Reviewerly/exHarmony huggingface dataset:
Forensics analysis of FacePlay application to seek digital artifacts on data ownership and privacy

Smartphones are indispensable items for more than 95% of young people today. And of course, intelligent applications on phones are also born more to meet the needs. Thanks to its unique ease of use and modern technology, the applications are beautiful to users. Nowadays, the term AI (Artificial Intelligence) is more and more popular and close to Android phone users thanks to the addition of AI to their functions. Big phone brands like Samsung, Huawei, or Google make significant machine learning improvements to their phone cameras. Especially with the application of filming and taking photos that can edit faces and create animations. Therefore, it has attracted countless users, especially selfie enthusiasts who have used their images to post apps to animate their pictures and videos. Android devices are now more popular; as of 2021, there are more than 3 billion active Android devices, three times more than IOS. Besides, there are applications related to editing, creating images, effects for users on Android. The use of personal photos by users for such applications may inadvertently cause the applications to violate privacy. Therefore, it is necessary to carry out forensic investigations of applications to detect access violations and theft of user privacy. This paper performs forensic analysis of the FacePlay app to analyze, evaluate, and warn users about FacePlay privacy issues.
Ontology-Based Natural Language Processing for Process Compliance Management

Process compliance with relevant regulations and de-facto standards is a mandatory requirement for certifying critical systems. However, it is often carried out manually, and therefore perceived as complex and labour-intensive. Ontology-based Natural Language Processing (NLP) provides an efficient support for compliance management with critical software system engineering standards. This, however, has not been considered in the literature. Accordingly, the approach presented in this paper focuses on ontology-based NLP for compliance management of software engineering processes with standard documents. In the developed ontology, the process concerns, such as stakeholders, tasks and work products are captured for better interpretation. The rules are created for extracting and structuring information, in which both syntactic features (captured using NLP tasks) and semantic features (captured using ontology) are encoded. During the planning phase, we supported the generation of requirements, process models and compliance mappings in Eclipse Process Framework (EPF) Composer. In the context of reverse compliance, the gaps with standard documents are detected, potential measures for their resolution are provided, and adaptions are made after the process engineer approval. The applicability of the proposed approach is demonstrated by processing ECSS-E-ST-40C, a space software engineering standard, generating models and mappings, as well as reverse compliance management of extended process model.
Decoy state HD QKD system for secure optical communication

Quantum key distribution (QKD) is an unconditionally secure way of sharing secret cryptographic keys on a public channel. Recently, high dimension QKD protocols have gained focus to achieve better security against various attack models. Here, we focus on the robustness of high dimensional QKD protocol against two realistic attack models, i.e. photon number splitting (PNS) attack and beam splitter (BS) attack. We present the high dimensional quantum key distribution system that incorporates a decoy-state scheme with complex structured light (OAM modes) using the KMB09 protocol. The complete QKD system design with experimental setup details and evaluation results are presented. The experimental results show that our proposed decoy-state scheme is robust against both PNS and BS attacks.
Reasoning about Authentication and Secrecy in the Signal Protocol

Increasing concern about security and privacy in instant messaging applications has led many users to question whether their messages are really properly encrypted. Signal is emerging as one of the more popular instant messaging applications that promises to protect messages by providing authentication between users, and protection from an adversary learning previous and future keys if it happens upon one key. Studies of the Signal application use traditional cryptographic proof techniques and show that it is secure. In contrast, little work has been down to analyse security of Signal in the setting of &#x2018;who knows what&#x2019;. We present a security analysis of the Signal protocol using epistemic logic to express security properties and analyse its behaviour. We find the protocol is indeed secure. By focusing on expressing security properties before implementation, this work contributes to the development of standards for security-by-design rather than security as an afterthought.
Cryptocurrency and Associated Bourse Analysis using Machine Learning and Knowledge Graphs

Data science has a goal to discover hidden patterns in raw data. This is exceptionally useful when discussing the stock market. The way crypto currency has taken over the world is something that wasn&#x0027;t predicted 20 years ago. It seems that regular market indicators aren&#x0027;t being implemented in the case of crypto currencies such as bit coin. Crypto currency seems to evade inflation as well. For analysts to perform analyses on these entities there are umpteen tools. One instance is the knowledge graph, which is a collection of interlinked descriptions of objects, events, or concepts. It puts data in context through linking and semantic metadata. Historical data present across many trading and finance websites enable analysts and enthusiasts to try machine learning and deep learning algorithms to predict the subsequent nature of that market. The paper has summarized and analyzed all the possible algorithms to prepare the data available regarding the lifetime and volatility of a currency state. A knowledge graph linking all the related information was also materialized along with a UML-style ontological graph is done to aid and support proper inference from the knowledge representation model.
Convolutional Neural Network for Nigerian Traditional Male Attire Classification

Online business has brought about tremendous positive change globally; however, it comes with some challenges. Thousands of different products are posted on a daily basis. Thus, automatic tagging and classification are becoming extremely important for business environment. In effort to aid with classification problems, this paper finds a CNN structure with the minimal computational expense to classify male attires for three main ethnic groups in Nigeria; Yoruba, Igbo, and Hausa, while keeping prediction accuracy high. We tested several manually developed network structures and compared their accuracy with popular image classification networks, namely AlexNet. We collected a dataset of 1000 pictures, in 4 different classes. We achieved 72% of test accuracy on the shallow network built from scratch, performing better than AlexNet. The preliminary study shows that with proper image processing, a shallow network can be used for prediction, thus saving huge computational expense.
A Privacy-Preserving Cross-domain Network access Services Using Sovrin Identifier

The number of cross-domain roaming users through Wi-Fi has expanded considerably in recent years. It is important to authenticate users from various institutions to ensure the confidentiality and reliability of the network. Existing solutions like eduroam utilize a centralized framework to identify user&#x0027;s accounts that generate confidentiality and security concerns. We propose a blockchain-based cross-domain authentication mechanism that provides accountability and anonymity without any third-party dependence. In contrast to previous centralized ways, our scheme authenticates its user and server anonymously and widely distributed way of delivering roaming service without privacy breaching concerns. Using a Hyperledger blockchain, we integrate our system with Sovrin identifier to authenticate the user that retains confidentiality and reliability when the user accesses the network.
Managing Cyber Security with Quantum Techniques

Recent advancements in quantum information theory and quantum computation intend the possibilities of breaking the existing classical cryptographic systems. To mitigate these kinds of threats with quantum computers we need some advanced quantum-based cryptographic systems. The research orientation towards this is tremendous in recent years, and many excellent approaches have been reported. In this article, we discuss the probable approaches of the quantum cryptographic systems from implementation point of views to handle the post-quantum cryptographic attacks.
An Effective Contrast Enhancement Approach for Wide Dynamic Range Image

Wide dynamic range image enhancement plays an important role in image processing applications. This study presents a low-complexity automatic method for wide range image contrast enhancement. According to a large number of experimental results that a narrow range image enhancement problem can be described easily by Gaussian curve. In this paper, we divide wide dynamic range image into regular cells and then applying an optimized image remapping method based on contrast gain redistribution function groups. By improving the bilinear fusion method between cells and simplifying the mean and variance of the contrast gain redistribution function, the algorithm complexity is reduced significantly and the whole process is fully automatic. Experiments show that the presented method enhances the visual effect of the wide dynamic range image effectively and fast, making it suitable for real-time applications.
Estimation of groundwater heavy metal pollution indices via an amalgam of stack ensemble learning

The Densu Basin is in the southern part of Ghana, with varied sources of contaminants because of agricultural activities, rapid urbanization, and industrialization. This paper evaluates heavy metal levels and specifically develops a prediction model for decision making. The prediction model adopted is a combination of artificial neural networks (as a first model-learner) and gradient boosting regressor (as a meta-learner). The model performed well with a strong correlation of 99.0 % between the predicted and the actual pollution indices, with root-mean-squared-log-errors of 0.025 and 0.012 for heavy metal pollution index and heavy metal evaluation index, respectively. Also, the results show that Mn and Pb are the two main relevant features necessary for the prediction of heavy metal indices.
Using argument mapping to improve clarity and rigour in written intelligence products

Intelligence products should be clear and rigorous. Intelligence organisations often attempt to improve clarity and rigour with training in thinking and writing. It is assumed that (1) training enhances clarity and rigour in products, and (2) greater clarity and rigour leads to better decisions. We describe three studies exploring these assumptions. These studies concern an initiative in a biosecurity context, based on a form of argument mapping. Results indicate that reports improved, but were inconclusive about the quality of decisions. We discuss implications of this research for intelligence.
Investigation on Face Alignment Factor for Generating Forensic Sketch Using Deep Convolutional Generative Adversarial Network

Over the years, deep learning algorithm has been rapidly improved by various architectures and optimization. Convolutional neural network (CNN) is usually the prominent architecture used for deep learning due to its effectiveness. Recently, a new architecture which is Generative Adversarial Network (GAN), becomes a popular research topic. GAN can be categorized as a generative model that is usually used to generate data similar to data distribution of what it is trained. This type of generative model is used for prediction and generating new data similar to the original distribution. Deep convolutional GAN (DCGAN) has shown that it can generate images effectively by introducing convolution layers into normal GAN. This architecture helps in generating synthetic images where the data is limited. This paper investigates face sketch images synthesis to study how face alignment affects the generated image using the DCGAN. The results show that using aligned images as training data improved the generated images visually and when trained with classifier it shows that it has 88% similarity to original images compared to using unaligned images.
NAPHash: Efficient Image Hash to Reduce Dataset Redundancy

Fast detection of duplicates in large image collections requires robust and efficient functions for image hashing. This paper presents the novel image hash function NAPHash based on the popular existing perceptional hash approach PHash. Normalized absolute values of DCT coefficients are used to create an efficient hash representation. NAPHash uses Hamming distances as the key metric and provides invariance against basic image rotation and mirroring operations. Our evaluations show its applicability and efficiency on large public image datasets as well as the positive impact on the performance of learned representations when NAPHash is used for reducing redundancies in the training data.
Feature Selection Criteria for Clustering Based on Support Vectors

Existing feature selection for clustering task usually relies on some statistical methods or some clustering quality evaluation, both of which need an assumption of data distribution. That heavily affects the generalization ability of feature selection in applications. This paper proposes feature selection criteria derived from support vector technique to address this problem. Firstly the least power support vector clustering (LPSVC) is presented. Based on products of LPSVC, an institutive criterion and a derivative criterion are proposed. These two LPSVC-based criteria keep data distribution shape as much as possible after features are removed. And they are equipped with selection algorithm to discard unimportant features. Empirical evidence suggests the proposed criteria are of fine quality.
Representation learning and retrieval

Recent advances in Deep Learning (DL), which inherently incorporate representation learning, have altered the landscape of representation learning tasks, such as image retrieval. This chapter aims to provide an introduction to recent deep representation learning methods, with special emphasis to the content based image retrieval task that will equip the reader with the necessary knowledge to apply these methods in practice. Particularly, we first present how a well-known DL method, known as autoencoders, can be used for representation learning tasks. Subsequently, we present a set of representative representation learning methods that aim to learn feature spaces that produce efficient retrieval-oriented representations, exploiting the geometric structure of the data in an unsupervised manner, as well as discriminative representations that exploit the class category of the data or the user's feedback using relevance feedback. Finally, we present a representation learning method suitable for retrieving objects that belong to classes that were not seen during the training process.
The data analysis and smart discriminant method based on fisher model

Fisher discriminant model was used to find the rule between discriminant factors and water quality types, and the discriminant function model of water inrush source was established, which was tested by water samples to be judged. On the basis of previous studies, the discriminant method model selects 6 kinds of conventional hydrochemical ions, pH and total dissolved solids, and a total of 8 kinds of hydrochemical factors to analyze.
Deep Learning Approaches for Arabic Sentiment Analysis - Case Study: Detection of Potential Suicide Cases

It is becoming increasingly clear that the Internet and social media have become a very rich source of information on the subject of suicide, which facilitates the study of the opinions, feelings and ideas that has suicidal tendencies. Those sources of information can be used to build a predictive system capable of processing sentiments expressed in suicidal notes to detect and predict suicidal tendencies before committing the act of suicide. In this work, we are interested in building a predicting system based on different approaches of deep learning models capable of processing suicidal note written in Arabic language. Despite the fact that the Arabic language is one of the languages that are widely spoken and written, both in real life and through social media, it is noticeable that there is a significant lack in terms of sentiment analysis and opinion mining systems dedicated to this language. In this paper, we have established a particular process to predict if the writer of a given note has more likely suicide tendencies or not. We can recap this process in five steps: first step is a pretreatment step; it is based on Arabic light-stemming algorithms. The second step concern data augmentation. The third step has a double goal; features extraction and features representation. The fourth step concern the building of the predictive algorithm; two deep learning models are used: CNN and LSTM. The fifth and final step is a validation step; in which we can determine the best model.
An Adaptive Hybrid Bat Algorithm with Genetic Operations and Dynamic Inertia Weight

Swarm intelligence in a bat algorithm (BA) provides social learning, and genetic operations for reproducing individuals in a genetic algorithm (GA) offer global search ability in solving complex optimization problems. The integration of a BA and a GA provides an opportunity for improved search performance. However, existing studies adopt only one genetic operation of the GA, or design hybrid algorithms that divide the overall population into multiple subpopulations evolving in parallel with weak interactions. Differing from them, this work proposes an improved Hybrid Bat Algorithm with Genetic Algorithm (HBG) where GA and BA are combined in a highly integrated way. Specifically, the genetic operations of the GA are performed on previous search information of the bats of the BA to produce new exemplars that are of high-diversity and high-quality. Guided by these exemplars, the efficiency and global search capability of the BA are both improved. This approach is evaluated by using widely-adopted problems from four test suites. Experiments by comparison show that HBG outperforms its widely-used and recently proposed peers in terms of effectiveness, search accuracy, local optima avoidance, and robustness.
Attribution

The matter of attribution has to do with identifying the author (or even the most likely candidates) for a text whose authorship is doubtful, collaborative, or unknown. Such work has been practiced down the centuries, most often for the rectification of literary history but also in political and theological disputation where the authenticity of a document is at issue. Its apparent value in legal inquiry is limited by the fact that few criminals offer a substantial corpus of their writings. The notorious US Unabomber, who might well have written himself into gaol without benefit of other evidence, was a striking exception.
Research Statement

Deep learning has brought about a paradigm shift in our quest for general artificial intelligence. Powered by concurrent technological advances neural nets have in recent times beaten all previous benchmarks in playing hard strategy games like chess and Go, [18], [17] and have also radically pushed forward the technology towards self-driving cars [8]. But on the other hand the methods employed to make deep learning practical remain highly mysterious and challenging to prove guarantees about. During my PhD. I have been extremely passionate about figuring out mathematically rigorous ways to understand deep-learning.
Deep learning for robotics examples using OpenDR

Robotics often involve solving challenging perception, planning, and action problems. Deep Learning (DL) provided powerful tools for solving these problems, by employing neural architectures that are trained in an end-to-end fashion. However, these DL tools are not always straightforward to use, due to their steep learning curve. Apart from this, a number of domain-specific additional limitations, for example, computational constraints of embedded devices, is further hindering the adoption of DL on various fields, including robotics. To address these limitations, the Open Deep Learning toolkit for Robotics (OpenDR) has been developed aiming at making DL tools for robotics easily accessible and lowering the access barrier to them, both for researchers and the industry. To this end, OpenDR provides fast, easy-to-use and open source implementations for DL methods that can be used in various robotics settings. At the same time, training DL models for robotics often requires a vastly different paradigm compared to computer vision tasks, dictating a cointegrated training and simulation pipeline that differs from traditional DL training approaches that are using static data sets. In this chapter, we provide an overview of the OpenDR toolkit, present how it can be used to solve practical perception tasks, and then provide an introduction to cointegrated simulation and training approaches.
Real-Time Siamese Visual Tracking with Lightweight Transformer

Trackers based on Siamese have demonstrated more remarkable performance in visual tracking. The majority of existing trackers typically compute target template and search image features independently, then utilize cross-correlation to predict the possibility of an object appearing at each spatial position in the search image for target localization. This paper proposes a Siamese network for feature enhancement and aggregation between the target template and the search image by utilizing a lightweight transformer with several linear self- and cross-attention layers. With anchor-free head prediction, the suggested framework is simple and effective. Extensive experiments on visual tracking benchmarks such as VOT2018, UAV123, and OTB100 demonstrates that our tracker achieves state-of-the-art performance and operates at a real-time frame rate of 39 fps.