We expect that there are several local optimal programs in the wall-following problem, which use different combinations of sensors. An experiment is conducted to investigate whether MMGP can get local optimal programs simultaneously for the wall-following problem. This experiment compares MMGP with a simple GP. The experimental results reveal that MMGP can achieve higher acquisition ratio of local optimal programs than the simple GP.
Chapter 6. A More In-depth Look at Electronic Laboratory Notebooks

As more and more scientific data is being created in digital formats, the need to be able to link together the data with the methods used to obtain it becomes ever more urgent. Digital data without the context provided by the notes we typically collect in our paper notebooks is meaningless. Electronic notebooks have the potential to revolutionise the way that scientists record their research. Not only can electronic notebooks enable us to create better links between the research activities recorded and the data they produce, but they are also easier to read, easier to search, enable sharing and collaboration, and can improve quality and efficiency in working practices. Despite the benefits, the majority of scientists are still relying on their trusty paper notebooks to capture the record of their research. In this chapter, we take a more in-depth look at electronic notebooks, and Electronic Laboratory Notebooks (ELNs) in particular. We take a look at the numerous benefits of electronic notebooks over paper notebooks and discuss a number of different ELNs to provide examples of differences and overlaps in their capabilities and target audience. We also look at some experiences of using an ELN from different disciplines, and how an ELN can facilitate and support scientific research in different ways. We also take a look at what some of the potential barriers are to the adoption of ELNs and why some scientists are reluctant to use them.
Urban Sensing: Towards Security Efficient Sensing Network Scheme

The recent advancements in information and communication technologies are tackling problems in cities that do strive for urbanization; from economy and transportation, to sustainable and luxury life style. The concept of smart cities is being widespread and several suggested sensing paradigms are currently adopted. Participatory sensing applications offer plenty advantages over the traditional networking sensors, however; its applicability is highly dependent on the voluntary participation of its owners which can be strictly bounded by security challenges. This paper highlights the security concerns in the context of urban sensing scheme and their direct implications on the embracement of such a paradigm. Moreover, it surveys suggested countermeasures to overcome the possible exposure of user's private information. The research's findings formulate the required knowledge upon which more robust and rigid urban sensing scheme can be visualized.
Using High Performance Computing in Vehicles to Create Image Datasets for Deep Learning

Modern vehicles are equipped with multiple sensors that help engineers to automate driving functions as much as possible. Among other types of sensors, these vehicles are equipped with multiple cameras that enable capturing the video data of the surrounding vehicles. These video data can be later used in training deep neural networks that are typically used within the vehicles to detect objects of interest around them. However, before we can use the video data for training, we must annotate them. Nowadays, the annotation process is typically semi-automated, where the initial annotations are added in a datacenter and then fine-tuned by human annotators. In this paper, we investigate the possibility to utilize high performance computing inside the vehicles in order to add initial annotations at the edge, instead of using the datacenter. With this approach, we expect a more scalable overall solution for the annotation process, because we utilize the computing power that is already available in the vehicles. For this investigation, we focus on heterogeneous automotive platforms of Intel® and analyze their potential for the annotation tasks at the automotive edge.
Parallel Framework for Evolutionary Black-box optimization with Application to Algebraic Cryptanalysis

In this study, a framework aimed at implementing pseudo-Boolean optimization algorithms is presented. In this framework, called EvoGuess objective functions are calculated using the Monte Carlo random sampling method. The framework is used to construct algebraic attacks on several cryptographic keystream generators. An original cryptanalysis problem is first reduced to the Boolean satisfiability problem (SAT). The obtained problem can be simplified by assigning guessed bits values from some set of variables. A problem of finding a guessed bits set, which corresponds to a SAT-based attack with relatively low runtime estimation, is studied. This problem is considered as an optimization problem of a special black-box fitness function on the Boolean hypercube. To solve this optimization problem we develop EvoGuess, in which several evolutionary strategies are implemented. Also, the framework allows to use an arbitrary CDCL-based SAT solver. In computational experiments, SAT-based guess-and-determine attacks on several well-known keystream generators are constructed.
Legal basis for the use of technical means of copyright protection in the USA

Introduction. The legal regulation of the copyright protection through technical means in the United States of America (hereinafter referred to as the USA) is by far the most developed. This is due to the fact that law enforcement in this area in the USA has been formed for over more than one century and over this time, a lot of experience has been accumulated. At the same time the USA, through the adoption of the Digital Millennium Millennium Copyright Act of 1998, were the first to implement in its legislation the WIPO Internet treaties that established the legal regulation of the use of technical protection at the international level. In this regard legal approaches to regulating the use of technical means of copyright protection are of particular interest both from the scientific point of view and from the point of view of law enforcement. The article is devoted to the study of the legal basis for the use of technical means of copyright protection in the USA. Materials and methods. The methodological ground of the research include the following general scientific and special methods of cognition of legal phenomena and processes: dialectical, formal-legal, comparative-legal, formal-logical, structural-functional. Results. The authors found that US law regulates not only the ban on circumvention of technical means of copyright protection, but also establishes exemptions from this ban, which is called «the doctrine of free use of works”, which details the cases when a person has the right on legal grounds to get access to a certain work. The US legislation also defines the criteria according to which the technical means of copyright protection is recognized as such. Two conditions must be met in this regard: effective control over access to the protected work and effective protection of copyright. Analysis of the US current legislation allows us to come to a conclusion about sufficiently effective measures of civil-law protection and the development of the institution of civil liability, which is for offenses related to circumvention of technical means of protection. Discussion and Conclusions. The «doctrine of free use of works” has been esplored, and cases of its application have been determined. The approaches of the legislator in the USA to the definition of technical means of copyright protection are analyzed. The mechanism and conditions for applying civil liability in the case of establishing the circumvention of technical means of copyright protection are investigated.
A Multi-level Polygonal Approximation-Based Shape Encoding Framework for Automated Shape Retrieval

Due to exponential growth of image data, textual annotation of every image object has failed to provide practical and efficient solution to the problem of image mining. Computer vision-based methods for image retrieval have been drawing significant attention with the progress in achieving fast computational execution power. One of the fundamental characteristics of an image object is its shape which plays a vital role to recognize the object at a primitive level. We have studied the scope of a shape descriptive framework based on multi-level polygonal approximations for generating features of the shape. Such a framework explores the contour of an object at multiple approximation stages and captures shape features of varying significance at each approximation stage. The proposed algorithm determines polygonal approximations of a shape starting from coarse-level to more refined-level representation by varying number of polygon sides. We have presented a shape encoding scheme based on multi-level polygonal approximation which allows us to use the popular distance metrics to compute shape dissimilarity score between two objects. The proposed framework when deployed for similar shape-retrieval task demonstrates fairly good performance in comparison with other popular shape-retrieval algorithms.
Data Reduction in MMBD Computing

Internet of Things technology is emerging very quickly in human facilities of all types, such as smart home and industry, which leads to a large boom in multimedia big data due to the connection of approximately 50 billion devices to the internet in 2020. It is really a challenging task to manage the IoT multimedia data regarding storage and transmission. The only way to handle this complicated storage and transmission problem is the process of compression techniques. Multimedia data is compressed by reducing its redundancy. Compression algorithms face numerous difficulties because of the large size, high streaming rate, and the high quality of the data, due to their different types and modality of acquisition. This chapter provides an overarching view of data compression challenges related to big data and IoT environment. In this chapter, we provide an overview of the various data compression techniques employed for multimedia big data computing, such as run-length coding, Huffman coding, arithmetic coding, delta modulation, discrete cosine transform, fast Fourier transform, joint photograph expert group, moving picture expert group, and H.261, including the essential theory, the taxonomy, necessary algorithmic details, mathematical foundations, and their relative benefits and disadvantages.