Reviewerly/exHarmony huggingface dataset:
Towards Developing a Real-Time Hand Gesture Controlled Wheelchair

The number of wheelchair patients is increasing every day all over the world. Safe navigation and independent mobility are key issues for wheelchair users especially those suffering from dexterity inhibiting disorders and mobility impairments. Following modern technologies, this paper designs a control system prototype to easily navigate the wheelchair by performing finger gestures rather than operating a traditional joystick. Hand or finger gesture recognition using deep Convolutional Neural Network (CNN) and interaction with a powered wheelchair are major features of this system. This system is focused more on the flexibilities of wheelchair users by ensuring less movement of the hand as well as the cost of the implementation. To achieve these goals, some necessary steps have been performed, i.e., locating and continuously tracking the hand region, feeding the CNN model using Raspberry pi for gesture recognition, and perform navigation. After testing, our proposed system has achieved the recognition accuracy of 98.33% and a response time of 34 ms with a Raspberry Pi 4 device (1.5 GHz of CPU frequency), which is suitable for real-time wheelchair control application with reasonably low-cost.
Semantische Äquivalenz in Geburtserzählungen: Anwendung von Word Embeddings

Abstract The present study focuses on serially occurring narrations of ‘everyday’ life, more specifically on birthing as narrated by mothers on online forums; the underlying idea being that these narrations happen against the background of cultural narratives. The present paper uses word embedding models to detect typical topics and actors in these narrations. The calculation of word embeddings automatically constructs semantic spaces, where semantic relations (synonymy in particular) can be modeled. This method offers a way to think of synonymy as ‘functional equivalence in discourse’. The present study relies on previous work with n-grams (Bubenhofer, 2018). N-grams are sequences of words that often appear together; their sequential order in different narrations gives insight in narrative patterns. A further step in the analysis is the construction of ‘narrative topoi’, which is achieved through clustering neighboring vectors. The emerging clusters can in turn be grouped into five narrative elements of ‘telling a birth story’: 1) disruption of daily life, 2) personnel, 3) body, 4) fear, 5) joy. While it seems obvious that certain themes ‘belong’ into the narration of a delivery, it is less obvious with what vocabulary these themes are expressed. The presented method of clustering word-embedding-profiles adds tremendously to the modelling of a narrative. Its advantages lie in its potential to show lexical variation, as it also includes rare, non-conformative orthographical variants. Furthermore, it allows for a discourse-specific (and usage-based) view on semantic relations. The same applies to relations between semantic clusters. Seen from a discourse linguistics or cultural analysis perspective, word embeddings renew our understanding of semantics. This shows particularly fruitful if used to analyze (discourse dependent) derivations between semantic spaces.
Application of Cloud Accounting in Comprehensive Budget Management of Agricultural Enterprises under Big Data

Cloud accounting is a new technology developed on the basis of network information technology under the background of big data, which can provide convenient services for accounting and management of enterprises. The development of agricultural enterprises is directly related to the development of local agricultural economy. However, the application of cloud accounting in agricultural enterprises is less, and the application in comprehensive budget management is insufficient, which hinders the efficient docking of agricultural enterprises and economy. Therefore, this study believes that agricultural enterprises can use the advantages of big data and cloud accounting platform to build a more information-based comprehensive budget management system, in order to further improve the core competitiveness of agricultural enterprises.
The Eleventh Power Residue Symbol

Abstract This paper presents an efficient algorithm for computing 11 th -power residue symbols in the cyclo-tomic field <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>ℚ</m:mi> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:msub> <m:mi>ζ</m:mi> <m:mrow> <m:mn>11</m:mn> </m:mrow> </m:msub> </m:mrow> <m:mo>)</m:mo> </m:mrow> <m:mo>,</m:mo> </m:mrow> </m:math> $ \mathbb{Q}\left( {{\zeta }_{11}} \right), $ where 11 is a primitive 11 th root of unity. It extends an earlier algorithm due to Caranay and Scheidler (Int. J. Number Theory, 2010) for the 7 th -power residue symbol. The new algorithm finds applications in the implementation of certain cryptographic schemes.
A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings

Graph Neural Networks (GNNs) are a framework for graph representation learning, where a model learns to generate low dimensional node embeddings that encapsulate structural and feature-related information. GNNs are usually trained in an end-to-end fashion, leading to highly specialized node embeddings. However, generating node embeddings that can be used to perform multiple tasks (with performance comparable to single-task models) is an open problem. We propose a novel meta-learning strategy capable of producing multi-task node embeddings. Our method avoids the difficulties arising when learning to perform multiple tasks concurrently by, instead, learning to quickly (i.e. with a few steps of gradient descent) adapt to multiple tasks singularly. We show that the embeddings produced by our method can be used to perform multiple tasks with comparable or higher performance than classically trained models. Our method is model-agnostic and task-agnostic, thus applicable to a wide variety of multi-task domains.
Nonconvex Total Generalized Variation Model for Image Inpainting

It is a challenging task to prevent the staircase effect and simultaneously preserve sharp edges in image inpainting.For this purpose, we present a novel nonconvex extension model that closely incorporates the advantages of total generalized variation and edge-enhancing nonconvex penalties.This improvement contributes to achieve the more natural restoration that exhibits smooth transitions without penalizing fine details.To efficiently seek the optimal solution of the resulting variational model, we develop a fast primal-dual method by combining the iteratively reweighted algorithm.Several experimental results, with respect to visual effects and restoration accuracy, show the excellent image inpainting performance of our proposed strategy over the existing powerful competitors.
Situation-Aware Updates for Cyber-Physical Systems

The growing trend of integrating software processes and the physical world created new paradigms, such as, the Internet of Things and Cyber-Physical Systems. However, as applications of these paradigms are rolled out, the management of their software and hardware components becomes a challenge, e.g., an update of a mobile system must not only consider the software but also the surrounding context, such as, internet connection, current position and speed, or users activities, and therefore must be aware of their current context and situations when executed. The timing of executing management tasks is crucial as the surrounding state of the system and its context must hold long enough to enable robust execution of management tasks. We propose an approach to model software updates in a Situation-Aware manner, enabling to observe context, such as, time, environment, application state and people, hence, enabling to execute update tasks at the right time and in the right time-frame. We implemented our approach based on a train scenario within the OpenTOSCA Ecosystem.
Implementation of a Lightweight Semantic Segmentation Algorithm in Road Obstacle Detection

Due to deep learning’s accurate cognition of the street environment, the convolutional neural network has achieved dramatic development in the application of street scenes. Considering the needs of autonomous driving and assisted driving, in a general way, computer vision technology is used to find obstacles to avoid collisions, which has made semantic segmentation a research priority in recent years. However, semantic segmentation has been constantly facing new challenges for quite a long time. Complex network depth information, large datasets, real-time requirements, etc., are typical problems that need to be solved urgently in the realization of autonomous driving technology. In order to address these problems, we propose an improved lightweight real-time semantic segmentation network, which is based on an efficient image cascading network (ICNet) architecture, using multi-scale branches and a cascaded feature fusion unit to extract rich multi-level features. In this paper, a spatial information network is designed to transmit more prior knowledge of spatial location and edge information. During the course of the training phase, we append an external loss function to enhance the learning process of the deep learning network system as well. This lightweight network can quickly perceive obstacles and detect roads in the drivable area from images to satisfy autonomous driving characteristics. The proposed model shows substantial performance on the Cityscapes dataset. With the premise of ensuring real-time performance, several sets of experimental comparisons illustrate that SP-ICNet enhances the accuracy of road obstacle detection and provides nearly ideal prediction outputs. Compared to the current popular semantic segmentation network, this study also demonstrates the effectiveness of our lightweight network for road obstacle detection in autonomous driving.
A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations

User-POI rating matrix is one of the current research hotspot of POI recommendation algorithms, the goal of which is to obtain the POIs with the highest user satisfaction. However, most existing POI recommendation algorithms ignore the diversity of recommended POI list, and the POIs in the recommended list are usually similar to each other, which cannot effectively broad the user’s perspectives. To solve this problem, a diverse and personalized recommendation method EBPRMF (Embedded Bayesian Personalized Ranking Matrix Factorization) that integrates the embedded features of the geographic-social relationships of POIs is proposed. First, by embedding and compressing the geographic and social relationships between POIs, a geographic-social relationship embedding model is constructed to evaluate the coupled correlation between POIs. The correlation between all POIs forms a POI correlation matrix. And then the spectral clustering method is leveraged to cluster the POIs according to the correlation matrix, and several different POI clusters can be obtained. Next, the Bayesian personalized ranking matrix factorization model is proposed to select POI that is close to the user’s preference from each cluster, and finally a recommendation list of POIs that is both diverse and personalized is obtained. Experimental results demonstrate that the geographic-social relationship embedding model can well represent the location and social relationship between POIs, and has a good clustering effect. The diversity of recommendation list is high, and the sorted list can be well satisfied with user preferences.
Independent Sources of Information-Theoretic Descriptors of Electronic States

The need for resultant measures of the Information-Theoretic (IT) content of molecular electronic wavefunctions, combining the information contributions due to the probability and phase/current distributions, is reemphasized. Complementary measures of the state entropy (disorder) and information (order) contents are reexamined, the continuities of wavefunction components are summarized, and the probability acceleration concept is used to determine the current and information sources. The experimental elimination of the state uncertainties is discussed and limitations in this information-acquirement process imposed by the Heisenberg indeterminacy principle are commented upon.
Wildlife Detection and Recognition in Digital Images Using YOLOv3: Extended Abstract

Recent advances in hardware capability and machine learning techniques enable convenient monitoring of wildlife and their living environments. In this work, we apply Deep Learning (DL) methods to detect and recognize wildlife in digital images and report the experimental results conducted in a commodity workstation. Specifically, YOLOv3 and YOLOv3-Tiny are used to detect and classify several classes of animals based on 9051 digital images and they achieve 75.2% and 68.4% mean average precision, respectively.
The Empirical Watershed Wavelet

The empirical wavelet transform is an adaptive multi-resolution analysis tool based on the idea of building filters on a data-driven partition of the Fourier domain. However, existing 2D extensions are constrained by the shape of the detected partitioning. In this paper, we provide theoretical results that permits us to build 2D empirical wavelet filters based on an arbitrary partitioning of the frequency domain. We also propose an algorithm to detect such partitioning from an image spectrum by combining a scale-space representation to estimate the position of dominant harmonic modes and a watershed transform to find the boundaries of the different supports making the expected partition. This whole process allows us to define the empirical watershed wavelet transform. We illustrate the effectiveness and the advantages of such adaptive transform, first visually on toy images, and next on both unsupervised texture segmentation and image deconvolution applications.
Pea-KD: Parameter-efficient and accurate Knowledge Distillation on BERT

Knowledge Distillation (KD) is one of the widely known methods for model compression. In essence, KD trains a smaller student model based on a larger teacher model and tries to retain the teacher model's level of performance as much as possible. However, existing KD methods suffer from the following limitations. First, since the student model is smaller in absolute size, it inherently lacks model capacity. Second, the absence of an initial guide for the student model makes it difficult for the student to imitate the teacher model to its fullest. Conventional KD methods yield low performance due to these limitations. In this paper, we propose Pea-KD (Parameter-efficient and accurate Knowledge Distillation), a novel approach to KD. Pea-KD consists of two main parts: Shuffled Parameter Sharing (SPS) and Pretraining with Teacher's Predictions (PTP). Using this combination, we are capable of alleviating the KD's limitations. SPS is a new parameter sharing method that increases the student model capacity. PTP is a KD-specialized initialization method, which can act as a good initial guide for the student. When combined, this method yields a significant increase in student model's performance. Experiments conducted on BERT with different datasets and tasks show that the proposed approach improves the student model's performance by 4.4% on average in four GLUE tasks, outperforming existing KD baselines by significant margins.
An Efficient Lightweight Cryptography Hash Function for Big Data and IoT Applications

This paper introduces a lightweight cryptography hash function for big data and IoT applications. The proposed design employs S-Box, linear transformation, and bit permutation functionalities. Conventional hash functions require memory and time to process big data and IoT applications. Therefore, a secure and quick lightweight cryptography protocol is needed. This design provides the security requirements of the conventional hash functions and the specifications of the lightweight cryptography protocols. The proposed design is tested and verified using three of the lightweight requirements: speed, memory and power consumption. The results show that the proposed design outperforms other lightweight protocols in terms of speed, memory and power consumption. Moreover, the security analysis shows that this design posses the general security requirements of the conventional hash protocols.
Big Healthcare Data: Survey of Challenges and Privacy

The last century witnessed a dramatic leap in the shift towards digitizing the healthcare workflow and moving to e-patients' records. Health information is consistently becoming more diverse and complex, leading to the so-called massive data. Additionally, the demand for big data analytics in healthcare organizations is increasingly growing with the aim of providing a wide range of unprecedented potentials that are considered necessary for the provision of meaningful information about big data and improve the quality of healthcare delivery. It also aims to increase the effectiveness and efficiency of healthcare organizations; provide doctors and care providers better decision-making information and help them in the early detection of diseases. It also assists in evidence-based medicine and helps to minimize healthcare cost. However, a clear contradiction exists between the privacy and security of big data and its widespread usage. In this paper, the focus is on big data with respect to its characteristics, trends, and challenges. Additionally, the risks and benefits associated with data analytics were reviewed.
Pengaruh Kemampuan Matematika Terhadap Kemampuan Computational Thinking Pada Anak Usia Sekolah Dasar

Computational thinking ability need to be trained from an early age through the mathematics ability approach at the primary schools level. This study aims to analyze the effect of the analysis of mathematic ability to computational thinking abilities in primary schools age in sdn 2 sekarteja with 2 measurement variables including: computational thinking ability and math ability. This type of research is quantitative using descriptive methods. The population of this study was class V SDN 2 Sekarteja with a total of 30 students and the entire population becomes the sample, the type of research sample is saturated sample. Data collection techniques in this study used tests and documentation. The technique used to analyze the data was descriptive statistics. The results of this study indicated that, with n = 30 and a significant level of 5%, r table = 0.361, while the result rcount = 0.304. The results obtained were rcount ≤ rtabel, then Ha was rejected and Ho was accepted and the direction of the correlation was negative. It can be concluded that there was no significant relationship between the computational thinking abilities of elementary school age children in terms of mathematics abilities at SDN 2 Sekarteja
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation

In this study, linguistic knowledge at different levels are incorporated into the neural machine translation (NMT) framework to improve translation quality for language pairs with extremely limited data.Integrating manually designed or automatically extracted features into the NMT framework is known to be beneficial.However, this study emphasizes that the relevance of the features is crucial to the performance.Specifically, we propose two methods, 1) self relevance and 2) word-based relevance, to improve the representation of features for NMT.Experiments are conducted on translation tasks from English to eight Asian languages, with no more than twenty thousand sentences for training.The proposed methods improve translation quality for all tasks by up to 3.09 BLEU points.Discussions with visualization provide the explainability of the proposed methods where we show that the relevance methods provide weights to features thereby enhancing their impact on low-resource machine translation.
Neural random subspace

The random subspace method, also known as the pillar of random forests, is good at making precise and robust predictions. However, there is as yet no straightforward way to combine it with deep learning. In this paper, we therefore propose Neural Random Subspace (NRS), a novel deep learning based random subspace method. In contrast to previous forest methods, NRS enjoys the benefits of end-to-end, data-driven representation learning, as well as pervasive support from deep learning software and hardware platforms, hence achieving faster inference speed and higher accuracy. Furthermore, as a non-linear component to be encoded into Convolutional Neural Networks (CNNs), NRS learns non-linear feature representations in CNNs more efficiently than contemporary, higher-order pooling methods, producing excellent results with negligible increase in parameters, floating point operations (FLOPs) and real running time. Compared with random subspaces, random forests and gradient boosting decision trees (GBDTs), NRS demonstrates superior performance on 35 machine learning datasets. Moreover, on both 2D image and 3D point cloud recognition tasks, integration of NRS with CNN architectures achieves consistent improvements with only incremental cost.
Lossless Image Compression Using Wavelet and Vector Quantization.(Dept.E)

In this paper, we propose a new low bit rate scheme that combines the Discrete Wavelet transform (IWT) and Vector Quantization (VQ) for image compression with high Compression Ratio (CR) in order to achieve acceptable quality of output images, Image compression using the combination of DWT willy VQ has been considered in many recent works; here in our paper the DWT was performed on several gray scale standard images, each of (256X256) byte. We used 4 images as the training images of the train VO using LBG algorithm and got the final optimal codebook. Then we used another two images to compare our output image with JPEG 2000 standard winch based on Wavelet and Scalar quantization. Our proposed system used a VO codebook with Variable cod hook size and dimensions were used to compress wavelet Transform) coefficients resulting using Danny wavelet types. Our pro system is slowed so that the best Wavelet types that you can use for compression is COIF 2. Our proposed system gave better PSNR than G2000 standard at high (CR), above than (5L), and this achieved a VQ codebook size (1024 -512) with dimension equal to 256. In the paper a typical system for the application of image compression system and new scientific results are present.
BYteam at SemEval-2020 Task 5: Detecting Counterfactual Statements with BERT and Ensembles

We participate in the classification tasks of SemEval-2020 Task: Subtask1: Detecting counterfactual statements of semeval-2020 task5(Detecting Counterfactuals).This paper examines different approaches and models towards detecting counterfactual statements classification.We choose the Bert model.However, the output of Bert is not a good summary of semantic information, so in order to obtain more abundant semantic information features, we modify the upper layer structure of Bert.Finally, our system achieves an accuracy of 88.90 % and F1 score of 86.30 % by hard voting, which ranks 6th on the final leader board of the in subtask 1 competition.