Reviewerly/exHarmony huggingface dataset:
Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation

Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver. However, only transmitting task-relevant information without introducing any redundancy may cause robustness issues in learning due to the channel variations, and the JSCC which directly maps the source data into continuous channel input symbols poses compatibility issues on existing digital communication systems. In this paper, we address these two issues by first investigating the inherent tradeoff between the informativeness of the encoded representations and the robustness to information distortion in the received representations, and then propose a task-oriented communication scheme with digital modulation, named discrete task-oriented JSCC (DT-JSCC), where the transmitter encodes the features into a discrete representation and transmits it to the receiver with the digital modulation scheme. In the DT-JSCC scheme, we develop a robust encoding framework, named robust information bottleneck (RIB), to improve the communication robustness to the channel variations, and derive a tractable variational upper bound of the RIB objective function using the variational approximation to overcome the computational intractability of mutual information. The experimental results demonstrate that the proposed DT-JSCC achieves better inference performance than the baseline methods with low communication latency, and exhibits robustness to channel variations due to the applied RIB framework.
Prototype robot rover with Arduino, LabVIEW and mobile devices

Terrestrial robot prototype all – terrain explorer, for exploration of areas that pose a danger to humans was developed. Mega Arduino microcontroller was used for remote control of the robot's movements as well as shipping and receiving signals in a wireless way. The visual interface is adapted to be used with mobile devices, with which the robot can be controlled, and also signal processing for decision- making. This visual interface was developed in LabVIEW executable mode, so this software not need be installed on the mobile device. A comparison between RF technologies and Wi - Fi point to point, no significant difference was found between them, with respect to the wireless functionality of the robot. The terrestrial prototype designed has four wheels, it is functional is independent and can be moved into different types of surface and places where humans cannot access, either for safety or for the same site. The proof of concept was successful, the prototype is expandable in functionality, appropriate technologies and application-specific functions can be integrated into the overall scheme.
Constraints Penalized Q-learning for Safe Offline Reinforcement Learning

We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while satisfying safety constraints given only offline data, without further interaction with the environment. This problem is more appealing for real world RL applications, in which data collection is costly or dangerous. Enforcing constraint satisfaction is non-trivial, especially in offline settings, as there is a potential large discrepancy between the policy distribution and the data distribution, causing errors in estimating the value of safety constraints. We show that na\"ive approaches that combine techniques from safe RL and offline RL can only learn sub-optimal solutions. We thus develop a simple yet effective algorithm, Constraints Penalized Q-Learning (CPQ), to solve the problem. Our method admits the use of data generated by mixed behavior policies. We present a theoretical analysis and demonstrate empirically that our approach can learn robustly across a variety of benchmark control tasks, outperforming several baselines.
Mimicking a Complete Life-Cycle of Fiat Currency in One E-cash System

The electronic cash was introduced by Chaum in 1982 and now many e-cash systems have been proposed in order to mimic the fiat currency. Bitcoin provides us with an attractive way to construct a decentralized e-cash system. Ideally, we would like to make the system more practical, for example, the users can be able to transfer coins between each other multiple times and they can also withdraw arbitrary amount of coins rather than one or the predefined number, so that in the spend protocol the user can spend any amount of valid coins. In this paper, we propose a provably secure and more practical e-cash system. Firstly, it can provide the anonymous transfer of coins between users, so that the merchant can spend the received coins further; secondly, the user can withdraw arbitrary amount of coins rather than the one or predefined number; thirdly, during the transfer of coins, the coins have a fixed size; finally, the fair exchange between the users can also be achieved.
Uncertainty-Oriented Textual Marker Selection for Extracting Relevant Terms from Job Offers

Automated resume ranking aims at selecting and sorting pertinent resumes, among those sent to answer a given job of er. Most of the screening and elimination process relies on the resumes’ content, marginally including information of the job of er. In this sense, currently available resume ranking approaches lack of accuracy in detecting relevant information in job of ers, which is imperative to assure that selected resumes are pertinent. To improve the extraction of relevant terms that represent significant information in job of ers, we study the uncertainty-oriented selection of 16 textual markers – 10 obtained by examining the behaviour of expert recruiters and 6 from the literature – according to two approaches: fuzzy logistic regression and fuzzy decision trees. Results indicate that globally, fuzzy decision trees improve the F1 and recall metrics, by 27% and 53% respectively, compared to a state-of-the-art term extraction approach.
Prediction Algorithm of Guiding Effectiveness based on Fuzzy Data Network Structure Specification with Character Color Class

This paper summarizes the short-term market forecast of teaching effectiveness and the theoretical basis of fuzzy theory and deep belief network, studies the learning algorithm of DBN model, and constructs a fuzzy deep learning network model for stock price forecasting through experiments. For the first time, the deep learning network was applied to the market prediction of teaching effectiveness, and the model effect was tested and analyzed, and the DBN was determined through experiments to have the most structure. The abnormal teaching evaluation data was eliminated by using the improved cosine dissimilarity algorithm, and the normalization method was adopted. The standardization method standardizes the evaluation data, and uses the traditional K-modes algorithm to perform a preliminary cluster analysis on the evaluation data, and points out three problems.
A Study of the Impact of Canara Bank’s Loan Grants to Businesses on the Post-pandemic from A Borrower’s Perspective

The purpose of the study was to examine the performance of Canara Bank on post covid and their relationship with the borrowers. Banks are important social organizations rendering various financial services to their customers. A well-structured questionnaire was employed to collect data from the respondents with a sample size numbering 75. It focuses on the study of Canara Banks towards business on post covid from the borrower's perspective. Statistical tools like simple percentage analysis, Chi-Square test, regression analysis were used in this study. This analysis revealed certain engaging results regarding the customer expectation towards loan granting facilities.
FORMATION OF A MOTIVATIONAL-VALUE ATTITUDE TO REGULAR PHYSICAL LOADS IN TECHNICAL UNIVERSITES

The article discusses the formation of a motivational-value attitude to regular physical activity of students of technical universities in order to attract them to a healthy lifestyle.
Smart Health Care System for Elders’ Home to Monitor Physical and Mental Health in a Controlled Environment

Nowadays, with the busyness of lifestyle, the focus on parents has diminished. At the same time, the need for nursing homes has increased. As the number of elderly people in nursing homes increases, the number of elderly people living there cannot be considered individually. And nowadays, people are not willing to work in such places. Because they have to work without pay or at low wages. About 95% of seniors in nursing homes have poor health. And their behavior patterns are very different. For these reasons, the authority to manage nursing homes faces a major challenge. They have a big role to play in giving each adult proper medication and healing their mental level. As a solution to this problem, authors hope to compare it with technology and provide a solution. Authors decided to create the relevant items and software for this. This is done using an adult friendly handrail and camera system in the proposed methods. Advanced ML, image processing and hardware parts are used in this technology world. This allows you to focus on each adult 24 h throughout the day. One of the main benefits of this is that it pays more attention with less staff. According to our survey, this has proved to be an essential factor for nursing homes. By creating this, it will be possible to provide healthier and more productive services to the elderly.
Compressed and Encrypted Acquisition of Transient Signals

A shockwave signal has the characteristics of short duration and wide dynamic range, so the wireless test system needs to collect it at a continuous high sampling rate, resulting in a short node life cycle and a large amount of redundant data during the test process. Moreover, test data with confidentiality, if it is still recorded and transmitted in plaintext, will challenge information security. Therefore, a compressed and encrypted acquisition framework based on hyperchaotic compressed sensing and semi-tensor is proposed in this paper. Firstly, a new four-dimensional hyperchaotic system is used to generate a compressed sensing measurement matrix to improve the encryption and anti-noise performance. Secondly, for saving memory space and improving computing efficiency, the semi-tensor product is introduced to reduce the dimension of the measurement matrix exponentially. The experimental results show that the proposed framework can realize the compressed and encrypted acquisition of transient signals with small memory space and low computational complexity. And the acquisition results can be directly transmitted to meet the requirements of real-time wireless transmission bandwidth and information security.
Extreme-scale Talking-Face Video Upsampling with Audio-Visual Priors

In this paper, we explore an interesting question of what can be obtained from an $8\times8$ pixel video sequence. Surprisingly, it turns out to be quite a lot. We show that when we process this $8\times8$ video with the right set of audio and image priors, we can obtain a full-length, $256\times256$ video. We achieve this $32\times$ scaling of an extremely low-resolution input using our novel audio-visual upsampling network. The audio prior helps to recover the elemental facial details and precise lip shapes and a single high-resolution target identity image prior provides us with rich appearance details. Our approach is an end-to-end multi-stage framework. The first stage produces a coarse intermediate output video that can be then used to animate single target identity image and generate realistic, accurate and high-quality outputs. Our approach is simple and performs exceedingly well (an $8\times$ improvement in FID score) compared to previous super-resolution methods. We also extend our model to talking-face video compression, and show that we obtain a $3.5\times$ improvement in terms of bits/pixel over the previous state-of-the-art. The results from our network are thoroughly analyzed through extensive ablation experiments (in the paper and supplementary material). We also provide the demo video along with code and models on our website: \url{http://cvit.iiit.ac.in/research/projects/cvit-projects/talking-face-video-upsampling}.
Analysis on Norms of Word Embedding and Hidden Vectors in Neural Conversational Model Based on Encoder-Decoder RNN

A neural conversational model (NCM) based on an encoder-decoder recurrent neural network (RNN) with an attention mechanism learns different sequence-to-sequence mappings from what neural machine translation (NMT) learns even when based on the same technique. In the NCM, we confirmed that target-word-to-source-word mappings captured by the attention mechanism are not as clear and stationary as those for NMT. Considering that vector norms indicate a magnitude of information in the processing, we analyzed the inner workings of an encoder-decoder GRU-based NCM focusing on the norms of word embedding vectors and hidden vectors. First, we conducted correlation analyses on the norms of word embedding vectors with frequencies in the training set and with conditional entropies of a bi-gram language model to understand what is correlated with the norms in the encoder and decoder. Second, we conducted correlation analyses on norms of change in the hidden vector of the recurrent layer with their input vectors for the encoder and decoder, respectively. These analyses were done to understand how the magnitude of information propagates through the network. The analytical results suggested that the norms of the word embedding vectors are associated with their semantic information in the encoder, while those are associated with the predictability as a language model in the decoder. The analytical results further revealed how the norms propagate through the recurrent layer in the encoder and decoder.
Sample Selection Approach with Number of False Predictions for Learning with Noisy Labels

In recent years, deep neural networks (DNNs) have made a significant impact on a variety of research fields and applications. One drawback of DNNs is that it requires a huge amount of dataset for training. Since it is very expensive to ask experts to label the data, many non-expert data collection methods such as web crawling have been proposed. However, dataset created by non-experts often contain corrupted labels, and DNNs trained on such dataset are unreliable. Since DNNs have an enormous number of parameters, it tends to overfit to noisy labels, resulting in poor generalization performance. This problem is called Learning with Noisy labels (LNL). Recent studies showed that DNNs are robust to the noisy labels in the early stage of learning before over-fitting to noisy labels because DNNs learn the simple patterns first. Therefore DNNs tend to output true labels for samples with noisy labels in the early stage of learning, and the number of false predictions for samples with noisy labels is higher than for samples with clean labels. Based on these observations, we propose a new sample selection approach for LNL using the number of false predictions. Our method periodically collects the records of false predictions during training, and select samples with a low number of false predictions from the recent records. Then our method iteratively performs sample selection and training a DNNs model using the updated dataset. Since the model is trained with more clean samples and records more accurate false predictions for sample selection, the generalization performance of the model gradually increases. We evaluated our method on two benchmark datasets, CIFAR-10 and CIFAR-100 with synthetically generated noisy labels, and the obtained results which are better than or comparative to the-state-of-the-art approaches.
A Cost Effective and Energy Efficient Algorithm for Cloud Computing

Cloud-Computing offers high performance solution to solve complex engineering and scientific tasks by deploying resources at geo-diverse locations. With the large-scale demand of scientific and engineering jobs, huge number of cloud data centres needs to be constructed to fulfil the requirement of the jobs. The extensive use of cloud data centres leads to increases in cost as well as energy consumption. In this paper, an agent based Cost-Effective Energy Efficient scheduling algorithm, namely, CEEE has been proposed. To establish the effectiveness of the proposed algorithm, simulation is performed on CloudSim environment. The proposed algorithm is compared with state of art scheduling algorithms namely, Randam and MaxUtil. The experimental results demonstrates that CEEE algorithm is capable of refining energy efficiency with reduced cost. The proposed algorithm outperforms the prevailing algorithms in terms of energy consumption, resource utilization, number of hosts in sleep mode and completion time.
CNN-Based Optical Character Recognition for Isolated Printed Gujarati Characters and Handwritten Numerals

Optical character recognition (OCR) technologies have made significant progress in the field of language recognition. Gujarati is a more difficult language to recognize compared to other languages because of curves, close loops, the inclusion of modifiers, and the presence of joint characters. So great effort has been laid into the literature for Gujarati OCR. Recently deep learning-based CNN models are applied to develop OCR for different languages but Convolutional Neural Networks (CNN) models are not yet giving a satisfactory performance to recognize Gujarati characters. So, this paper proposes a revolutionary Gujarati printed characters and numerals recognition CNN models. CNN-PGC (CNN for - Printed Gujarati Character) and CNN-HGC (CNN for - Handwritten Gujarati Character) are two optimally configured Convolutional Neural Networks (CNNs) presented in this research for printed Gujarati base characters and handwritten numbers, respectively. Concerning particular performance indicators, the suggested work's performance is evaluated and proven against that of other traditional models and with the latest baseline methods. Experimental analysis has been carried out on well-segmented newly generated Gujarati base characters and numerals dataset which includes 36 consonants, 13 vowels, and 10 handwritten numerals. Variation in the database is also taken into consideration during experiments like size, skew, noise blue, etc. Even in the presence of printing irregularities, writing irregularities, and degradations the proposed method achieves a 98.08% recognition rate for print characters and a 95.24 % recognition rate for handwritten numerals which is better than other existing models.
Realize the safe storage and efficient sharing of educational resources based on IPFS+ blockchain

Guided by the framework Construction of University High-quality Education Resource Platform Based on Block Chain [1], the paper introduces IPFS as the data repository of education resources, and realizes the safe storage and efficient sharing of university high-quality education resources by combining IPFS+ block chain technology, and completes the bottom construction of university high-quality education resource platform.Ensure the efficient and secure uploading and sharing of educational data.
Continuous Ship Unloader Availability Analysis Using Association Rules Method with Apriori Algorithm

&#x0D; Petrokimia Gresik is a company engaged in the argo industry. In an effort to increase productivity, PT. Petrokimia Gresik has a special port used for loading and unloading activities. During loading and unloading activities, special equipment is needed to make it easier to move cargo. Not infrequently this equipment suffers damage that is not known what object affects the damage. Based on the 2021 Asset Utilization Data, internal tools whose availability is still below the target of more than 1% are CSU I and 81.66%. The percentage of equipment availability that is below the target causes the process of unloading raw materials to be not optimal and can cause demurrage costs or company fines to the ship if the cause of the damage is not immediately identified and addressed. This study aims to help companies obtain information about objects that affect CSU I and CSU II experiencing breakdowns. The role of data mining that will be used in this research is association rules with a priori algorithms. Data processing is assisted by Microsoft Excel, RapidMiner software and WEKA software. There are no association rules that are formed with the application of a minimum support value of 50% and a minimum value of 50% confidence, both in CSU I and CSU II data processing. The association rules formed by applying a minimum support value of 20% and a minimum confidence value of 50% for CSU I data processing are 3 rules, while the association rules obtained for CSU II data processing are 2 rules. Based on the rules formed in CSU I and CSU II, the breakdown item that is likely to be damaged is Vertical – Motor 2M1.&#x0D;
Accurate solar-power integration: Solar-weighted Gaussian quadrature

In this technical note, we explain how to construct Gaussian quadrature rules for efficiently and accurately computing integrals of S(x)f(x) where S(x) is the solar irradiance function tabulated in the ASTM standard and f(x) is an arbitary application-specific smooth function. This allows the integral to be computed accurately with a relatively small number of f(x) evaluations despite the fact that S(x) is non-smooth and wildly oscillatory. Julia software is provided to compute solar-weighted quadrature rules for an arbitrary bandwidth or number of points. We expect that this technique will be useful in solar-energy calculations, where f(x) is often a computationally expensive function such as an absorbance calculated by solving Maxwell's equations.
Breaking Time Invariance: Assorted-Time Normalization for RNNs

Methods such as Layer Normalization (LN) and Batch Normalization (BN) have proven to be effective in improving the training of Recurrent Neural Networks (RNNs). However, existing methods normalize using only the instantaneous information at one particular time step, and the result of the normalization is a preactivation state with a time-independent distribution. This implementation fails to account for certain temporal differences inherent in the inputs and the architecture of RNNs. Since these networks share weights across time steps, it may also be desirable to account for the connections between time steps in the normalization scheme. In this paper, we propose a normalization method called Assorted-Time Normalization (ATN), which preserves information from multiple consecutive time steps and normalizes using them. This setup allows us to introduce longer time dependencies into the traditional normalization methods without introducing any new trainable parameters. We present theoretical derivations for the gradient propagation and prove the weight scaling invariance property. Our experiments applying ATN to LN demonstrate consistent improvement on various tasks, such as Adding, Copying, and Denoise Problems and Language Modeling Problems.
Accepted Tutorials at The Web Conference 2022

This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% of these are hands on.