Reviewerly/exHarmony huggingface dataset:
Automatic modulation classification in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e970" altimg="si69.svg"><mml:mi>α</mml:mi></mml:math>-stable noise using graph-based generalized second-order cyclic spectrum analysis

An innovative automatic modulation classification using graph-based generalized second-order cyclic spectrum analysis in the background of α-stable noise is presented in this paper. In our proposed method, the three-dimensional generalized second-order cyclic spectrum of the modulated signal is first constructed from the nonlinear Hilbert transform. According to the generalized second-order cyclic spectrum, the signal is mapped onto a set of direct weighted rings in the graph domain. Then, the graph features of the modulated signal are extracted from the lower-upper decomposition of the corresponding adjacency matrices. The Hamming distance is employed to measure the distinctions between the feature parameters derived from the training and test data, and the modulation scheme can be identified thereby. Monte Carlo simulation results demonstrate that the proposed approach can achieve better classification accuracy than other existing methods for the α-stable noise scenario.
Designing The Built-In Microcontroller Control Systems of Executive Robotic Devices Using The Digital Twins Technology

The article presents approaches to the development of robotic systems based on the use of virtual prototypes, known as digital twins. Also, usage of such systems for walking robotic mechanisms are demonstrated by the example of developing an experimental debugging platform that supports both the design of algorithms for robot behavior at the upper level of the control hierarchy and the design of control algorithms for its nodes at the lower level. Authors used different basic tooling systems integrated with each other through cross-platform tools, such as Gazebo, V-Rep, IAR Embedded Workbench, Quartus, Fuzzy51 and several others.
Intensity Guided Depth Upsampling Using Edge Sparsity and Super-Weighted $L_0$ Gradient Minimization

Although depth cameras acquire depth in dynamic scenes, the captured depth images are often noisy and of low resolution. Depth images have the physical nature of being represented by smooth regions and edges in between them, i.e. depth images have high edge sparsity in the gradient domain. In this paper, we propose intensity guided depth upsampling using edge sparsity and super-weighted L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient minimization. First, we get mutual structure between intensity and depth using joint mutual structure filtering. Second, we generate an initial depth image using recursive interpolation. Next, we generate weights for L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient minimization based on gradient and entropy of the intensity image, and upsample the depth image using super-weighted L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient minimization. In super-weighted L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient minimization, we combine two main terms: Hybrid data fidelity and weighted L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient regularization. The hybrid data fidelity term combines both zero-order and first-order data differences to suppress staircase artifacts, while the weighted L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gradient regularization term preserves depth structures and removes noise. Finally, we further refine the depth image using adaptive fast weighted median filtering. Experiments on Middlebury and realworld scene datasets verify that the proposed method produces edge preserving depth upsampling results and outperforms state-of-the-arts in terms of both visual quality and quantitative measurements.
THE DEVELOPMENT OF GÖDEL’S ONTOLOGICAL PROOF

Abstract Gödel’s ontological proof is by now well known based on the 1970 version, written in Gödel’s own hand, and Scott’s version of the proof. In this article new manuscript sources found in Gödel’s Nachlass are presented. Three versions of Gödel’s ontological proof have been transcribed, and completed from context as true to Gödel’s notes as possible. The discussion in this article is based on these new sources and reveals Gödel’s early intentions of a liberal comprehension principle for the higher order modal logic, an explicit use of second-order Barcan schemas, as well as seemingly defining a rigidity condition for the system. None of these aspects occurs explicitly in the later 1970 version, and therefore they have long been in focus of the debate on Gödel’s ontological proof.
Empowering Qualitative Research Methods in Education with Artificial Intelligence

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches on learning and to understand the ways skills and knowledge are acquired by learners. One of these is qualitative research, a scientific method grounded in observations that manipulates and analyses non-numerical data. It focuses on seeking answers to why and how a particular observed phenomenon occurs rather than on its occurrences. This study aims to explore and discuss the impact of artificial intelligence on qualitative research methods. In particular, it focuses on how artificial intelligence have empowered qualitative research methods so far, and how it can be used in education for enhancing teaching and learning.
PassMan: A New Approach of Password Generation and Management without Storing

Password has become a critical part of one's personal, social, and professional life. We need passwords to secure personal information regardless of the platform. People need passwords for almost every system they use. Secured passwords are hard to generate. It is harder to remember and manage them. Password managers claim immense importance in this circumstance, but not all the password managers available to use can always provide the proper security for the passwords and other information given to them. Those are vulnerable when it comes to protecting the information from hackers. This paper presents a proposition of a new and improved approach for password management systems. This approach does not need to store the password anywhere like the existing ones. It will only save three parameters set by the user to identify them in the local storage of the device where they installed the system. Our hope is, the system should provide the users with a safer feeling to use password manager systems as it becomes more secured and non-volatile.
Determining Factors Pertaining to Cloud Security Adoption Framework in Government Organizations: An Exploratory Study

In wealthy developing countries like the Kingdom of Saudi Arabia (KSA), the adoption of cloud computing is progressing slowly compared to the developed countries.To accelerate the cloud computing adoption, this study takes place exploring and investigating the associated security factors with cloud computing that influence organisations' desire for adopting the services of cloud computing.This exploratory study has been conducted through two steps: (1) develop a framework based on security factors discussed in related studies and (2) validate the security factors' relationships within the developed security framework.In the validation study, an instrument was distributed to 217 IT experts in different Saudi government organisations.Correlation analysis is used to explore the relationship(s) among items and factors.The results suggested that the security factors were significantly correlated with each other.The internal consistency reliability analysis results were great.Worthy Cronbach's alpha results also indicated that the items used to measure each factor were independent measures, which are positivity correlated with one another.The key findings of the exploratory factor analysis revealed three main components named as security benefits, security risks, and security awareness.Overall, the implications of this exploratory study provide a significant contribution towards cloud adoption in Saudi organisations.The outcomes of this study will serve as valuable information for policymakers, practitioners, and researchers.
Computational Stylometry and Machine Learning for Gender and Age Detection in Cyberbullying Texts

The aim of this paper is to show the importance of Computational Stylometry (CS) and Machine Learning (ML) support in author's gender and age detection in cyberbullying texts. We developed a cyberbullying detection platform and we show the results of performances in terms of Precision, Recall and F -Measure for gender and age detection in cyberbullying texts we collected.
Keystroke Mobile Authentication: Performance of Long-Term Approaches and Fusion with Behavioral Profiling

In this paper we evaluate the performance of mobile keystroke authentication according to: (1) data availability to model the user; and (2) combination with behavioral-based profiling techniques. We have developed an ensemble of three behavioral based-profile authentication techniques (WiFi, GPS Location, and App usage) and a Keystroke state-of-the-art recognition approach. Algorithms based on template update are employed for profiling systems meanwhile bidirectional recurrent neuronal networks with a Siamese training setup is used for the keystroke system. Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. This database comprises smartphone sensor signals acquired during natural human-mobile interaction. Our results show that it is necessary 6 days of usage data stored to achieve the best performance in average. The template update allows to improve the equal error rate of keystroke by a relative 20%–30% performance.
Identifying Biases in Politically Biased Wikis through Word Embeddings

With the increase of biased information available online, the importance of analysis and detection of such content has also significantly risen. In this paper, we aim to quantify different kinds of social biases using word embeddings. Towards this goal we train such embeddings on two politically biased MediaWiki instances, namely RationalWiki and Conservapedia. Additionally we included Wikipedia as an online encyclopedia, which is accepted by the general public. Utilizing and combining state-of-the-art word embedding models with WEAT and WEFAT, we display to what extent biases exist in the above-mentioned corpora. By comparing embeddings we observe interesting differences between different kinds of wikis.
Secure Random Phase Key Exchange Schemes for Image Cryptography

We propose novel random phase key exchange schemes for image cryptography, which are complex sinusoidal waveform versions of the Diffie-Hellman (DH) key exchange algorithm. We demonstrate that the proposed schemes can be particularly helpful to establish a shared secret key in Fourier optics-based image cryptosystems since the problem of symmetric secret key establishment is one of the fundamental difficulties in Fourier optics-based image cryptography. We also propose a ring-type key exchange system in which multiple users can securely share a secret key, which is an extension of the key exchange scheme for two users. The schemes are verified by numerical simulations.
User experience design for older adults

This paper describes a study that combined interview and observation methods to explore the user experiences of 23 users aged 70+. Interviews involved questions about how and when users learned to use computers and the internet, as well as their usual online activities and difficulties completing those activities. Observations involved two sessions for each user: first, a naturalistic observation where the researcher recorded the participant interacting with the computer(s) in their home, demonstrating their typical internet use; second, a structured task analysis where participants completed a series of increasingly complex operations online, including searching for information on a specific topic, using web mapping tools, and interacting with a government website. Participants followed a think-aloud protocol during both observations, explaining the steps that they were taking and sharing their thought process and feelings with the researcher. The rich qualitative data from this work provides insights into the needs of this population in four specific areas---physical, cognitive, educational, and cultural---as well as design strategies for addressing those needs.
Raman Spectroscopy Study of Phosphorites Combined with PCA-HCA and OPLS-DA Models

Phosphorite is a nonrenewable strategic resource, a convenient and rapid method of phosphorite grade identification and classification is important to improve phosphate utilization. In this study, Raman spectroscopy has been combined with principal component analysis and hierarchical clustering analysis (PCA-HCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models for an investigation of different grade phosphorite samples. Both the PCA-HCA and OPLS-DA models showed that different grade phosphorite samples can be clearly distinguished by statistically analyzing the Raman spectra after smoothing, baseline correction, and first derivation. In addition, the S-line study on the OPLS-DA model clearly demonstrated that the symmetrical stretching vibrational mode of phosphate near 960 cm−1 had a much more significant contribution than other vibrational modes for the differentiation of different grade phosphorite samples.
CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection

Pedestrian detection in the wild remains a challenging problem especially when the scene contains significant occlusion and/or low resolution of the pedestrians to be detected. Existing methods are unable to adapt to these difficult cases while maintaining acceptable performance. In this paper we propose a novel feature learning model, referred to as CircleNet, to achieve feature adaptation by mimicking the process humans looking at low resolution and occluded objects: focusing on it again, at a finer scale, if the object can not be identified clearly for the first time. CircleNet is implemented as a set of feature pyramids and uses weight sharing path augmentation for better feature fusion. It targets at reciprocating feature adaptation and iterative object detection using multiple top-down and bottom-up pathways. To take full advantage of the feature adaptation capability in CircleNet, we design an instance decomposition training strategy to focus on detecting pedestrian instances of various resolutions and different occlusion levels in each cycle. Specifically, CircleNet implements feature ensemble with the idea of hard negative boosting in an end-to-end manner. Experiments on two pedestrian detection datasets, Caltech and CityPersons, show that CircleNet improves the performance of occluded and low-resolution pedestrians with significant margins while maintaining good performance on normal instances.
An autonomous drone surveillance and tracking architecture

In this work, we present a computer vision and machine learning backed autonomous drone surveillance system, in order to protect critical locations.The system is composed of a wide angle, high resolution daylight camera and a relatively narrow angle thermal camera mounted on a rotating turret.The wide angle daylight camera allows the detection of flying intruders, as small as 20 pixels with a very low false alarm rate.The primary detection is based on YOLO convolutional neural network (CNN) rather than conventional background subtraction algorithms due its low false alarm rate performance.At the same time, the tracked flying objects are tracked by the rotating turret and classified by the narrow angle, zoomed thermal camera, where classification algorithm is also based on CNNs.The training of the algorithms is performed by artificial and augmented datasets due to scarcity of infrared videos of drones.
Comprehensible Context-driven Text Game Playing

In order to train a computer agent to play a text-based computer game, we must represent each hidden state of the game. A Long Short-Term Memory (LSTM) model running over observed texts is a common choice for state construction. However, a normal Deep Q-learning Network (DQN) for such an agent requires millions of steps of training or more to converge. As such, an LSTM-based DQN can take tens of days to finish the training process. Though we can use a Convolutional Neural Network (CNN) as a text-encoder to construct states much faster than the LSTM, doing so without an understanding of the syntactic context of the words being analyzed can slow convergence. In this paper, we use a fast CNN to encode position-and syntax-oriented structures extracted from observed texts as states. We additionally augment the reward signal in a universal and practical manner. Together, we show that our improvements can not only speed up the process by one order of magnitude but also learn a superior agent.
Innovations in Housing for Smart Cities

This paper presents research being conducted to develop sustainable and affordable housing that leverages population health strategies for smart cities. We are currently developing a multifamily prototype housing unit that demonstrates best practices in aging-in-place strategies and telehealth technology. We are also investigating prefabricated construction methodologies that will be used to bring plug-and-play technology infrastructure to the exploding senior housing demographic. Gigabit networks allow smart cites to collect and analyze vast amounts of data. Sensors embedded in the built environment are able to collect information on topics such as human vital signs (heart rate, breathing, body temperature, weight, blood pressure, hydration), physical activity (activities of daily living, falls, gait, sleep), environmental factors (temperature, humidity, air quality, water quality, weather), and pharmaceutical regimens (overdosing, missing doses). Population health strategies utilize the collection and analysis of such data to deliver health care more affordably and effectively, sometimes before we know we need it. Some of the more advanced technologies will include motion sensors/fall detection, gait analysis (determination of early onset Alzheimer's and Parkinson's), automated light-emitting diode (LED) smart-spectrum lighting (optimizing circadian rhythms), smart mirrors, smart toilets (hydration monitoring/diuretic dosage), sleep sensors (ballistocardiography), and automated medicine dispensers (coupled with smart toilets for heart medication).
A Research on Generative Adversarial Networks Applied to Text Generation

Using deep learning methods to generate text, a sequence-to-sequence model is typically used. This kind of models is very effective in dealing with tasks that have a strong correspondence between input and output, such as machine translation. Generative Adversarial Networks(GAN) is a generation model that has been proposed in recent years, which has achieved good results in generating continuous and divisible data such as images. This paper proposes an improved model based on GAN, specifically using the transformer network structure instead of the original general Convolutional Neural Network or Recurrent Neural Networks as generator, and using the reinforcement learning algorithm Actor-Critic to improve the model training method. By comparing experiments, and selecting the perplexity, the BLEU score, and the percentages of unique n-gram to evaluate the quality of the generated sentences. The results show that the improved model proposed in this paper perform better than comparative models on above three evaluation indexes. This verifies its effectiveness in text generation.
A new MapReduce solution for associative classification to handle scalability and skewness in vertical data structure

Associative classification is a promising methodology in information mining that uses the association rule discovery procedures to build the classifier. But they have some limitations like: they are not able to handle big data as they have memory constraints, high time complexity, load imbalance and data skewness. Data skewness occurs invariably when big data analytics comes in picture and affects the efficiency of an approach. This paper presents the MapReduce solution for associative classification in respect of vertical data layout. To handle these problems we have proposed two algorithms MR-MCAR-F (MapReduce-Multi Class Associative Classifier-MapReduce fast algorithm) and MR-MCAR-L (MapReduce-Multi Class Associative Classifier Load parallel frequent pattern growth algorithm). Also in this paper, MapReduce solution of Tid List and Database coverage has been proposed. We have used three type of pruning techniques viz. database coverage, global and distributed pruning. The proposed approaches have been compared with latest approach from the literature survey in terms of accuracy, computation time and data skewness. The existing scalable approaches cannot handle skewness while, our proposed method handles it in a very effective manner. All the experiments have been performed on six datasets which have been extracted from UCI repositories on the Hadoop framework. Proposed algorithms are scalable solutions for associative classification to handle big data and data skewness.
Deep Reinforcement Learning for Weak Human Activity Localization

Human activity localization aims at recognizing contents and detecting locations of activities in video sequences. With an increasing number of untrimmed video data, traditional activity localization methods always suffer from two major limitations. First, detailed annotations are needed in most existing methods, i.e., bounding-box annotations in every frame, which are both expensive and time consuming. Second, the search space is too large for 3D activity localization, which requires generating a large number of proposals. In this paper, we propose a unified deep Q-network with weak reward and weak loss (DWRLQN) to address the two problems. Certain weak knowledge and weak constraints involving the temporal dynamics of human activity are incorporated into a deep reinforcement learning framework under sparse spatial supervision, where we assume that only a portion of frames are annotated in each video sequence. Experiments on UCF-Sports, UCF-101 and sub-JHMDB demonstrate that our proposed model achieves promising performance by only utilizing a very small number of proposals. More importantly, our DWRLQN trained with partial annotations and weak information even outperforms fully supervised methods.