Reviewerly/exHarmony huggingface dataset:
Word Level Language Identification on Code-Mixed English-Bodo Text

Abstract Since social media has become an active part of one’s life, people express their views freely in mixed informal languages on such platforms. So, in a multi-lingual country like India, it becomes really difficult for conventional language detectors to identify such languages. This paper mainly aims to detect the language at word level where the code mixed text can be in English-Bodo-Assamese. The data for the same is collected from some related Facebook pages and various classification algorithms are used to predict and compare the accuracy with which the detection is done.
Analysis of Voice Cues in Recognition of Sarcasm

Background: The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The entire process is referred to as voice analytics. Objective: The paper aims at analyzing and synthesizing the phonetics of voice. The work focuses on the facts of voice analytics i.e. basic blocks of ‘Glottal signature’. The glottal signature and unique voice cues are evaluated to derive the relationship for utterance of emotional words which leads to sentimental expression cues. An effort is made to map further to understand sarcasm behavior in the sounds made by human speech. Methods: The basic blocks of unique features identified in the work are Intensity, Pitch, Formants related to speak, read, interactive and declarative sentences solely on voice cues not on linguistic theory. It is also tested to identify derived features that maps to fine-grained details of voice cues to drill up usage in sarcasm detection. Results: Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences and derived parameters. Conclusion: The work carried out in the paper also supports the analysis of voice segmentation labelling, analyzes the unique features of voice cues, understanding physics of voice, the process is further carried out to recognize sarcasm.
Once-for-All: Train One Network and Specialize it for Efficient Deployment on Diverse Hardware Platforms

We address the challenging problem of efficient inference across many devices and resource constraints, especially on edge devices. Conventional approaches either manually design or use neural architecture search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally prohibitive (causing $CO_2$ emission as much as 5 cars' lifetime) thus unscalable. In this work, we propose to train a once-for-all (OFA) network that supports diverse architectural settings by decoupling training and search, to reduce the cost. We can quickly get a specialized sub-network by selecting from the OFA network without additional training. To efficiently train OFA networks, we also propose a novel progressive shrinking algorithm, a generalized pruning method that reduces the model size across many more dimensions than pruning (depth, width, kernel size, and resolution). It can obtain a surprisingly large number of sub-networks ($> 10^{19}$) that can fit different hardware platforms and latency constraints while maintaining the same level of accuracy as training independently. On diverse edge devices, OFA consistently outperforms state-of-the-art (SOTA) NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1.5x faster than MobileNetV3, 2.6x faster than EfficientNet w.r.t measured latency) while reducing many orders of magnitude GPU hours and $CO_2$ emission. In particular, OFA achieves a new SOTA 80.0% ImageNet top-1 accuracy under the mobile setting ($<$600M MACs). OFA is the winning solution for the 3rd Low Power Computer Vision Challenge (LPCVC), DSP classification track and the 4th LPCVC, both classification track and detection track. Code and 50 pre-trained models (for many devices & many latency constraints) are released at this https URL.
Semantic Description of Objects in Images Based on Prototype Theory

This research aims to build a model for the semantic description of objects based on visual features extracted from images. We introduce a novel semantic description approach inspired by the Prototype Theory. Inspired by the human approach used to represent categories, we propose a novel Computational Prototype Model (CPM) that encodes and stores the object’s image category’s central semantic meaning: the semantic prototype. Our CPM model represents and constructs the semantic prototypes of object categories using Convolutional Neural Networks (CNN). The proposed Prototype-based Description Model uses the CPM model to describe an object highlighting its most distinctive features within the category. Our Global Semantic Descriptor (GSDP) builds discriminative, low-dimensional, and semantically interpretable signatures that encode the objects’ semantic information using the constructed semantic prototypes. It uses the proposed Prototypical Similarity Layer (PS-Layer) to retrieve the category prototype using the principle of categorization based on prototypes. Using different datasets, we show in our experiments that: i) the proposed CPM model successfully simulates the internal semantic structure of the categories; ii) the proposed semantic distance metric can be understood as the object typicality score within a category; iii) our semantic classification method based on prototypes can improve the performance and interpretation of CNN classification models; iv) our semantic descriptor encoding significantly outperforms others state-of-the-art image global encoding in clustering and classification tasks.
Animal image recognition based on convolutional neural network

In recent years, cameras have been used to monitor poaching in many places, and deep learning technology has been used to identify images to solve the problem of sentencing for forest resource enforcement cases. To accurately classify the collected images, this article uses data enhancement technology to rotate the original image, uses principal component analysis to reduce dimensionality to remove redundant features, and realizes the classification of small samples using transfer learning. Experiments show that the recognition rate of this method to animals reaches 88%.
Revisiting Locally Supervised Learning: an Alternative to End-to-end Training

Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E) training of deep networks usually suffers from high GPUs memory footprint. This paper aims to address this problem by revisiting the locally supervised learning, where a network is split into gradient-isolated modules and trained with local supervision. We experimentally show that simply training local modules with E2E loss tends to collapse task-relevant information at early layers, and hence hurts the performance of the full model. To avoid this issue, we propose an information propagation (InfoPro) loss, which encourages local modules to preserve as much useful information as possible, while progressively discard task-irrelevant information. As InfoPro loss is difficult to compute in its original form, we derive a feasible upper bound as a surrogate optimization objective, yielding a simple but effective algorithm. In fact, we show that the proposed method boils down to minimizing the combination of a reconstruction loss and a normal cross-entropy/contrastive term. Extensive empirical results on five datasets (i.e., CIFAR, SVHN, STL-10, ImageNet and Cityscapes) validate that InfoPro is capable of achieving competitive performance with less than 40% memory footprint compared to E2E training, while allowing using training data with higher-resolution or larger batch sizes under the same GPU memory constraint. Our method also enables training local modules asynchronously for potential training acceleration.
My Security: An interactive search engine for cybersecurity

Large volumes of Cybersecurity-related data is generated every day from various sources at high speed to adapt to the fast-evolving landscape of cybersecurity.It drives the emergence of challenges such as the efficient gathering of in-demand information from unstructured and heterogeneous data sources.After collecting sufficient data, it is hard for users to understand the message hidden behind without adequate security domain knowledge.To help address this problem, in this paper, we present My Security, an innovative search engine for gathering, managing, and understanding cybersecurity-related data.My Security is based on a novel indexing approach that stores both the information of data sources (e.g., publication date, authorship) and the pragmatics messages, including security category (e.g., ransomware, data breach) and corresponding security components (e.g., time of the event, impacted systems).With the established index, users can retrieve cybersecurity information through comprehensive approaches.Fetched results are provided with interpretations leveraged from pragmatics indexing.Additional data mining and visualization techniques enhance the interactivity of My Security by presenting the retrieved results in a clear and comprehensible manner with cybersecurity expertise.It is demonstrated that My Security is efficient at satisfying users' requirements for searching security data and helping people gain better insights into cybersecurity.
Development of Smartband to Monitor from Home the Vital Signs for Patients with SARS COV 2 Through a Mobile Application from the Central Military Hospital (HOMIL) Bogota 2020

After the arrival of SARS COV2 (COVID 19) many biomedical devices have been innovated to mitigate the impact of the pandemic.Despite this, none have been developed to monitor patients with mild symptoms or the asymptomatic from their homes, forcing many hospitals worldwide to send these patients home with poor surveillance measures.The main objective of this project is to monitor from the HOMIL the vital signs of COVID 19 patients, who are currently isolated in their homes.For this purpose, a biomedical device in the form of a lowcost smartband has been developed.It is linked to a mobile application connected to a database where heart rate, oxygen saturation, and temperature parameters are sent in real time.The development was proposed in phases, which involve physical design, data acquisition and processing, and subsequent trial at the HOMIL.The smartband design and the mobile application has been developed, wireless communication between sensors has been achieved.Furthermore, the plan is to provide the HOMIL with the capability of initiating emergency protocols.The use of the smartband will be encouraged in patients who have tested positive for COVID 19 through PCR and will be taken off once they test negative.
La vigilancia científica como apoyo a la gestión educativa. Un análisis de la formación en Automatización Industrial.

This research presents an innovative methodology, which allows the articulation of different participants in the academic community to transform processes in educational institutions. It uses a bibliometric analysis of scientific literature, supported by expert judgments. To present it, the field of Industrial Automation was analyzed by means of recognized software for bibliometric analysis (Bibliometrix de R) and analysis of Vosviewer, Sci2 and gephi networks. The allowed us to identify the main authors, journals, countries, institutions and relevant documents, in addition to trends in this field. Based on the findings, it is concluded that it is necessary to update the curricular designs and create new programs related to industrial automation. It also provides relevant information to update the training plan for teachers on issues related to wireless sensors and security of industrial systems. The research also provides teachers with an overview of the past, present and future of industrial automation, which will allow them to improve their educational practice.
Multi-Robot Path Planning using Dijkstra’s Algorithm with Multi-layer Dictionaries

Path planning is the first task for a Robot to autonomously navigate, especially for autonomous Robots.For multi-Robot systems the process is more complex than a single Robot system.The commonly known algorithms for path planning usually finds solutions for single Robot systems and do not propose ideas for multi-Robot systems.In this paper an enhanced Dijkstra's algorithm for multi-Robot systems with a multi-layer dictionary is used to navigate multiple Robots on an indoor map autonomously and simultaneously.Simulation and Experimental results show that the proposed enhanced algorithm was able to generate paths for the multiple Robots that where navigating through the map simultaneously and assigning optimal or feasible paths for the Robots to navigate through.
Large Remote Sensing Image Segmentation with Stitching Strategy Based on Dominant Color

Large remote sensing image segmentation is a crucial issue in object-based image analysis. It is common sense that a segmentation framework consists of three components: (1) dividing largeremote sensing image into blocks for overcoming the constraint of computer memory; (2) executing segmentation algorithm for each block individually; (3) stitching segmentation results of all blocks into a complete result for eliminating artificial borderscreated by dividing blocks. However, there is a lack of mature technologies to eliminate artificial borders produced by dividing blocks. In this paper, we proposed a new stitching strategy based on the dominant color similarity measure and modified thetraditional methodof dominant color similarity measure to make itmoresuitable for measuring the similarity of two segmented regions. A multi-scale segmentation algorithm is adopted for segmenting each block. External memory is used to store intermediate segmentation results and exchange data with internal memory. We tested the algorithm with three different images and validated that the algorithm can implement the segmentation for large remote sensing images in a common computer. Experiments demonstrate that the stitchingstrategy based on the similarity measure of dominant color can effectively eliminate artificial borders.
KEoG: A knowledge-aware edge-oriented graph neural network for document-level relation extraction

Document-level relation extraction (RE) has attracted more and more attentions recently. Edge-oriented graph neural network (EoG) is a new neural network exhibiting greater potential than previous node-oriented graph neural networks for document-level RE. In this paper, we propose a novel EoG, called knowledge-aware edge-oriented GNN (KEoG) for document-level RE. In KEoG, we further introduce not only two types of nodes to represent documents and external knowledge respectively, but also soft F-Measure loss function to solve the inherent class imbalance problem in document-level RE. Experiments conducted on two document-level datasets show that KEoG outperforms other state-of-the-art methods for comparison on both intra-sentence and inter-sentence relation extractions, indicating that KEoG is an effective extension of EoG.
Relational Generalized Few-Shot Learning

Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available. Although this few-shot learning setup has received a lot of attention recently, most proposed methods focus on discriminating novel classes only. Instead, we consider the extended setup of generalized few-shot learning (GFSL), where the model is required to perform classification on the joint label space consisting of both previously seen and novel classes. We propose a graph-based framework that explicitly models relationships between all seen and novel classes in the joint label space. Our model Graph-convolutional Global Prototypical Networks (GcGPN) incorporates these inter-class relations using graph-convolution in order to embed novel class representations into the existing space of previously seen classes in a globally consistent manner. Our approach ensures both fast adaptation and global discrimination, which is the major challenge in GFSL. We demonstrate the benefits of our model on two challenging benchmark datasets.
Path Planning of Mobile Robot Based on Improved Particle Swarm

Path planning is one of the key technologies of robot navigation and control, in path planning, there are some problems in the application of ant colony algorithm, such as slow convergence speed, poor optimization results and incomplete search. In order to improve the mobile robot's ability to search the optimal path to the target point in the global static environment. In this paper, a double improved fusion algorithm of particle swarm optimization and ant colony algorithm is proposed to solve the path planning problem. Firstly, the occupied grid map is constructed based on visual slam technology of depth camera in static environment; Secondly, the improved particle swarm optimization -ant colony algorithm is used for path planning in the grid map: The sub optimal solution is obtained by using the advantages of global search ability and search speed of improved particle swarm optimization, which is transformed into the increment of initial pheromone distribution in the improved ant colony algorithm, and the exact solution of the path problem is solved by using the positive feedback mechanism of the improved ant colony algorithm; Finally, a robot experimental platform is built to verify the effectiveness and practicability of the improved particle swarm optimization and ant colony fusion algorithm. The experimental results show that the fusion algorithm has a certain guiding role for mobile robot path planning.
Analysis of Student Learning Independence as the Basis for the Development of Digital Book Creations Integrated by Realistic Mathematics

The aim of this study was to determine the category of independence of SMAN Bukittinggi students in learning mathematics and elaborate a number of thoughts and concepts that believe in the importance of digital book creations integrated by realistic mathematics educations in order to achieve independent learning. The method used is descriptive qualitative through the questionnaires and interviews with the mathematics teacher in Senior High School Bukittinggi and several randomly selected students. The results obtained were the category of independence of SMAN Bukittinggi students in learning mathematics was low. Based on elaborate, a few thoughts and concepts believes in the importance of Digital Book Creations Integrated by Realistics Mathematics Educations in order to achieve independent learning. For this reason, it is necessary to carry out research into the development of a creative digital book integrated with realistic mathematics education to guide self-regulated learning.
Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective

Deep learning had already demonstrated its power in medical images, including denoising, classification, segmentation, etc. All these applications are proposed to automatically analyze medical images beforehand, which brings more information to radiologists during clinical assessment for accuracy improvement. Recently, many medical denoising methods had shown their significant artifact reduction result and noise removal both quantitatively and qualitatively. However, those existing methods are developed around human-vision, i.e., they are designed to minimize the noise effect that can be perceived by human eyes. In this paper, we introduce an application-guided denoising framework, which focuses on denoising for the following neural networks. In our experiments, we apply the proposed framework to different datasets, models, and use cases. Experimental results show that our proposed framework can achieve a better result than human-vision denoising network.
ALBA: Reinforcement Learning for Video Object Segmentation.

We consider the challenging problem of zero-shot video object segmentation (VOS). That is, segmenting and tracking multiple moving objects within a video fully automatically, without any manual initialization. We treat this as a grouping problem by exploiting object proposals and making a joint inference about grouping over both space and time. We propose a network architecture for tractably performing proposal selection and joint grouping. Crucially, we then show how to train this network with reinforcement learning so that it learns to perform the optimal non-myopic sequence of grouping decisions to segment the whole video. Unlike standard supervised techniques, this also enables us to directly optimize for the non-differentiable overlap-based metrics used to evaluate VOS. We show that the proposed method, which we call ALBA outperforms the previous stateof-the-art on three benchmarks: DAVIS 2017 [2], FBMS [20] and Youtube-VOS [27].
Sparse Representation Based Despeckling of SAR Images using STDTCWT

Synthetic Aperture Radar (SAR) is a coherent acquisition system and hence images obtained from Synthetic Aperture Radar are difficult to analyze due to the multiplicative speckle noise present in the images. Therefore, despeckling of SAR images is a primary speckle reduction task in SAR image processing. All the existing Despeckling methods focus on speckle reduction but not all methods can preserve the image edge features in SAR images. This paper propose a despeckling algorithm via sparse representation using hybrid Shearlet Transform and Dual Tree Complex wavelet transform that posses both shift invariant and direction selectivity properties in images. The experimental results show that the proposed hybrid Shearlet and Dual Tree Complex Wavelet Transform outperforms the existing methods of denoising in terms of PSNR, ENL, and EPI for MSTAR dataset images. The proposed methodology not only preserves the edges but also enhances the image visually.
Integrating gesture control board and image recognition for gesture recognition based on deep learning

ABSTRACTDue to the rise of the Internet of Things, more devices can connect with the Internet. A large amount of data is collected from devices that could be used for different applications. The development of hardware equipment for the Internet of Things not only use for industrial but also for smart homes. The smart home covers broad topics, including remote control of home applications, sensing of humans, temperature-controlling air conditions, and security monitors. When we carry out these topics, the human-machine interface is essential for system applications. A gesture recognition system is applied to many real applications. The reason is that the accuracy rate and real factors are complicated. They commonly use of gesture control service in the market is the sensor board of gesture control. The principle is using the electric field to change and determine the gestures. The limitation requires the close operation, and there is a problem of critical point sensitivity. In this paper, we use the gesture control board to combine with gesture image recognition methods to perform the double authentication gesture recognition. Raspberry Pi is the control center to integrate the intelligent light bulb. HUE makes a gesture recognition system. The results explain that the accuracy rate of the gesture recognition proposed is 90%. Meanwhile, it is higher than the SVM method.