Reviewerly/exHarmony huggingface dataset:
Robust Gaussian Approximate Fixed-Interval Smoother With Outlier Detection

We consider that existing nonlinear state estimators cannot always yield a satisfactory performance when the distribution of the measurement noise deviates from the assumed Gaussian distribution. This letter investigates the non-linear robust fixed-interval smoothing problem where the measurement vectors are partially disturbed by outliers. The proposed smoother employs a binary indicator variable modelled with a beta-Bernoulli distribution to determine whether the measurement vector is an outlier. Moreover, to address the inaccurate process noise covariance matrix, an inverse Wishart distribution was chosen as the conjugate prior of the process noise covariance matrix. Then, the variational Bayesian inference method was applied to estimate the state trajectory, inaccurate process noise covariance matrix, and a set of binary indicator variables. Traditional target tracking simulation results indicate the effectiveness and stability of the derived smoother, and the proposed smoother has demonstrated a marked improvement in estimation accuracy and robustness.
Virtual Reality as an Educational Tool for Elementary School

The new information and communication technologies (ICTs), through virtual and augmented realities, present themselves as innovative and useful digital tools in many different areas. The educational scenario, in the current context of a constant and ever-changing world, requires a pedagogical approach in constant transformation and innovation within the digital culture. This research will present an overview on the stage of VR current apps and educational relation with them and propose the beginning of adoption of this technology on classes as part of a work that seeks to create a social innovation experiment with public school teachers for the newly created “Media-Education” discipline in order to disseminate virtual reality and 360° camera knowledge and applications, as well as to understand how schools and educators are up to date and are positioning themselves in front of the new technologies. It is mandatory to be updated on the new apps and technologies and create new approaches where virtual reality can be a useful tool.
Secure Smart Home Systems: A Blockchain Perspective

The unprecedented growth of the smart home industry brings increasing amount of IoT devices into people's home. The transmission of data and execution of commands across connected network were meant to bring security, comfort and accessibility. However, the inter-connectivity and complexity of smart home systems leads to higher vulnerability to cyberattacks, network congestion and information leaks. In this paper, we first analyze the limitations of existing centralized approaches. Then we propose a blockchain-enabled solution to help build a secure smart home system. We demonstrate the design architecture and implementation methods as a proof of concept. We believe that the proposed blockchain-enabled solution can improve security while offering intelligent control of smart home systems.
A context‐aware recommender method based on text and opinion mining

Abstract A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context‐aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context‐aware recommendation method based on text mining (CARM‐TM). The method includes two techniques to extract context from reviews: CIET.5 embed , a technique based on word embeddings; and RulesContext , a technique based on association rules. In this work, we have extended our previous method by including CEOM , a new technique which extracts context by using aspect‐based opinions. We call our extension of CARM‐TOM (context‐aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context‐aware recommender based on matrix factorization. To evaluate CARM‐TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM‐TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context‐aware recommender system.
Conditional probability logic, lifted Bayesian networks, and almost sure quantifier elimination

We introduce a formal logical language, called conditional probability logic (CPL), which extends first-order logic and which can express probabilities, conditional probabilities and which can compare conditional probabilities. Intuitively speaking, although formal details are different, CPL can express the same kind of statements as some languages which have been considered in the artificial intelligence community. We also consider a way of making precise the notion of lifted Bayesian network, where this notion is a type of (lifted) probabilistic graphical model used in machine learning, data mining and artificial intelligence. A lifted Bayesian network (in the sense defined here) determines, in a natural way, a probability distribution on the set of all structures (in the sense of first-order logic) with a common finite domain D. Our main result (Theorem 3.14) is that for every "noncritical" CPL-formula φ(x¯) there is a quantifier-free formula φ⁎(x¯) which is "almost surely" equivalent to φ(x¯) as the cardinality of D tends towards infinity. This is relevant for the problem of making probabilistic inferences on large domains D, because (a) the problem of evaluating, by "brute force", the probability of φ(x¯) being true for some sequence d¯ of elements from D has, in general, (highly) exponential time complexity in the cardinality of D, and (b) the corresponding probability for the quantifier-free φ⁎(x¯) depends only on the lifted Bayesian network and not on D. Some conclusions regarding the computational complexity of finding φ⁎ are given in Remark 3.17. The main result has two corollaries, one of which is a convergence law (and zero-one law) for noncritial CPL-formulas.
Amplifying the Security of Functional Encryption, Unconditionally

Security amplification is a fundamental problem in cryptography. In this work, we study security amplification for functional encryption (FE). We show two main results: Furthermore, both of our amplification results preserve compactness of the underlying FE scheme. Previously, amplification results for FE were only known assuming subexponentially secure LWE. Along the way, we introduce a new form of homomorphic secret sharing called set homomorphic secret sharing that may be of independent interest. Additionally, we introduce a new technique, which allows one to argue security amplification of nested primitives, and prove a general theorem that can be used to analyze the security amplification of parallel repetitions.
PDE Evolutions for M-Smoothers in One, Two, and Three Dimensions

Local M-smoothers are interesting and important signal and image processing techniques with many connections to other methods. In our paper, we derive a family of partial differential equations (PDEs) that result in one, two, and three dimensions as limiting processes from M-smoothers which are based on local order-p means within a ball the radius of which tends to zero. The order p may take any nonzero value $$>-1$$ , allowing also negative values. In contrast to results from the literature, we show in the space-continuous case that mode filtering does not arise for $$p \rightarrow 0$$ , but for $$p \rightarrow -1$$ . Extending our filter class to p-values smaller than $$-1$$ allows to include, e.g. the classical image sharpening flow of Gabor. The PDEs we derive in 1D, 2D, and 3D show large structural similarities. Since our PDE class is highly anisotropic and may contain backward parabolic operators, designing adequate numerical methods is difficult. We present an $$L^\infty $$ -stable explicit finite difference scheme that satisfies a discrete maximum–minimum principle, offers excellent rotation invariance, and employs a splitting into four fractional steps to allow larger time step sizes. Although it approximates parabolic PDEs, it consequently benefits from stabilisation concepts from the numerics of hyperbolic PDEs. Our 2D experiments show that the PDEs for $$p<1$$ are of specific interest: Their backward parabolic term creates favourable sharpening properties, while they appear to maintain the strong shape simplification properties of mean curvature motion.
Helminth Egg Automatic Detector (HEAD): Improvements in development for digital identification and quantification of helminth eggs and their application online

Helminths are parasitic worms that constitute a major public health problem. Conventional analytical techniques to evaluate helminth eggs in environmental samples rely on different steps, namely sedimentation, filtration, centrifugation, and flotation, to separate the eggs from a variety of particles and concentrate them in a pellet for direct observation under an optical microscope. To improve this process, a new approach was implemented in which various image processing algorithms were developed and implemented by a Helminth Egg Automatic Detector (HEAD). This allowed identification and quantification of pathogenic helminth eggs of global medical importance and it was found to be useful for relatively clean wastewater samples. After the initial version, two improvements were developed: first, a texture verification process that reduced the number of false positive results; and second, the establishment of the optimal thresholds (morphology and texture) for each helminth egg species. This second implementation, which was found to improve on the results of the former, was developed with the objective of using free software as a platform for the system. This does not require the purchase of a license, unlike the previous version that required a Mathworks® license to run. After an internal statistical verification of the system was carried out, trials in internationally recognized microbiology laboratories were performed with the aim of reinforcing software training and developing a web-based system able to receive images and perform the analysis throughout a web service. Once completed, these improvements represented a useful and cheap tool that could be used by environmental monitoring facilities and laboratories throughout the world; this tool is capable of identifying and quantifying different species of helminth eggs in otherwise difficult environmental samples: wastewater, soil, biosolids, excreta, and sludge, with a sensitivity and specificity for the TensorFlow (TF) model in the web service values of 96.82% and 97.96% respectively. Additionally, in the case of Ascaris, it may even differentiate between fertile and non-fertile eggs.
Enterprise Mobile Device Management Requirements and Features

Mobile devices have now become a fundamental human necessity, not only due to their wide set of communication features but mostly due to their immense computing capabilities. The accessibility of mobile services has increased substantially thus leading to the concept of Bring Your Own Device (BYOD) to increase the efficacy and productivity of employees in an enterprise. Though BYOD gives organizations numerous benefits, it also presents great risks. Many enterprises have deployed Mobile device management (MDM) solution to secure BYOD environment. In this work we first determine the security risks associated with BYOD environment, followed by identification of requisite security requirements a MDM solution must support to mitigate these risks. On the basis of these requirements, our approach examines and compares leading commercial MDM solutions; an analysis, not only helpful in selection of appropriate solution for an enterprise but also suitable to provide guidelines for development of a customized solution.
An Effective Model for Handling the Big Data Streams Based on the Optimization-Enabled Spark Framework

The recent advancements in information technology tend to maximize the volume of the data used in day-to-day life. In big data era, analysis and extraction of knowledge from huge-scale data sets is a key issue in the research due to the complexity in the analysis of big data. Numerous techniques are devised for streaming big data. However, the techniques are highly dynamic, which may result in concept drift. Accordingly, this paper introduces a technique based on optimization for handling the concept drift effectively. The proposed dragonfly moth search (DMS) algorithm-based spark architecture is developed for handling the big data stream classification without any concept drift. Specifically, big data stream classification is done based on the proposed DMS-based stacked auto-encoder (SAE) in spark and the big data stream classification involves two phases, namely online phase and offline phase. The incremental data is tackled effectively in the online phase, where the concept drift is checked based on error, and in case of any concept drift, the rough set theory is employed, which is defined, based on DMS algorithm. The proposed DMS algorithm is the integration of dragonfly algorithm (DA) and moth search algorithm (MS), which aims at feature selection and optimal tuning of the SAE.
Neural Network Language Model Compression With Product Quantization and Soft Binarization

Large memory consumption of the neural network language models (NN LMs) prohibits their use in many resource-constrained scenarios. Hence, effective NN LM compression approaches that are independent of NN structures are of great interest. However, previous approaches usually achieve a high compression ratio at the cost of obvious performance loss. In this paper, two recently proposed quantization approaches, product quantization (PQ) and soft binarization are effectively combined to address the issue. PQ decomposes word embedding matrices into a Cartesian product of low dimensional subspaces and quantizes each subspace separately. Soft binarization uses a small number of float scalars and the knowledge distillation technique to recover the performance loss during the binarization. Experiments show that the proposed approaches can achieve a high compression ratio, from 70 to over 100, while still maintaining comparable performance to the uncompressed NN LM on both PPL and word error rate criteria.
Designing mobile language learning with Arabic speaking migrants

Learning the language is crucial to be included in a new society. For migrants, the smartphone is a commonly used device for staying connected, which could also be used for language learning purposes. This research concerns mobile literacy with newly arrived Arabic speaking migrants to Sweden and the use of mobile learning as a means for integration. The purpose is to investigate how mobile technology can be designed to support migrants’ language learning process. The research concerns technology development where versions of a mobile application (app) are explored from a bottom-up perspective with Arabic speaking migrants. A qualitative method approach is applied, built on design principles focusing on the construction of situated artefacts and evaluation of performance. The results show that intuitive design and engaging content with connections to everyday social situations play important parts in sustaining motivation to engage with an app.
Modeling uncertain processes with interval random vector functional-link networks

This paper presents a new approach to building an interval model for an industrial process with uncertainty that employs an interval neural network (INN), which can solve problems such as model structure demands and complexity limitations in the conventional unknown but bounded (UBB) errors method. A new architecture for an interval random vector functional-link network (IRVFLN) and its learning algorithm with penalty factors are proposed, to solve the problems such as the local minima, slow convergence, and very poor sensitivity to learning rate settings in the interval feed-forward neural networks with error back-propagation (IBPNNs). As an application case study, the IRVFLN is used to model the glutamic acid fermentation process under the condition of bounded-error data, and the test results indicate that the accuracy of the IRVFLN model meets the manufacturing requirements. The comparison is performed with IBPNN, and the results demonstrate that the proposed network outperforms IBPNN both on effectiveness and efficiency. Also, a comparison is given with a crisp (point-valued) approach using RVFLN, and the results show that the crisp approach is less reliable when existing uncertainties in measuring or process.
An optimized fringe generator of 3D pavement profilometry based on laser interference fringe

As pavement distresses have more negative influences on the driving safety and comfortableness,pavement distress detection and maintenance becomes more significant. 3D pavement profilometry can help analyze the type and severity of the pavement distress, which provides a basis for pavement maintenance. This paper has employed laser interference fringe on 3D pavement profilometry. This new technology has great research value and prospect on improving the measurement accuracy.However,the fringe generators used in previous 3D profilometry technologies performed close-range projection (< 1 m) with laser interference fringe on the object surfaces being measured which have small sizes (diameter < 0.1 m), so the area of the fringe region was small.To extract the 3D profile information of the pavement surface,it is imperative to increase the projection distance to enlarge the area of fringe region. However, increasing the projection distance will cause the fringe spatial frequency and the measurement accuracy to decrease.In order to solve the problem,this study optimizes the configuration of the fringe generator based on previous researches, that enables it to emit interference fringes with higher spatial frequencies. It is proved that the new fringe generator makes it possible to extract the 3D pavement profile.After validation, the results show that the measurement error reaches ±0.38 mm when the fiber core interval is 0.75 mm and the projection distance is 1200 mm.
Augmented Reality in Descriptive Geometry

In this paper is investigated the possibility of Augmented Reality (AR) technologies contextualizing in teaching methods for “Descriptive Geometry” (DG) student course. The aim of the investigation was the study of the current state of knowledge and practice in the field of DG students teaching with the help of AR-technologies, and identification of key issues affecting the adoption by users (teachers and students) of AR-technologies as a modern educational tool in the. Conducted an analysis of existing researches in the field of modern educational tool in the field of DG. Has been carried out the analysis of current investigations in the field of DG students teaching based on AR-technology. The key problems affecting the adoption by users of AR-technologies as an educational tool in the field of DG have been determined. Existing methods of DG students teaching using AR-technologies in St. Petersburg Mining University are gradually completed and updated. The work results showed that students have a positive perception of educational classes on DG course based on AR-technologies. Students successfully solve DG problems using AR-technology based on Vuforia platform; create 3-D models of geometric entities in SketchUp, and labels for camera fixing based on AutoCAD. When creating the software, the compiled C ++ programming language is used, based on which scripts (markers) are written that lift 3-D models of objects to given planes. The study results will be useful for developers of AR-platforms, AR-applications in the field of DG students training. They will allow avoid projects that may cause problems with the convenience of AR-applications using, what, in turn, will lead to the rejection of users from the introduction of this technology when getting students education in DG.
Social Conformity in Immersive Virtual Environments: The Impact of Agents’ Gaze Behavior

Immersive virtual reality (IVR) can induce an experience of "social presence" which can, in turn, increase social influence. Non-verbal behavior such as eye contact is an important component of human communication and, therefore, an important factor in creating social presence. This paper presents an experimental study that elaborates social influence through conformity with a group of virtual agents within an immersive virtual environment (IVE). Specifically, it investigates the impact of the agents' gaze behavior on social presence and influence. An experiment based on the Asch (1951) paradigm using two levels of agents' gaze behavior (Eye Contact condition vs. No-Eye Contact condition) was conducted. The results showed that participants conformed with the agents as they gave significantly more incorrect responses to the trials that the agents also gave an incorrect response, compared to those trials that the agents gave correct answers. However, no impact of the agents' gaze behavior on conformity was observed, even if the participants in the Eye Contact condition reported a higher sense of social presence. In addition, self-reported measures showed a number of social effects that occurred only in the eye contact condition, indicating that the agents' gaze behavior has an impact on participants' experience.
Image-Based Recommendation Engine Using VGG Model

Image retrieval is broadly classified into two parts—Description-based image retrieval and content-based image retrieval. DBIR involves annotating the images and then using text retrieval techniques to retrieve them. This requires vast amount of labor and is expensive for large image databases. Semantic gap is another major issue in DBIR. Different people may perceive the contents of the image differently and hence annotate it differently, e.g., a bright shiny silver car for one person can be boring dull gray car for another. Thus, no objective keywords for search can be defined, whereas CBIR aims at indexing and retrieving images based on their visual contents like color, texture, and shape features. For a content-based image retrieval system, the performance is dependent on how the feature vector is represented and what similarity metrics is chosen. The disadvantage with visual content descriptor is that it is very domain specific, e.g., color correlogram and color histogram for color feature extraction, Tamura and GLCM for texture feature extraction, local binary pattern and HOG for face recognition, SIFT and SURF for object detection, etc. Hence, convolution neural network which is not domain specific would be a better approach.