Reviewerly/exHarmony huggingface dataset:
Prediction of face age progression with generative adversarial networks

Face age progression, goals to alter the individual's face from a given face image to predict the future appearance of that image. In today's world that demands more security and a touchless unique identification system, face aging attains tremendous attention. The existing face age progression approaches have the key problem of unnatural modifications of facial attributes due to insufficient prior knowledge of input images and nearly visual artifacts in the generated output. Research has been continuing in face aging to handle the challenge to generate aged faces accurately. So, to solve the issue, the proposed work focuses on the realistic face aging method using AttentionGAN and SRGAN. AttentionGAN uses two separate subnets in a generator. One subnet for generating multiple attention masks and the other for generating multiple content masks. Then attention mask is multiplied with the corresponding content mask along with an input image to finally achieve the desired results. Further, the regex filtering process is performed to separates the synthesized face images from the output of AttentionGAN. Then image sharpening with edge enhancement is done to give high-quality input to SRGAN, which further generates the super-resolution face aged images. Thus, presents more detailed information in an image because of its high quality. Moreover, the experimental results are obtained from five publicly available datasets: UTKFace, CACD, FGNET, IMDB-WIKI, and CelebA. The proposed work is evaluated with quantitative and qualitative methods, produces synthesized face aged images with a 0.001% error rate, and is also evaluated with the comparison to prior methods. The paper focuses on the various practical applications of super-resolution face aging using Generative Adversarial Networks (GANs).
Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis With Auxiliary Clinical Variables and Incomplete Multi-Modality Data

The integrative analysis of complementary phenotype information contained in multi-modality data (e.g., histopathological images and genomic data) has advanced the prognostic evaluation of cancers. However, multi-modality based prognosis analysis confronts two challenges: (1) how to explore underlying relations inherent in different modalities data for learning compact and discriminative multi-modality representations; (2) how to take full consideration of incomplete multi-modality data for constructing accurate and robust prognostic model, since a host of complete multi-modality data are not always available. Additionally, many existing multi-modality based prognostic methods commonly ignore relevant clinical variables (e.g., grade and stage), which, however, may provide supplemental information to promote the performance of model. In this paper, we propose a relation-aware shared representation learning method for prognosis analysis of cancers, which makes full use of clinical information and incomplete multi-modality data. The proposed method learns multi-modal shared space tailored for prognostic model via a dual mapping. Within the shared space, it equips with relational regularizers to explore the potential relations (i.e., feature-label and feature-feature relations) among multi-modality data for inducing discriminatory representations and simultaneously obtaining extra sparsity for alleviating overfitting. Moreover, it regresses and incorporates multiple auxiliary clinical attributes with dynamic coefficients to meliorate performance. Furthermore, in training stage, a partial mapping strategy is employed to extend and train a more reliable model with incomplete multi-modality data. We have evaluated our method on three public datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrate the superior performance of the proposed method.
Emotion-infused deep neural network for emotionally resonant conversation

The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent years become a popular research topic in the field of natural language processing. We therefore aim at improving current chatbots with the addition of natural emotions. In contrast to previous work, we intend to distinguish fine-grained emotion differences between words in order to better understand emotion expressions in sentences. Our approach infuses fine-grained emotion content into the response generation process to make the dialog more emotionally resonant. The experimental results demonstrate that this method can classify emotions more effectively. In addition, the proposed hybrid model, which consists of recurrent and convolutional neural networks with additional emotion-specific valence-arousal features, can correctly identify five emotions with a 67.89% overall F1-score. We further evaluate the subjective quality of the responses and discover that the infusion of fine-grained emotion information substantially improves the quality and fluency of automatically generated empathetic conversation. We conclude that the proposed model can greatly improve the efficiency and usability of a conversational chatbot system.
Image recognition based on deep residual shrinkage Network

Deep residual shrinkage network is a novel and improved algorithm based on deep residual shrinkage network by introducing attention mechanism and soft threshold function. The core of the deep residual shrinkage network can be expressed as: when processing the image sample data, the important information features are extracted by the attention mechanism, and then the negligible information features are eliminated by the soft threshold function, only important information is retained, and these important information features are processed to get the final results, so as to improve the accuracy. The deep residual shrinkage network was applied to MNIST dataset and CIFAR-10 dataset to determine the feasibility of this method in image recognition, and then the network was applied to the facial expression dataset made by the author based on FER2013. Experimental results show that this method can effectively improve the accuracy of image recognition.
Real time object-based video forgery detection using YOLO (V2)

Video forgery detection is a challenging task nowadays due to fake video forwarding. Copy-Move type of attack is especially mostly practiced to tamper with the original contents of a video or an image. Copy-Move attack mainly deals with object-based video forgery. Traditional methods are quiet slow and not strong enough to detect complex Copy-Move attacks. So, automatic tamper detection in videos related to speed and accuracy is a challenging task. This paper proposes a new approach for the detection of Copy-Move attack in passive blind videos. Object-based forgery detection approach is implemented using fast and real-time object detector “You Only Look Once -Version 2″:YOLO (V2). The system is trained on the benchmark dataset videos for the automatic detection of forged objects within the video with a 0.99 confidence score. Trained YOLO (V2) model is accurately able to classify and localize the forged and non-forged objects within the given input video. The results and experimental analysis demonstrates that the proposed YOLO (V2) model achieved excellent results for detecting plain and complex Copy-Move attacks such as scaling, rotation, flipping. The performance excellent for object-based forgery detection for speed and accuracy than existing similar state-of-art deep learning approaches.
Topic Difficulty: Collection and Query Formulation Effects

Several recent studies have explored the interaction effects between topics, systems, corpora, and components when measuring retrieval effectiveness. However, all of these previous studies assume that a topic or information need is represented by a single query. In reality, users routinely reformulate queries to satisfy an information need. In recent years, there has been renewed interest in the notion of “query variations” which are essentially multiple user formulations for an information need. Like many retrieval models, some queries are highly effective while others are not. This is often an artifact of the collection being searched which might be more or less sensitive to word choice. Users rarely have perfect knowledge about the underlying collection, and so finding queries that work is often a trial-and-error process. In this work, we explore the fundamental problem of system interaction effects between collections, ranking models, and queries. To answer this important question, we formalize the analysis using ANalysis Of VAriance (ANOVA) models to measure multiple components effects across collections and topics by nesting multiple query variations within each topic. Our findings show that query formulations have a comparable effect size of the topic factor itself, which is known to be the factor with the greatest effect size in prior ANOVA studies. Both topic and formulation have a substantially larger effect size than any other factor, including the ranking algorithms and, surprisingly, even query expansion. This finding reinforces the importance of further research in understanding the role of query rewriting in IR related tasks.
Analyzing the Impact of Forensic Accounting in the Detection of Financial Fraud: The Mediating Role of Artificial Intelligence

In the recent times, the financial sector has witnessed number of financial scams in the markets raising concerns for the professionals and the auditors accountable for ensuring transparency and accuracy in the day-to-day auditing activities. Globally, the use of artificial intelligence has prominently increased the ability of a machine combined with human intelligence and has added to the ability of traditional methods to deal with financial information manipulation. The study aims to identify the mediating role of artificial intelligence tools in the forensic accounting and the detection of financial frauds. The study has developed a research model using structural equation modeling to examine the influence of forensic accounting in the detection of financial frauds. The results can help professionals understanding the benefits of AI technology in the forensic accounting domain that can facilitate combating fraud.
Quaternion-Based Dictionary Learning and Saturation-Value Total Variation Regularization for Color Image Restoration

Color image restoration is a critical task in imaging sciences. Most variational methods regard the color image as a Euclidean vector or the direct combination of three monochrome images and completely ignore the inherent color structures within channels. To better describe the relationship of color channels, we represent the color image as the so-called pure quaternion matrix. Note that the celebrated dictionary learning method has attracted considerable attention for image recovery in the past decade. Following this idea, we propose a novel quaternion-based color image recovery method. This model combines the advantages of dictionary learning and the total variation method for color image restoration. The new strategy used in the proposed model manages to handle the color image restoration problem in the quaternion space. Moreover, the new proposed model can be easily solved by the classical alternating direction method of multipliers (ADMM) algorithm. Numerical results demonstrate clearly that the performance of our proposed dictionary learning method is better than some state-of-the-art color image dictionary learning and total variation methods in terms of some criteria and visual quality.
Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information

Entity relation extraction plays an important role in the biomedical, healthcare, and clinical research areas. Recently, pre-trained models based on transformer architectures and their variants have shown remarkable performances in various natural language processing tasks. Most of these variants were based on slight modifications in the architectural components, representation schemes and augmenting data using distant supervision methods. In distantly supervised methods, one of the main challenges is pruning out noisy samples. A similar situation can arise when the training samples are not directly available but need to be constructed from the given dataset. The BioCreative V Chemical Disease Relation (CDR) task provides a dataset that does not explicitly offer mention-level gold annotations and hence replicates the above scenario. Selecting the representative sentences from the given abstract or document text that could convey a potential entity relationship becomes essential. Most of the existing methods in literature propose to either consider the entire text or all the sentences which contain the entity mentions. This could be a computationally expensive and time consuming approach. This paper presents a novel approach to handle such scenarios, specifically in biomedical relation extraction. We propose utilizing the Shortest Dependency Path (SDP) features for constructing data samples by pruning out noisy information and selecting the most representative samples for model learning. We also utilize triplet information in model learning using the biomedical variant of BERT, viz., BioBERT. The problem is represented as a sentence pair classification task using the sentence and the entity-relation pair as input. We analyze the approach on both intra-sentential and inter-sentential relations in the CDR dataset. The proposed approach that utilizes the SDP and triplet features presents promising results, specifically on the inter-sentential relation extraction task. We make the code used for this work publicly available on Github.1.
Identifying Drone Operator by Deep Learning and Ensemble Learning of IMU and Control Data

Drone flight controls and ground stations are known to be vulnerable to attacks. Besides posing a threat to integrity and confidentiality of drone data, their vulnerabilities endanger safety. Onboard continuous authentication is a vital countermeasure to hijacking attempts. Motivated by the success of Machine Learning (ML) techniques in the field of behavioral biometrics, this paper investigates the use of sensor readings generated onboard drones and of control data reaching them from the ground to feed an onboard ML model continuously authenticating pilots. We analyze fifteen inertial measurement units (IMU) and four radio control signals obtained from the drone's onboard sensors or coming from its remote controller, to identify the controlling pilot. We investigate three sequence classification schemes. In the first scheme, raw sensor sequences are directly fed to a deep Long/Short-Term Memory (LSTM) learner. In the second scheme, frequency-domain features are extracted from the data sequences and interpreted by an ensemble of random trees. In the third scheme, instantaneous sensor readings are classified using the same ensemble learning technique as in the second scheme, yet a final decision fusion method is adopted to provide a sequence-based decision. We compare the three schemes in terms of accuracy, complexity, and delay. The winning scheme is validated and tested against an unseen intruder scenario. Our tests show that an LSTM model trained with data from 19 users is able to identify the operating user at a 97% accuracy, while it can identify an unknown intruder at an average accuracy of 73%.
The Emergence Threat of Phishing Attack and The Detection Techniques Using Machine Learning Models

With the increasing number of the internet users, cybercrime is increasing at a high rate. Among all the cyber- attack, phishing has now confirmed so successful and the number one attack vector. Throughout our investigation of phishing attack, we find that it is the most uses attack and used many ways to attack the targeted user. Attacks by phishing URL, phishing email and phishing websites are very popular way of phishing attack. But now-a-days with the increasing popularity of social media and online gaming, attackers are targeting this media for the phishing attack. Machine learning is giving a new era for both the attackers and the users who want to prevent this attack. In our work we build a twitter spear phishing bot using machine learning. We did experiment on the detection of phishing url, phishing email and phishing website. For the detection of phishing url we used various classifier and with the higher accuracy we focus on the timing to train the dataset. We found that XGBoost classifier gives the higher accuracy 94.44% and took less time. For the detection of phishing email, we used naïve bayes classifier and got the accuracy of 95.15%. In our website detection techniques, we used different classifier and found that Random Forest Classifier gives the higher accuracy of 96.80%
Code Clone Detection—A Systematic Review

Code segments are similar or identical to each other by copy-paste the original code at different locations with or without modifications are called code clones. Code clones are either by intentional or unintentional. In software development reprocess, the original code is most common. If any bug exists in original code segment, the bug remains in all the cloned code. As a result, bug propagation in cloning process affects in software maintenance. Detection and corrections are required. Code clone detection appears as an ongoing research area to solve these issues. In this paper, we carried out literature survey of 43 studies to summarize techniques and tools utilized for code clone detection. Six code clone detection techniques are based on text, token, tree, metric, graph, and hybrid to detect type-1, type-2, type-3, and type-4 clones. Our study is concluded with several studies that exist to detect the types 1–4 clones. There is a requirement of novel approach to develop to detect all four types of clones together. In addition, need to develop more approaches to simplify to create program dependency graph for type-4 clone detection.
Weakly Supervised Person Search with Region Siamese Networks

Supervised learning is dominant in person search, but it requires elaborate labeling of bounding boxes and identities. Large-scale labeled training data is often difficult to collect, especially for person identities. A natural question is whether a good person search model can be trained without the need of identity supervision. In this paper, we present a weakly supervised setting where only bounding box annotations are available. Based on this new setting, we provide an effective baseline model termed Region Siamese Networks (R-SiamNets). Towards learning useful representations for recognition in the absence of identity labels, we supervise the R-SiamNet with instance-level consistency loss and cluster-level contrastive loss. For instance-level consistency learning, the R-SiamNet is constrained to extract consistent features from each person region with or without out-of-region context. For cluster-level contrastive learning, we enforce the aggregation of closest instances and the separation of dissimilar ones in feature space. Extensive experiments validate the utility of our weakly supervised method. Our model achieves the rank-1 of 87.1% and mAP of 86.0% on CUHK-SYSU benchmark, which surpasses several fully supervised methods, such as OIM [36] and MGTS [4], by a clear margin. More promising performance can be reached by incorporating extra training data. We hope this work could encourage the future research in this field.
Fast and Efficient Group Key Exchange in Controller Area Networks (CAN)

The security of vehicle communication buses and electronic control units has received much attention in the recent years. However, while essential for practical deployments, the problem of securely exchanging cryptographic keys between electronic control units on the CAN bus received little attention so far. In this work, we evaluate group extensions of a regular key exchange protocol, i.e., the elliptic curve version of the Diffie-Hellman protocol, by using both a standardized NIST elliptic curve as well as the faster, more recently proposed Four <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {Q}$</tex-math></inline-formula> curve. We deploy protocol implementations and determine crisp performance bounds on real-world automotive-grade platforms with Infineon and ARM cores. For an up-to-date analysis, we use both CAN and its more recent extension CAN-FD as communication layers. Roughly, the computational runtime of the key exchange protocol scales logarithmically or linearly with the number of nodes, depending on the protocol version. The computational time proves to be more critical than bandwidth due to the more demanding elliptic curve operations.
Geochemical indices as efficient tools for assessing the soil weathering status in relation to soil taxonomic classes

The classification of a soil at different taxonomic levels provides abundant information on the weathering and physical–chemical properties of the soil. Geochemical indices are quantitative models that correlate soil weathering status to soil geochemical properties. Therefore, the use of such indices provides a quantitative criterion for determining the intensity of weathering as well as help to recognising soil classes. Furthermore, geochemical indices can be used to understand various aspects of soil agricultural, environmental and engineering properties. The study area was located in Mazandaran province, northern Iran, with a Mediterranean climate, and we assessed the potential use of quantitative geochemical indices to assess the evolutionary stages of soils. The classes of studied soils which were determined according to soil taxonomy were compared versus the results of X-ray fluorescence (XRF) analysis and multiple geochemical indices to determine the intensity of weathering. In total, 25 geochemical indices, which were classified as atomic proportion, mobile/mobile, immobile/mobile, mobile/immobile alkaline element-based, mobile/immobile silica-based and immobile/immobile elements were used to assess the stage of soil weathering. Principal components analysis (PCA) of XRF results and the calculated geochemical indices showed high correlation coefficients with soil classifications at different soil taxonomic levels. The results obtained from PCA also showed that the mobile/immobile (alkali element-based and silica-based) as well as the immobile/mobile elements are efficient tools to identify soil classes.