Reviewerly/exHarmony huggingface dataset:
REPFINDER: Finding Replacements for Missing APIs in Library Update

Libraries are widely adopted in developing software projects. Library APIs are often missing during library evolution as library developers may deprecate, remove or refactor APIs. As a result, client developers have to manually find replacement APIs for missing APIs when updating library versions in their projects, which is a difficult and expensive software maintenance task. One of the key limitations of the existing automated approaches is that they usually consider the library itself as the single source to find replacement APIs, which heavily limits their accuracy.In this paper, we first present an empirical study to understand characteristics about missing APIs and their replacements. Specifically, we quantify the prevalence of missing APIs, and summarize the knowledge sources where the replacements are found, and the code change and mapping cardinality between missing APIs and their replacements. Then, inspired by the insights from our study, we propose a heuristic-based approach, REPFINDER, to automatically find replacements for missing APIs in library update. We design and combine a set of heuristics to hierarchically search three sources (deprecation message, own library, and external library) for finding replacements. Our evaluation has demonstrated that REPFINDER can find replacement APIs effectively and efficiently, and significantly outperform the state-of-the-art approaches.
Impacts of metaheuristic and swarm intelligence approach in optimization

Optimization is a searching technique by which we can find the best solution from all the feasible solutions. There are two types of optimization problems depending up on the type of variable that is continuous and discrete. Discrete optimization is one kind of optimization technique where we will wait for an object like an integer, permutation, or graph from a countable set. In another side, continuous optimization deals with continuous variables including constrained problems and multimodal problems, whereas Metaheuristic is a high-level procedure which is designed to find and generate a heuristic that may produce a sufficiently good solution to optimization problems, especially with incomplete or imperfect information or limited computation capacity. It is describing always observed results based on computer experiments with the algorithms. Furthermore, the metaheuristics have been demonstrated by the scientific community to be a feasible, and often superior, alternative to more traditional (exact) methods of mixed-integer optimization such as branch and bound and dynamic programming. It is more flexible than traditional methods because at first, metaheuristic frameworks are defined in general terms which can be adapted to fit the needs of most real-life optimization problems and at second, metaheuristics do not put any demands on the formulation of the optimization problem (like requiring constraints or objective functions to be expressed as linear functions of the decision variables). Therefore, in this chapter, we will discuss the impact of metaheuristics and swarm intelligence varieties techniques in the optimization field to achieve good performance.
From facial images of different quality to score based LR

A simple method is proposed to assess the quality of a trace facial image in the context of the facial recognition system used using the similarity scores with low quality different-source facial images, defined as the Confusion Score (CS). Methods are proposed to calculate the probability of finding the correct facial image in a database using low quality images for investigational purposes using the CS, as well as calculation of the Likelihood Ratio (LR) for comparison of low quality trace facial images with good quality reference facial images, based on the assessed CS of the trace image. Improvement of performance of an LR-system using training datasets stratified on CS over the use of pooled data is demonstrated. Examples of using the proposed approach in simulated case examples are presented.
Aging Facial Recognition for Feature Extraction Using Adaptive Fully Recurrent Deep Neural Learning

Abstract The face recognition (FR) process identifies or confirms a person with the help of images and videos. The traditional aging FR (AFR) method encounters numerous issues as a result of age-related biological transformation. An adaptive fully recurrent deep neural learning (AFRDNL) technique is presented in this research to improve FR accuracy (FRA) with minimal time complexity (TC). Feature extraction and classification are two processes included in the proposed technique. The AFRDNL technique gathers facial images from the database and trains the face images with three layers. To learn the facial features, three hidden layers are employed in the AFRDNL technique. Finally, the AFRDNL technique utilizes the Gaussian activation function for matching extracted testing features with earlier saved training features. This process is repeated until a minimal training error with a higher FRA is achieved. Experimental evaluation is carried out with three image datasets like Face and Gesture Recognition Research Network, MORPH and Cross-Age Labeled faces in the Wild (CALFW) datasets using different metrics, such as FRA, false-positive rate (FPR), TC and F1score. The experimental results confirm that the AFRDNL technique effectively increases the FRA and F1score and minimizes the FPR and TC more effectively than the state-of-the-art methods.
Deep Learning Based Anomaly Detection for Muti-dimensional Time Series: A Survey

Abstract Multi-dimensional time series are multiple sets of variables collected in chronological order, which are the results of observing a certain potential process according to a given sampling rate. It also has the ability to describe space and time and is widely used in many fields such as system state anomaly detection. However, multi-dimensional time series have problems such as dimensional explosion and data sparseness, as well as complex pattern features such as periods and trends. Such characteristics lead to rule-based anomaly detection methods suffer from poor detection effects. In the big data scenario, deep learning method begins to be applied to anomaly detection tasks for multi-dimensional time series due to its wide coverage and strong learning ability. This work first summarizes the definitions of anomaly detection for multi-dimensional time series and the challenges it faces. Related methods are sorted out, and then the deep learning-based method is emphasized. The existing work and its advantages and disadvantages are summarized. Finally, the shortcomings of the existing algorithms are clarified and the future research direction is explored.
Modelling human-centric aspects of end-users with iStar

Human-centric characteristics of the end-users of software systems, such as gender, age, emotions, personality, language, culture, and physical and mental impairments, play an essential role in the uptake and usage of the software. Current software tools suffer from the lack of in-depth elicitation and understanding of these human-centric requirements during the design and modelling of the system. This can lead to ineffective and hard to use software for some users. In this paper, to be able to account for contextual variables in human interaction with diverse characteristics, we propose an approach for using personas and contexts to model human-centric aspects of the software in goal models. To achieve this, we select the iStar language due to its ability to model social, intentional and strategic dimensions, and propose an extension of it to model human-centric aspects of the software. Our novel approach is illustrated with two examples. We conducted user evaluation studies to understand how users model human aspects, and also to measure the effectiveness of our approach. Results show the lack of consideration of the human-centric aspects in existing modelling frameworks and how our extended model can simplify the understanding and addressing of such aspects. This shows and encourages that more research on modelling human aspects of the end-users is required to achieve human-centred modelling.
Content-adaptive convolutional neural network post-processing filter

Neural Network (NN)-based coding techniques are being developed for hybrid video coding schemes, such as the Versatile Video Coding (VVC) standard. In-loop filters and postprocessing filters are two types of coding tools that aim to improve the visual quality of the reconstructed content. These tools are usually trained on large video or image datasets with varying content, but they are rarely adaptive to different content types. This problem is addressed with the proposed content-adaptive Convolutional Neural Network (CNN) post-processing filter. The proposed approach is content-adaptive in two ways. Firstly, a relatively simple CNN is pre-trained on a general video dataset and then fine-tuned on the video to be coded. Since only the bias terms of the CNN are fine-tuned, the signalling overhead is reduced. Secondly, a scaling factor indicates the influence of the CNN post-processing filter on the final reconstruction. The CNN post-processing filter is evaluated on top of VVC Test Model (VTM) 11.0 with NN-based Video Coding (NNVC) 1.0 and, overall, it can save 2.37% (Y), 3.63% (U), 2.24% (V) Bjøntegaard Delta rate (BD-rate) in the Random Access (RA) configuration.
Fuzzy logic and fuzzy systems

A popular way to model knowledge in computers is by means of using IF-THEN rules. Fuzzy logic allows a straightforward manner to model them by using words instead of crisp values. This makes it more similar to human reasoning, which does not need precise rules for allowing the decision-making process in most of our daily life activities. Also, as it can be inferred, this kind of rules could be directly taken from experts (clinicians), looking for developing tools of computer-assisted diagnostic, whose final goal is not the replacement of the clinicians, but provide them of a support. In this chapter, the main fundamentals related to fuzzy logic are presented: logic operations, fuzzy inference systems, and fuzzy relations. Furthermore, they are also linked to more powerful hybrid systems, such as neuro-fuzzy models. Specifically, this could guide the reader to understand, design, integrate, and implement these concepts into novel hybrid systems.
Secure Client-Side Deduplication Scheme for Cloud with Dual Trusted Execution Environment

Data deduplication is the process that eliminates excessive redundant data in a dataset to improve storage and bandwidth utilization by significantly reducing the storage costs and increasing capacity. Therefore, a novel client-side secured dual-TEE (Trusted Execution Environment) based deduplication model (Dual-TEE) in the cloud is introduced. The proposed system prototype includes system setup, Data outsourcing, Proof of ownership (PoW) protocol initialize, and Data update. The traditional deduplication approach had a single TEE, which was prone to man-in-middle attacks and there has a high chance for authentication compromise. As a solution to this, dual TEE is implied in this research work, such that, the cryptosystem improves its ability to resist attacks. Moreover, a privilege set is assigned to each of the cloud users and the deduplication can be performed if and only if the correct privilege is with the cloud users. Further, dual TEE model attains a low value of 0.55179 for KPA analysis which is 7.088%, 7.5%, 1.83% better than the existing models like POW, POSD, and 3R-PoOR for file size 20. Hence, from the result, it is evident that the proposed Dual TEE secured model is secure to sustain data deduplication.
Privacy preservation in healthcare systems

With the progression in sensor technology and the low-cost electronic circuits’ proliferation, the Internet of Things (IoT) is emerging as a promising technology for enhancing people life in terms of making decisions and communicating with ease, especially in the healthcare domain, the IoT technology transforms the traditional hospital into a smart hospital enhanced with the benefit of using the sensors to get better diagnosis and monitoring to the patients, the patient data are collected and are sent through the internet to be analysed in the cloud, This produces growth in privacy and security risks such as sensitive data disclosure and using it in a bad way. Thus, privacy and security are considered as a challenge that needs to be addressed. In this paper, we present a new taxonomy of privacy preservation techniques in healthcare systems. In addition, we analyse and discuss several recent approaches that base on existing privacypreserving techniques.
Creating a Mathematical Model for Estimating the Impact of Errors in the Process of Reconstruction of Non-Uniform Code Structures on the Quality of Recoverable Video Images

Existing compression coding technologies are investigated using a statistical approach. The fundamental strategies used in the process of statistical coding of video information data are analyzed. Factors that have a significant impact on the reliability and efficiency of video delivery in the process of statistical coding are analyzed. A model for estimating the impact of errors in the process of reconstruction of uneven code structures on the quality of recoverable video images is being developed.The influence of errors that occur in data transmission channels on the reliability of the reconstructed video image is investigated.
Distributed State Estimation With Colored Noises

Most of the existing studies mainly consider the process and measurement noise as Gaussian white noises, while a large number of systems are influenced by colored noises in the practical applications. In this brief, a distributed state estimator with colored noises is proposed. Since every moment of colored noise sequence is related, we first make the colored noises equivalent to Gaussian white noises by augmenting the state matrix. Then, we design a state estimator with colored noises, and further analyze the convergence property of the estimation error covariance. Ultimately, simulation results indicate that the designed distributed state estimator can effectively address colored noises and ensure high accuracy and performance of state estimation, and demonstrate the relationship between colored noise parameters and estimation performance as well.
On the Security of Encrypted JPEG Image with Adaptive Key Generated by Invariant Characteristic

Recently, a JPEG encryption scheme with adaptive key is proposed to improve the security of encrypted images. However, it still has a security risk under chosen plaintext attack (CPA) since the adaptive key is generated by invariant characteristic of the number of AC codes (ACCs) in minimum coded units (MCUs) (NAM). This paper analyzes the possibility of ACCs encryption being attacked by the CPA from three stages: (1) the adaptive key can be forged from the NAM of encrypted image; (2) the diversity of run-length sequence in minimum coded units (MCUs) can realize the estimation of MCUs permutation sequence; (3) the plaintext images can be constructed with the unique ACCs in the same run-length to estimate ACCs permutation sequence. Therefore, this paper constructs plaintext images with same NAM and run-length sequence as encrypted image to achieve CPA attack. The estimation of the permutation sequences is based on the analyses of last two phases. Experimental results show that the higher the correct rate of MCUs permutation sequence, the higher the correct rate of ACCs permutation sequence. Texture images or images with higher quality factors are more likely to be completely attacked by the proposed CPA.
Intergroup Cascade Broad Learning System With Optimized Parameters for Chaotic Time Series Prediction

The prediction of chaotic time series can well describe the physical properties and influencing factors of a sequence of numerical data points in successive order and has played an increasingly crucial role in various scientific and engineering communities. These time series data require the prediction model to have strong dynamic feature representation ability, which often leads to excessive calculation burden. As an emerging and effective learning method, a broad learning system (BLS) can be trained quickly by incremental learning, and the system can be reconstructed without requiring a lengthy retraining process, which has been shown to facilitate superior time-saving performance. However, the historical information of time series data is often ignored, resulting in the lack of dynamic feature representation ability of the original BLS algorithm. In this article, a novel intergroup cascade BLS (ICBLS) method and its variant with optimized parameters, termed MOICBLS, are proposed, which can combine the previous knowledge with current information to determine the output results by constructing a novel intergroup cascade structure for the enhancement nodes. The new algorithms try to retain the time-saving advantage of BLS and greatly enhance the extraction ability of dynamic features of chaotic time series data. In addition, it cannot be ignored that the network structure parameters have an unquestionable impact on the performance of a BLS. In response, two conflicting goals of prediction accuracy and diversity are formulated as multiobjective optimization functions in the training phase to generate the optimized network structure parameters needed by multiple candidate models. The experimental results on two benchmark chaotic time series dataset, the Lorenz system and Rossler system, and a geomagnetic disturbance storm time chaotic time series dataset demonstrate that our proposed algorithms are effective for prediction of chaotic time series, and the prediction accuracy is better than some existing approaches.
“Do You Know You Are Tracked by Photos That You Didn’t Take”: Large-Scale Location-Aware Multi-Party Image Privacy Protection

Most existing image privacy protection works focus mainly on the privacy of photo owners and their friends, but lack the consideration of other people who are in the background of the photos and the related location privacy issues. In fact, when a person is in the background of someone else’s photos, he/she may be unintentionally exposed to the public when the photo owner shares the photo online. Not only a single visited place could be exposed, attackers may also be able to piece together a person’s travel route from images. In this article, we propose a novel image privacy protection system, called LAMP, which aims to light up the location awareness for people during online image sharing. The LAMP system is based on a newly designed location-aware multi-party image access control model. Unlike previous works on small scales, the LAMP system is highly efficient and scalable as it can enforce privacy protection for billions of users on social networks in real time. The LAMP system automatically detects the user’s occurrences on photos regardless the user is the photo owner or not. Once a user is identified and the location of the photo is deemed sensitive according to the user’s privacy policy, the user’s face will be replaced with a synthetic face. A prototype of the system was implemented and evaluated to demonstrate its applicability in the real world.
Adoption of Artificial Intelligence Integrated Customer Relationship Management in Organizations for Sustainability

Customer Relationship Management (CRM) is considered a strategic initiative of a business organization. To develop CRM activities, a huge volume of customer data is analyzed, which helps the organizations identify customers’ purchase intentions. But analysis of such huge volume of customer data in an accurate and quicker way is difficult by humans and organizations face numerous challenges and sometimes crisis situations without any automated system. Thus, Artificial Intelligence (AI) technology integrated with CRM could be a solution to overcome such challenges and can provide a solution for overcoming such crisis, often without human intervention. AI-CRM could provide sustenance to the organizations in terms of consistent decision-making. In this context, this chapter has discussed how AI integrated CRM system could help the organizations to overcome decision-making challenges and can provide sustainable solutions through its several functionalities. This chapter has also discussed the security and privacy issues related to usage of customers’ personal data and how to overcome such challenges.
Sign Language Interpreter Using Machine Learning

Communication is the key point for interaction and we humans use different languages through which we share our views and opinions. But for certain people i. e., the hearing impaired or the deaf and dumb they are not able communicate well because of their disability. According to World Health Organization (WHO) it is estimated that hearing loss affects approximately four hundred sixty-six million people globally with thirty-four millions of these being children. Sign language is one of the possibilities through which they can communicate. It is a coded language in which they use hand gestures. Communication with the rest of the world is very difficult for hearing-impaired people and hence they are isolated, in order to overcome these circumstances, we present a project that is helpful to such people. This project presents a solution with the use of Machine Learning that will identify hand gestures and translate it into speech/text and the vice versa that is it convert from speech/text to gesture. A webcam will be used to capture the region of interest i.e, recognize the hand motion as well as the gesture that the hand is indicating. Based on the recognized gestures, the recorded soundtrack will be played.
From Theory of Rhetoric to the Practice of Language Use: The Case of Appeals to Ethos Elements

Abstract In their book Commitment in Dialogue , Walton and Krabbe claim that formal dialogue systems for conversational argumentation are “not very realistic and not easy to apply”. This difficulty may make argumentation theory less well adapted to be employed to describe or analyse actual argumentation practice. On the other hand, the empirical study of real-life arguments may miss or ignore insights of more than the two millennia of the development of philosophy of language, rhetoric, and argumentation theory. In this paper, we propose a novel methodology for adapting such theories to serve as applicable tools in the study of argumentation phenomena. Our approach is both theoretically-informed and empirically-grounded in large-scale corpus analysis. The area of interest are appeals to ethos, the character of the speaker, building upon Aristotle’s rhetoric. Ethotic techniques are used to influence the hearers through the communication, where speakers might establish, but also emphasise, weaken or undermine their own or others’ credibility and trustworthiness. Specifically, we apply our method to Aristotelian theory of ethos elements which identifies practical wisdom , moral virtue and goodwill as components of speakers’ character, which can be supported or attacked. The challenges we identified in this case and the solutions we proposed allow us to formulate general guidelines of how to exploit rich theoretical frameworks to the analysis of the practice of language use.