Reviewerly/exHarmony huggingface dataset:
A novel subset-based polynomial design for enhancing the security of short message-digest with inflated avalanche and random responses

The data breach and the integrity violation of remote data remain significant issues in the domain of information security. A provably-secure hash function aids in providing solutions to integrity-related issues. Nevertheless, the choice of a provably-secure hash function has to be made with caution from the perspective of security. This research study attempts to identify the weakness of contemporary keyless hash functions and proposes an algorithm called a provably secure subset hash function (PSSHF). The objectives of the studies are reinforcing the internal structure of random oracle (RO), intensifying stochastic deportment, presenting computationally infeasible conditions for reverse decoding, and forestalling block-level and differential attacks through subsets and polynomial functions. The avalanche response of PSSHF is 50.06% and is higher than that of its contemporary variants. Likewise, the Near-collision response of PSSHF is 49.94% and is the least among its other similitudes. The empirical analysis of the effect of avalanche proves the novel design modifies 93.78% of output symbols besides excelling its other counterparts on random behavior. The runtime response proves the PSSHF processes short messages with acceptable delay. Therefore, the proposed PSSHF can be considered a perfect replacement for its similitudes in respect of the short messages for higher security.
A Framework of Runtime Monitoring for Correct Execution of Smart Contracts

Smart contracts have been subjected to several attacks that have exploited various vulnerabilities of languages like Solidity, which has resulted in huge financial losses. The functioning and deployment of smart contracts are somewhat different from classical programming environments. Once a smart contract is up and running, changing it, is very complicated and nearly infeasible as the contract is expected to be immutable when created. If we find a defect in a deployed smart contract, a new version of the contract has to be created and deployed with concurrence from the stakeholders. Further, when a new version of an existing contract is deployed, data stored in the previous contract does not get transferred automatically to the newly refined contract. We have to manually initialize the new contract with the past data which makes it very cumbersome and not very trustworthy. As neither updating a contract nor rolling back an update is possible, it greatly increases the complexity of implementation and places a huge responsibility while being deployed initially on the blockchain. The main rationale for smart contracts has been to enforce contracts safely among the stakeholders. In this paper, we shall discuss a framework for runtime monitoring to prevent the exploitation of a major class of vulnerabilities using the programmers’ annotations given in the smart contracts coded in Solidity. We have chosen several phrases for annotation mimicking declarations of concurrent programming languages so that the underlying run-time monitors can be automatically generated. The annotations simply reflect the intended constraints on the execution of programs relative to the object state relative to observables like method calls, exceptions, etc. Such a framework further adds to the advantage of debugging at the source level as the original structure is preserved and also enhances the trust of the user as the run-time monitoring assertion logs provide a rough proof-outline of the contract.
Differential Privacy from Locally Adjustable Graph Algorithms: k-Core Decomposition, Low Out-Degree Ordering, and Densest Subgraphs

Differentially private algorithms allow large-scale data analytics while preserving user privacy. Designing such algorithms for graph data is gaining importance with the growth of large networks that model various (sensitive) relationships between individuals. While there exists a rich history of important literature in this space, to the best of our knowledge, no results formalize a relationship between certain parallel and distributed graph algorithms and differentially private graph analysis. In this paper, we define locally adjustable graph algorithms and show that algorithms of this type can be transformed into differentially private algorithms. Our formalization is motivated by a set of results that we present in the central and local models of differential privacy for a number of problems, including k-core decomposition, low out-degree ordering, and densest subgraphs. First, we design an $\varepsilon$-edge differentially private (DP) algorithm that returns a subset of nodes that induce a subgraph of density at least $ \frac{D^{*}}{1+\eta}-O(\operatorname{poly}(\log n)/\varepsilon)$, where $D^{*}$ is the density of the densest subgraph in the input graph (for any constant $\eta\gt 0$). This algorithm achieves a two-fold improvement on the multiplicative approximation factor of the previously best-known private densest subgraph algorithms while maintaining a near-linear runtime. Then, we present an $\varepsilon$-locally edge differentially private (LEDP) algorithm for k-core decompositions. Our LEDP algorithm provides approximates the core numbers (for any constant $\eta\gt 0$) with $(2+\eta)$ multiplicative and $O(\operatorname{poly}(\log n)/\varepsilon)$ additive error. This is the first differentially private algorithm that outputs private k-core decomposition statistics. We also modify our algorithm to return a differentially private low out-degree ordering of the nodes, where orienting the edges from nodes earlier in the ordering to nodes later in the ordering results in out-degree at most $O(d+$ poly $(\log n)/\varepsilon$) (where d is the degeneracy of the graph). A small modification to the algorithm also yields a $\varepsilon$-LEDP algorithm for $(4+\eta,O(\operatorname{poly}(\log n)/\varepsilon))$ approximate densest subgraph (which returns both the set of nodes in the subgraph and its density). Our algorithm uses $O(\log^{2}n)$ rounds of communication between the curator and individual nodes.
A CAD System for Software Effort Estimation

The mentioned article investigates the use of approaches and techniques of machine learning especially the unsupervised learning ways in order to compute effort of software tools and application. Here, I have made use of mainly the historical dataset in collaboration with unsupervised learning techniques to predict the effort. First of all, to cluster the projects from the historical dataset then utilized the unsupervised category of machine learning approach as m-medoids in clustering with multiple similarity measures. Apart from this, we facilitate the results by using machine learning approaches to anticipate software effort by imputing missing values from past records. Furthermore, it is being experimented using ISBSG and CSBSG dataset which demonstrate that unsupervised learning as k-medoids clustering produced superior performance. In addition to this, it has been observed that kulzinsky coefficient proved to be best in estimating the similarities index of projects and imputation of missing imputation enhanced the efficiency of unsupervised approach of machine learning in software effort prediction.
Machine Learning Application for Image Forgery Detection

We have proposed a novel machine learning based algorithm for the detection of image forgery without the availability of any prior information. Computation of local features is performed from the morphological information of image histograms and used to extract discriminating parameters. Images are assumed to be characterized by different parameters in different regions. Such parameters are learned by the image processing application with machine learning algorithms itself together on the segmentation in genuine and doubtful parts. A proposed supervised algorithm results on a wide range of images are very encouraging and proved to be sufficient for reliable forgery detection.
Predicting preeminent Machine Learning Approach on Stars

Numerous statistical methods, including “machine learning”, “predictive modeling” and “data mining” are included in predictive analysis.. One of the most intriguing and fascinating recent developments in artificial intelligence is machine learning. With the rise in technology the numbers of algorithms are also increasing for training models and based on the dataset the algorithms are being selected for training a good model with higher accuracy. In this paper I have used a stars dataset imported from Kaggle for predicting the spectral classes of the stars M and O based on the temperature, and have used regression algorithms for predicting it, since it contains continuous real values and regression algorithms work best for this type of cases for predictions and outputs with higher accuracy. By implementing the algorithms, I found that Random Forest Regressor works best with a higher R2_score.
Green Cloud Based Efficient Security Mechanism for Aadhaar Voting System

Elections are crucial in forming governments in countries like India. The value of democracy lies in the “Votes” flung by people, through which they elect their representatives for forming the government. At present Electronic Voting Machine (EVM) is used which has certain disadvantages with respect to security aspects. The main goal of the paper is to design a reliable voting machine with improved security and ease of accessibility while also addressing the shortcomings of the present system. Here, electronic components such as fingerprint sensors, microcontroller, LCD display and Global System for Mobile (GSM) modules are used. The entire process will take place in a secured green cloud domain, thereby reducing the security risks and utilize the computer infrastructure more efficiently with curtailed energy consumption.
Improved mine microseismic event recognition method combining neural network and transfer learning

The data collected by the coal mine monitoring system contains a lot of noise, which is difficult to accurately identify with traditional methods. It can be processed by deep learning methods to mine the deep-level features of the data, making the identification more efficient. Both natural earthquakes and coal mine microseisms belong to the category of vibration. The waveform characteristics, focal mechanism, and monitoring targets are similar. Transfer learning can transfer the characteristics of large sample seismic data set to the model of small sample microseismic data set. Therefore, this paper proposes an improved mine microseismic event recognition method based on transfer learning. First, in view of the low accuracy of microseismic event recognition in the existing deep learning CNN method, a non-recursive SimRank-based model (SimRank CNN) is proposed. Secondly, in view of the problem that the small-sample microseismic data set is not enough to support the training of the neural network model and the recognition accuracy is not high, a source domain feature transfer learning method based on SimCNN is proposed. In order to make the model have a better ability to extract features. Furthermore, a LSTM-based time-series feature transfer learning method is proposed and the Transfer learning SimCNN transfer model (T-SimCNN) is constructed. Finally, the performance of the T-SimCNN transfer learning model has been improved, and the recognition accuracy of microseismic events can reach 95%.
Digital Wellbeing: Designers’ Perspectives on Where the Responsibility Lies

The pervasiveness of technology and the increasing number of studies on technology misuse, high dependency and addiction in the last decade raised an intriguing question of where the responsibility of preserving users’ wellbeing lies. As a number of scholars advocated that both users and designers share the responsibility, others pointed fingers towards designers since they are the creators of products and platforms that are not only usable but overly captivating. In this paper, we discuss designers’ opinions on the roles and responsibilities regarding the design for digital wellbeing. We present an initial analysis results of 20 interviews with UX designers and HCI researchers and professionals. As a general finding, the majority of the participants believed that the responsibility is on designers and tech companies as they exhibit more power over the digital product design and its implications, while users were occasionally blamed for their lack of self-control and weak digital literacy on topics related to online risks and ethics.
Detection of Vertical Poles in a Road Environment Using Monocular Images based on YOLOv4

Autonomous vehicles use a variety of software-based detection algorithms to detect the static and dynamic objects in their surroundings. Detected objects are then used as bases for the higher-level tasks required for the safe operation of the vehicles. Due to their abundance in most road environments, poles are some of the key static landmarks used for the tasks of autonomous vehicle mapping and localization. However, most of the work done in the field of pole-like object detection has been focused on the use of LiDAR-generated 3D point clouds. This study explores the validity of monocular camera-based pole detection approaches as means of redundancy for environmental perception. It uses the YOLOv4 object detector combined with the MiDaS depth estimator to ultimately produce pole detections on inverse depth maps. To train and test the YOLOv4 model, a new 3000-image dataset based on BDD100K was created by manually generating accurate pole ground truth labels. The final model produced by the study is capable of predicting poles with an mAP of over 71%.
Detection of Alzheimer's Disease Using Deep Learning, Blockchain, and IoT Cognitive Data

Telemedicine has the potential to be a good resource for early disease diagnosis, provided that it is utilised in the correct manner. The Internet of Things (IoT) is a concept that has developed in recent years as people have become more aware that they are continuously being watched. As a result of the increased prevalence of neurodegenerative disorders like Alzheimer's disease (AD), biomarkers for these conditions are in high demand for early-stage resource prognosis. Because of the precarious nature of the situation, it is absolutely necessary for these structures to offer remarkable qualities such as accessibility and precision. Deep learning strategies could be useful in fitness applications in situations in which there are a large number of data points to be analysed. Excellent data for a decentralized Internet of Things device that is based on block chain technology. By utilizing a connection to the internet that is of a high speed, it is feasible to obtain a prompt answer from these structures. It is not possible to run deep learning algorithms on smart gateway devices since they do not have sufficient computational capacity. In this study, we investigate the potential for increasing the speed of data flow in the healthcare industry while simultaneously improving data quality through the incorporation of blockchain-based deep neural networks into the control system. Experiments are being conducted to evaluate the speed and accuracy of real-time fitness tracking for the purpose of classifying groups. We are able to determine if diseases of the brain are benign or malignant by employing a model that utilises deep learning. For the purpose of determining the relative severity of each condition, the research examines the symptoms of several different mental diseases and compares them to those of Alzheimer's disease, moderate cognitive impairment, and normal cognition. The research calls for a number of different procedures. The majority of the data is used to train the classifiers, while the remainder of the data is utilised in conjunction with an ensemble model and meta classifier to classify individuals into the appropriate categories. The OASIS-three database is a long-term study that incorporates neuroimaging, cognitive, clinical, and biomarker measurements. This study focuses on healthy ageing as well as Alzheimer's disease. When comparing the outcomes of the simulation to those acquired from the real world, the OASIS-three database (AD), in addition to the ADNI UDS dataset, is employed as a comparison tool. The findings show that answers to questions about this issue can be arrived at quickly and categorized utilizing an in-depth methodology (98% accuracy).
Generating Smart Contracts for Blockchain-Based Resource-Exchange Systems

Blockchain technology allows to store data in a secure and decentralized manner. The combination of blockchains with smart contracts allows for a novel range of applications. However, the creation of such applications requires IT skills, which introduces a burden for businesspeople that would like to incorporate blockchains combined with smart contracts in their business processes. To overcome this issue in the context of resource exchange across organizations and customers, we developed a framework that allows businesspeople to generate blockchain applications for such resource exchanges, without requiring any programming or blockchain knowledge. In this paper, we elaborate on the module that generates the smart contracts necessary to implement the specifications of use cases. In the current implementation, smart contracts are generated for Solidity, which is a smart contract language supported by many blockchain implementations, but this is not a limitation of the approach.
Development of intelligent acupuncture applications and related technologies

The study focuses on developing mobile applications based on intelligent acupuncture, classified into education and intelligent diagnosis and treatment. The mobile application function is divided into two directions: assisting acupoint positioning and perfecting acupoint knowledge system. The study does a relative review on Android and IOS, showing that the number of Android users is rather more prevailing than IOS. It suggests that intelligent acupuncture mobile applications should comply with the trend of acupuncture globalization in the future, developing multilingual versions. Further, the content should be characterized by the experience of different famous doctors to avoid homogenization. Technically, intelligent applications should continue to develop three-dimensional and augmented reality technology, to optimize the accuracy of acupoint positioning.
Cue prompt adapting model for relation extraction

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Position-aware and structure embedding networks for deep graph matching

Graph matching refers to the process of establishing node correspondences based on edge-to-edge constraints between graph nodes. This can be formulated as a combinatorial optimization problem under node permutation and pairwise consistency constraints. The main challenge of graph matching is to effectively find the correct match while reducing the ambiguities produced by similar nodes and edges. In this paper, we present a novel end-to-end neural framework that converts graph matching to a linear assignment problem in a high-dimensional space. This is combined with relative position information at the node level, and high-order structural arrangement information at the subgraph level. By capturing the relative position attributes of nodes between different graphs and the subgraph structural arrangement attributes, we can improve the performance of graph matching tasks, and establish reliable node-to-node correspondences. Our method can be generalized to any graph embedding setting, which can be used as components to deal with various graph matching problems answered with deep learning methods. We validate our method on several real-world tasks, by providing ablation studies to evaluate the generalization capability across different categories. We also compare state-of-the-art alternatives to demonstrate performance.
Entanglement and coherence in a system of two atoms in the presence of Kerr medium and field dissipation

In the present manuscript, we investigate qualitatively the quantum entanglement and coherence in two atoms coupled to a quantized field in the existence of Kerr medium and Ising interaction. We explore the effects of the atom–atom (A–A) interaction and field nonlinearity on the dynamics of quantumness measures by examining the A–A concurrence, von Neumann entropy, l1 norm coherence and Mandel’s parameter. We also illustrate the relations between the information quantifiers in the presence and absence of field dissipation.
Adversarial data hiding with only one pixel

Making adversarial samples to fool deep neural network (DNN) is an emerging research direction of privacy protection, since the output of the attacker's DNN can be easily changed by the well-designed tiny perturbation added to the input vector. However, the added perturbation is meaningless. Why not embed some useful information to generate adversarial samples while integrating the functions of copyright and integrity protection of data hiding? This paper solves the problem by modifying only one pixel of the image, that is, data hiding and adversarial sample generation are achieved simultaneously by the only one modified pixel. In CIFAR-10 dataset, 11 additional bits can be embedded into the host images sized 32 × 32, and the successful rate of adversarial attack is close to the state-of-the-art works. This paper proposes a new idea to combine data hiding and adversarial sample generation, and gives a new method for privacy-preserved processing of image big data.
Neural POS tagging of shahmukhi by using contextualized word representations

Part of Speech (POS) tagging has a preliminary role in building natural language processing applications. This paper presents the development and evaluation of the first POS tagged corpus along with a Bi-directional long-short memory (BiLSTM) network based POS tagger for Shahmukhi (Western Punjabi) at this scale. A balanced corpus of 0.13 million words has been annotated which contains text from 14 different text domains. A Shahmukhi POS tagset has been devised by studying the applicability of the CLE Urdu POS tagset and tagging guidelines have also been designed for annotation. A multi-step corpus evaluation process has been employed for tagged corpus including grammar-based and n-gram based consistency evaluations. The average inter-annotator agreement for all domains is 95.35% along with an average Kappa coefficient of 0.94. The performance of the BiLSTM POS tagger has been compared with the well-known language independent TreeTagger and the Stanford POS tagger. The accuracy of the tagger has been further improved by employing transfer learning by training context-free (Word2Vec) and contextualized (ELMo) word representations on a corpus of 14.9 Shahmukhi words which has been collected from World Wide Web. The tagger performed with an f-score of 96.11 and the accuracy of 96.12%. For a morphologically-rich and low-resourced language, these POS tagging results are quite promising.