Reviewerly/exHarmony huggingface dataset:
Spatial Gradient Guided Learning and Semantic Relation Transfer for Facial Landmark Detection

Pixel-wise losses are widely used in heatmap regression networks to detect facial landmarks, however, those losses are not consistent with the evaluation criteria in testing, which is evaluating the error between the highest pixel position in the predicted heatmap and the ground-truth heatmap. In this paper, we proposed a novel spatial-gradient consistency loss function (called Grad loss), which maintains a similar spatial structure in the heatmap with ground-truth. To reduce the quantization error caused by downsampling in the network, we also propose a new post-processing strategy based on the Gaussian prior. To further improve face alignment accuracy, we introduce Spatial-Gradient Enhance attention and Relation-based Reweighing Module to transfer semantic information and spatial information between high-resolution and low-resolution representations. Extensive experiments on several benchmarks (e.g., 300W, AFLW, COFW, WFLW) show that our method outperforms the state-of-the-art by impressive margins.
Comparative Analysis of Seam Carving in Images

Owing to the incessant advancements in technology, there are a number of electronic gadgets of different shapes and sizes available with everyone today. As such, the content available through the Internet in the form of online websites, videos, etc., may be viewed through these varied digital devices. Most of the content is multi-modal with images forming a large chunk of this digital content. Hence, the digital images may be viewed using a number of diverse display devices having a multiplicity of resolutions. This makes viewing images challenging as they need to be retargeted for the different resolutions and resized as per space. Simple image resizing methods include interpolation and cropping. However, interpolation blurs the image and cropping alters the image composition and is not always desirable. In this work, we present a dynamic programming-based image resizing technique named seam carving. Furthermore, we present a detailed performance analysis for seam carving. We carry out our experiments on a dataset of 100 images having diverse content, size, number of seams and carry out seam removal using different filters. As per our results, the factors image size, number of seams removed and image content affect the performance of the seam carving algorithm.
When DNS Goes Dark: Understanding Privacy and Shaping Policy of an Evolving Protocol

The Domain Name Service (DNS) is the most widely deployed Internet service. Stark in its simplicity, it maps host names to routable IP addresses. Replacing a collection of names mapped to IP addresses stored in a disk file, modern DNS is now a scalable, hierarchical, decentralized, and world-wide naming system for Internet resources. Because of its ubiquity and importance, as well as the fact that queries are performed in cleartext, DNS remains an attractive target for malicious and benign actors. But the rising interest in privacy driven by pervasive monitoring and the commodification of personal data has triggered the need for encryption in key Internet protocols, including DNS. DNS is evolving to increasingly support confidentiality and privacy; DNS over TLS (DoT) and DNS over HTTPS (DoH) have been standardized with encryption between the user and DNS server. While these protocols appear to preserve user privacy, unintended consequences within the DNS ecosystem still exist. We explore the effects of encrypted DNS on the privacy of individuals, and ascertain whether these protocols and their operational reality have achieved the goal of increased user privacy. Further, we examine privacy policies of twelve public DNS providers in the context of select articles from the EU General Data Protection Regulation (GDPR) legislation. Finally, we examine the important role standards bodies, regulatory agencies, and non-government organizations (NGO) can play to ensure continued user privacy even as the DNS protocol continues to evolve. We propose approaches that address the unique privacy attributes of DNS: mostly invisible to the user, yet offering operators of DNS resolver services uniquely-detailed visibility into user behavior.
Semi-supervised Affinity Propagation Clustering Algorithm Based on QPSO

The affinity propagation algorithm is a distance-based clustering algorithm, which has the advantages of fast clustering speed, high accuracy, and no need to specify the number of clusters. The quantum particles swarm optimization algorithm has powerful parameter optimization capabilities. In this paper, we use the quantum particles swarm optimization algorithm, named QPSO-SAP, to optimize the bias parameters. Experiments show that the improved AP algorithm improves the clustering performance significantly on complex structured data sets.
The Impact of Deep Learning on the Semantic Machine Learning Representation

Deep learning (DL) is one of the core subsets of the semantic machine learning representations (SMLR) that impact on discovering multiple processing layers of non-linear big data (BD) transformations with high levels of abstraction concepts. The SMLR can unravel the concealed explanation characteristics and modifications of the heterogeneous data sources that are intertwined for further artificial intelligence (AI) implementations. Deep learning impacts high-level abstractions in data by deploying hierarchical architectures. It is practically challenging to model big data representations, which impacts on data and knowledge-based representations. Encouraged by deep learning, the formal knowledge representation has the potential to influence the SMLR process. Deep learning architecture is capable of modelling efficient big data representations for further artificial intelligence and SMLR tasks. This chapter focuses on how deep learning impacts on defining deep transfer learning, category, and works based on the techniques used on semantic machine learning representations.
A Framework for Designing Self-sustaining Ecosystems with Blockchain

Blockchain is a foundational technology where the idea of a distributed database and trust is established through mass collaboration and smart contracts. It is being claimed to be the next major socio-technical advancement after the invention of the Internet. In this paper we present a framework that supports experts in designing self-sustaining ecosystems leveraging distributed ledger technology. The critical building blocks of the framework are value exchange mapping, determining an evolutionary distributed ledger technology architecture, governance modelling, and token engineering. The goal of the framework is to establish a Minimum Viable Ecosystem that is self-sustaining in itself while having a positive-sum game as the basis to attain organic network effects. The framework has been evaluated through three case studies of prominent distributed ledger technology projects. The results of the case study were positive and evident of the need for such frameworks to help experts to think strategically, critically and precisely.
The Challenges of Empirically Comparing Cybercriminals and Traditional Offenders

In the theoretical literature, many differences between cybercriminals and traditional criminals are discussed. On the one hand, the empirical literature tends to focus only on cybercrime and usually does not empirically compare cybercriminals with traditional criminals. However, such a comparison enables an empirical evaluation on the extent to which traditional criminological theories and concepts are equally important in explaining cybercrime. Comparing such different types of crime committed in completely different contexts is, however, also very challenging. One needs to find comparable samples of criminals and find research methods suitable for both groups. Based on the experiences resulting from empirical comparisons of cybercriminals and traditional criminals, this chapter will discuss several of these challenges. In addition to methodological choices, it will also discuss differences between cybercriminals and traditional criminals in, for example, response rates or privacy awareness, as this may be useful in both comparative research as well as research specifically focused on cybercrime.
Identifying and sensing emotional quotient weightage in the outcome using Speech Dialogue

Abstract Emotion is a very important part of any interaction whether it is human-human or human- computer interaction as an emotion conveys the essence of the interaction. Decision making is also influenced by the emotional state of a person. If we can capture emotions from a human-computer interaction then it will be very beneficial for the computer to learn from that interaction. It can be efficiently used by an artificial intelligence system to understand and give response accordingly. The method proposed in this paper uses a machine learning based approach to detect emotion from the text obtained from the speech dialogue.
Model-free Path Following Control of Unmanned Surface Vehicle Based on Adaptive Line-of-sight Guidance

Abstract A path following control scheme based on virtual transition line-of-sight (LOS) guidance law is proposed for model-free unmanned surface vehicle (USV). In the guidance part, an adaptive LOS circle radius based on the transition layer is designed to improve the convergence speed of USV. Aiming at reducing the overshoot of turning, a small angle transition steering strategy is proposed. In the heading and speed control part, the intelligent adaptive S-plane (IAS) heading controller and intelligent adaptive integral S-plane (IAIS) speed controller with strong robustness are designed respectively. In order to improve the following efficiency, a speed solver based on heading deviation and path point distance is designed. The effectiveness of the guidance strategy and the control algorithms of heading and speed proposed in this paper are verified by the comparative simulations.
Three Channels for Gray Level Co-occurrence Matrix (GLCM) to detect Acute Lymphoblastic Leukemia (ALL) Images

Leukemia is one of the dangerous diseases causing the death of people. Based on our literature review, numerous methods have been utilized for leukemia detection with various performances. Therefore, this research aims to employ the three channels for GLCM to detect ALL based on the Lymphoblastic Leukemia Image Database (ALL-IDB2) dataset. This research has some novelties. Firstly, the proposed method related to segmentation has separated the main object and background and removed the noises. Secondly, Feature extraction of the proposed method has depicted the object in three-dimensional form, so that the object can be visualized in more details than one-dimensional. Therefore, this research contributes by proposing a novel method of GLCM to detect ALL. We have employed 260 images of ALL-IDB2 and conducted twenty experiments for each scenario. Our proposed method has proven that three channels of GLCM and testing sets using Manhattan and Euclidean Distance achieved 91.54% of maximum accuracy. It has better accuracy than a combination of the GLCMshape-based feature-SVM classifier method. Furthermore, we have demonstrated that the quantity of training sets has given an impact on accuracy performance. Our experiments show higher numbers of the training sets obtained higher accuracy. Besides accuracy, we also calculated the false-negative and false-positive. The average of the false-negative results contributes to stability. It means that the difference of the results among scenarios is insignificant and almost similar. Meanwhile, false-negative tends to decrease in percentage. This is inversely proportional to the increase in the amount of the training set used. Additionally, the use of large amounts of training sets can reduce misclassification for the false-negative rate.
Application of Bayesian Hierarchical Finite Mixture Model to Account for Severe Heterogeneous Crash Data

Various techniques have been proposed in the literature to account for the observed and unobserved heterogeneity in the crash dataset. Those include techniques such as the finite mixture model (FMM), or hierarchical techniques. The FMM could provide a flexible framework by providing various distributions for various individual observations. However, the shortcoming of the standard FMM is that it cannot account for the heterogeneity in a single model’s structure, and the data needs to be disaggregated to its resultant subsamples. That would result in a loss of information. On the other hand, a second plausible approach is to use a hierarchical technique to account for the data heterogeneities, being based on various explanatory variables, and based on engineering intuition. In the context of traffic safety, while some researchers, for instance, considered the seasonality, some others considered highway systems or even genders. However, a question might arise: are the same observations within a same hierarchy homogenous? Are all the observations within different clusters heterogeneous? Additionally, how about other variables? Although the results in the literature highlighted accounting for the structure of the dataset would result in an acceptable interclass correlation (ICC), and also result in a significant improvement in terms of reduction in the deviance information criteria (DIC), there is no justification why to use those specific hierarchies and reject others. A more reasonable approach is to let the algorithm come up with the best distributions based on the provided parameters and accommodate observations to the related mixtures. In that approach those observations that belong to various subjective hierarchies, e.g., winter versus summer, but found to be similar would be set in a similar cluster. That is why we proposed this methodology to implement an objective hierarchy of the FMM to be used for the hierarchical technique. Here, due to the label switching problem of the FMM in the context of Bayesian, the FMM first conducted in the context of maximum likelihood estimates, and then assigned observations were used for the final analysis. The results of the DIC highlighted a significant improvement in the model fit compared with a subjective assigned hierarchy based on highway system. Additionally, although the subjective model resulted in a very low ICC due to so much heterogeneity in the dataset, the implemented methodology resulted in an acceptable ICC (0.3), justifying the use of hierarchy. The Bayesian hierarchical finite mixture model (BHFMM) is one of earliest application in traffic safety studies. The findings of this study have important implications for the future studies to account for a higher heterogeneity of the crash dataset based on the distance of observations to each cluster.
Transfer Learning for classifying front and rear views of vehicles

Abstract Various computer systems have been proposed to classify vehicles according to several criteria (category, brand, model). Unfortunately, there is not much research on the classification of views, especially front and rear views. Several factors make this classification very difficult including similarity in shape, size, and color. This work aims to classify front and rear views of vehicles using the Transfer Learning (TL) approach. Here, we used a pre-trained CNN (AlexNet) that has been trained on more than a million images and can classify images into 1000 object categories. Thus, we transferred its learned knowledge and applied it to our new task (Classifying vehicle views). We conducted then two experiments. The first experiment has two scenarios: the first scenario is devoted to Transfer Learning using the AlexNet model, and the second scenario aims to build a network from scratch inspired from AlexNet. Experimental results reveal that the Transfer Learning approach gives high results. On the other hand, in the second experiment, we decided to use TL-AlexNet to extract features and train them with an SVM classifier instead of fully connected layers. And also, we combined the SVM with the fully connected layers. The accuracy rates have been improved after this experiment.
Learning-Based Coordination Model for On-the-Fly Self-Composing Services Using Semantic Matching

Forecasts announce that the number of connected objects will exceed 20 billion by 2025. Objects, such as sensors, drones or autonomous cars participate in pervasive applications of various domains ranging from smart cities, quality of life, transportation, energy, business or entertainment. These inter-connected devices provide storage, computing and activation capabilities currently under-exploited. To this end, we defined “Spatial services”, a new generation of services seamlessly supporting users in their everyday life by providing information or specific actions. Spatial services leverage IoT, exploit devices capabilities (sensing, acting), the data they locally store at different time and geographic locations, and arise from the spontaneous interactions among those devices. Thanks to a learning-based coordination model, and without any pre-designed composition, reliable and pertinent spatial services dynamically and fully automatically arise from the self-composition of available services provided by connected devices. In this paper, we show how we extended our learning-based coordination model with semantic matching, enhancing syntactic self-composition with semantic reasoning. The implementation of our coordination model results in a learning-based semantic middleware. We validated our approach on various experiments: deployments of the middleware in various settings; instantiation of a specific scenario and various other case studies; experiments with hundreds of synthetic services; and specific experiments for setting up key learning parameters. We also show how the learning-based coordination model using semantic matching favours service composition, by exploiting three ontological constructions (is-a, isComposedOf, and equivalentTo), de facto removing the syntactic barrier preventing pertinent compositions to arise. Spatial services arise from the interactions of various objects, provide complex and highly adaptive services to users in seamless way, and are pertinent in a variety of domains such as smart cities or emergency situations.
Quantum state tomography and purity measurement of a multiqubit system with random single-pulse rotations

Quantum state tomography and other measures of the global properties of a quantum state are indispensable tools in understanding many body physics through quantum simulators. Unfortunately, the number of experimental measurements of the system required to estimate these global quantities scales exponentially with system size. Here, we consider the use of random-axis measurements for quantum state tomography and state purity estimation. We perform a general analysis of the statistical deviation in such methods for any given algorithm. We then propose a simple protocol which relies on single-pulse X/Y rotations only. We find that it reduces the basis of the exponential growth, calculating the statistical variance to scale as $\sum_{a,b}\left\lvert\Delta\rho_{ab}\right\rvert^{2}\sim 5^{N}/N_{\rm tot}$ for full tomography, and $\left(\Delta\mu\right)^{2}\sim 7^{N}/N_{\rm tot}^{2}$ for purity estimation, for $N$ qubits and $N_{\rm tot}$ measurements performed.
An Adaptive Method for Recognition of Dual-pointer Mechanical Instrument Based on Hough Transform

Abstract For automatic and efficient reading of dual-pointer mechanical instruments in petroleum engineering applications, an adaptive recognition method based on Hough transform is proposed. Firstly, the instrument dial is detected by Hough transform. Then the center of circle is located, as well as the pointer is recognized. Finally, the linear relationship between the dial scale and the pointer is adopted to calculate the pointer reading. The experimental results show that the algorithm achieves high application value for the dual-pointer mechanical instrument with uniform scales.
Through the Looking Glass: Designing Agents for MAS Based Shape-Shifting Technology Using the STEAM Approach

The aim of this paper is to explore the development of shape shifting technology through the development of the Dod. The Dod is an agent design which was developed as part of a PhD study completed in 2018. It represents an attempt to close the gap between the copious research on agent behavior and communication and the sparse development of a physical design (i.e. a body) that will eventually contain the agent’s behavior mechanism. This exploration will cover how the Dod’s development (multifunction/purpose), strongly guided by the STEAM (Science, Technology, Engineering, Arts, Mathematics) approach, is appropriate to adapting into future applications. An important result of this study is the recognition for the importance of developing multidisciplinary experts. Designing shape-shifting technology requires knowledge of many different fields and in order to create viable design blueprints, it is necessary for designers to learn to acquire, understand and apply knowledge from a diverse range of subjects.
A Preprocessing Methodology by Using Additional Steganography on CNN-based Steganalysis

There exists a need of “image steganalysis” which reveals whether steganographic signals are embedded in an image to improve information security. Among various steganalysis, Convolutional Neural Networks (CNN) based steganalysis is promising since it can automatically learn the features of diverse steganographic algorithms. However, we discover the detection performance of CNN is degraded when an image is intentionally reduced by the nearest-neighbor interpolation before steganography. This is because spatial frequency in a reduced image gets high, which disturbs the training. In order to overcome this shortcoming, in this paper, we propose a preprocessing methodology by using additional steganography on CNN-based steganalysis. In the proposed preprocessing, steganographic signals are additionally embedded into both reduced original images and reduced steganographic ones since a difference of spatial frequencies between them gets obvious, which helps CNN learn features. Whenever reduced images are trained in CNN or inspected whether they are steganographic ones or not, steganography is applied to them once by the proposed preprocessing. Thus, an image is regarded as a steganographic one if the trained model judges steganography is applied to it twice; otherwise it is an original one. Since the proposed methodology is very simple, its computational cost is low. Our evaluation shows accuracy in a model with the proposed preprocessing is 10.6% higher than that in the conventional one. Besides, even in the situation where another steganography is additionally embedded, the proposed preprocessing yields 7% higher accuracy compared with the conventional one.