Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Deliver Commitments using Active Capacity and Baseload Planning in Agile Software Development

Faster and continuous delivery models to deploy products and value-added features to customers especially in Cloud environments, have motivated organizations to move towards Agile methodologies of software development. Organizations look for software delivery models that generate minimum wastage during the development and deployment processes, while providing maximum value to the end users, in delivery cycles ranging from days to weeks. In this process, a methodology that is designed and developed to enable the development teams to plan the release commitments with minimum wastage is being highlighted. With this methodology, teams can plan the capacity requirements well in advance, provide commitments to the stakeholders, identify as well as compare the planned vs actual efforts so that the teams can retrospect on the impediments. Additionally, stakeholders can track the progress of the backlogs based on the efforts logged by the team members and decide the further course of action for upcoming sprint(s) and release(s).
Color Image Encryption Algorithm Based on Lorenz Hyperchaotic System

A color image encryption algorithm based on Lorenz hyperchaotic system and GF (257) domain is proposed. Firstly, We get the initial value of each state of the hyperchaotic Lorenz system from the input key, obtain a pseudorandom sequence with a fixed length by iterating the hyperchaotic Lorenz system. Secondly, convert the input three-dimensional space color image into three two-dimensional R, G and B grayscale images. After expanding each two-dimensional grayscale image into a one-dimensional vector, the non-repeated scrambling algorithm based on the pseudorandom sequence is used for scrambling operation and the diffusion algorithm based on GF (257) domain multiplication is used for diffusion processing. Finally, the three encrypted two-dimensional grayscale images are merged into the final encrypted image. The analysis of simulation experiment results and performance analysis verify that the algorithm has good encryption effect and has high robustness against common attacks.
A Few Shot Transfer Learning Approach Identifying Private Images With Fast User Personalization

As online image sharing has become commonplace, researchers have acknowledged the need to assist users in detecting sensitive (or private) images. However, image privacy classification tasks have shown to be nontrivial, as the designation of an image sensitivity requires considerations of the visual concepts in the image. In this paper, we propose an innovative framework that combines the power of knowledge transfer for efficient, personalized learning of individuals’ privacy preferences toward images.Our approach defines a meta-model, which, given the query image and a small set of labeled images (used for the user-privacy customization), identifies if the query image is private for a target user. A generic user can efficiently customize this model by providing a small labeled training set. Moreover, our proposed framework includes transfer learning techniques to import basic patterns for image processing learned from other domains. Transfer learning enables fast and accurate processing of images, and allows few shot learning to focus on customization. This helps speed up the training process and avoid risk of overfitting. Our proposed framework significantly outperforms several baselines, including advanced object-oriented approaches and other CNN-based methods.
Use of Machine Learning in Application of Artificial Intelligence

In modern days technology machine learning is one of the best discovery is in science, and it is one of the critical inventions in intellectualize, which is made artificially. To make this type of revolution, any product the programming behind it is not so complicated and even this type of programming language are used daily. So it is quite sure that it is not so difficult to execute as the program behind it is quite familiar. According to some specific case studies when every human individual is searching some queries through searching instrument via browser, the desired result comes conveniently than ever before, and all of this revolution only can happen because in present times machine level language learning has gone so far and not only that but also very few important researches are also happening around the whole world to improve this specific section of artificial intelligence better than ever. In modern days internet surfing cannot be more convenient without machine learning and artificial intelligence. This study reflects the idea if using machine learning in the application of Artificial Intelligence (A). For the last few years one of the biggest tech giants Google and along with Microsoft are usually implementing machine learning and artificial intelligence in their search engine and as well as their software-based products and not only that but they are also investing in this type of technology to improve as much as it can be in upcoming future as well. Not only in Google and Microsoft but also present generation social media is an essential thing in daily life, and by naming social media, the first thing that comes into mind is Facebook. This particular study reflects the explanation of using machine learning features in the application of Artificial Intelligence. This study explores the classifications of software categories where machine learning can be used. As the data are being more significant every day, this study shows it's necessary to improve machine learning algorithms so that they can be able to handle extensive data and return error-less and more precise output.
Integrating a Pentesting Tool for IdM Protocols in a Continuous Delivery Pipeline

Identity Management (IdM) solutions are increasingly important for digital infrastructures of both enterprises and public administrations. Their security is a mandatory prerequisite for building trust in current and future digital ecosystems. IdM solutions are usually large-scale complex software systems maintained and developed by several groups of ICT professionals. Continuous Delivery (CD) pipeline is adopted to make maintenance, extension, and deployment of such solutions as efficient and repeatable as possible. For security, CD pipeline is also used as a continuous risk assessment to quickly evaluate the security impact of changes. Several tools have been developed and integrated in the CD pipeline to support this view in the so called DevSecOps approach with the notable exception of a tool for protocol pentesting and compliance against standards such as SAML 2.0, OAuth 2.0 and OpenID Connect. To fill this gap, we propose an approach to integrate Micro-Id-Gym—a tool for the automated pentesting of IdM deployments—in a CD pipeline. We report our experience in doing this and discuss the advantages of using the tool in the context of a joint effort with Poligrafico e Zecca dello Stato Italiano to build a digital identity infrastructure.
Analytic Color Imbalance Normalization for Digital Color Fringe Profilometry on Deforming Membrane Surface

Digital Color Fringe Profilometry (DCFP) is a modification of conventional fringe pattern profilometry (FPP). Three different phase fringe patterns are encoded into a single frame colored fringe to overcome the multi-frame issue of phase shifting interferometry. Even so, non-linear intensity and color imbalance are still major problems in the application of color fringe profilometry. Due this problem, modulated sinusoidal fringe pattern is distorted due to undesired harmonic component. This paper proposes an analytic color imbalance normalization based on heuristic interpolation of distorted fringe which performed through line-by-line processing. Phase maps is extracted using Hilbert transform followed by a simple phase unwrapping and an offset removal based on the absolute phase map of the reference plane. The final step is averaging of the surface profiles from all color channels. The proposed scheme contributes to a better consistency and higher quality of deforming surface reconstruction.
Effect of ICT-based Rehabilitation Exercise Program on Upper Extremity Muscle Strength and Compliance in Healthy Adults

The purpose of this study was to study the effect of an Information and communication technology (ICT)-based rehabilitation exercise program on upper extremity function and compliance of the health adults. A total of 20 subjects were enrolled in this study and assigned to an experimental group and a control group. The experimental group performed upper extremity exercises using an ICT-based rehabilitation exercise program, while the control group performed self-exercise without exercise equipment. Interventions were provided for 4 weeks, 4 times a week, 30 minutes per day in both groups. For primary outcome measurement, Handgrip were measured using a dynamometer, and for secondary outcome measurement, compliance with rehabilitation exercise was measured using a numeric rating scale. As a result of this study, the experimental group not only showed a statistically significant improvement in grip strength but also showed higher motivation and fun/interest in exercise compliance than the control group (all, p < 0.05). Therefore, this study suggests that the ICT-based rehabilitation exercise program can be an effective rehabilitation program in a clinical setting.
The Image Super-Resolution with Hopfield Neural Network

Single Image Super-Resolution (SISR) aims to generate a high-resolution (HR) image of a given low-resolution (LR) image. Most of the existing deep learning-based super-resolution methods either take too much time to train or require too many network resources, and the resulting super-resolution results are not as good as expected, which makes the SISR problem still needs to be studied by people all the time (To address this issue, we propose a method). To address this issue, we propose a method. In order to solve this problem, we try to combine the idea of vector quantization and discrete Hopfield networks to propose two new super-resolution methods, the first one suppose for 8-bit gray-level image that consists of 8-layers (bitplanes) of binaries. In this way it is possible to express each bitplane as single binary image for Hopfield neural network, which overcomes the problem that traditional Hopfield networks can only handle binary images. The second one is to fuse the idea of BP algorithm and Hopfield network, which greatly saves the number of neurons compared with the traditional Hopfield network. Compared with other super-resolution methods both of the above methods obtain satisfactory PSNR and SSIM scores, as well as visual effects.
A Study on the Instructors" Perceptions of Online Distance Learning in the Physical Education Practical Class due to COVID-19: A Case Study on H University

The purpose of this study was to provide academic basic data which can help in the transformation of the future-oriented physical education paradigm through the process of synthesizing and analyzing the strengths and weaknesses and improvement plans of online distance learning for practical subjects. For this, survey using an open questionnaire and in-depth interview were conducted by 5 instructors who were in charge of online classes of practical subjects in 2020 at H university. To analyze the collected data, the inductive analysis method was used to classify concepts and categories by domain after verifying that the transcribed data matches the recorded data through the writing task. The results of this study could be summarized as follows:BR First, in the perception of online distance learning for practical subjects, ‘repetitive learning and detailed observation of movements were possible’ and ‘accuracy and objectivity in evaluation’, instructors’ self-development through the reflective reflection’ were found as the advantages of online distance learning in practical subjects. On the other hand, ‘difficulty in selecting a place’, ‘difficulty in immediate-specific feedback due to lack of physical assistance’, and ‘low learning effect compared to a large amount of tasks’ were found as disadvantages of online distance learning in practical subjects. BR Second, as for the prospects for online distance learning in the college of physical education, the contents of ‘acceleration of online distance learning’ and ‘promotion of physical education using blended learning’ were derived. As improvement requirements for online distance learning, online system that is easy to understand and access should be established, and cost support for online content development should be provided. Moreover, online classes and instructors must be thoroughly managed.
DUSE: A New Benchmark Dataset for Drug User Sentiment Extraction

Social media continuously produce a huge volume of data in different formats and different domains. In particular, patients&#x2019; and caregivers&#x2019; written medical texts play an important role among individuals, medical doctors, and drug developers for understanding drug users&#x2019; sentiment. However, automatic sentiment detection is a challenging problem in medical settings due to a lack of data with age group, gender, treatment duration, and so on. Therefore, we present a drug review dataset for the most reviewed 100 drugs. Especially, we collected 88K instances from WebMD which is one of the largest online health service providers. Empirically, we explore strongly labeled data and weakly labeled data for automatic sentiment detection using BERT, which learns context-dependent features. We show that the BERT model yields better accuracy than the baseline models.
Prediction of Hospital Outpatient Volume Based on Combined Neural Network

The hospital outpatient volume has temporal property, and the importance of features is different. In this paper, a new neural network model was proposed in order to the regression prediction of outpatient volume, which adds attention layers to the original network model. In our paper, we use the powerful feature extraction ability of convolution neural network(CNN) to extract important features of data, and the attention mechanism is added in CNN, it helps the model to solve the blindness problem of CNN feature extraction, so that our feature extraction model can gives more sources on the important features and weaken the influence of other features. We connect the output of CNN to the input of long short-term memory network(LSTM), and add an attention layer to the LSTM hidden layers in order to better learn the time characteristics, the output of the last time step of LSTM is connected to a deep neural network to predict the results. The comparison with common algorithms shows that the error of the algorithm proposed in this paper is smaller.
Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

In telemedicine, the realization of reversible watermarking through information security is an emerging research field.However, adding watermarks hinders the distribution of pixels in the cover image because it creates distortions (which lead to an increase in the detection probability).In this article, we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security.The proposed method selects two adjacent gray pixels whose least significant bit (LSB) is different from the relevant message bit and then calculates the distortion degree.We use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel values.Experimental results show that the designed method is robust to different attacks and has a high PSNR (peak signal-to-noise ratio) value.The MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 dB.In addition, the proposed algorithm is tested against the latest technology on standard images, and it is found that the average PSNR of our proposed reversible watermarking technology is higher (i.e., 51.71 dB).Numerical results show that the algorithm can be extended to normal images and medical images.
Cross domain optimization problem with hyperheuristic approach using size stochastic move acceptance

Cross domain optimization problem is a complex optimization problem because in each of the problem contains a different characteristic which can be solved with hyperheuristic. Hyperheuristic has two kind of search space, low-level heuristic (LLH) and move acceptance that work into the domain barrier. In this paper, an effort is made to develop a strategy in the high-level heuristic (HLH) order to regulate the selection process for the LLH and will be followed by a solution acceptance mechanism. The method that will be used is size stochastic move acceptance (SM) approach as a solution acceptance mechanism in the HLH setting, combined with the self-adaptive (SAD) LLH selection method.
Privacy-preserving Breast Cancer Prediction via Inner-Product Functional Encryption

Machine learning is increasingly being used in the medical domain in recent years. However, due to increasing privacy concerns and requirements imposed by privacy-related regulations, the deployment of machine learning solutions in the medical field poses a significant challenge. As a new type of advanced encryption primitive, functional encryption allows fine-grained access control and selective computation on ciphertext, making it very suitable for privacy protection machine learning scenarios. In this paper, we propose a privacy-preserving breast cancer predication system based on a provable secure inner-product functional encryption scheme. The medical server trains the logistic regression model based on breast cancer data, and then a client encrypts his test data using the inner-product functional encryption scheme, and sends the encrypted test data to the medical server for predication, and the medical server complete the prediction by running the decryption algorithm of the inner-product functional encryption scheme, and sends the predication result to the client. Experimental results show that the proposed system has a significant effect on breast cancer data privacy protection, and achieves a high accuracy, which are close to the accuracy of the non-privacy protection logistic regression model on breast cancer data.
Efficient Covering of a Target Object Using a Prediction Technique

In recent years, as the shooting of video and the spread and viewing of video over the Internet have increased exponentially, the damage caused by the exposure of personal information has occurred. In this paper, we propose a method that robustly extracts the target object exposed personal information from continuous input video and then blocks the image using the image blurring technique while quickly tracking the extracted object using a prediction algorithm. The proposed method first extracts the target object exposed the personal information region from the input video using a neural network-based learning algorithm. The detected object is then blocked by applying image blurring while being quickly tracked using a position prediction algorithm. Experimental results show that the proposed method effectively protects personal information by quickly tracking and blurring the target object exposed personal information from the received video. The proposed method is expected to be useful for many practical applications such as privacy protection, video surveillance, object detection and tracking.
A Hybrid Algorithm for Document Clustering Using Optimized Kernel Matrix and Unsupervised Constraints

Document clustering plays a dominant role in data mining, and grouping of data makes information retrieval easier. Significant information can be mined from a collection of documents by clustering them effectively. Several researches that concentrate on clustering documents are available in the literature. In the former works, document clustering is performed by using methodologies such as Term Weight-based Hybridized Harmony K-Means (TW-HHKM) and Coverage Factor-based Hybridized Harmony K-Means (CF-HHKM) searches. Clustering is normally performed using K-means algorithm, and cluster centroids are optimally found by using Harmony Search Algorithm (HSA). The main challenge faced by the existing methods is the reduced accuracy as unrelated documents may be grouped together. To overcome this problem, Novel Feature Weighting and Feature Selection-based Hybrid Scheme for Document Clustering (NFW-FS-HSDC) with optimized and unsupervised constraint kernel matrix K-means and Harmony Search Method (HSM) is introduced for accurate clustering of documents. The weights of the data instance and softness parameter decide the performance of spherical kernel K-means clustering. The parameter values for different datasets are identified after many trials. The kernel parameters are determined by applying Particle Swarm Optimization (PSO)-based technique. The proposed NFW-FS-HSDC algorithm optimizes the kernel matrix based on the feature ranges of the dataset by utilizing PSO. The exploratory tests are conducted on Newsgroup and Text REtrieval Conference (TREC) dataset, and it is obvious that the proposed NFW-FS-HSDC offers better Precision, Recall, F-Measure, Average Distance to Document Centroid (ADDC), Entropy, Overall Similarity and Cluster Purity.
Fuzzy Cognitive Maps for Software Fault Prediction

Detection of faulty modules in the early stages of the software development life cycle is crucial for the testing procedures. Several software metrics are collected during and end of the development process to represent software modules. By utilizing these collections of module representation, machine learning methods are utilized to predict the fault-prone modules. However, these methods lack interpretability and generalization utilities. In other words, these solutions are highly dependent on the underlying dataset since they aim to discover the hidden relationship between the input to the output in one direction. Addressing these issues, a fuzzy cognitive map has been first proposed to both provide interpretability and to eliminate data dependency for software fault prediction. The proposed cognitive map was learned from experts without the need or dependency on a prior project dataset. In addition to the input-output relations, the relations between inputs were jointly considered. Basing two Mamdani fuzzy inference systems' prediction performance, the proposed map could provide more plausible and accurate decisions in predicting the faulty modules.
Parsing Meaning from the 152 ZIP Code Data

The Bronx held top position for 2nd wave ZC mean and median % positive (%pos). A month after crest, the mean and median %pos of Bronx ZCs exceeded 10%. For the three other boroughs, associations between ZC SE factors and %pos tightened over time, even into post-crest weeks. The Bronx associations began tighter than the other three boroughs but disappeared during the crest. Infections swamped the Bronx and allowed no SE haven. With the Bronx crest exception, SE factors clearly associated with patterns of %pos over the boroughs. The high R-squares of the SE multivariate associations with %pos reveal failure of public health efforts. Testing intensity in the boroughs reflected race and class. Manhattan was tested most; the Bronx, least. The frequent negative association of % Black with % pos in the multivariate regressions hinted that testing within the Black neighborhoods was biased toward better-educated, wealthier residents. Public health resources were allotted in a deeply discriminatory manner.