Reviewerly/exHarmony huggingface dataset:
Modeling Metaheuristic Optimization with Deep Learning Software Bug Prediction Model

Software testing is an effective means of verifying software stability and trustworthiness. It is essential in the software development process and needs a huge quantity of resources such as labor, money, and time. Automated software testing can be used to save manual work, shorten testing times, and improve testing performance. Recently, Software Bug Prediction (SBP) models have been developed to improve the software quality assurance (SQA) process through the prediction of bug parts. Advanced deep learning (DL) models can be used to classify faults in software parts. Because hyperparameters have a significant impact on the performance of any DL model, a proper hyperparameter optimization approach utilizing metaheuristic methods is required. This paper provides a unique Metaheuristic Optimization with Deep Learning based SBP (MODL-SBP) methodology to ensure software dependability and trustworthiness. The suggested technique entails creating a hybrid Convolution Neural Network (CNN) bi-directional long short-term memory (BiLSTM) to forecast software problems. Furthermore, the Chaotic Quantum Grasshopper Optimization Algorithm (CQGOA) is used for hyperparameter optimization of the CNN-BiLSTM models, which enhances predictive accuracy. To demonstrate the superior performance of the MODL-SBP technique, a wide range of simulations are performed on benchmark datasets, with the results highlighting the superior performance of the proposed model over other recent techniques.
A Blockchain-Based Risk Assessment Model for Heterogeneous Identity Alliance

Heterogeneous identity alliance technology solves the identity management problem under heterogeneous networks by building a unified and trusted identity management system, while the risk assessment system of heterogeneous identity alliance can analyze potential security problems in heterogeneous identity alliance in advance, so as to realize timely supervision and maintenance of identity when crossing domains and reduce the proliferation of security problems. A blockchain-based risk assessment model for heterogeneous identity alliance is proposed for existing risk assessment systems that are generally centralized architectures with single point of failure, internal mischief, and loss of control of user data. The model uses attribute encryption to guarantee the secure storage of privacy data, while ensuring that the control of risk assessment-related data is always in the hands of the data owner, and simplifies the three-stage PBFT (Practical Byzantine Fault Tolerance) consensus to two stages to improve the efficiency of risk assessment result processing. Finally, a comparison experiment shows that compared to the three-stage PBFT, the two-stage PBFT reduces the number of communications to reach consensus and improves the throughput by about 7%.
NFW: Towards National and Individual Fairness in Face Recognition

Face recognition has been a long-standing research field and especially boosted by the development of convolutional neural network (CNN). However, existing CNN based FR methods are known to exhibit bias - subjects in a certain demographic group can be better recognized than other groups. While previous works mostly focus on the fairness among different races, we argue that it is essential and favorable to measure the national as well as individual fairness of existing FR methods. As to achieve this, we contribute a dataset called National Faces in the World (NFW) to measure the fairness of representative state-of-the-art FR methods across different countries and individuals. We make comprehensive comparisons on our proposed NFW dataset and illustrate different degrees of bias of existing FR methods. We hope to facilitate more fair FR research with the proposed NFW. The dataset will be public for the whole research community at https://github.com/God-BlessYou/NFW .
An Automatic Facial Landmarking for Children With Craniofacial Malformations

BACKGROUND AND OBJECTIVES Two to three thousand syndromes modify facial features: their screening requires the eye of an expert in dysmorphology. A widely used tool in shape characterization is geometric morphometrics based on landmarks, which are precise and reproducible anatomical points. Landmark positioning is user-dependent and time consuming. Many automatic landmarking tools are currently available, but have mainly been trained using photographic databases of healthy adults. Here we report a method for building an automatic landmarking tool for frontal and lateral facial photographs as well as photographs of external ears. We chose Treacher Collins (TC) syndrome because it is the most frequent mandibulofacial dysostosis in human and clinically recognizable although highly variable in severity. METHODS We extracted photographs from the photographic database of the maxillofacial surgery and plastic surgery department of the Hôpital Necker - Enfants Malades in Paris, France with the diagnosis of TC syndrome. The control group was built from children admitted for craniofacial trauma or skin lesions. After testing two methods of object detection by bounding boxes, a Haar Cascade based tool and a Faster Region-based Convolutional Neural Network (Faster R-CNN) based tool, we used three different automatic annotation algorithms: the patch-based AAM (Active Appearance Model), the holistic AAM and the Constrained Local Model (CLM). The final error corresponding to the distance between the points placed by automatic annotation and those placed by manual annotation was reported. RESULTS We included respectively 1664, 2044 and 1375 manually annotated frontal, profiles and ears photographs. Object recognition was optimized with the Faster R-CNN based detector. The best annotation model was the patch-based AAM (p < 0.001 for frontal faces, p = 0.082 for profile faces and p < 0.001 for ears). Pre-training on public photos did not improve the performance of the model. CONCLUSION We defined a pipeline to create automatic annotation models adapted to faces with congenital anomalies, an essential prerequisite for research in dysmorphology.
Smart Recruitment System Using Deep Learning with Natural Language Processing

Recruitment, a worldwide industry with millions of resumes being uploaded on N-number of hiring websites for countless jobs on daily routine, with the great evolution in online-based hiring process. Job requirement is considered one of the major activities for humans which is a very strenuous job to find a fruitful talent. Every job seeker will have their novel to organize the data blocks with unique style and format for representation, thereby the resumes became an extraordinary illustration of data which is unstructured. Choosing a fantastic resume from a collection of unstructured resumes is very simple with a resume parser, which adapts the process of extracting structured data from a collection of unstructured resumes. Machine understandable output is obtained using the set of instructions that investigates and abstracts resume/CV data. This helps to store and analyse data automatically. This paper proposes a smart recruitment system (SRS) which includes resume classification using deep learning along with natural language processing (NLP) techniques and automatic questionaries' which measures technical proficiency of an applicant, and by using syntactical similarity and semantic similarity measurements to identifying the suitable candidate according to the requirement of the company skill set. To abstract the significant data for hiring process using natural language process, we propose to use named entity recognition. The proposed procedure irrespective of format whether it is a pdf or word document of the resume, will pick up the data associated with the candidate-like work experience and education. Parsing and ranking the resume makes the hiring process easy and efficient.
Anonymous Certificateless Multireceiver Encryption Scheme for Mobile Communication Devices

With the rapid development of wireless communication technologies and the popularity of mobile communication devices, people can easily access the Internet through their mobile communication devices. Compared with the traditional wired communication, this brings great convenience to people’s lives, but at the same time, it also brings greater challenges in terms of security and privacy. In order to protect the confidentiality of the transmitted message and prevent the identity information of message recipients from being leaked, some certificateless multireceiver encryption (CLMRE) schemes that could realize the anonymity of the receivers have been proposed. But most of the proposed schemes are either under the random oracle model (ROM), or based on bilinear pairings and hash-to-point (HTP) operations, which lead to expensive operation costs in encryption or decryption. In the ROM, in order to gain the value of the hash function, the adversary makes a query to the challenger, but in the standard model (SM), the adversary can compute the hash function directly. The cryptographic scheme based on the SM has higher security than the cryptographic scheme based on the ROM. Therefore, it is very meaningful and necessary to design an efficient CLMRE scheme without bilinear pairings and HTP operations under the SM, which is suitable for mobile communication devices. In this article, we propose the first CLMRE scheme without bilinear parings and HTP operations in the SM, and we prove that this scheme is indistinguishable and anonymous against Type-I adversary <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {A}_\text{I}$</tex-math></inline-formula> and Type-II adversary <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {A}_{\text{II}}$</tex-math></inline-formula> . Additionally, we compare our scheme’s performance in calculation and communication with several previous schemes. The analysis shows that our novel scheme based on the SM is efficient in terms of calculation, communication, and it is suitable for mobile communication devices.
Causal Relationship Between Financial Development, Economic Growth, and Income Inequality: Panel Data Evidence from Asian and North African Countries

This study address whether financial development and economic growth will decrease income inequality. A sample of 11 Asian and 4 North African countries was examined over 1996–2019. Various tests for stationarity, cointegration and causality, and robust estimation methods were applied. The results confirm a long-run cointegrating relationship between the variables. Based on FE regressions, pooled OLS, and system GMM estimates, it is shown that there is a significant growth threshold effect in the inequality–growth nexus. It implies that the long-run rise in real income per capita reduces inequality. It was found an inverted U-shaped relationship between economic growth and income inequality. Our findings also show a significant financial development threshold effect in the finance–income inequality nexus, confirming an inverted U-shaped relationship between all the financial development proxies and income inequality. Furthermore, the Granger causality tests show a long-run unidirectional causality running from all the proxies of financial development to income inequality. In contrast, there is evidence of a long-run unidirectional causality running from income inequality to economic growth.
A Text Classification Approach to Api Type Resolution for Incomplete Code Snippets

The Stack Overflow Q&A platform boasts an active community of users, who often include code snippets in their questions and answers. Several development tools rely on these code snippets as a source of information. Intended as examples for humans, however, the code snippets may not form a compilation unit on their own. For instance, snippets illustrating how to use an API might lack the import statements for the corresponding API types. It becomes a problem to determine the fully-qualified name of API types in incomplete snippets.We present RESICO, a machine learning-based text classification approach to resolving the simple name of API types to their fully-qualified names. RESICO is trained on a corpus of Java programs for which a compiler can determine the fully-qualified names. For a Random Forest classifier, we evaluate the type resolution accuracy of the resulting model on the original and on an extended version of datasets of snippets previously used to evaluate the current state-of-the-art approach which is based on information retrieval. Results show that our approach outperforms the state-of-the-art one, although the training phase is considerably slower. We observe that most of its incorrect type resolutions are not due to ambiguities among the simple names for API types, but due to similarities among the contexts in which these types are used. This in itself forms an actionable observation for future work.
E3d: An Efficient 3d Cnn for the Recognition of Dairy Cow's Basic Motion Behaviour

Accurately and rapidly recognizing the basic motion behaviors (lying, standing, walking, drinking, and feeding) is helpful to better understand the health status of dairy cows. Existing algorithms cannot effectively deal with the problem of large parameters, thus difficult to load and use on portable edge devices. In this paper, an E3D (Efficient 3D CNN) algorithm was proposed to solve the problems of existing algorithms. Based on the 3D convolution combined with Dwise (Depthwise Separable Convolution) in the SandGlass-3D module, E3D could directly and efficiently process the Spatial-Temporal information of the video. The ECA (Efficient Channel Attention) was introduced to filter channel information for accuracy improvement. A total of 1009 basic behavior videos with different scenes and postures of dairy cows were divided into training set, validation set, and testing set at a ratio of 3:1:1. Experimental results showed that the precision, recall, params, and FLOPs of the E3D were 98.17%, 97.08%, 2.35 M, and 0.98 G, respectively. The accuracy of E3D was 7.29%, 4.06%, 5.31%, and 12.46% higher than C3D, I3D, P3D, and S3D, respectively. It indicated that the proposed network was suitable for accurately and rapidly recognizing the basic motion behaviors of dairy cows in natural environments.
The End of the Crypto-Diversification Myth

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A Comparative Study of Deep Learning Models for Word-Sense Disambiguation

Word-sense disambiguation (WSD) has been a persistent issue since its introduction to the community of natural language processing (NLP). It has a wide range of applications in different areas like information retrieval (IR), sentiment analysis, knowledge graph construction, machine translation, lexicography, text mining, information extraction, and so on. Analysis of the performance of deep learning algorithms with different word embeddings is required to be done since various deep learning models are deployed for the task of disambiguation of word sense. In this paper, comparison of several deep learning models like CNN, LSTM, bidirectional LSTM, and CNN + LSTM is done with trainable as well as pretrained GloVe embeddings with common preprocessing methods. Performance evaluation of temporal convolutional network (TCN) model is done along with the comparison of the same with the formerly mentioned models. This paper shows that using GloVe embeddings may not result in better accuracy in the case of word-sense disambiguation, i.e., trainable embeddings perform better. It also includes a framework for evaluating deep learning models for WSD and analysis of the usage of embeddings for the same.
Statistics of Orientation Relationships in Crystallography

The orientation of a crystal can be described by a rotation that transforms it into some standard position. Because crystals tend to be symmetrical, this rotation is known only up to multiplication by an element of the symmetry group. The first part of this paper summarises the embedding approach to studying crystallographic orientations. The second part introduces methods for handling the crystallographic orientations of pairs of crystals, for which the first and second crystals in each pair can have different symmetry groups. A key concept is that of orientation relationship, a directional form of hidden regression, in which pairs of orientations are related by a common rotation. Some real and simulated data sets are analysed to illustrate how our methods can be applied.
Is CNN Really Looking at Your Face?

Face recognition has dramatically improved its performance with the advent of deep learning, especially Convolutional Neural Networks (CNNs), while they have raised a new issue: the difficulty in interpreting the results. The question is what CNN looks at in a face image to identify a person. To answer this question, this paper presents a simple and novel analysis of deep face recognition based on facial parts. We evaluate the recognition accuracy of face images with specific regions masked using face segmentation labels. Our analysis clarifies what CNNs really need in face images for face recognition. The paper concludes with an application of face recognition models to general visualization methods and the problems contained in some classical face image datasets.
Inferring a Multi-perspective Likelihood Graph from Black-Box Next Event Predictors

Deep learning models for next event prediction in predictive process monitoring have shown significant performance improvements over conventional methods. However, they are often criticized for being black-box models. Without allowing analysts to understand what such models have learned, it is difficult to establish trust in their abilities. In this work, we propose a technique to infer a likelihood graph from a next event predictor (NEP) to capture and visualize its behavior. Our approach first generates complete cases, including event attributes, using the NEP. From this set of cases, a multi-perspective likelihood graph is inferred. Including event attributes in the graph allows analysts to better understand the learned decision and branching points of the process. The results of the evaluation show that inferred graphs generalize beyond the event log, achieve high F-scores, and small likelihood deviations. We conclude black-box NEP can be used to generate conforming cases even for noisy event logs. As a result, our visualization technique, which represents exactly this set of cases, shows what the NEP has learned, thus mitigating one of their biggest criticisms.
Interpretation of Hindi Compound Nouns Using Word2Vec Embeddings

The present work outlines the result of Hindi compound noun interpretation using an SVM classifier and a Hindi word2vec embedding. The word2vec embedding is used to represent the individual noun constituents. The work focuses on classifying the different semantic relations that are found between the constituents of Compound nouns. The Hindi compound noun dataset used for this experiment is 1500 compound noun data annotated with four semantic relations. We have classified the dataset into four major semantic relations Purpose, Modifier, Topic, and Others, based on the occurring frequency of the relations in the dataset, then performed a multiclass classification using an SVM classifier. The introduction of the Word2Vec model with an SVM classifier has outperformed our previous result of interpretation of compound nouns on the same data set using only an SVM classifier.
Secure Image Transmission Through Ofdm System Using One Dimensional Chaotic Map

Information security considers as one of the main goals when it is transmitted through a wireless communication or even it is required to be saved in a PC. Incorporation of a secure and highly reliable of digital data such as (video, audio, images or text) that be required to transmit from source to destination through communication channel, is turning into a top of the requirements in present-day wi-fi communications. There are two phases to achieve confidentiality, the data level and the network level. Cryptographic techniques are using in data level security. This study focuses on data-level security phase to establish a secure image transmission through an AWGN channel, where (OFDM) Orthogonal Frequency Division Multiplexing system is used with different encryption techniques, in tow schemes using cipher, permutation, and scrambling algorithms to ensure secure image transmission using MATLAB program. Several statistical tests were used to check the quality of the encryption algorithm, which are the entropy, correlation coefficient, NPCR, UACI, histogram. On the other hand, the image transmission quality is evaluated using Peak-Signal-to-Noise Ratio (PSNR), and Bit-Error-Rate (BER) at different Signal-to-Noise-Ratio (SNR) conditions. The numerical results show that the tow stage-OFDM system proposed in both schemes present an overall performance improvement among the earlier cryptosystems. The statistical analysis tests prove that the proposed schemes are considered as simple and robust algorithms for security point of view.
Computationally efficient integrity verification for shared data in cloud storage

The cloud model offers hypothetically unlimited computing and storage resources. Cloud storage service is the best storage option compared to conventional storage options. However, the cloud storage model poses data security and integrity threats. Data is exposed to serious security threats while outsourcing to a cloud service provider (CSP). When a user outsources important sensitive data to a CSP, the data integrity verification is an obvious needed task. Furthermore, the data auditing scheme should be computationally efficient to perform the data auditing even with an android device. In this chapter, we propose a computationally efficient auditing scheme Certificate-Less Integrity Verification for Shared data (CL-IVS). CL-IVS does not have a key escrow problem as the user’s private key is made up of two parts with a partial key generated by the key generation center (KGC) and the secret value chosen by the data owner. Subsequently, in CL-IVS we have introduced a new tag generation method by which we can reduce the number of tags generated for the blocks thereby reducing computational overhead. Hence, the proposed data auditing scheme CL-IVS reduces the computation, communication, and storage cost without compromising on providing efficient data integrity checking. Extensive security and performance analysis show that the proposed scheme is secure in the random oracle model and proves the efficiency by implementation results.
A Comparative Study to Determine the Photovoltaic Annual Energy Generation

Renewable energy sources have been growing worldwide, and solar energy is a significant part of such sources. An essential step in planning studies, including the problem of optimal location and sizing of photovoltaic generators, is estimating how much energy the panel will generate over time. For that, two aspects must be considered: the stochastic nature of the input variables and the way to calculate the generator’s output power. In a brief literature review, twelve approaches were identified to determine the PV output power. Therefore, this article compares such methods through actual meteorological data and energy generated by a solar power plant. As expected, models that consider the influence of ambient temperature on output power performed better than those that do not. When performing Monte Carlo simulations, the correlation between the input variables must be considered appropriately, so that the estimate is even more realistic. Keywords— Photovoltaic generation, output power, real data, correlation, Monte Carlo simulation.