Reviewerly/exHarmony huggingface dataset:
Pose-aware Multi-level Feature Network for Human Object Interaction Detection

Reasoning human object interactions is a core problem in human-centric scene understanding and detecting such relations poses a unique challenge to vision systems due to large variations in human-object configurations, multiple co-occurring relation instances and subtle visual difference between relation categories. To address those challenges, we propose a multi-level relation detection strategy that utilizes human pose cues to capture global spatial configurations of relations and as an attention mechanism to dynamically zoom into relevant regions at human part level. Specifically, we develop a multi-branch deep network to learn a pose-augmented relation representation at three semantic levels, incorporating interaction context, object features and detailed semantic part cues. As a result, our approach is capable of generating robust predictions on fine-grained human object interactions with interpretable outputs. Extensive experimental evaluations on public benchmarks show that our model outperforms prior methods by a considerable margin, demonstrating its efficacy in handling complex scenes.
A Multi-stage Approach to Facilitate Interaction with Intelligent Environments via Natural Language

Due to the proliferation of Internet of Things (IoT) devices and the emergence of the Ambient Intelligence (AmI) paradigm, the need to facilitate the interaction between the user and the services that are integrated in Intelligent Environments has surfaced. As a result, Conversational Agents are increasingly used in this context, in order to achieve a natural, intuitive and seamless interaction between the user and the system. However, in spite of the continuous progress and advancements in the area of Conversational Agents, there are still some considerable limitations in current approaches. The system proposed in this paper addresses some of the main drawbacks by: (a) automatically integrating new services based on their formal specification, (b) incorporating error handling via follow-up questions, and (c) processing multiple user intents through the segmentation of the input. The paper describes the main components of the system, as well as the technologies that they utilize. Additionally, it analyses the pipeline process of the user input, which results in the generation of a response and the invocation of the appropriate intelligent services.
Pengembangan Media Pembelajaran Berbasis Aplikasi Lectora Inspire Pada Mata Pelajaran IPS Kelas V Di SDN 197 Sapolohe Kecamatan Bontobahari Kabupaten Bulukumba

This study studies on the development of media-based learning applications Lectora inspire on class V IPS courses at SDN 197 Sapolohe, Bontobahari Sub-district, Bulukumba district. The purpose of the research is to describe (1) The initial overview of the media needs of the application-based Lectora inspire in IPS class V subjects; (2) Overview of the results of the development of media-based learning application Lectora inspire on the subject of IPS class V, and (3) the level of media feasibility learning application-based Lectora inspire on the subjects of IPS class V at SDN 197 Sapolohe subdistrict Bontobahari, Bulukumba regency. This research approach is research and development, with the Borg &amp; Gall development model. The process of development of media-based learning application Lectora Inspire on IPS subjects through 8 stages, namely research introduction and data collection, planning, product development, validity test (expert test), revision I, test small group, Revision II, and final product. Data collection techniques using poll instruments. Analysis of the data used is quantitative descriptive data analysis. The level of media feasibility learning application Lectora Inspire on IPS subjects based on a material expert assessment of 4.35, a media expert of 3.8, a teaching practitioner (teacher) of 4.55 and a student of 4.54 that gained an average A score of 4.31 with a very good category that shows the results that the learning media is worthy to be used in the learning process for students of class V SDN 197 Sapolohe, Bontobahari Sub-district, Bulukumba Regency.
Sliding window property testing for regular languages

We study the problem of recognizing regular languages in a variant of the streaming model of computation, called the sliding window model. In this model, we are given a size of the sliding window $n$ and a stream of symbols. At each time instant, we must decide whether the suffix of length $n$ of the current stream ("the active window") belongs to a given regular language. Recent works showed that the space complexity of an optimal deterministic sliding window algorithm for this problem is either constant, logarithmic or linear in the window size $n$ and provided natural language theoretic characterizations of the space complexity classes. Subsequently, those results were extended to randomized algorithms to show that any such algorithm admits either constant, double logarithmic, logarithmic or linear space complexity. In this work, we make an important step forward and combine the sliding window model with the property testing setting, which results in ultra-efficient algorithms for all regular languages. Informally, a sliding window property tester must accept the active window if it belongs to the language and reject it if it is far from the language. We consider deterministic and randomized sliding window property testers with one-sided and two-sided errors. In particular, we show that for any regular language, there is a deterministic sliding window property tester that uses logarithmic space and a randomized sliding window property tester with two-sided error that uses constant space.
Pedagogical Approach to Developing the Hiring Practices of Higher Education Administrators

Most student affairs professionals will serve in a managerial and/or supervisory role at some point in their careers, yet we found only 11% of higher education graduate preparatory programs have required coursework focusing on this competency area.This situation is disconcerting, as there seems to be an assumption within the student affairs field that new professionals have the formal training and experiences needed to immediately be placed into these managerial and/or supervisory roles.In an effort to address this discrepancy, and in particular staffing practices, one higher education graduate preparatory program developed a course on staffing practices using an innovative pedagogical approach rooted in the theoretical framework of experiential learning.This approach enabled future student affairs professionals to apply theory-to-practice and develop the supervision skills they will need
Editable AI: Mixed Human-AI Authoring of Code Patterns

Developers authoring HTML documents define elements following patterns which establish and reflect the visual structure of a document, such as making all images in a footer the same height by applying a class to each. To surface these patterns to developers and support developers in authoring consistent with these patterns, we propose a mixed human-AI technique for creating code patterns. Patterns are first learned from individual HTML documents through a decision tree, generating a representation which developers may view and edit. Code patterns are used to offer developers autocomplete suggestions, list examples, and flag violations. To evaluate our technique, we conducted a user study in which 24 participants wrote, edited, and corrected HTML documents. We found that our technique enabled developers to edit and correct documents more quickly and create, edit, and correct documents more successfully.
PENGAPLIKASIAN CERTAINTY FACTOR PADA SISTEM PAKAR UNTUK MENDIAGNOSA PENYAKIT CAMPAK RUBELLA

Rubella measles is a disease that can infect to the pregnant mothers, toddlers, children, and adults. The purpose of this study is applicate the certainty factor method to the expert system application based on website to diagnose the rubella measles disease. The utilization of information technology into the expert system application can help society to detect the rubella measles disease early. Certainty factor is one method that can be used to handle a problem that is uncertain the answer. In this study, the certainty factor method has been applied on expert system application to diagnose rubella measles based on symptoms of the disease felt by patients. The result of this study is an expert system based on the website can provide information and diagnose symptoms of rubella measles following the questions proposed by the system and symptoms of the disease felt by the patient.
Implementation of a Rapidly Executing Robot Controller

This article describes the implementation of a high level and rapidly executing robot controller. It can be used to emulate the trajectory a robot will follow, given a set of robot motion commands. A geometric path parameterisation is used, which is tuned to match observed controller behaviour. It is also shown how this path is time stamped, while fulfilling velocity and acceleration constraints to produce a robot trajectory. The behaviour of the controller can emulate the behaviour of official controllers from robot vendors by parameter identification. The trajectory has been compared to an ABB 6640 robot, using an ABB virtual controller environment, and to sampled robot trajectory experiments of the KUICA robots KR 30-3 and IIWA. Good agreement with the geometric trajectories of the ABB robot and the IIWA robot has been achieved. For the KR 30-3 robot the proposed method does not fully capture the observed controller behaviour, but the resulting deviations are still within fractions of a degree for the test trajectories used.
Review: Image Encryption Techniques based on Chaotic Map

Due to the tremendous developments in communication networks especially in the Internet network, which used to exchange many types of data by many people. Data security has become a major concern. Therefore, there is an increasing interest in the use of encryption and decryption methods. Many encryption methods have been developed to maintain data security, one of these methods that commonly used in recent are chaotic encryption systems. Where many methods were suggested to encrypt images using a chaotic map because their features such as sensitivity to initial conditions and unpredictability of random behaviors etc... In this paper, we attempt to review and describe many methods used to encrypt images and make a comparison among these methods.
Camera calibration method in specific bands for the near-infrared dynamic navigator

The positional accuracy of a near-infrared (NIR) dynamic navigator is remarkably affected by two factors. One is the calibration accuracy of the navigator’s two NIR cameras, and the other is the accuracy of feature point extraction. The current lack of accurate calibration devices for NIR cameras limits further application. Therefore, in this study, an NIR camera calibration device was designed by placing the NIR light source and heat dissipation system at the back of a Halcon transparent glass template. Usage of camera calibration in a specific band and the gray centroid method based on elliptic boundary to extract feature points can further improve the accuracy of vision system calibration and measurement. Repeated tests and verifications showed that the reconstruction accuracy (&lt;0.1 pixels) of the binocular vision system calibrated by the NIR calibration device in a specific band was better than that calibrated by traditional methods.
Generic Encodings of Constructor Rewriting Systems

Rewriting is a formalism widely used in computer science and mathematical logic. The classical formalism has been extended, in the context of functional languages, with an order over the rules and, in the context of rewrite based languages, with the negation over patterns. We propose in this paper a concise and clear algorithm computing the difference over patterns which can be used to define generic encodings of constructor term rewriting systems with negation and order into classical term rewriting systems. As a direct consequence, established methods used for term rewriting systems can be applied to analyze properties of the extended systems. The approach can also be seen as a generic compiler which targets any language providing basic pattern matching primitives. The formalism provides also a new method for deciding if a set of patterns subsumes a given pattern and thus, for checking the presence of useless patterns or the completeness of a set of patterns.
Conditional Hardness of Earth Mover Distance

The Earth Mover Distance (EMD) between two sets of points $A, B \subseteq \mathbb{R}^d$ with $|A| = |B|$ is the minimum total Euclidean distance of any perfect matching between $A$ and $B$. One of its generalizations is asymmetric EMD, which is the minimum total Euclidean distance of any matching of size $|A|$ between sets of points $A,B \subseteq \mathbb{R}^d$ with $|A| \leq |B|$. The problems of computing EMD and asymmetric EMD are well-studied and have many applications in computer science, some of which also ask for the EMD-optimal matching itself. Unfortunately, all known algorithms require at least quadratic time to compute EMD exactly. Approximation algorithms with nearly linear time complexity in $n$ are known (even for finding approximately optimal matchings), but suffer from exponential dependence on the dimension. In this paper we show that significant improvements in exact and approximate algorithms for EMD would contradict conjectures in fine-grained complexity. In particular, we prove the following results: (1) Under the Orthogonal Vectors Conjecture, there is some $c&gt;0$ such that EMD in $Ω(c^{\log^* n})$ dimensions cannot be computed in truly subquadratic time. (2) Under the Hitting Set Conjecture, for every $δ&gt;0$, no truly subquadratic time algorithm can find a $(1 + 1/n^δ)$-approximate EMD matching in $ω(\log n)$ dimensions. (3) Under the Hitting Set Conjecture, for every $η= 1/ω(\log n)$, no truly subquadratic time algorithm can find a $(1 + η)$-approximate asymmetric EMD matching in $ω(\log n)$ dimensions.
Application of distributed ledger technology as one of the digital integration mechanisms of supply chains

The article has been devoted to the system analysis of supply chains digital integration. The main goal is to study the practical significance of the blockchain in terms of digital integration of supply chains, to identify areas for further development. World experience of implementing blockchain projects in supply chains has been analyzed, trends in the use of digitalization technologies in logistics have been identified, components of digital supply chain integration have been considered. The general scheme of work with information is the collection of relevant data from news messages, evaluation of requests on the Internet by statistics analysis tools “Yandex. Choice of words” and “Google Trends”, analysis of legal and law enforcement documents using the search and analytics capabilities reference-legal systems, study of intellectual property through the service “Yandex. Patents». Systematization of the data was carried out by expert methods by the specialists of the Logistics Department from the State University of Management. The phenomenon of the popularity of the term “blockchain” has been examined, a plenty of papers, news articles, legal acts, devoted to the blockchain (distributed registry) and related with it technologies has been studied. The shortcomings of the blockchain, complicating the practical commercial application of technology in logistics and the economy as a whole, has been systematized; domestic legal support, regulating the issues of blockchain, cryptocurrency and related concepts has been analyzed; innovations (technologies, inventions, platforms and systems), related to the digitalization of the economy have been classified, and the components of digital integration of supply chains and logistics business processes have been formulated. The results of this study can be used to consolidate information about the opportunities and problems of blockchain in connection with related technologies, as well as in making decisions about the application of blockchain in logistics in order to integrate modern supply chains.
The Effect of Emotional Intelligence and Self Effycacy Towards Students Achievement

&lt;pre&gt;This research aims at revealing the effect of emotional intelligence and self-efficacy towards learning achievement of the 8th grade students in SMP Islam Terpadu Ar-Rahman south of Jakarta. This type of research is a survey method. Data instruments are emotional intelligence questionnaires and self-efficacy questionnaires, the mathematics learning achievement data from the daily test scores of students through the description questions. The data analysis technique used is multiple regression analysis. The results of the regression analysis of significance between emotional intelligence on mathematics learning achievement showed the results as much as 0,551 (0,551 &amp;gt; 0,05), self-efficacy towards mathematics learning achievement as much as 0.333 (0.333&amp;gt; 0.05). The significance of variance analysis was obtained at 0.350 (0.350&amp;gt; 0.05). The results of this data analysis show that emotional intelligence and self-efficacy together no meaningful affect the learning achievement of mathematics. The implication of this study is that students who have high and moderate emotional intelligence and self-efficacy do not have a significant effect on improving learning achievement, thus emotional intelligence and self-efficacy are not the main factors that influence student learning achievement.&lt;/pre&gt;
End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning

In this work, a novel, end-to-end motion planning method is proposed for quadrotor navigation in cluttered environments. The proposed method circumvents the explicit sensing-reconstructing-planning in contrast to conventional navigation algorithms. It uses raw depth images obtained from a front-facing camera and directly generates local motion plans in the form of smooth motion primitives that move a quadrotor to a goal by avoiding obstacles. Promising training and testing results are presented in both AirSim simulations and real flights with DJI F330 Quadrotor equipped with Intel RealSense D435. The proposed system in action can be found in https://youtu.be/pYvKhc8wrTM.
Semantic Web Technologies for Data Curation and Provenance

The Reproducibility issue even if not a crisis, is still a major problem in the world of science and engineering. Within metrology, making measurements at the limits that science allows for, inevitably, factors not originally considered relevant can be very relevant. Who did the measurement? How exactly did they do it? Was a mistake made? Was the equipment working correctly? All these factors can influence the outputs from a measurement process. In this work we investigate the use of Semantic Web technologies as a strategic basis on which to capture provenance meta-data and the data curation processes that will lead to a better understanding of issues affecting reproducibility.
Disentangling Speech and Non-Speech Components for Building Robust Acoustic Models from Found Data.

In order to build language technologies for majority of the languages, it is important to leverage the resources available in public domain on the internet - commonly referred to as `Found Data'. However, such data is characterized by the presence of non-standard, non-trivial variations. For instance, speech resources found on the internet have non-speech content, such as music. Therefore, speech recognition and speech synthesis models need to be robust to such variations. In this work, we present an analysis to show that it is important to disentangle the latent causal factors of variation in the original data to accomplish these tasks. Based on this, we present approaches to disentangle such variations from the data using Latent Stochastic Models. Specifically, we present a method to split the latent prior space into continuous representations of dominant speech modes present in the magnitude spectra of audio signals. We propose a completely unsupervised approach using multinode latent space variational autoencoders (VAE). We show that the constraints on the latent space of a VAE can be in-fact used to separate speech and music, independent of the language of the speech. This paper also analytically presents the requirement on the number of latent variables for the task based on distribution of the speech data.
Parallel graph-based anomaly detection technique for sequential data

In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable.
Iterative Decoding of Reed-Solomon Codes based on Non-binary Matrices

A novel iterative approach for soft-decision decoding of Reed-Solomon codes is presented that employs symbol-level belief propagation on an alternative parity-check matrix representation of the code. Construction of a suitable matrix is discussed from the viewpoint of iterative decoding, and certain conditions are derived on existence of structures detrimental for decoding. Simulation results demonstrate that the novel scheme performs substantially better than hard-decision decoding, especially with high rate codes, while being of much lower complexity than existing soft-decision decoding methods. Proposed method is also well-suited for efficient hardware implementations.
Challenges and Opportunities for Spatial Data Infrastructure Development in Mozambique

The importance of access to spatial data for development and resource management is widely acknowledged worldwide. Unrestricted, reliable and efficient access to accurate, timely, and up-to-date spatial data may be achieved through a spatial data infrastructure. Thus, most developed countries implemented and continue to develop their spatial data infrastructure. In Africa, there is a growing number of governments committed to spatial data infrastructure development. This paper aims to contribute to initialize an spatial data infrastructure in Mozambique. We identified and characterized through a survey the government institutions producing, sharing, and using spatial data in the country to estimate their potential contribution to the development of the Mozambican spatial data infrastructure. We found 12 institutions producing 15 thematic datasets which can constitute the core of the spatial data infrastructure for Mozambique. Two government agencies have the technical skills and policies to make spatial data available to the public. Based on the possible contribution of these institutions, this paper proposes an spatial data infrastructure for Mozambique based on four pillars: i) organizational framework; ii) legal framework; iii) technical framework; and iv) accessibility.