Reviewerly/exHarmony huggingface dataset:
LAW ENFORCEMENT AND TECHNOLOGICAL FACILITIES IN CHANGING AGENDA OF THE PUBLIC SAFETY AND SECURITY

It is&#x0D; possible to state that law enforcement officers have heavier responsibilities&#x0D; as well as having more diverse and confusing works in comparison to the last&#x0D; decade. It is obvious that the most fundamental reasons behind this situation&#x0D; are the instability in the world and changing percept of the public safety and&#x0D; security. Fighting against crime and criminal techniques which vary from&#x0D; antiterrorism to cybercrimes have changed the working style of law enforcement&#x0D; officers thanks to technology. This study, within this framework, aims to&#x0D; determine what the anticipated technological needs are and what should be&#x0D; attached importance to in order that law enforcement officers work effectively&#x0D; and efficiently within the next years. This study has been carried out in a&#x0D; theoretical foundation by reviewing the literature. In this study, the first&#x0D; step will be followed by explaining changing threats of the public safety and security and the&#x0D; technological facilities that law enforcement officers in the world utilized.&#x0D; Aftermath, the facilities of technology which are capitalized on as part of Law&#x0D; Enforcement Units in Turkey will be highlighted.
The Effects of Additional Factors on Subjective Quality Assessments

In Quality of Experience, the users' degrees of delight or annoyance are quantized by direct human interaction via subjective quality assessments. There are many factors that may influence the users' responses, and standards have been laid down to strictly control the monitoring conditions. In this paper, we analyze limitations of Mean Opinion Score (MOS) assessment to portray the influence of additional factors on user behavior while assessing multimedia content. We show that the frequency of watching online video content as well as the user delight with the content play a significant role in her final feedback. Our study emphasizes the need to use additional metrics along with MOS for content ratings to obtain a more accurate measure of the user experience.
Culture and Context Impact on Mobile Tech Application in Organizational Learning

While the significance of mobile technologies to support deep learning has been recognized, the impact factors of applying these technologies into various organizational learning environments still remain poorly understood. This chapter has explored the community of inquiry (CoI) framework as the lens to understand culture and context impact on mobile technology application to facilitate learners' engagement and educational experiences. Two sharp-contrast case studies are selected to provide the practical insight of the research. Results infer that usage of mobile technology has a positive impact on organizational learning, which leads to greater learning engagement as long as culture and context are considered as key issues to select appropriate mobile technologies. These factors decide the mobile native functions, delivery platforms, and provided learning contents, which are key elements in the proposed CoI framework in the context of m-learning.
A Deterministic Annealing Approach to Switched Predictor Design for Adaptive Compression Systems

Adaptive prediction is important in the compression of non-stationary signals, and a common remedy is to switch between appropriately designed prediction modes. This paper presents a near optimal procedure to design prediction modes for an adaptive compression system. The main challenges include: instability and mismatched statistics during closed loop design; and the severe non-convexity of the cost function trapping the system in poor local minima. The statistical mismatch is circumvented through a largely open loop (hence stable) design that is devised to asymptotically optimize the prediction modes for closed loop operation. The non-convexity of the cost function is handled by the deterministic annealing paradigm, a powerful non-convex optimization framework devised to avoid poor local minima. Experimental results provide substantial gains validating the efficacy of the proposed design technique.
Distributed Differentially-private Canonical Correlation Analysis

We propose a distributed differentially-private canonical correlation analysis (CCA) algorithm to use on multi-view data. CCA finds a subspace for each view such that projecting the views onto these subspaces simultaneously reduces the dimension and maximizes correlation. In applications involving privacy-sensitive data, such as medical imaging, distributed privacy-preserving algorithms can let data holders maintain local control of their data while participating in joint computations with other data holders. Differential privacy is a framework for quantifying the privacy risk in such settings. However, conventional distributed differentially-private algorithms introduce more noise to guarantee a given level of privacy compared to their centralized counterparts. Our differentially-private CCA employs a noise-reduction strategy to achieve the same utility level as CCA on centralized data. Experiments on synthetic and real data show the benefit of our approach over conventional methods.
Maximum a Posteriori on a Submanifold: a General Image Restoration Method with GAN

We propose a general method for various image restoration problems, such as denoising, deblurring, super-resolution and inpainting. The problem is formulated as a constrained optimization problem. Its objective is to maximize a posteriori probability of latent variables, and its constraint is that the image generated by these latent variables must be the same as the degraded image. We use a Generative Adversarial Network (GAN) as our density estimation model. Convincing results are obtained on MNIST dataset.
Interactive Weblog as a Source of Social Study of Junior High School Students

Weblogs are the concept of approving the internet today. In the use of blogs, students do not need technical knowledge and equipment, easy use and management. The attractive and functional personal web space in the blog usually has a main menu from the core that allows the blog, based on one page displayed on the main page of the blog on the internet network. The development of the current blog comes to the utilization of learning, with blogs and the internet as a source of learning the level of insight in student material is increasingly widespread. Especially for social science subjects which not only require theory in learning but also practice in the field, in this case the blog is very useful to help in the illustration of the practice. The results of the feasibility analysis based on the material validation of the material produced were 3.64 with the category "Good" on all aspects of the material. The results of the feasibility analysis based on media expert validation of the blog were developed in 4 categories "Good" in all aspects. The results of the feasibility analysis according to the teacher on blog products amounted to 4.45 with the category "Very Good". The results of the trial using learning media according to students amounted to 4.33 with the category "Very Good". This is consistent with research conducted where with the use of blogs, students increasingly have increased learning activeness and ease in accessing learning resources that have an impact on student success in learning
Overview of Face Recognition Methods

With the continuous development of information technology, the demand for security and safety is gradually improving. For the consideration of security, face recognition has been studied for many decades. With the development of information technology, face recognition is widely used in our daily life, especially in security systems, information security, human-computer interaction. Researches are committed to improving the recognition accuracy and response speed of the face recognition system. The state-of-art of face recognition has been significantly improved by the appearance of deep learning. Although these systems perform well on large amounts of web collected facial data, the performance and accuracy are still limited when they are applied in actual scenarios. There is still a long way to go to improve the recognition accuracy of face recognition system in real scenarios. This paper gives a comprehensive description of a series of face recognition methods. In this paper, we introduce the definition and development of face recognition, and also indicate main challenges in this domain. Furthermore, some classical popular methods in the development of face recognition technology are described in detail. Finally, the application of face recognition technology will be introduced.
Multi-spectral Image Denoising with Shared Dictionaries and Low-rank Representation

As a 3-order tensor, a multi-spectral image (MSI) has dozens of spectral bands, which can deliver more faithful representation for real scenes. However, MSIs are often corrupted by noise in the sensing process, which deteriorates the performance of higher-level classification and recognition tasks. In this paper, we propose a novel tensor dictionaries learning method for MSI denoising, where two shared dictionaries are learned from MSI groups of similar blocks in the spatial domain and the spectral domain, respectively. In addition, we enforce a low rank structure for the representations of MSI groups under the learned dictionaries, which captures the latent structure in MSIs. Our experiments demonstrate that the proposed method achieves the best performance in comparison with the state-of-the-art methods.
Automatic Jazz Melody Composition Through a Learning-Based Genetic Algorithm

In this study, we automate the production of good-quality jazz melodies through genetic algorithm and pattern learning by preserving the musically important properties. Unlike previous automatic composition studies that use fixed-length chromosomes to express a bar in a score, we use a variable-length chromosome and geometric crossover to accommodate the variable length. Pattern learning uses the musical instrument digital interface data containing the jazz melody; a user can additionally learn about the melody pattern by scoring the generated melody. The pattern of the music is stored in a chord table that contains the harmonic elements of the melody. In addition, a sequence table preserves the flow and rhythmic elements. In the evaluation function, the two tables are used to calculate the fitness of a given excerpt. We use this estimated fitness and geometric crossover to improve the music until users are satisfied. Through this, we successfully create a jazz melody as per user preference and training data.
SORCER: A Decentralised Continuous Integration Platform for Service-Oriented Software Systems

Continuous integration (CI) is a key practice where developers integrate frequently via a shared repository to enable automated build, test, and release of software systems. While enabling CI in a centralised development environment has been a common practice, no much work has been done to effectively support CI of decentralised service-oriented systems where centralised repositories are unavailable. This paper presents SORCER, a decentralised interface-based continuous integration platform that makes it easy for developers to perform integrated build and test of service-oriented systems whose service constituents are owned and managed by different organisations to only expose their interfaces without access to their source codes.
Determination of strain fields on two-dimensional images using the STC method

In this paper, a novel method called the Strain Tensor using Computational Fourier transform moiré (STC) method is proposed to obtain strain maps in a 2D lattice image such as an HRTEM or HAADF-STEM image. The method is a modification of the CFTM method, but it has two essential merits. Firstly, the method provides a 2 × 2 strain tensor using two independent Bragg peaks, and can hence be used to calculate both the axial and shear components, whereas the CFTM method can be used to calculate only the axial strain. Secondly, the STC method can provide more reliable results than the CFTM method by excluding the weak Bragg peaks of low signal/noise ratio on a digital diffractogram. In addition, the STC method is better than the GPA method because it does not require an unwrapping process. In this work, the STC method is demonstrated using a computer-generated image, showing that the method matches exactly with the CFTM method. Furthermore, the results for an experimental image reveal that the STC method can be an alternative method for strain analysis when the CFTM method cannot provide reliable results owing to weak Bragg peaks of low signal/noise ratio.
Wavelet Boundary Element Methods: Adaptivity and Goal-Oriented Error Estimation

This article is dedicated to the adaptive wavelet boundary element method. It computes an approximation to the unknown solution of the boundary integral equation under consideration with a rate $$N_{\text{dof}}^{-s}$$ , whenever the solution can be approximated with this rate in the setting determined by the underlying wavelet basis. The computational cost scale linearly in the number N dof of degrees of freedom. Goal-oriented error estimation for evaluating linear output functionals of the solution is also considered. An algorithm is proposed that approximately evaluates a linear output functional with a rate $$N_{\text{dof}}^{-(s+t)}$$ , whenever the primal solution can be approximated with a rate $$N_{\text{dof}}^{-s}$$ and the dual solution can be approximated with a rate $$N_{\text{dof}}^{-t}$$ , while the cost still scale linearly in N dof. Numerical results for an acoustic scattering problemAcoustic scattering problem and for the point evaluation of the potential in case of the Laplace equation are reported to validate and quantify the approach.
Data Mining and Recommendation of Engineering Note Items in MBD Dataset

To meet the requirement of product full 3D digitalization development, a data mining and recommendation method based on the association rules about engineering note items in MBD (Model Based Definition) dataset is proposed. The helpful knowledge and experience can be obtained from MBD dataset's creation history based on association rule theory in data mining. In this method, all the design, manufacturing and inspection standards and information used in the product's development process, which can be called engineering note items, are analyzed, decomposed, encoded, managed and released. Then FP-growth algorithm is taken to get association rules from the MBD dataset's history records, and the potential association relationship among engineering note items can be revealed. Finally, the recommendation and the completeness check about the engineering note items are realized based on the association rules. The MBD dataset definition system has been developed and implemented in an enterprise based on this method. The practice is proved that this method could effectively reveal the latent association rules from the history records and the MBD dataset's creation efficiency and quality by engineering note items recommendation is improved.
Strengthening fawkescoin against double spending attack using merkle tree

Cryptocurrency is a digital currency with cryptographic security so that it is not easily to be faked. Recently cryptocurrency is widely used for transactions. Therefore, preserving its integrity and security is important. The technology underlying the digital currency is the Blockchain, as applied to Fawkescoin. But for securing the transaction, fawkescoin has a disadvantage when the fork occurs because it can provide opportunity to conduct double spending attack. To overcome this problem, Merkle tree proposed by applying DSA digital signatures. The application of DSA on Merkle tree is used to verify data without knowing the contents of the data. Based on the experiment results and analysis, the security of the proposed method can prevent fawkescoin against double spending attack on transactions when forking occurs than the previous method.
Registration-based Image Segmentation using Lattice Boltzmann Method

Image segmentation is the first and important step leading to image analysis and interpretation. Using image segmentation we can divide the image into different regions. In this study, a Lattice Boltzmann model is proposed for registration-based CTA segmentation. The energy model is established and can be minimized by variational method. The gradient descent flow was solved by the Lattice Boltzmann method. In the experiments, a reference image was selected from CTA image series and artificially segmented by experts. By aligning the images to the reference image, the bias of the voxels are obtained. At the same time the contours of the desired object are estimated at the low contrast region.
Applications of neural networks in the diagnosis of lymphoproliferative diseases

The work is devoted to one of the promising directions in the field of artificial intelligence - recognition of blood cells and bone marrow to differentiate leukocytes by type in the diagnosis of lymphoproliferative diseases using computer microscopy. The article presents a study using neural networks to classify cells by type.
Video Quality Assessment for Encrypted HTTP Adaptive Streaming: Attention-based Hybrid RNN-HMM Model

End-to-end encryption challenges mobile network operators to assess the quality of the HTTP Adaptive Streaming (HAS), where the quality assessment is coarse-grained, e.g., detecting if there exist stalling during the whole playback. Targeting on this issue, this paper proposes an attention-based hybrid RNN-HMM model, which integrates HMM with attention mechanism to predict the player states. The model is trained and evaluated based on the download speed and player state sequences of encrypted video sessions collected from YouTube. Experiment results show that the proposed model is able to recognize the player states with 86.53% ~ 94.35% accuracy, and thus achieves to assess video quality in a fine-grained manner, where how long the stalling lasts and when the stalling occurs can be evaluated effectively from the download speed sequence even when encryption is employed.
ArUcoRSV: Robot Localisation Using Artificial Marker

Robot Soccer (RS) vision system is designed for robot soccer competition. Most RS system implemented colour patches as main marker detector for robot localisation and ball detection. It requires complicated procedures for colour and camera calibration and highly affected by light to run smoothly while it suffers parallax issue. This paper aims to improvise the procedures and performance by minimising the calibration process for robot localisation and produces accurate marker detection for robot identification. ArUcoRSV utilises marker detection using ArUco patches to solve light limitation and able to perform camera calibration processes quick and reliable. It applies an automated perspective using marker detection, refining rejected marker and pose estimation. Experimental results for robot calibration and localisation using ArUcoRSV achieved significant improvements in detection rate with high positional accuracy. This system is expandable and robust to deal with various types of robots including low cost robots.
Fuzzy Unordered Rule Induction Algorithm Application Basic Programming Language Competence: A Rule-Based Model

Objective: A rule-based model using Fuzzy Unordered Rule Induction Algorithm (FURIA) aimed to be developed in this study.Methods/Statistical Analysis: This study utilized a Machine-Learning algorithm called FURIA in developing the model.Graduates of the K to 12 programs in the Philippines who enrolled in the Bachelor of Science in Information Technology in a State University were the respondents of this study.Respondents were assessed using a self-made questionnaire in basic programming using Java programming language and validated both face validation and the internal reliability test with a Cronbach alpha value of 83.Furthermore, the steps performed in this study were the variable definition and data collection, data processing and cleaning, machine-learning algorithm application, verifying and interpreting the model, and reporting the model and synthesizing.Findings: This study revealed seven (7) rule-based models with a substantial rating in Kappa statistics and a very high accuracy rating in predicting the competencies by class.Applications/ Improvements: The researcher believed the need to unveil the competence of the Senior High School graduates in the K to 12 programs of the Department of Education in the Philippines in basic programming, which aimed to retool the type of teaching strategies to fit the competencies of the student.
Adversarial Detection of Concealed VoIP Traffic

network attacker wants to transmit VoIP traffic streams covertly. He tries to evade the detection system by manipulating the VoIP streams through padding and shifting operations, so as to conceal them amidst the Internet traffic. A defender (the detection system) wants to detect the manipulated VoIP streams. Tackling this problem from an adversarial perspective, we provide two contributions: i) we obtain a highly stylized representation of VoIP streams in terms of transmission frequency $\mathcal{F}$ and payload $\mathcal{L}$, and characterize the $(\mathcal{F},\ \mathcal{L})$ region achievable by the attacker’s transformation; ii) we formulate the VoIP detection game, and find both theoretical conditions as well as a practical algorithm to find the Nash equilibrium of the game. Simulations over real network traces, and comparison with standard, adversary-unaware tools, show the advantages of the adopted adversarial perspective.
Recommendation system using hybrid collaborative filtering methods for community searching

Community is a group that has the possibility to connect to each other or between members of other groups. However, nowadays many people difficult to get information about the communities that are around them. Recommendation system can help people to find the right community. This paper proposes a recommendation system to help people find appropriate communities using Hybrid Collaborative Filtering methods. This system can help people to search for the suitable community based on their location and category. It can also provide recommendations based on their interests and criteria.