Reviewerly/exHarmony huggingface dataset:
Detecting hidden webcams with delay-tolerant similarity of simultaneous observation

Small, low-cost, wireless cameras are becoming increasingly commonplace making surreptitious observation of people more difficult to detect. Previous work in detecting hidden cameras has only addressed limited environments in small spaces where the user has significant control of the environment. To address this problem in a less constrained scope of environments, we introduce the concept of similarity of simultaneous observation where the user utilizes a camera (Wi-Fi camera, camera on a mobile phone or laptop) to compare timing patterns of data transmitted by potentially hidden cameras and the timing patterns that are expected from the scene that the known camera is recording. To analyze the patterns, we applied several similarity measures and demonstrated an accuracy of over 87% and F1 score of 0.88 using an efficient threshold-based classification. We used our data set to train a neural network and saw improved results with accuracy as high as 97% and an F1 score over 0.95 for both indoors and outdoors settings. We further extend this work against an attacker who is capable of delaying when the video is sent. With the new approach, we see increased F1 scores above .98 for the original data and delayed data. From these results, we conclude that similarity of simultaneous observation is a feasible method for detecting hidden wireless cameras that are streaming video of a user. Our work removes significant limitations that have been put on previous detection methods.
Modified Artificial Bee Colony Algorithm for Identification of a Discrete Model of Atmospheric Pollution Dynamics by Nitrogen Dioxide

The existing environmental monitoring systems of cities control the concentration of nitrogen dioxide in the ground-layer of atmosphere. The measurement results are complemented by mathematical modeling. In these cases, the mathematical model is formulated in the form of differential equations, the solution of which is the dynamics of pollution, depending on the motor vehicles traffic intensity. It has been proposed to use an inductive approach to construct such models in the article. To solve the problem of structural identification, a modified algorithm of the bee colony is proposed and substantiated.
Application of Machine Learning for Aviation Safety Risk Metric

A risk metric is one of the key tools to monitor the safety performance of complex systems. This paper explains the methodologies employed to develop a new comprehensive risk metric based on Machine Learning to measure the safety performance of the National Airspace System (NAS) in the airport surface environment. Unlike existing metrics that focus primarily on incidents and close calls, this metric incorporates all relevant surface incidents and accidents and assigns an appropriate severity measure proportional to their consequences and in relation to a potential worst outcome of an accident.
The Game and Integration between Online Education and Traditional School Education

The development of the Internet accelerates the reform of the education industry, and the market of online education is becoming larger and larger. The profit mode of online education platform determines the flow awareness of enterprises, which will compete with the education feelings shaped under the support of traditional school education scenes. Online education is a new form of education, the flow of the platform expands the quality education resources, and it is also a supplement to the traditional school education, which is the foundation of online education, there is a harmonious and beneficial relationship between these two platforms in the game.
A Novel Perspective in Designing a Low Quantum Cost Synchronous Reversible Counters

Reversible circuits are one of the most promising technologies that allow the reproduction of the inputs from the observed outputs and thus reducing the power dissipation in the form of heat. Many basic and universal logic gates i.e. nand, nor, or, and etc. are irreversible and result in energy consumption during calculations. In the reversible technology, like quantum circuits energy is not consumed during the entire calculation process and these circuits can be used for low-power and quantum communication applications circuits. Counters are considered as one of the most important sequential circuits used in many real-time applications. This paper presents a new approach to design a quantum cost-effective reversible up/down counter circuit using exclusive-or sum of product (ESOP) expressions and TR gates. The proposed counter circuits are simulated in the Xilinx Vivado simulator to verify the functionality of the proposed designs. The comparative study indicates that the proposed counters are superior to the best previous designs in terms of quantum cost (QC) and gate count (GC). Moreover, they are very close to or better than the previously available designs in terms of garbage outputs (GO) and Ancilla inputs (AI).
Evaluation of Recognition of Water-meter Digits with Application Programs, APIs, and Machine Learning Algorithms

Image recognition of digits of water meters is useful and timesaving. Many exist programs/APIs available for image recognition but not specially for this task. Therefore, this paper investigated and compared correctness of those 5 programs/APIs, i.e. Anyline, Line, Google Vision, Microsoft Azure Computer Vision, and Naver (Clovar) OCR with 32 water-meter images with 3 different perspectives (straight, upside-down, right-rotation). In addition, those images were manually cropped into single-digit images. Then, they were tested with AutoML, Tesseract and some machine learning algorithms (KNN, SVM and CNNs). trained with 1,500 pictures (28*28 pixels) of a single digit of water meter images. The experimental results showed that Anyline got 96.9% of accuracy for the straight images. Whereas, GoogLeNet got 78.9% and 74.2% for right-rotation and upside-down images, respectively. On the other hand google vision, Anyline and Line got 34.4%, 0% and 18.8% accuracy for straight, right-rotation and upside-down images, respectively. In addition, dataset of EMNIST and Google Street View were used for training of CNN. However, about 12% and 40% correctness were achieved for those dataset, respectively.
Form Follows Brain Function: A Computational Mapping Approach

The strong association between computation and the built environment gives birth to new research approaches to urban design. Although these approaches enable the implementation of urban systems based on various collections of datasets, they usually neglect human experience. To address this problem, our research identifies and quantifies spatial information and patterns of urban organization based on experience, by gathering two parallel types of user data: sensory and recollection. The presented experiment, executed in Copenhagen, Denmark with 23 participants, involves a walking task, and a recollection task. The walking task is 1 km walk from the Copenhagen Court House building to the Royal Danish Theatre through Strøget Street, while wearing eye-tracking spectacles. The recollection task includes the underlining – on a 3D photorealistic digital model – of the elements that the participants can recall from their walk. We monitor the eye movement of the participants to locally characterize the urban path by computing four spatial attributes: a) The fixations on elements (counted through iMotions Software), b) The distance from the viewer (taken by GPS points through FUMapp software), c) The position of the stimuli related to the eye-level (calculated in FUMapp), and d) The degree of spatial transformations on street intersections (through spatial variables in FUMapp). Then we compare the eye-tracking and the recollection data and we evaluate their heat maps. The originality of this research is twofold: First, for the first time a substantial amount of quantifiable optical data related to urban walking is recorded; Second, for the first time this experiment is performed on the public street. The proposed method could offer a quantifiable basis for predicting eye fixation locations, determining human centric guidelines for urban design. Additional modes of body sensors could yield even more all-encompassing body-metrics related to the urban walking experience.
A Novel Noise Removal in Digital Mammograms based on Statistical Algorithms

The noise removal is being a substantial phase for the computer-assisted detection (CAD) based breast cancer diagnosis using mammogram medical images. A proficient method for the salt-and-pepper or impulse noise eradication in digital mammograms is implemented. The approach depends on the statistical measures like mean, median and standard deviation quantities. This calculates the new intensity which is to be substituted in the impulse area by determining those measures in neighbour points of the taken mammogram images. The proposed is simply an iterative method that aims to take away the salt and pepper otherwise impulse noise devoid of affecting the boundaries and other major significant portions of the image. The approach is compared with several existing methods and it provides enhanced noise removal performance over others.
State of the Art in Digital Forensics for Small Scale Digital Devices

As the society becomes more reliant on Small Scale Digital Devices, the uses of these devices is becoming more prevalent. This makes SSDDs commonly present at crime scenes, creating a need for the development of SSDD forensics. The area is relatively new but is witnessing fast paced development. The development of SSDDs is a less evolved field despite the excellent progress in analytical tools and research development. The purpose of this study is to develop a guide that provides researchers and forensic examiners with the required knowledge to develop appropriate standards and procedures with which to approach SSDDs within the investigative context. This paper presents the current state of the art in SSDDs including smartphones, drones, smart toys, wearable technologies, and gaming consoles.
Representation learning for entity type ranking

The type of an entity is a key piece of information to understand what an entity is and how it relates to other entities mentioned in a document. Search engine result pages (SERPs) often surface facts and entity type information from a background Knowledge Graph (KG) in response to queries that carry a semantic information need. In a KG, an entity usually holds multiple type properties. It is then important to, given an entity in a KG, rank entity types attached to the entity by relevance to a certain user and information need as not always the most popular type is the most informative within a textual context.
Gaze-Adaptive Lenses for Feature-Rich Information Spaces

The inspection of feature-rich information spaces often requires supportive tools that reduce visual clutter without sacrificing details. One common approach is to use focus+context lenses that provide multiple views of the data. While these lenses present local details together with global context, they require additional manual interaction. In this paper, we discuss the design space for gaze-adaptive lenses and present an approach that automatically displays additional details with respect to visual focus. We developed a prototype for a map application capable of displaying names and star-ratings of different restaurants. In a pilot study, we compared the gaze-adaptive lens to a mouse-only system in terms of efficiency, effectiveness, and usability. Our results revealed that participants were faster in locating the restaurants and more accurate in a map drawing task when using the gaze-adaptive lens. We discuss these results in relation to observed search strategies and inspected map areas.
Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training

Magnetic Resonance Imaging (MRI) of the brain can come in the form of different modalities such as T1-weighted and Fluid Attenuated Inversion Recovery (FLAIR) which has been used to investigate a wide range of neurological disorders. Current state-of-the-art models for brain tissue segmentation and disease classification require multiple modalities for training and inference. However, the acquisition of all of these modalities are expensive, time-consuming, inconvenient and the required modalities are often not available. As a result, these datasets contain large amounts of unpaired data, where examples in the dataset do not contain all modalities. On the other hand, there is smaller fraction of examples that contain all modalities (paired data) and furthermore each modality is high dimensional when compared to number of datapoints. In this work, we develop a method to address these issues with semi-supervised learning in translating between two neuroimaging modalities. Our proposed model, Semi-Supervised Adversarial CycleGAN (SSA-CGAN), uses an adversarial loss to learn from unpaired data points, cycle loss to enforce consistent reconstructions of the mappings and another adversarial loss to take advantage of paired data points. Our experiments demonstrate that our proposed framework produces an improvement in reconstruction error and reduced variance for the pairwise translation of multiple modalities and is more robust to thermal noise when compared to existing methods.
Motion Planning for Active Data Association and Localization in Non-Gaussian Belief Spaces

This paper presents a method for motion planning under uncertainty to resolve situations where ambiguous data associations result in a multimodal hypothesis on the robot state. Simultaneous localization and planning for a lost (or kidnapped) robot requires that given little to no a priori pose information, a planner should generate actions such that future observations allow the localization algorithm to recover the correct pose of a mobile robot with respect to a global reference frame. We present a Receding Horizon approach, to plan actions that sequentially disambiguate a multimodal belief to achieve tight localization on the correct pose in finite time. In our method, disambiguation is achieved through active data associations by picking target states in the map which allow distinctive information to be observed for each belief mode and creating local feedback controllers to visit the targets. Experimental results are presented for a kidnapped physical ground robot operating in an artificial maze-like environment.
Evolution of SOMs’ Structure and Learning Algorithm: From Visualization of High-Dimensional Data to Clustering of Complex Data

In this paper, we briefly present several modifications and generalizations of the concept of self-organizing neural networks—usually referred to as self-organizing maps (SOMs)—to illustrate their advantages in applications that range from high-dimensional data visualization to complex data clustering. Starting from conventional SOMs, Growing SOMs (GSOMs), Growing Grid Networks (GGNs), Incremental Grid Growing (IGG) approach, Growing Neural Gas (GNG) method as well as our two original solutions, i.e., Generalized SOMs with 1-Dimensional Neighborhood (GeSOMs with 1DN also referred to as Dynamic SOMs (DSOMs)) and Generalized SOMs with Tree-Like Structures (GeSOMs with T-LSs) are discussed. They are characterized in terms of (i) the modification mechanisms used, (ii) the range of network modifications introduced, (iii) the structure regularity, and (iv) the data-visualization/data-clustering effectiveness. The performance of particular solutions is illustrated and compared by means of selected data sets. We also show that the proposed original solutions, i.e., GeSOMs with 1DN (DSOMs) and GeSOMS with T-LSs outperform alternative approaches in various complex clustering tasks by providing up to 20 % increase in the clustering accuracy. The contribution of this work is threefold. First, algorithm-oriented original computer-implementations of particular SOM’s generalizations are developed. Second, their detailed simulation results are presented and discussed. Third, the advantages of our earlier-mentioned original solutions are demonstrated.
The Impact of Addressee Factor for Scientific Text Writing

An addressee factor, as a constituent characteristic of a scientific text, takes into account a specific recipient to implement high-quality scientific communication. The need to consider this factor is explained by the search for ways to popularize scientific knowledge dissemination by means of scientific publications. The author’s understanding of the aggregate “portrait” of the reader – his/her social, intellectual, cultural, educational parameters – should be reflected in the content and structure of the scientific written product. The authors present the analysis of such key concepts as “scientific text”, “addressing of the text”, “addressee”, “sender”. The article defines the characteristics of an article as a product of scientific communication which reflects the specificity of the individual-personal perception of information by the sender. Here is an attempt to prove that the nature of scientific interaction is becoming more individualized and personalized. There is an urgent need to find means and methods of the sender identifying the potential requirements of the addressee of the scientific message. The authors prove the necessity of ensuring unambiguous perception of scientific information through implementation of addressing as a constituent category of text. We represent techniques and methods to make the text addressed in the structure of scientific discourse. The article shows how addressing of the text helps to achieve scientific interaction – to cause the recipient to replicate – to perform a certain verbal, mental, emotional reaction.
Image Quality Assessment Using a Combination of Hand-Crafted and Deep Features

No-reference image quality assessment is an important area of research and has gained significant interest over the past years. Full-reference image quality assessment is not feasible in some scenarios where the reference image is not available. Hand-crafted features are statistics of natural scene images which are used to train a regression algorithm. Deep learning based approaches learn discriminatory features from images. This paper proposes a hybrid method to Image Quality Assessment (IQA) which uses combination of hand-crafted and deep features. The hand-crafted features are extracted in multi-scale and color space configuration in order to capture greater details. Deep features are extracted using transfer learning of Vgg19. Dimensionality reduction is achieved using principal component analysis and features with 95% variance are retained and a final feature set of 102 transformed features is obtained. Gaussian process regression using the squared exponential kernel is used for modeling. The final model is tested on seven benchmark databases for correlation of estimated image quality and mean of subjective score. A comparison with twelve state-of-the-art methods is performed and superior performance is achieved.
Periodicity in Data Streams with Wildcards

We investigate the problem of detecting periodic trends within a string S of length n, arriving in the streaming model, containing at most k wildcard characters, where k = o(n). A wildcard character is a special character that can be assigned any other character. We say that S has wildcard-period p if there exists an assignment to each of the wildcard characters so that in the resulting stream the prefix of length n − p equals the suffix of length n − p. We present a two-pass streaming algorithm that computes wildcard-periods of S using $\mathcal {O}(k^{3} \text {polylog} n)$ bits of space, while we also show that this problem cannot be solved in sublinear space in one pass. We also give a one-pass randomized streaming algorithm that computes all wildcard-periods p of S with $p<\frac {n}{2}$ and no wildcard characters appearing in the last p symbols of S, using $\mathcal {O}(k^{3}\log ^{9} n)$ space.
CNN-Based Real-Time Indian Sign Language Recognition System

Sign language is a way by which deaf and dumb people can express their thoughts and feelings. Deaf and dumb people use hand shape, body movements, and facial expressions for communication. In this work, a vision-based Indian Sign Language Recognition system using a convolutional neural network (CNN) is implemented. The sign images are captured by a USB camera. To segment the hand region, background subtraction method is used. Two databases have been created for ISL. Each of the databases contains 26 alphabets having a total of 52,000 images. The system has been also tested in real time, where the sign alphabets by four signers in front of the camera are captured and it correctly recognized almost all of the signs. Accuracy of around 99.40% is obtained.
Toward Reconfiguration of BPaaS Based on SaaS Services using Clustering and Boolean Satisfiability

Business Process as a Service (BPaaS) met a considerable proliferation and an exponential emergence as a new Cloud service for delivering complex and complete business applications. To better position themselves in the harshly competitive Cloud services market, BPaaS providers should offer to their clients, flexible services responding both to their functional and non-functional requirements. This article proposes a Reconfigurable BPaaS-based SaaS services Federation system (RBPF) allowing flexible support of user requirements changes. RBPF proposes to end-users minimal on the fly compositions whenever a reconfiguration is required. To do so, Boolean Satisfiability Problem-based algorithms are adopted to select the minimal reconfiguration. To reduce composition candidates research space, we propose new algorithms to group BPaaS services that are likely to compose within the same cluster. The clustering process leverages semantic Linked Open Data similarity measures to match service capabilities and identify composable ones.
Lessons Learned from Designing Adaptive Training Systems

Adaptive training systems use different types of context information to adapt to the individual needs, preferences, and progress of the trainee. In general, this is achieved by automatically adjusting the appearance and behaviour of the training system. This adaptation can be based on parameters related to the training task to be performed, the environment, or the user himself. Among the latter, the focus of current research shifts from the traces of a user in the system (e.g., to derive his level of knowledge) to more transient aspects (such as his current mental or emotional state). Though this is clearly desirable from a pedagogical point of view, new challenges from an ethical perspective arise with the availability of emotion-sensitive technology. Following the case study of the emotion-based training system EVA, we analyse ethical challenges that might be associated with such software. This leads us to 16 ethical guidelines that were designed in a participative and interdisciplinary process. We also discuss the consequences from these guidelines for our further research and development in that area.