Reviewerly/exHarmony huggingface dataset:
Latent-Separated Global Prediction for Learned Image Compression.

Over the past several years, we have witnessed the impressive progress of learned image compression. Recent learned image codecs are based on auto-encoders, that first encode an image into low-dimensional latent representations and then decode them for reconstruction. To capture spatial dependencies in the latent space, prior works exploit hyperprior and spatial context model to facilitate entropy estimation. However, they are hard to model effective long-range dependencies of the latents. In this paper, we explore to further reduce spatial redundancies among the latent variables by utilizing cross-channel relationships for explicit global prediction in the latent space. Obviously, it will generate bits overhead to transmit the prediction vectors that indicate the global correlations between reference point and current decoding point. Therefore, to avoid the transmission of overhead, we propose a 3-D global context model, which separates the latents into two channel groups. Once the first group is decoded, the proposed module will leverage the known group to model spatial correlations that guide the global prediction for the unknown group and thus achieve more efficient entropy estimation. Besides, we further adopt split attention module to build more powerful transform networks. Experimental results demonstrate that our full image compression model outperforms standard VVC/H.266 codec on Kodak dataset in terms of both PSNR and MS-SSIM, yielding the state-of-the-art rate-distortion performance.
Data Encryption for IoT Applications Based on Two-Parameter Fuss–Catalan Numbers

The Internet of Things (IoT) brings a new set of security concerns, especially regarding data integrity and confidentiality. Encryption protects data from unauthorized access or a potential attacker in IoT applications using an algorithm and a key to transform a plaintext into an encrypted text. This chapter presents an analysis of the Fuss–Catalan number properties (or objects) and the possibility of their application in data encryption. We present a new cryptographic method based on the lattice path combinatorics. The presented method consists of five phases: definition, conversion, generation, selection, and inversion. The basic step is the definition of input parameters, followed by converting text (or image) to binary notation and split binary sequence into blocks. Then, the next step is generating the Fuss–Catalan key and defining additional parameters in the lattice path space. The next phase is bit selection on the lattice path based on the generated key. The last step is the inversion of the selected bits. The proposed method was implemented in the Java Net Beans environment. In the experimental part of this chapter, we present a comparative analysis of the proposed method and some of the previously published methods.
CREST project focused on neuromorphic devices and networks

The artificial neural network is a type of electronic circuit modelled after the human brain. It contains thousands of artificial neurons and synapses that, in general, assemble to execute algorithms that can allow the neural network to incorporate a large amount of input data. One of the algorithms is known as deep learnig (DL), which is a kind of statistical processing to learn and infer several features of the big data while consuming tremendous energy. A team, led by Dr Isao H Inoue of the National Institute of Advanced Industrial Science and Technology (AIST), is working on a five-and-a-half-year CREST project until March 2025 to develop a novel neuromorphic architecture that can do the learning and inference without using such an algorithm, thus in low power consumption.
Routing Attention Shift Network for Image Classification and Segmentation

Deep neural networks as fundamental tools of deep learning have evolved remarkably in various tasks; however, the computational complexity and resources costs rapidly increased when using deeper networks, which challenges the deployment of the resource-limited devices. Recently, shift operation is considered as an alternative.to depthwise separable convolutions, using 60% fewer parameters compared spatial convolutions. Its basic block is composed by shift operations and 1 $$\times $$ 1 convolution in the intermediate feature maps. Previous works focus on optimizing the redundancy of the correlation between shift groups, making shift to be a learnable parameter, which yields more time to train and higher computation. In this paper, we propose a “dynamic routing” strategy to seek the best movement for shift operation based on attention mechanism, termed Routing Attention Shift Layer (RASL), which measures the contribution of channels to the outputs without back propagation. Moreover, the proposed RASL shows strong generalization to many tasks. Experiments on both classification and semantic segmentation tasks demonstrate the superior performance of the proposed methods.
Motion and Gesture Controlling

Hand gesture is an innovative technique of interaction between human and computer. Hand gesture recognition is a technology that is becoming increasingly relevant, given the recent growth and popularity of Virtual and Augmented Reality technologies. In this paper, we represent a novel approach for better human computer interaction (HCI) where we are using hand gesture recognition using real-time camera in order to overcome the drawbacks of existing methods and more user friendly. Our method is to use a camera and computer vision technology, such as image segmentation and gesture recognition, to control keyboard tasks and Hand motions are acquired using web camera based on color detection.
Best Practices – Promising Initiatives at TCS

Best practices are implemented for better performance of the organisation - they improve productivity, increase efficiency of operations and also help in optimization of resources. They are generally accepted as superior to any alternative. The use of Information Communication Technology (ICT) and digitization have changed the organizational culture of libraries. New well defined, user focused best practices using ICT in the library field are being initiated to ensure success.This paper describes some of the best practices followed at Information Resource Centre (IRC), Tata Consultancy Services (TCS), a global leader in IT services.Paper discusses importance and benefits of introducing best practices such as Internal Audit, Webex Review Meet, Pop up library and Book for Book at TCS IRCs. These are the processes that are being implemented to improve IRC’s processes, activities; optimization and deliver value add services to its users.
An Ensemble Learning Approach to Improve Tracking Accuracy of Multi Sensor Fusion

Finding or tracking the location of an object accurately is a key problem in defence applications as well as the problem of object localization in the fields of robotics and computer vision. Radars fall into the spectrum of high-end defence sensors or systems on which the security and surveillance of the entire world depends. There is much focus on the topic of Multi Sensor Fusion (MSF) in recent years with radars as the sensors. In this paper, we focus on the problem of asynchronous observation of data which can reduce the tracking accuracy of an MSF system comprised of radars of different types at different locations. Our solution utilizes a machine learning approach to train models on hundreds of hours of (anonymized real) Multi Sensor Fusion data provided by radars performing tracking activity across the Indian airspace. Our approach comprises of 3 steps: In step 1, we train an ensemble model of logistic regression and Xgboost to predict Splitting error. In step 2, we use Xgboost to predict the second type of error, namely Merging error to improve the tracking accuracy further. The third step uses nearest neighbour search to compensate for the predicted errors by retaining the data points removed in the first step while maintaining the tracking accuracy. Our experimental results show that the trained models provide reasonable predictions of errors and increase the accuracy of tracking by 15% while retaining 100% of the data.
La competencia digital docente: una perspectiva global

The transformation of our society into an information and knowledge society mediated by Information and Communication Technologies (ICT) as well as the demand for quality education and the need to make a reflexive use of ICT in favor of processes of teaching and learning, pose challenges and reflections in education due to the demands that these transformations generate in how society is organized, works, relates and learns (Valencia-Molina &amp; Serna-Collazos, 2016). This article addresses the term digital competence, as well as the analysis of four frames of reference for the measurement of this competence that every teacher participating in the knowledge society should develop, enhance and evaluate for the improvement of their teaching practice by incorporating the Technology in their educational processes. The frameworks of UNESCO, ISTE and INTEF that propose dimensions of observation and grouping of competences associated with this competence are analyzed, to establish similarities and differences in the search for a model framework.
Place-based design as method of accessing memories and meanings: Historical augmentation in the harbor promenade of Lahti

In this chapter, we present an analysis of participant experiences of an augmented reality (AR) application that was piloted in the harbor promenade area (Satamaraitti) in the city of Lahti, Finland. The application was designed to digitally augment the historically interesting promenade and its surrounding area, which is a popular site for outdoor enjoyment. Overall, we chose to utilize an architectural, place-based approach to inform the participatory design process, the design of the application, the research method, and the analysis of our participants’ experiences. Here, we focus on the latter, employing the concepts of embodiment and emplacement to connect our analysis to existing research literatures. Our findings demonstrate the importance of participants’ personal histories with the place in question—that our AR application was able to connect these individual personal histories to the identity of the place itself, rendering the experience meaningful for our participants. While these results are exploratory and must be understood within their context, they suggest that designers of urban technologies would do wisely to connect their designs with the identity of place as a method of engaging also their participants’ individual experiences in a meaningful way.
Web Based Application for Cloud Security and Compliance

cloud computing service is the most important services for many of the organizations. The service providers must ensure of their security and protection techniques to guarantee to protect the client data. There are some cloud security guidelines that supports the cloud data security are need to be followed by the vendors. Despite the fact, the vendors are facing the mess of security and protection controls and eventually leads to an confusion among the consumers on concerning the safety efforts and whether their measures satisfies the security measures. An inclusive report to survey the risk and security issues faced by cloud consumers have depicted to control the hazards. In light of this investigation, an ontology describing the cloud security controls, threats and compliances framed. a web based cloud application is designed to recommend the cloud security policies from the ontology such that it also helps the existing cloud providers. Security strategies can also be planned by the consumers by utilizing the web application that describing the ontology
COMPARISON OF ADAPTIVE BOOSTING AND BOOTSTRAP AGGREGATING PERFORMANCE TO IMPROVE THE PREDICTION OF BANK TELEMARKETING

Background: Telemarketing is an effective marketing strategy lately, because it allows long-distance interaction making it easier for marketing promotion management to market their products. But sometimes with incessant phone calls to clients that are less potential to cause inconvenience, so we need predictions that produce good probabilities so that it can be the basis for making decisions about how many potential clients can be contacted which results in time and costs can be minimized, telephone calls can be more effective, client stress and intrusion will be reduced. strong. Method: This study will compare the classification performance of Bank Marketing datasets from the UCI Machine Learning Repository using data mining with the Adaboost and Bagging ensemble approach, base algorithm using J48 Weka, and Wrapper subset evaluation feature selection techniques and previously data balancing was performed on the dataset, where the expected results can be known the best ensemble method that produces the best performance of both. Results: In the Bagging experiment, the best performance of Adaboost and J48 with an accuracy rate of 86.6%, Adaboost 83.5% and J48 of 85.9%Conclusion: The conclusion obtained from this study that the use of data balancing and feature selection techniques can help improve classification performance, Bagging is the best ensemble algorithm from this study, while for Adaboost is not productive for this study because the basic algorithm used is a strong learner where Adaboost has Weaknesses to improve strong basic algorithm.
Open Event Trigger Recognition Using Distant Supervision with Hierarchical Self-attentive Neural Network

Event trigger recognition plays a crucial role in open-domain event extraction. To address issues of prior work on restricted domains and constraint types of events, so as to enable robust open event trigger recognition for various domains. In this paper, we propose a novel distantly supervised framework of event trigger extraction regardless of domains. This framework consists of three components: a trigger synonym generator, a synonym set scorer and an open trigger classifier. Given the specific knowledge bases, the trigger synonym generator generates high-quality synonym sets to train the remaining components. We employ distant supervision to produce instances of event trigger, then organizes them into fine-grained synonym sets. Inspired by recent deep metric learning, we also propose a novel neural method named hierarchical self-attentive neural network (HiSNN) to score the quality of generated synonym sets. Experimental results on three datasets (including two cross-domain datasets) demonstrate the superior of our proposal compared to the state-of-the-art approaches.
Cross-grained context guided Chinese entity extraction with graph convolutional network

Entity extraction is an attractive but challenging research filed. In recent years, the development of deep learning has promoted entity extraction techniques. However, owing to underutilizing multi-grained context, existing entity extraction methods obtain limited performance. Actually, cross-sentence information is capable of relieving the problem of ambiguity in entity extraction, especially for Chinese language. Instead of exploiting single intra-sentence context, we propose a cross-grained context guided Chinese entity extraction model by mining and integrating inter-sentence, intra-sentence and document-level information. Specifically, a document graph is constructed to simultaneously represent cross-grained information for a Chinese article using dependency syntax. Certain rules are designed to link syntactic trees of sentences to a weighted graph. Then an entity extraction model is established using GCN network to capture deep relations between words on the graph. Finally, cross-grained information embedded by GCN is used to predict entity mentions via multiclassification. Experiment results show that our method has achieved comparable performance to two baselines on Chinese discourse-level literature datasets, with average accuracy, recall, and F1 scores of 79.02%, 67.41%, and 78.08%, respectively.
Research support services of Panjab University library during lockdown period due to COVID-19: A survey

The study explores the problems and prospects of newly introduced service “RemoteXs” by the A C Joshi library for the faculty of Panjab University during the lockdown due to COVID-19 The paper discusses expectations and experiences of users, advantages of new research support services, challenges faced by faculty while accessing literature through RemoteXs service An online survey was carried out;framed questionnaire was sent to 650 academicians through Whatsapp and e-mail 148 respondents submitted their views on access and usage of e-resources through RemoteXs service of University Library Data on most frequently used resources and documents type has been analysed Some of the problems while accessing e-resources through RemoteXs have also been discussed in the paper Some of the suggestions and remarks on faculty members have been included in the study to know the user's perception and requirements © 2020 All Rights Reserved
Upper bound on mutual quantum information between two partially mixed qubits

Polarization entangled photons become partially mixed when transmitted through channels with polarization mode dispersion. We experimentally build a channel that maximizes the amount of mutual quantum information shared between two qubits for a given amount of entanglement left in the two photon system.
Research on Image Encryption Based on Wavelet Transform Integrating with 2D Logistic

In order to protect them from theft, it is necessary to encrypt image data. When considering the security algorithm of image information security, we must consider its particularity. Two-dimensional Logistic chaotic mapping is introduced in the design of pixel gray value substitution, and two substitution ideas are adopted. That is to say, the low-frequency coefficient matrix after wavelet decomposition is scrambled first, and the image after wavelet transform is replaced by the pixel value. The encryption system constructed by the chaotic dynamic model can still solve the information better after the multimedia information is processed by some signals. The combination of the lifting wavelet transform in the frequency domain and the chaotic sequence in the spatial domain is used for image encryption, which has good encryption effect and practical prospect.
Role of Siraiki in Teaching/Learning English at Graduation Level: A Case Study from the Southern Punjab, Pakistan

The focus of the present research was to find out the Role of Siraiki Language in Teaching/Learning English Language at graduation Level. By this study the researcher attempted to expose the intentions for which the students and instructors make use of Siraiki in their English language classroom inside and outside activities and in the same way, to show the actions and the situations in which they select for not using their L1. To explore a clear comprehension of the current subject matter, the research targeted on the 577 students and the 156 teachers. Data was collected through questionnaires. The data was examined through SPSS (statistical package for social sciences) 23 version. Data were inspected using descriptive analysis, Analysis of variance (ANOVA), T-Test and Cronbach's alpha. The findings of the present study showed that the students and the teachers indicated highly positive perceptions concerning the use of Siraiki language in EFL classroom.
Artificial Intelligent Robot for Enforcement Surveillance (AIRES)

This project represents an idea to provide a quick, easy plug and play drone and robot for the military as assistance in the war zone area. Most of the time army has to wait for the drone to collect information from the battlefield or terrorist site, it requires a lot of time and money as the drones fly from an Air Base. These are usually known as UAVs, and they are huge, often detected by the RADAR. The idea is to make a smaller drone which a soldier can carry in his backpack, can fly and use it for quick surveillance before entering a danger zone. As it is difficult to get into the buildings with large drones, this project can help to make surveillance's even inside of the building as we will be using smaller drones. Most of the military drones are very expensive and therefore cannot be used every time, and if the drone crashes, the loss is beyond recovery. Hence such drones are operated only in rare cases. To make a drone that can be used for the military purpose, the control system must be more precise, and the drone must sustain windy conditions and cold weather. The challenge is when the drone has to slip through the wind and also to maintain the speed judged by the pilot. Therefore, powerful motors which achieve high velocity to function against the wind are used. Also, expertise is required in piloting the drone.
Customizable GAN: Customizable Image Synthesis Based on Adversarial Learning

In this paper, we propose a highly flexible and controllable image synthesis method based on the simple contour and text description. The contour determines the object’s basic shape, and the text describes the specific content of the object. The method is verified in the Caltech-UCSD Birds (CUB) and Oxford-102 flower datasets. The experimental results demonstrate its effectiveness and superiority. Simultaneously, our method can synthesize the high-quality image synthesis results based on artificial hand-drawing contour and text description, which demonstrates the high flexibility and customizability of our method further.
Integración didáctica de “apps” relacionadas con la producción animal en la docencia universitaria del Grado de Ingeniería Agroalimentaria y Agroambiental

The objective of this work is the didactic integration of apps that are used in the management of livestock farms in the Degree in Agro-Food and Agro-Environmental Engineering. The academic results indicated that the scores improved by 1.67 points when the apps were included in the teaching. In addition, a questionnaire was carried out for the student, resulting that more than 66% of the students answered "Totally agree" to the sentence "The proposed task has made it easier for me to learn the subject". This was also the response for 33% of the students to the sentences "The use of apps has helped me understand the subject" and "I would recommend the use of apps in other subjects of the subject". The answer "Neither agree nor disagree" was the answer selected by 66% of the students for the sentence "The use of apps has brought me closer to that of the livestock sector". The conclusions of this work are that the introduction of apps in teaching improves the academic performance of the student as they facilitate learning. It would be advisable to use this methodology to other subject.
PERAN PEREMPUAN DALAM KUMPULAN CERPEN BOBO EDISI JANUARI-DESEMBER 2019 (KAJIAN SASTRA FEMINISME)

This research is motivated to see the role of women in the collection of short stories Bobo January-December 2019 edition. The object of this research is the Bobo short stories January-December 2019 edition. This type of research is descriptive qualitative. The data collection technique used was literature study. The data analysis technique was carried out by using the Moser model analysis technique. The results of the research obtained from the January-December 2019 edition of Bobo's short stories show the productive and reproductive roles of women.