Reviewerly/exHarmony huggingface dataset:
Data in the Internet of Things Era: The Propertization of Data in Light of Contemporary Business Practices

Data is at the heart of the digital age or, as some put it, “the new oil”, “the next natural resource” and the “currency of the 21st century” that everything depends on to function and operate. The Internet of Things (IoT), which aims to connect things to the Internet and interconnect them with each other, could have a huge impact on data generation and dissemination as there will be data streams from countless sources. The centrality of data and its countless value in today’s world do not, however, end contention and controversial views about its nature. Every stakeholder looks at data from his own perspective. Merchants and the business community view data as a valuable asset and commodity that could generate huge profits. In the existing legal systems, courts traditionally excluded information (data) from the ambit of property and current statutes seem to be silent about the issue of dealing with data as property. This paper attempts to study the propertization of data in light of contemporary business practices and the existing laws through consulting some relevant statutory laws and court cases. The aim of the discussion is to pay attention to the gap between business practices and current legal protection available for data or information. This paper is believed to be useful and timely as data propertization is an ongoing contentious issue that surfaces with every new wave of data-generated technology such as the IoT.
Intuitive Physics Guided Exploration for Sample Efficient Sim2real Transfer

Physics-based reinforcement learning tasks can benefit from simplified physics simulators as they potentially allow near-optimal policies to be learned in simulation. However, such simulators require the latent factors (e.g. mass, friction coefficient etc.,) of the associated objects and other environment-specific factors (e.g. wind speed, air density etc.,) to be accurately specified. As such a complete specification can be impractical, in this paper, we instead, focus on learning task-specific estimates of latent factors which allow the approximation of real world trajectories in an ideal simulation environment. Specifically, we propose two new concepts: a) action grouping - the idea that certain types of actions are closely associated with the estimation of certain latent factors, and; b) partial grounding - the idea that simulation of task-specific dynamics may not need precise estimation of all the latent factors. We demonstrate our approach in a range of physics-based tasks, and show that it achieves superior performance relative to other baselines, using only a limited number of real-world interactions.
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data

Interleaved texts, where posts belonging to different threads occur in a sequence, commonly occur in online chat posts, so that it can be time-consuming to quickly obtain an overview of the discussions. Existing systems first disentangle the posts by threads and then extract summaries from those threads. A major issue with such systems is error propagation from the disentanglement component. While end-to-end trainable summarization system could obviate explicit disentanglement, such systems require a large amount of labeled data. To address this, we propose to pretrain an end-to-end trainable hierarchical encoder-decoder system using synthetic interleaved texts. We show that by fine-tuning on a real-world meeting dataset (AMI), such a system out-performs a traditional two-step system by 22%. We also compare against transformer models and observed that pretraining with synthetic data both the encoder and decoder outperforms the BertSumExtAbs transformer model which pretrains only the encoder on a large dataset.
DOTS STRATEGIC WITH EXTREME MACHINE LEARNING METHOD IN THE CLASSIFICATION OF DISEASE TRANSMISSION IN TB PATIENTS

Machine Learning Model The type of data to be grouped determines the type similarity measurement techniques are used. The exact image measurements are used for determine the similarity of the two data points in the dataset. Therefore, we studied an image dataset of TB patients is an important step in its treatment to anticipate early detection with the aim of helping the DOTS plan strategy with the community. The results showed that there were 9 men with DOTS and EML algorithms (73.3%). While for women, the DOTS and EML algorithms were 5 people (26.4%). The age was determined between 12 people (25.3%) while those and 7 people (55.5). Sufficient knowledge of DOTS and EML algorithms among 30 people (66.7%). Minimum knowledge is 2 people and middle knowledge is 13 people (34.3%). Based on the final results of the DOTS and EML algorithm, there were 26 people (46.9%) and tall as many as 6 people (54.1%).
WITHDRAWN: Implementation of CNN based address generation unit and data path unit for the performance trade-off

Due to their applications in ground breaking technologies such as autonomic drive, biosecurity imaging and natural language processing, convolutionary neural networks become increasingly common. The complexity of the underlying algorithms is also increased with this growth in adoption. This pattern often includes repercussions for calculation platforms, i.e. The memory access control unit GPUs, FPGA or ASIC-based accelerators are in particular, the AGU (Address Generation unit). Current accelerators typically have Datapath AGUs that have limited adaptability in algorithm evolution. For new algorithms, new hardware is needed, which in time, energy and reusability is an extremely inefficient method. In this research 6 algorithms with various hardware consequences are evaluated and a completely programmable AGU (PAGU) that can be adapted to those algorithms is presented. The results are presented. These are normal, screwed, dilated, sampled and padded turf, and Max Pooling algorithms. The proposed AGU architecture is a very long instructions Word based Application Specific Direction Processor with specialised components, including hardware counters and zero overhead loops, as well as a powerful instruction set architecture (ISA). The goal was to reduce flexibility vs. area, capability and productivity. Results show that PAGU shows nearly an excellent efficiency, one cycle per address, for every algorithm under consideration except Up sampled Convolution for which 1,7 cycles per address are used for a working test network of Semantic Segmentation. The PAGU area is roughly. 4.6 times larger than the Data path method, which is nevertheless acceptable due to the high degree of versatility.
An extension of the Cardioid distributions on circle

A new family of distributions on the circle is introduced which is a generalization of the Cardioid distributions. The elementary properties such as mean, variance, and the characteristic function are computed. The distribution is shown to be either unimodal or bimodal. The modes are computed. The symmetry of the distribution is characterized. The parameters are shown to be canonic (i.e. uniquely determined by the distribution). This implies that the estimation problem is welldefined. We also show that this new family is a subset of distributions whose Fourier series has degree at most 2 and study the implications of this property. Finally, we study the maximum likelihood estimation for this family.
Enhancing entanglement of two-qubit states from amplitude damping channel using single-qubit unitary operation and local filtering operation

We propose a scheme to enhance entanglement from amplitude damping or correlated amplitude damping decoherence. We show that entanglement sudden death time can be prolonged by the initial single-qubit operation combined with local filtering operation. For the amplitude damping channel case, we give the optimal single-qubit operation for arbitrary pure state [Formula: see text]. For the correlated amplitude damping channel case, we find that single-qubit operation on the initial state can not only enhance the final entanglement but also avoid entanglement sudden death. Compared to the previous schemes, the optimal operations and local filtering operations used in our scheme are independent with the decay parameters of the environment.
Imaginary-to-Real Generative Network for Marine Organism Image Extrapolation

Marine organism research plays an extremely important role in ocean science. Underwater computer vision is a main measure to observe organism. In this work, we devote on drawing the entire organism image by a visible part. Inspired by the human ability to “imagine-draw” an image, we propose a novel Imaginary-to-Real Generative Network, ImReNet for short, facing the task's challenges and overcoming the semantic inaccuracies and background distortions problems. We devise a method of image generation with an imaginary output as guidance, involving a novel underwater-color loss to deal with the similar chromatic characteristics of underwater images. Experiments conducted on datasets covering both Fish and Plankton present that our method achieves good performance on image extrapolation. Our code will be made publicly available.
Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus.

Cross-target generalization is a known problem in stance detection (SD), where systems tend to perform poorly when exposed to targets unseen during training. Given that data annotation is expensive and time-consuming, finding ways to leverage abundant unlabeled in-domain data can offer great benefits. In this paper, we apply a weakly supervised framework to enhance cross-target generalization through synthetically annotated data. We focus on Twitter SD and show experimentally that integrating synthetic data is helpful for cross-target generalization, leading to significant improvements in performance, with gains in F1 scores ranging from +3.4 to +5.1.
Development of Key Performance Indicators in Ammunition Demilitarization Facility Using the Balanced Score Card

Ammunition Demilitarization facility (ADF) should be set up the feasible goals and continue to operate, taking into account non-profit characteristics. However, due to the lack of performance measurement methods in ADF, which are essential to national policy at a significant cost each year, the reliability of the evaluation results can be insufficient. In this paper, the Balanced Score Card (BSC) method was applied that could be evaluated to reflect the financial and non-financial features. The relevant literature research and army regulations reflected the results of various interviews of the expert group. The extraction of success performance area in ADF was confirmed using the BSC method and the Decision Variable (DV) candidate was created to use regression for selecting the DV. Additionally, the key performance indicator was presented by verification the feasibility of content by conducting the survey of experts. The implications of this paper are as follows. First, the proposed BSC model was found to be suitable for practical use in ADF reflecting the non-profit characteristics. Second, accurate evaluation of ADF can contribute to long-term development of ADF. Finally, it can be applied to the management process of the other military sector, so it can be expected to play a role in providing basic data and spreading it to other areas.
On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions

Recent complementary strands of research have shown that leveraging information on the data source through encoding their properties into embeddings can lead to performance increase when training a single model on heterogeneous data sources. However, it remains unclear in which situations these dataset embeddings are most effective, because they are used in a large variety of settings, languages and tasks. Furthermore, it is usually assumed that gold information on the data source is available, and that the test data is from a distribution seen during training. In this work, we compare the effect of dataset embeddings in mono-lingual settings, multi-lingual settings, and with predicted data source label in a zero-shot setting. We evaluate on three morphosyntactic tasks: morphological tagging, lemmatization, and dependency parsing, and use 104 datasets, 66 languages, and two different dataset grouping strategies. Performance increases are highest when the datasets are of the same language, and we know from which distribution the test-instance is drawn. In contrast, for setups where the data is from an unseen distribution, performance increase vanishes.
The Use of Concept Maps in the Process of Independent Study of Educational Material in Higher Mathematics

The purpose of the article is to analyze the impact of concept mapping on the level and quality of assimilation of learning material in higher mathematics in the process of independent study. &#x0D; Methodology. Based on a review of the main provisions of modern theory of learning and generalization of research results presented in scientific papers on the scope of application of concept maps in the learning process, such a training experiment was conducted. For students of the two experimental groups on a certain topic of the discipline “Higher Mathematics” were offered to build conceptual maps in addition to the common teaching methods. It considered as an independent creative task. In the other two groups, which were under control, students had to study the same topic of the discipline, using traditional methods. &#x0D; Results. It was determined that the results of the colloquium composed of students of experimental groups were significantly higher than those of students of control groups who did not use mapping as a method of learning during independent work. The average score obtained by students of experimental groups for the colloquium was almost 80 points, while for students of control groups it was only 72 points. Using Student’s test, the significance of the difference between the values of the sample averages of these indicators proved. If we compare the average scores for different types of tasks, the most significant was the difference in the performance of heuristic tasks, which reflect the ability to apply the acquired knowledge to solve practical problems of economic content.&#x0D; Conclusions. The positive influence of the use of concept mapping in independent work of students established, which proves the thesis about the feasibility of using concept mapping as a learning tool. Further introduction of concept maps in the educational process should be based on the development of complex theoretical and practical, as well as competency-oriented tasks.
Fall recognition algorithm for the elderly based on home service robot

The elderly living alone may not be able to stand up when they fall, nor can they timely call the police or inform others, so they may face the risk or even life-threatening. Researchers have developed a lot of schemes based on the fall process, but some of these schemes are too difficult to promote because they are inconvenient to wear and some are too limited because of the location of the camera. This paper proposes an accidental fall reorganization method based on the symmetry principle of home service robot. The Kinect can obtain the monitoring video, frame information of the human body is extracted through OpenPose, and four key parameters are used to identify the fall: speed of descent at the center of the articulatio coxae, the human body centerline angle with the ground, and the aspect ratio of rectangle outside human body, and whether the old man has stood. Finally, the home service robot can confirm whether the alarm is needed with the elderly through voice interaction. This method has 97.3% success rate to recognize the fall down behavior.
The Role of Blockchain and IoT in Modern Energy Systems

Blockchain and Internet-of-Things (IoT) are emerging as key features of the modern energy system. This chapter focuses on these technological advancements in the context of transition of the energy sector into the Internet-of-Energy (IoE) environment. Here, we explore how blockchain and IoT fit into this transition and deal with issues relating to their development and deployment in the sector. Although the interest in Blockchain- and IoT-based innovation in energy is growing, its widespread deployment usage is rightly encumbered by the critical nature of the application that necessitates proven reliability and alignment with the future goals of the sectors through rigorous development, implementation, and testing. We investigate how applications of Blockchain and IoT principles progressively seek to meet the demands of future energy systems, and extricate the current and future challenges with respect to inter alia context, architecture, regulations, security, and uptake. We also unpack the details of the various challenges with the different components and subsystems of blockchain- and IoT-based technologies the energy sector that affect security, privacy, and reliability, and offer technical insight into opportunities and resolutions pertaining to the same. This chapter is intended to provide researchers, developers, and policymakers with an understanding of the possibilities of blockchain and IoT in the energy sector through equipping them with knowledge of the application principles, state-of-the-art, technological limitations/barriers as well as current and future developments encompassing this technological confluence.
A Multi-Target Detection and Tracking Technology Based on Labeled PHD Filtering

In modern air combat, group target attacks in a complex electromagnetic environment put forward higher requirements for seeker multi-target resolution. The missile-borne phased array radar technology enables the radar seeker to detect and track multiple targets. The current missile-borne radar seeker usually detects and tracks the most attractive target in a multi-target scenario. Based on the airborne radar multi-target detection technology and the random finite set multi-target detection method, this paper models the multi-target state and the multiple measurements obtained by the radar separately, and distinguishes the state of different targets at the same time by introducing a label probability hypothesis density filter. To achieve the purpose of multi-target detection and tracking. The simulation results show that the radar seeker using the labeled PHD filtering algorithm can simultaneously detect and distinguish multiple targets in real time, and give the track of multiple targets.
Multi-Dimensional Failure Modeling for Shared Data in Cooperative Systems

Autonomous systems will share data to enrich their environmental model and provide cooperative functionality. However, as shared data might be imprecise or inaccurate, its failure characteristics have to be analyzed by the receiving system before using the data. A corresponding failure model for describing failure characteristics was proposed by Jäger et al. (2018), but is limited to one-dimensional sensory data. In this work, we extend the failure model to support multi-dimensional feature data as well. We exemplary evaluate the approach by modeling the failure characteristics of a lane detection system of a simulated car. By comparing it to state-of-the-art failure modeling techniques, we can show that the model accurately predicts failure amplitudes of previously unseen tracks even when trained on limited data.
Multidomain Pretrained Language Models for Green NLP

When tackling a task in a given domain, it has been shown that adapting a model to the domain using raw text data before training on the supervised task improves performance versus solely training on the task. The downside is that a lot of domain data is required and if we want to tackle tasks in n domains, we require n models each adapted on domain data before task learning. Storing and using these models separately can be prohibitive for low-end devices. In this paper we show that domain adaptation can be generalised to cover multiple domains. Specifically, a single model can be trained across various domains at the same time with minimal drop in performance, even when we use less data and resources. Thus, instead of training multiple models, we can train a single multidomain model saving on computational resources and training time.
Mining Suggestions from imbalanced datasets of online reviews using SMOTE- Random Multimodel Deep Learning

Suggestion mining is a relatively new area & is challenged by issues like the complexity in a task or manual formulation, the knowledge of sentence-level semantics, figurative sentences, handling long & complex words, context dependence, & also very imbalanced class distribution. Deep learning is an industry that can be highly competitive in machine learning. We use the Random Multimodel Deep Learning (RMDL) approach in this paper to address the problem of suggestion mining using the SemEval-2019 Task 9 data sets. Though its data sets are very imbalanced and unstructured, we have utilized SMOTE techniques to extract class imbalance problems. To solve the imbalanced dataset problem, SMOTE (synthetic Minority oversampling technique) is a widely used over-sampling tool. Experimental findings show that the advantages of SMOTE to manage complex data and imbalanced data set are superior to our current SMOTE-RMDL (SMO-RMDL) model of the existing research process.
Autonomous Inpainting Algorithm for Wire Removal from Image

With the increase in digital photography methods and demand for image quality enhancement procedures, the removal of unwanted parts of an image such as hanging wires and cables remain a barrier for professional image editing tools. An appealing image with unwanted wires and cables appear disturbing to the viewer along with depreciating its morale. Although there exists manual tools for the removal of wires and cables, a novel autonomous approach for the removal of wires and cables from an image using image processing is required. Hence, this work proposes a ready-to-use algorithm that demonstrates successful in-painting for wire removal and verification on real-world images. The method readily finds and inpaints wires without requiring human efforts. Evaluation of the method on real-world images shows promising results and reliability.
Design, optimization and Real Time implementation of a new Embedded Chien Search Block for Reed-Solomon (RS) and Bose-Chaudhuri-Hocquenghem (BCH) codes on FPGA Board.

The development of error correcting codes has been a major concern for communications systems. Therefore, RS and BCH (Reed-Solomon and Bose, Ray-Chaudhuri and Hocquenghem) are effective methods to improve the quality of digital transmission. In this paper a new algorithm of Chien Search block for embedded systems is proposed. This algorithm is based on a factorization of error locator polynomial. i.e, we can minimize an important number of logic gates and hardware resources using the FPGA card. Consequently, it reduces the power consumption with a percentage which can reach 40 % compared to the basic RS and BCH decoder. The proposed system is designed, simulated using the hardware description language (HDL) and Quartus development software. Also, the performance of the designed embedded Chien search block for decoder RS\BCH (255, 239) has been successfully verified by implementation on FPGA board.