Reviewerly/exHarmony huggingface dataset:
Causality and Randomness as Objects of Perception in the Structure of Criminalistic Thinking

The author analyzes criminalistic mindset from two standpoints — as a set of guidelines and as a flexible system of tools for studying the crime incident — and notes that the process of investigating and solving crimes is represented in the cognition of the person studying it as a dynamic system developing from cause to effect. Thus, there is a transition from the a priori (fragmented) probability of evidential information to the a posteriori, reliable probability. The author stresses the importance of the analysis and synthesis mechanism of the investigator’s thinking, which allow them to avoid investigation mistakes or other distortions of information perception. Each consequence has a corresponding concrete cause, although one cause could lead to multiple consequences. Thus, the causality of a crime and the appearance of its traces are perceived in detail, i.e., the investigator studies the mechanism of how the crime was committed through the trace picture of crime consequences and chooses the most probable of all possible causes. The author points out the importance of verifying the obtained information because material information is subject to destructive physical and chemical processes, while the ideal information — to cognitive distortions of perception, memory and reproduction. However, even when the trace information is distorted, the cause-and-effect relations remain intact. The author stresses the significance of the version process in the establishment of the cause-and-effect relations. He also discusses the random factors present in the process of crime investigation and solution, which, on the one hand, have a negative impact on this process and, on the other, make it possible to resolve problem situations characterized by a lack of information. Randomness is most evident in the investigative situations of problematic or risky nature. It is noted that randomness acts as a strictly subjective feature dependent on the specifics of the criminalistic mindset of the subject of cognition, not on the features of the objective reality; the characteristics of randomness could differ at different levels of examining a problem. The author analyzes the structure of the consequences of random search as a decision-making method in the conditions of insufficient information: it is possible to continue random search, to update the version or to transform the investigative situation from the simple into the complex or vice versa.
Advanced Usability Through Constrained Multi Modal Interactive Strategies: The CookieBot

Service robots are becoming able to perform a variety of tasks and they are currently used for many different applications. For this reason people with different backgrounds and also without robotic experience need to interact with them. Enabling the user to control the motion of the robot end-effector, it is important to provide an easy and intuitive interface. In this work we propose an intuitive method for the control of a robot TCP position and orientation. This is done taking into account the robot kinematics in order to avoid dangerous configuration and defining rotational constraints. The user is enabled to interact with the robot and control its end-effector using a set of objects tracked by a camera system. The autonomy level of the robot changes depending on the different phases of the interaction for a better efficiency. An intuitive GUI has been developed to ease the interaction and help the user to achieve a better precision in the control. This is possible also through the scaling of the tracked motion, which is represented as visual feedback. We tested the system through multiple experiments that took into account how people with no experience interact with the robot and the precision of the method.
A Logic Programming Approach to Predict Enterprise-Targeted Cyberattacks

Although cybersecurity research has demonstrated that many of the recent cyberattacks targeting real-world organizations could have been avoided, proactively identifying and systematically understanding when and why those events are likely to occur is still challenging. It has earlier been shown that monitoring malicious hacker discussions about software vulnerabilities in the Dark web and Deep web platforms (D2web) is indicative of future cyberattack incidents. Based on this finding, a system generating warnings of cyberattack incidents was previously developed. However, key limitations to this approach are (1) the strong reliance on explicit software vulnerability mentions from malicious hackers, and (2) the inability to adapt to the ephemeral, constantly changing nature of D2web sites. In this chapter, we address those limitations by leveraging indicators that capture aggregate discussion trends identified from the context of hacker discussions across multiple hacker community websites. Our approach is evaluated on real-world, enterprise-targeted attack events of malicious emails. Compared to a baseline statistical prediction model, our approach provides better precision-recall tradeoff. In addition, it produces actionable, transparent predictions that provide details about the observed hacker activity and reasoning led to certain decision. Moreover, when the predictions of our approach are fused with the predictions of the statistical prediction model, recall can be improved by over 14% while maintaining precision.
On the proposal and evaluation of a test-enriched dataset for configurable systems

Configurable systems offer advantages compared to single systems since developers should maintain a unique platform to address a diversity of deployment contexts and usages. To ensure that all configurations correctly execute, developers spend considerable effort testing different system configurations. This testing process is essential because configurations that fail may potentially hurt user experience and degrade the reputation of a project. Previous studies have reported and created repositories of open-source configurable systems, although they neglected their test suites. Considering the importance of testing configurable systems, we reviewed the literature to find test suites of open-source configurable systems. As we found only 10 configurable systems with test suite available and considering that a test suite for configurable systems may be useful for different research topics, we created test suites for 20 additional configurable systems and evaluated the test suites coverage of all 30 configurable systems. Surprisingly, our test suites were able to find several failures in existing systems, mainly because of feature interactions, which enforces the need of test suites available for open source configurable systems. Aiming at finding common characteristics for fault-prone components (e.g., classes) on configurable systems, we group them based on software quality metrics (e.g., coupling between objects and lines of code). As result, we found that 44% of the configurable systems of our dataset have failures and these failures are concentrated in few classes.
Development of Deep Learning-Based Facial Recognition System

Recently, developed algorithms in the face recognition field that are based on deep learning technology have made significant progress. However, face recognition under unconstrained scenarios that is where illumination, image resolution, background clutter, facial pose, expression, occlusion and other factors are not controlled, still under heavy research. In this paper, we explore the problem of identifying a person of interest under this unconstrained conditions. To this end, we make the following contributions: firstly, we have implemented a Convolution Neural Network model (CNN) based on VGG16 architecture, using a fast open framework for deep learning called Keras. Then, a serie of experiments is conducted on the Labeled Faces in the Wild benchmark dataset (LFW), demonstrating that the proposed approach achieved state-of-the-art results.
On classical and Bayesian asymptotics in stochastic differential equations with random effects having mixture normal distributions

Delattre et al. (2013) considered a system of stochastic differential equations (SDEs) in a random effects setup. Under the independent and identical (iid) situation, and assuming normal distribution of the random effects, they established weak consistency of the maximum likelihood estimators (MLEs) of the population parameters of the random effects. In this article, respecting the increasing importance and versatility of normal mixtures and their ability to approximate any standard distribution, we consider the random effects having mixture of normal distributions and prove asymptotic results associated with the MLEs in both independent and identical (iid) and independent but not identical (non-iid) situations. Besides, we consider iid and non-iid setups under the Bayesian paradigm and establish posterior consistency and asymptotic normality of the posterior distribution of the population parameters, even when the number of mixture components is unknown and treated as a random variable. Although ours is an independent work, we later noted that Delattre et al. (2013) also assumed the SDE setup with normal mixture distribution of the random effect parameters but considered only the iid case and proved only weak consistency of the MLE under an extra, strong assumption as opposed to strong consistency that we are able to prove without the extra assumption. Furthermore, they did not deal with asymptotic normality of MLE or the Bayesian asymptotics counterpart which we investigate in details. Ample simulation experiments and application to a real, stock market data set reveal the importance and usefulness of our methods even for small samples.
A Form and API Data Management Platform for Progressive Web Application and Serverless Application Architecture

In the new global economy, web application has become a central issue for an enterprise organization with branches covering many countries around the world. One major issue in early business services research concerned in those organizations is that developing a web application that can support both global and local services at the same time is very difficult. Since at the local level, each country will have different languages, currencies, regulations. Therefore, to develop a system or web application to support business services around the world will have to be repeated the same, but with some details that are different, such as the same invoice form. However, to support services around the world that has repeated the same process but there is detailed information in each country or each language that is different. The objectives of this research are to determine whether to develop a platform that makes Form design to be shared in many countries more easily by automatically linking to the database via API. APIs can be embedded seamlessly into both the front-end and server side. The user interface can be designed smoothly. Export code can be used with HTML. In additional, it can work with serverless applications. Files can be managed in the form of JSON, csv and txt files. The results indicate that the designed platform can support the work that is intended to meet the objectives. Makes Form development in a web application very convenient and reduces many repetitive steps. In addition, data management is also effective. When creating this Form, it helps developers reduce the time it takes to create multiple Forms and reduces the form creation errors in the settings. Custom will help to keep the original Form.
Awareables

Wearable technology is a broad discourse that has evolved over decades, growing into an industry today that represents billions of dollars in product revenue, but stagnating as products become driven by technological developments rather than human needs. However, numerous isolated developments of a new class of wearables are emerging, being both aware of their environment and function, and able to physically adapt to user needs. Examined through this paper collectively, this new class of wearables are described as awareables, representing a shift in technology towards more life-like products. The context of this shift is broadly analyzed alongside similar shifts within the fields of architecture (responsive architecture), additive manufacturing (4D printing) and robotics (evolutionary robotics). The intent of this paper is to encourage new discourse and practical work, calling for researchers and product designers to think beyond gizmos, and instead consider more natural interactions between people and products inspired by nature.
Prototype to Mitigate the Risks of the Integrity of Cyberattack Information in Electoral Processes in Latin America

The problems of cyberattacks have been frequent in all areas for several years, the use of technology has become an indispensable element in the environment of the electoral process and its activities to mitigate the risks of information. Above all, if one considers that in a process of this nature, it is fundamental to guarantee the security of the system, since they have become the target of cyberattacks that have generated problems by compromising the integrity of the information necessary for this democratic exercise. The main objective was to provide a prototype to mitigate the risks of the integrity of cyberattack information in electoral processes in Latin America. It was used the deductive method and the exploration research technique were applied to study the information of the cited articles. It resulted in a Prototype to mitigate risks of the integrity of cyberattack information, Prototype risk matrix, and Mitigation approach. It was concluded that through the prototype evaluation methods and the risk mitigation process, risk and its value were identified according to the probability of occurrence and impact of cyberattacks, thus classifying them into levels and focusing mitigation by means of security strategies that generate higher priority and protect the integrity of the information before any type, this allows us to feedback and continuously improve the electoral processes.
Cryptanalysis of a Certificateless Signcryption Scheme

In 2016, Zheng and Li proposed a certificateless signcryption (CL-SC) scheme and claimed that their scheme is confidential (IND-CCA) and unforgeable (EUF-CMA) against both key replacement and malicious KGC attacks, in the standard model. In this paper, we propose both key replacement and malicious KGC attacks against confidentiality and unforgeability of their scheme. Our proposed attacks show that in contrast to Zheng and Li's claim, their scheme is neither confidential (IND-CCA) nor unforgeable (EUF-CMA) against key replacement and malicious KGC attacks.
Prediction of Semantically Correct Bangla Words Using Stupid Backoff and Word-Embedding Model

Word prediction is an essential technique used in different text entry environment to facilitate error-free writing. It also used as a helping hand for people with different types of disabilities. Word prediction technique is available in different languages. But developing an optimized Bangla word predictor is still a great research challenge. To overcome the challenge we propose a hybrid method to predict Bangla words. The stupid backoff language model is used to detect the most-probable words that may fit into the previously typed sentence by calculating the word sequence frequency. The novelty of this work is that it can provide the semantically correct words as a suggestion. The Word-Embedding model is used to maintain the semantic context of the word. To test this approach, a large corpus is built consisting of almost 0.5 million data. We compared our approach with other well-established methods. The proposed methodology surpasses them by obtaining 83% accuracy. The approach is also computationally efficient as the running time is linear with the prediction length.
Properties of constacyclic codes under the Schur product

For a subspace W of a vector space V of dimension n, the Schur-product space $$W^{\left\langle k \right\rangle }$$ for $$k \in {\mathbb {N}}$$ is defined to be the span of all vectors formed by the component-wise multiplication of k vectors in W. It is well known that repeated applications of the Schur product to the subspace W creates subspaces $$W, W^{\left\langle 2 \right\rangle }, W^{\left\langle 3 \right\rangle }, \ldots $$ whose dimensions are monotonically non-decreasing. However, quantifying the structure and growth of such spaces remains an important open problem with applications to cryptography and coding theory. This paper characterizes how increasing powers of constacyclic codes grow under the Schur product and gives necessary and sufficient criteria for when powers of the code and/or the dimension of the code are invariant under the Schur product.
Deep Facial Expression Recognition of facial variations using fusion of feature extraction with classification in end to end model

Expression recognition is an important direction for computers to understand human emotions and an important aspect of human-computer interaction. Expression recognition refers to the selection of an expression state from a still photo or video sequence to determine the emotional and psychological changes to the character.Spectral Supervised Canonical Correlation Analysis has been used for Feature extraction. For proper classification VGG119 and softmax has been used. Facial variations such as redundant information in image, illumination variance and overfitting have been addressed in this paper. The images have been preprocessed using face detection, data augmentation and image normalization. After down-sampling, Spectral Supervised Canonical Correlation Analysis (SSCCA) holds the dimensions with factor data which constructs affinity matrix that incorporates both the class information and local structure of the data points. Features with having massive discriminative details have been taken. In order to attain low frequency coefficients more effectively the local structural information will be effectively utilized using SSCCA. Data is further provided to VGG19 for proper training. Meanwhile, the proposed method is more effective and robust comparing other methods in the area.
Decentralized Service Platform for Interoperable Electro-Mobility Services Throughout Europe

While authorities are moving towards the adoption of more electro-mobility services in urban environments, their application in the real world remains still low due to a number of challenges. The integration of services from different providers using different data formats hinders interoperability; a secure and trustworthy data exchange is needed to preserve the rights of beneficiaries on the shared data. The NeMo project addresses those challenges by providing a decentralized service platform based on a distributed ledger to enable interoperable and secure data flow between service providers throughout Europe. This paper provides details about the architecture, enabling easy integration of electro-mobility services and demonstrates the approach by showing the overall flow from designing to using the electro-mobility services.
On automated cloud bursting and hybrid cloud setups using Apache Mesos

Summary Hybrid cloud technology is becoming increasingly popular as it merges private and public clouds to bring the best of two worlds together. However, due to the heterogeneous cloud installation, facilitating a hybrid cloud setup is not simple. Despite some commercial solutions being available to build a hybrid cloud, an open‐source implementation is still unavailable. In this paper, we try to bridge the gap by providing an open‐source implementation using the power of Apache Mesos. We build a hybrid cloud on top of multiple cloud platforms, private and public. Through comprehensive experimental results, we show that our solution is able to ensure resource bursting by leveraging the power of the public cloud.
Cryptography in the Healthcare Sector With Modernized Cyber Security

Cryptography is an indispensable tool used to protect information in any organization; providing secure transmission over the Internet. The major challenge faced by health-sector is data security, and to overcome this several advancements in medicine and biomedical research have proven to increase computer processing in data security. The study focuses on cryptography, the most emerging field in computer industries. Both artificial intelligence and quantum technology are both transforming the health sector in regard to cybersecurity. In this study, the AES algorithm is a cryptographic cipher used. One such application is implemented and is responsible for handling a large amount of the information in the health sector. An application with a double Hashing algorithm is accomplished to can maintain the data in a secure fashion.
Smooth Incremental Learning of Correlation Filters for Visual Tracking

Correlation Filters (CFs) are widely applied to visual tracking because of their effectiveness and efficiency. However, the online learning of CFs is problematic because of the increasing number of training samples as the tracking process goes on. To solve this issue, most CFs-based methods decouple the learning phase and updating phase of the correlation filters and then use a simple linear interpolation to fuse the newly learned correlation filters with the old ones for fast computing. Nevertheless, the linear interpolation may be an unwise way to update the model. In this letter, we propose a smooth incremental learning framework of CFs. In our method, the increments of the correlation filters are smoothly learned by minimizing an optimization problem in each frame, thus avoiding the ad hoc linear interpolation. The optimization problem can be efficiently solved via the Alternating Direction Method of Multipliers (ADMM). Furthermore, a rotation estimation strategy is introduced to enable the correlation filters to accurately estimate the rotation angle of the target. Experiments show the superiority of the incremental learning framework and the rotation estimation method.
Fusion of visual salience maps for object acquisition

The paradigm of visual attention has been widely investigated and applied to many computer vision applications. In this study, the authors propose a new saliency‐based visual attention algorithm applied to object acquisition. The proposed algorithm automatically extracts points of visual attention (PVA) in the scene, based on different feature saliency maps. Each saliency map represents a specific feature domain, such as textural, contrast, and statistical‐based features. A feature selection, based on probability of detection and false alarm rate and repeatability criteria, is proposed to choose the most efficient feature combination for saliency map. Motivated by the assumption that the extracted PVA represents the most visually salient regions in the image, they suggest using the visual attention approach for object acquisition. A comparison with other well‐known algorithms for point of interest detection shows that the proposed algorithm performs better. The proposed algorithm was successfully tested on synthetic, charge‐coupled device (CCD), and infrared (IR) images. Evaluation of the algorithm for object acquisition, based on ground truth, is carried out using synthetic images, which contain multiple examples of objects, with various sizes and brightness levels. A high probability of correct detection (greater than 90%) with a low false alarm rate (about 20 false alarms per image) was achieved.