Reviewerly/exHarmony huggingface dataset:
Energy Efficient MIMO-OFDM Spectrum Sensing Using Deep Stacked Spiking Delayed Feedback Reservoir Computing

This article introduces a novel energy efficient spectrum sensing method for multiple-input multiple-output and orthogonal frequency division multiplexing (MIMO-OFDM) systems in dynamic spectrum sharing (DSS) environments. To be specific, a spiking reservoir computing (RC) based approach is introduced for spectrum sensing of MIMO-OFDM systems to take advantage of the spatial and temporal correlations of the environment. In particular, we develop a spiking delayed feedback reservoir (DFR). DFRs are a new class of recurrent neural networks (RNNs) which tackle the vanishing gradient problem of traditional RNNs while spiking neurons are energy efficient and biologically plausible neuron models. We extend our introduced model in time and space domains to capture the spatial and temporal correlations in DSS environments. Conditional generative adversarial networks (cGANs) are introduced to tackle the data scarcity issue arose from applying machine learning techniques to MIMO-OFDM systems where training data is limited. Simulation results suggest that the probability of detection of the introduced RC based spectrum sensing at the low signal-to-noise (SNR) regime is significantly higher than the state-of-the-art techniques and the computational complexity is also reduced compared with traditional RNNs.
An enhanced color visual cryptography scheme based on the binary dragonfly algorithm

Visual cryptography is an encryption technique that decomposes a secret image into multiple shares that are digitally ‘overlapped’ based on logical operations to recover the original secret image. Each individual share cannot reveal clues about the secret image. Existing visual cryptography schemes suffer from problems such as pixel expansion, high computational complexity and poor decryption quality. To overcome these problems, we propose a new 2-out-of-2 secret sharing scheme for color images based on the binary dragonfly algorithm. The dragonfly algorithm is used to determine optimal color levels for the encryption process, which leads to reconstructed images with the higher quality upon decryption with minimal effect on the overall computational complexity. The proposed scheme is also non-expansible, whereby the share images are of the same size as the original secret image. This leads to reduced memory requirements as well as improved image quality. We analyze the strength of the proposed scheme based on a standard set of metrics which includes an evaluation of its resistance against cryptanalytic attacks, histogram, correlation, entropy and encryption quality. Results show that the proposed visual cryptography scheme has desired properties such as improved recovered image quality, near-ideal statistical characteristics and fast encryption speed, depicting an improvement over existing schemes.
Data augmentation with Mobius transformations

Abstract Data augmentation has led to substantial improvements in the performance and generalization of deep models, and remains a highly adaptable method to evolving model architectures and varying amounts of data—in particular, extremely scarce amounts of available training data. In this paper, we present a novel method of applying Möbius transformations to augment input images during training. Möbius transformations are bijective conformal maps that generalize image translation to operate over complex inversion in pixel space. As a result, Möbius transformations can operate on the sample level and preserve data labels. We show that the inclusion of Möbius transformations during training enables improved generalization over prior sample-level data augmentation techniques such as cutout and standard crop-and-flip transformations, most notably in low data regimes.
Fully abstract from static to gradual

What is a good gradual language? Siek et al. have previously proposed the refined criteria, a set of formal ideas that characterize a range of guarantees typically expected from a gradual language. While these go a long way, they are mostly focused on syntactic and type safety properties and fail to characterize how richer semantic properties and reasoning principles that hold in the static language, like non-interference or parametricity for instance, should be upheld in the gradualization. In this paper, we investigate and argue for a new criterion previously hinted at by Devriese et al.: the embedding from the static to the gradual language should be fully abstract. Rather than preserving an arbitrarily chosen interpretation of source language types, this criterion requires that all source language equivalences are preserved. We demonstrate that the criterion weeds out erroneous gradualizations that nevertheless satisfy the refined criteria. At the same time, we demonstrate that the criterion is realistic by reporting on a mechanized proof that the property holds for a standard example: GTLC µ , the natural gradualization of STLC µ , the simply typed lambda-calculus with equirecursive types. We argue thus that the criterion is useful for understanding, evaluating, and guiding the design of gradual languages, particularly those which are intended to preserve source language guarantees in a rich way.
Exploring popular topic models

Abstract Information from micro-blogging site such as Twitter is a huge repository of data. A lot of research is happening on sentiments, discovering patterns and prediction. One challenging task is dividing this humongous unstructured data into clusters. Several topic modeling methods are proposed by researchers. This paper presents a brief summary of topic modeling methods LDA, LSI and NMF and their applications. Experiments are conducted on the Twitter based datasets created using tweets on keywords Cauvery river, Lokpal bill and Rahul Gandhi. Paper covers a brief discussion on evaluating the accuracy of topics formed using perplexity, log-likelihood and topic coherence measures. Best topics formed are then fed to the Logistic regression model. The model created is showing better accuracy with LDA.
TangleCV

Connected vehicles are set to define the future of transportation; however, this upcoming technology continues to be plagued with serious security risks. If these risks are not addressed in a timely fashion, then they could threaten the adoption and success of this promising technology. This article deals with a specific class of attacks in connected vehicles, namely tampering attacks caused due to compromise of on-board sensors. Current centralized solutions that employ trusted infrastructure to protect against adversarial manipulation of information cannot validate the correctness of the shared data and do not scale well. To overcome these issues, decentralized protection mechanisms by means of blockchain technology have emerged as a promising research direction. However, current permission-less, linear blockchain-based solutions have low transaction performance and high computational cost, thereby making it difficult to adopt them for security in connected vehicles. In this article, we present TangleCV, a directed acyclic graph–based distributed ledger technique for connected vehicles to address data tampering threats in connected vehicular networks. We describe new validation steps, tip selection strategies, and cumulative weight definition for TangleCV that not only meets the timing constraints of the connected vehicular networks but also secures the network against threats due to tampering attacks. We describe how the reputation of the network is established in TangleCV using trust factors calculated on the basis of ability, integrity, and benevolence of the nodes in the network. We present numerical results that demonstrate that the average value of the time to first approval decreases by more than 70% as the network evolves from a low load to a high load in the case of the nearest neighbor strategy. We observe that more than 60% of the nodes are approved in a low-load network and this number increases to 80% in a high-load network for the nearest neighbor strategy. The standard deviation of error measurements for nodes experiencing tampering attack is around 60% higher as compared to nodes that do not experience such an attack.
Dimensionality Reduction for Human Activity Recognition Using Google Colab

Human activity recognition (HAR) is a classification task that involves predicting the movement of a person based on sensor data. As we can see, there has been a huge growth and development of smartphones over the last 10–15 years—they could be used as a medium of mobile sensing to recognize human activity. Nowadays, deep learning methods are in a great demand and we could use those methods to recognize human activity. A great way is to build a convolutional neural network (CNN). HAR using Smartphone dataset has been widely used by researchers to develop machine learning models to recognize human activity. The dataset has two parts: training and testing. In this paper, we propose a hybrid approach to analyze and recognize human activity on the same dataset using deep learning method on cloud-based platform. We have applied principal component analysis on the dataset to get the most important features. Next, we have executed the experiment for all the features as well as the top 48, 92, 138, and 164 features. We have run all the experiments on Google Colab. In the experiment, for the evaluation of our proposed methodology, datasets are split into two different ratios such as 70–10–20% and 80–10–10% for training, validation, and testing, respectively. We have set the performance of CNN (70% training–10% validation–20% testing) with 48 features as a benchmark for our work. In this work, we have achieved maximum accuracy of 98.70% with CNN. On the other hand, we have obtained 96.36% accuracy with the top 92 features of the dataset. We can see from the experimental results that if we could select the features properly then not only could the accuracy be improved but also the training and testing time of the model.
DisBot: A Portuguese Disaster Support Dynamic Knowledge Chatbot

This paper presents DisBot, the first Portuguese speaking chatbot that uses social media retrieved knowledge to support citizens and first-responders in disaster scenarios, in order to improve community resilience and decision-making. It was developed and tested using Design Science Research Methodology (DSRM), being progressively matured with field specialists through several design and development iterations. DisBot uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, it uses real-world safety knowledge, and infers a dynamic knowledge graph that is dynamically updated in real-time by a disaster-related knowledge extraction tool, presented in previous works. Through its development iterations, DisBot has been validated by field specialists, who have considered it to be a valuable asset in disaster management.
Explainable Recommendation with Comparative Constraints on Product Aspects

To aid users in choice-making, explainable recommendation models seek to provide not only accurate recommendations but also accompanying explanations that help to make sense of those recommendations. Most of the previous approaches rely on evaluative explanations, assessing the quality of an individual item along some aspects of interest to the user. In this work, we are interested in comparative explanations, the less studied problem of assessing a recommended item in comparison to another reference item.
Increasing the Density of Laboratory Measures for Machine Learning Applications

Background. The imputation of missingness is a key step in Electronic Health Records (EHR) mining, as it can significantly affect the conclusions derived from the downstream analysis in translational medicine. The missingness of laboratory values in EHR is not at random, yet imputation techniques tend to disregard this key distinction. Consequently, the development of an adaptive imputation strategy designed specifically for EHR is an important step in improving the data imbalance and enhancing the predictive power of modeling tools for healthcare applications. Method. We analyzed the laboratory measures derived from Geisinger’s EHR on patients in three distinct cohorts—patients tested for Clostridioides difficile (Cdiff) infection, patients with a diagnosis of inflammatory bowel disease (IBD), and patients with a diagnosis of hip or knee osteoarthritis (OA). We extracted Logical Observation Identifiers Names and Codes (LOINC) from which we excluded those with 75% or more missingness. The comorbidities, primary or secondary diagnosis, as well as active problem lists, were also extracted. The adaptive imputation strategy was designed based on a hybrid approach. The comorbidity patterns of patients were transformed into latent patterns and then clustered. Imputation was performed on a cluster of patients for each cohort independently to show the generalizability of the method. The results were compared with imputation applied to the complete dataset without incorporating the information from comorbidity patterns. Results. We analyzed a total of 67,445 patients (11,230 IBD patients, 10,000 OA patients, and 46,215 patients tested for C. difficile infection). We extracted 495 LOINC and 11,230 diagnosis codes for the IBD cohort, 8160 diagnosis codes for the Cdiff cohort, and 2042 diagnosis codes for the OA cohort based on the primary/secondary diagnosis and active problem list in the EHR. Overall, the most improvement from this strategy was observed when the laboratory measures had a higher level of missingness. The best root mean square error (RMSE) difference for each dataset was recorded as −35.5 for the Cdiff, −8.3 for the IBD, and −11.3 for the OA dataset. Conclusions. An adaptive imputation strategy designed specifically for EHR that uses complementary information from the clinical profile of the patient can be used to improve the imputation of missing laboratory values, especially when laboratory codes with high levels of missingness are included in the analysis.
Data Augmentation For Deep Learning Using Generative Adversarial Networks

Deep learning requires the use of several labeled images as training data. However, in practice, it is difficult to obtain a sufficient number of appropriate images, and it is particularly difficult to obtain diverse classes of images with labels. This poses a challenge in the training of convolutional neural networks (CNNs). Data augmentation, a technique for generating more data from pre-existing training data, plays an essential role in addressing the dearth of appropriate images. Thus, in this study, we propose a data augmentation method using image generation and a generative adversarial network (GAN) with geometric deformation. In the proposed method, a small dataset is repeatedly augmented via geometric deformation and used as training data for the auxiliary classifier-GAN. Finally, the performance of the CNN trained on the dataset generated by the proposed method is evaluated. Although the quality and accuracy of the results achieved were insufficient, it would be remiss to dismiss the potential of this approach. The challenge now lies in improving the quality of the generated images, thereby improving the accuracy of the CNN.
Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

Abstract Currently, Indonesia is still struggling against the Coronavirus, the number of cases of the Coronavirus is increasing every day. The virus that began to attack around December 2019 has become a pandemic and is causing serious problems because there is still no vaccine to prevent transmission. The Government of the Republic of Indonesia as the party responsible for this pandemic has made various efforts including the lockdown method in the form of a Large-Scale Social Restriction (PSBB) policy. Besides that, another effort is to present a digital information portal that can be accessed and able to present the development status of coronavirus patients. With the development of information and communication technology, this research is a find correlation between weather and Covid-19 Using Data Mining Algorithm in processing data sources from the government to produce knowledge that can be used to make decisions. The research data used came from Covid-19 surveillance data of the Ministry of Health of the Republic of Indonesia and the climatic conditions in Surabaya Indonesia in the form of the average temperature (°C), the average humidity (%), the average duration of sunlight (hour) taken from the Department of Meteorology of the Republic of Indonesia. Based on the results of the study it was found that the weather affected the number of Covid-19 patients. The highest order of correlation is the average duration of sunlight (hour), the average temperature (°C), the average humidity (%).
Special Session: Noise Characterization and Error Mitigation in Near-Term Quantum Computers

A detailed understanding of the noise processes and errors in near-term quantum computers is essential for accurate calibration of gates, and for achieving optimal performance when executing near-term applications such as quantum chemistry, optimization, or machine learning problems. This paper introduces several sources of noise in superconducting qubit based quantum computers such as those deployed by IBM, describes some of the main techniques for characterizing the resulting errors, and for mitigating the effects of certain errors without access to quantum error correction. We demonstrate experimental application of these techniques on an IBM Quantum system using the open source Qiskit software library.
A meta-engine for building domain-specific search engines

<h2>Abstract</h2> In recent years, domain-specific search (DSS) has emerged as a growing and important area of applied research in artificial intelligence (AI) and information retrieval (IR). Over the last 6 years of research, our group has developed a 'meta-engine' called myDIG (my Domain-specific Insight Graphs) that provides a relatively easy and customizable workflow for building DSSs without advanced technical training in crawling, information retrieval or user-interfaces. The myDIG system has been applied to some important and difficult use cases (most notably, fighting human trafficking), in addition to being used in classrooms by graduate students for building complex DSSs from scratch.
A Hybrid Layered Image Compressor with Deep-Learning Technique

This paper presents a detailed description of NCTU's proposal for learning-based image compression, in response to the JPEG AI Call for Evidence Challenge. The proposed compression system features a VVC intra codec as the base layer and a learning-based residual codec as the enhancement layer. The latter aims to refine the quality of the base layer via sending a latent residual signal. In particular, a base-layer-guided attention module is employed to focus the residual extraction on critical high-frequency areas. To reconstruct the image, this latent residual signal is combined with the base-layer output in a non-linear fashion by a neural-network-based synthesizer. The proposed method shows comparable rate-distortion performance to single-layer VVC intra in terms of common objective metrics, but presents better subjective quality particularly at high compression ratios in some cases. It consistently outperforms HEVC intra, JPEG 2000, and JPEG. The proposed system incurs 18M network parameters in 16-bit floating-point format. On average, the encoding of an image on Intel Xeon Gold 6154 takes about 13.5 minutes, with the VVC base layer dominating the encoding runtime. On the contrary, the decoding is dominated by the residual decoder and the synthesizer, requiring 31 seconds per image.
RBJ25 cryptography algorithm for securing big data

Abstract Cryptography is essential tool to protect data, information in computing system. In worldwide, billions of people using this cryptography methods in daily basis because It used the protect data. By and large the size of information imparted in web applications is developing and is in Peta-bytes and Exa-bytes. The customary security frameworks can't give successful insurance and confirmation to these sorts of informational collections. To over this issue in this work we have proposed a new cryptography algorithm RBJ25 with the matrix order N. This proposed algorithm contains three parts. In the first part based on the column operation of the matrix. In the second part secrete key generation and in the last part has five sub part to protect the information which will be discuss in detail in implementation part. Similarly in the decryption has three reverse parts. Finally, this algorithm is implemented and compared with traditional algorithms like AES and ChaCha20.
Based-nonequilibrium-environment non-markovianity, quantum Fisher information and quantum coherence

In this work, we investigate the non-Markovianity, quantum Fisher information (QFI) and quantum coherence of a qubit in a nonequilibrium environment and have obtained the expressions of QFI and quantum coherence as well as their relationship.We have also discussed in detail the influences of the different noise parameters on these quantum sffects.The results show that the suitable parameters of the nonequilibrium environment can retard the QFI and quantum coherence in both Markovian and non-Markovian regions.In addition, the smaller memory effects and the larger the jumping rate, the greater the QFI and quantum coherence.And a larger QFI naturally corresponds to a larger quantum coherence, which indicates that the quantum coherence can enlarge the QFI and can effectively enhance the quantum metrology.
Smoothing Adversarial Training for GNN

Recently, a graph neural network (GNN) was proposed to analyze various graphs/networks, which has been proven to outperform many other network analysis methods. However, it is also shown that such state-of-the-art methods suffer from adversarial attacks, i.e., carefully crafted adversarial networks with slight perturbation on clean one may invalid these methods on lots of applications, such as network embedding, node classification, link prediction, and community detection. Adversarial training has been testified as an efficient defense strategy against adversarial attacks in computer vision and graph mining. However, almost all the algorithms based on adversarial training focus on global defense through overall adversarial training. In a more practical scene, certain users would be targeted to attack, i.e., specific labeled users. It is still a challenge to defend against target node attack by existing adversarial training methods. Therefore, we propose smoothing adversarial training (SAT) to improve the robustness of GNNs. In particular, we analytically investigate the robustness of graph convolutional network (GCN), one of the classic GNNs, and propose two smooth defensive strategies: smoothing distillation and smoothing cross-entropy loss function. Both of them smooth the gradients of GCN and, consequently, reduce the amplitude of adversarial gradients, benefiting gradient masking from attackers in both global attack and target label node attack. The comprehensive experiments on five real-world networks testify that the proposed SAT method shows state-of-the-art defensibility against different adversarial attacks on node classification and community detection. Especially, the average attack success rate of different attack methods can be decreased by about 40% by SAT at the cost of tolerable embedding performance decline of the original network.