Reviewerly/exHarmony huggingface dataset:
From iterated revision to iterated contraction: Extending the Harper Identity

The study of iterated belief change has principally focused on revision, with the other main operator of AGM belief change theory, namely contraction, receiving comparatively little attention. In this paper we show how principles of iterated revision can be carried over to iterated contraction by generalising a principle known as the 'Harper Identity'. The Harper Identity provides a recipe for defining the belief set resulting from contraction by a sentence A in terms of (i) the initial belief set and (ii) the belief set resulting from revision by ¬A. Here, we look at ways to similarly define the conditional belief set resulting from contraction by A. After noting that the most straightforward proposal of this kind leads to triviality, we characterise a promising family of alternative suggestions that avoid such a result. One member of that family, which involves the operation of rational closure, is noted to be particularly theoretically fruitful and normatively appealing.
TH-NRDCSK: A Non-Coherent Time Hopping Chaotic System for Anti-Jamming Communications

Time hopping spread spectrum (THSS) system and chaotic system have been studied as candidates of the anti-jamming (AJ) systems with substantial advantages. However, THSS systems suffer from the strict synchronization prerequisite. In order to overcome the shortcomings of the THSS system and utilize the profits of the non-coherent chaotic system, we first propose a novel TH-NRDCSK system to enhance the bit error rate (BER) performance of the conventional non-coherent THSS system and relieve the strict synchronization issue of coherent THSS systems. Then, we analyze the performance of the proposed system under additive white Gaussian noise (AWGN) channel and jamming environment. Finally, we implement the simulation to demonstrate the consistency of the analysis and the simulation results and to disclose the effects of the system parameters on system's AJ performance. Moreover, the performance of the proposed system is compared to that of conventional counterparts to look at the benefits of integrating chaotic and THSS systems. The simulation results show that the proposed system significantly outperforms conventional counterparts in a practical environment.
Activity Recognition for Indoor Fall Detection in 360-Degree Videos Using Deep Learning Techniques

Human activity recognition (HAR) targets the methodologies to recognize the different actions from a sequence of observations. Vision-based activity recognition is among the most popular unobtrusive technique for activity recognition. Caring for the elderly who are living alone from a remote location is one of the biggest challenges of modern human society and is an area of active research. The usage of smart homes with an increasing number of cameras in our daily environment provides the platform to use that technology for activity recognition also. The omnidirectional cameras can be utilized for fall detection activity which minimizes the requirement of multiple cameras for fall detection in an indoor living scenario. Consequently, two vision-based solutions have been proposed: one using convolutional neural networks in 3D-mode and another using a hybrid approach by combining convolutional neural networks and long short-term memory networks using 360-degree videos for human fall detection. An omnidirectional video dataset has been generated by recording a set of activities performed by different people as no such 360-degree video dataset is available in the public domain for human activity recognition. Both, the models provide fall detection accuracy of more than 90% for omnidirectional videos and can be used for developing a fall detection system for indoor health care.
Evaluation Program of an Efficient Source Coding Algorithm Under the Condition of Converting Metric Spaces

In modern communication systems, the issues of ensuring the required quality of signal transmission are highly relevant. This article proposes an algorithm and a program built on its basis for evaluating the effective coding of a source of signals when they are converted in various metric spaces, and in particular for the currently widely used analog-digital conversion of a continuous signal into a binary one. The estimation algorithm takes into account the distortion indicators when encoding with a binary code, the likelihood of these distortions occurring during the transmission of message elements and the likelihood of a message appearing on the communication channel. In addition to the algorithm itself, its software implementation is proposed, created using the C # programming language. The software implementation has a graphical environment for the user.
Artificial Intelligence via Competitive Learning and Image Analysis for Endometrial Malignancies

Objective of this study is to investigate the potential of an artificial intelligence (AI) technique, based on competitive learning, for the discrimination of benign from malignant endometrial nuclei and lesions. For this purpose, 416 liquid-based cytological smears with histological confirmation were collected, each smear corresponded to one patient. From each smear was extracted nuclear morphometric features by the application of an image analysis system. Subsequently nuclei measurement from 50% of the cases were used to train the AI system to classify each individual nucleus as benign or malignant. The remaining measurement, from the unused 50% of the cases, were used for AI system performance evaluation. Based on the results of nucleus classification the patients were discriminated as having benign or malignant disease by a secondary subsystem specifically trained for this purpose. Based on the results it was conclude that AI based computerized systems have the potential for the classification of both endometrial nuclei and lesions.
Interferometric Radar For Spatially-Persistent Gesture Recognition in Human-Computer Interaction

We present a new method of spatially-persistent radar hand gesture recognition based on direct measurements of the angular velocity of hand motions for improved-human computer interaction. Traditional radar-based gesture recognition utilizes Doppler-based measurements, however the radial velocity and thus the Doppler time-frequency responses change significantly when the hand is not directly broadside to the radar, leading to degraded classification of gestures. Using a newly developed interferometric method of directly measuring angular velocity, we show that the time-frequency signatures of hand gestures remain persistent when moving away from broadside, indicating that the interferometric technique can be used for more robust and reliable wireless human-computer interaction. Experimental measurements using a 16.9 GHz continuous-wave interferometric radar demonstrate that for the Doppler mode the maximum response bandwidth degrades by 71% when the gesture moves to 30° off-broadside, leaving a negligible time-frequency response, while the interferometric signature generated from the same data degrades in maximum bandwidth by only 16%, with the response remaining largely identical.
Deterministic Trajectory Planning for Non-Holonomic Vehicles Including Road Conditions, Safety and Comfort Factors

Deterministic and real time calculation of safe and comfortable speed profiles is the main topic of this paper. Using vehicle properties and road characteristics, such as friction and road banking, safety limits for rollover and skidding are calculated and applied in the trajectory planning. To satisfy comfort criteria and obtain smooth speed profiles, jerk and acceleration of the vehicle are limited in the speed planning algorithm. For speed planner, an A* based search method is used to calculate a speed profile corresponding to shortest traveling time. In order to avoid stationary and moving obstacles, decoupled prioritized planning is used. A physical model is used to define the behavior of the vehicle in the speed planner, where jerk is main parameter for speed planner. The physical model enables the algorithm to take into account the safety and comfort limitations. The results attained from the search method are compared with optimal solutions in different test scenarios and the comparisons show the properties of the algorithm.
On the Equivalence Between Abstract Dialectical Frameworks and Logic Programs

Abstract Abstract Dialectical Frameworks ( ADF s) are argumentation frameworks where each node is associated with an acceptance condition. This allows us to model different types of dependencies as supports and attacks. Previous studies provided a translation from Normal Logic Programs ( NLP s) to ADF s and proved the stable models semantics for a normal logic program has an equivalent semantics to that of the corresponding ADF . However, these studies failed in identifying a semantics for ADF s equivalent to a three-valued semantics (as partial stable models and well-founded models) for NLP s. In this work, we focus on a fragment of ADF s, called Attacking Dialectical Frameworks ( ADF + s), and provide a translation from NLP s to ADF + s robust enough to guarantee the equivalence between partial stable models, well-founded models, regular models, stable models semantics for NLP s and respectively complete models, grounded models, preferred models, stable models for ADF s. In addition, we define a new semantics for ADF + s, called L -stable, and show it is equivalent to the L -stable semantics for NLP s.
A Blockchain Consensus Mechanism for Educational Administration System

Applying the blockchain to the school's educational system can provide credible educational information and ensure the reliable recording and maintenance of student qulifications, grades and other information. The blockchain-based educational system has many nodes, and the fast and accurate blockchain consensus mechanism is the key to system implementation. To this end, this paper proposes an improved consensus mechanism of equity authorization proof for the educational system. The paper constructs the consensus framework of group-based educational system, refines the node state and gives the state transition mechanism. On this basis, the paper proposes a voting scheme based on credit reward and punishment to ensure the effectiveness of proxy node election and timly elimination of malicious proxy nodes. The simulation results show that the probability that the abnormal node becomes the proxy node again is reduced from 90% to 3%, and the number of voting rounds required for the abnormal node to be kicked out of the proxy node set is reduced from 3 rounds to 1 round, which can effectively guarantee the consensus efficiency and proxy node credibility, support the design and implementation of the educational system based on blockchain.
Active Documentation: Helping Developers Follow Design Decisions

Good documentation has long been argued to be key to helping developers write code more quickly and consistently with design decisions, but is left largely disconnected from code. We propose a method for active documentation, where design decisions are made explicit as design rules and checked against code. Developers can discover how to follow a design rule by navigating to examples in their codebase. After editing code, developers receive immediate feedback about which design rules are satisfied and which are violated, notifying developers who miss design decisions about the existence of these design decisions. We implemented our approach in a prototype tool and conducted a user study. Compared to developers using a traditional design document, developers working in an unfamiliar codebase with active documentation were faster and more successful, using active documentation to learn how to follow design decisions through examples and receive immediate feedback on their changes.
Fully Adaptive Radar for Target Classification

In this paper, the fully adaptive radar (FAR) framework is applied to the problem of target classification. The FAR framework mimics the perception-action cycle of cognition and adapts its sensing based on its perception of the environment, system performance goals, and sensor costs. Performance is demonstrated on simulated data and on experimentally collected data from a pulse-Doppler radar testbed in which micro-Doppler features are used to classify the motion of a moving target. In both examples, the FAR classification algorithm is able to achieve the system performance goals while conserving system resources in a dynamically changing environment.
Can Tweets Predict Election Results? Insights from Twitter Analytics

Social media has emerged as a powerful tool where people can share their common point of interest. Political events such as elections are one such event that attracts many social media users, to share their common view point and support towards a political party. This paper tries to capitalize this power of social media in order to predict the outcome of 2018 Pakistan General elections using Twitter as a tool. We fetched 33,468 tweets related to Pakistan Election over a span of 10 days. To get better insights of the results various social media analytic techniques were employed. Our results depict that Imran Khan led PTI was clear favorite among masses, which actually coincided with actual election results, where PTI emerged as a single largest party, making Imran Khan its Prime Minister.
Particle filter based track-before-detect method in the range-doppler domain

In this paper, we discuss the application of the particle filter for the track-before-detect to detect the target in a low SNR situation. We investigate how to find a target in the range-doppler domain without using adaptive threshold level by CFAR. To do this, we propose the modified particle filter for simple calculation of particle weights. We design the radar simulator that includes full radar signal processing for algorithm verification. In spite of a low SNR situation where it is difficult to detect a target, the proposed algorithm can estimate the optimal position of a target. As the process of the particle filter is repeated, the particles converge to the optimum position considered as the estimated position of a target.
Analysis of Probability Density Functions in Existing No-Reference Image Quality Assessment Algorithm for Contrast-Distorted Images

Amongst all distortion types, contrast change is very crucial for visual perception of image quality. Contrast distortion may be caused by poor lighting condition and poor quality image acquisition device. Contrast-distorted image (CDI) is defined as image with low dynamic range of brightness. Most of existing image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for CDI, e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and No Reference-Image Quality Assessment (NR-IQA) for Contrast-Distorted Images (NR-IQA-CDI). The five features used in NR-IQA-CDI are the global spatial statistics of an image including the mean, standard deviation, entropy, kurtosis and skewness. The statistical model or the Probability Density Function (PDF) for each of the given moment features were estimated using a public image database with large number of natural scene images. Because of poor performance in two out of three image databases, where the Pearson Correlation Coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, thus motivate us to further investigated to detect the gabs in existing NR-IQA-CDI. The paper can address the problem of existing NR-IQA-CDI which the bell-curve like probability density function (pdf) of the contrast related features like standard deviation and entropy does not correlate well with the monotonic relation between the contrast features and the perceived contrast level.
Network System Design using Hyperledger Fabric: Permissioned Blockchain Framework

The network system is a series of clients connected and other peripheral devices. Based on their architecture they can be broadly classified into the client-server or peer-to-peer network. Database system for these networks is further classified into centralized and distributed database systems. The networks that we used, currently depend upon centralized database system. Data integrity is high in this type of system. However, recovery of lost data is complicated, sometimes impossible due to no redundancy. These drawbacks of the centralized database system can be overcome by designing a network using blockchain technologies. Blockchain is a type of distributive database system. In this system, data is spread among all clients, and there is no central server controlling the flow of data. Use of smart contracts, proof of work and encryption among nodes make the flow of data more secure and fast. To make this type of system, we are using hyperledger fabric and composer for designing our network system. Hyperledger fabric is an open source blockchain framework which helps to develop a network system. We are defining a fictitious airline company for which we are creating a network API to handle their business to business (B2B) network system. The necessary foundation is to leverage the hyperledger fabric for the formation of a business network that would enable efficient B2B collaboration leading to a better experience.
Kiss from a Rogue: Evaluating Detectability of Pay-at-the-Pump Card Skimmers

Credit and debit cards enable financial transactions at unattended "pay-at-the-pump" gas station terminals across North America. Attackers discreetly open these pumps and install skimmers, which copy sensitive card data. While EMV (“chip-and-PIN”) has made substantial inroads in traditional retailers, such systems have virtually no deployment at pay-at-the-pump terminals due to dramatically higher costs and logistical/regulatory constraints, leaving consumers vulnerable in these contexts. In an effort to improve security, station owners have deployed security indicators such as low-cost tamper-evident seals, and technologists have developed skimmer detection apps for mobile phones. Not only do these solutions put the onus on consumers to notice and react to security concerns at the pump, but the efficacy of these solutions has not been measured. In this paper, we evaluate the indicators available to consumers to detect skimmers. We perform a comprehensive teardown of all known skimmer detection apps for iOS and Android devices, and then conduct a forensic analysis of real-world gas pump skimmer hardware recovered by multiple law enforcement agencies. Finally, we analyze anti-skimmer mechanisms deployed by pump owners/operators, and augment this investigation with an analysis of skimmer reports and accompanying security measures collected by the Florida Department of Agriculture and Consumer Services over four years, making this the most comprehensive long-term study of such devices. Our results show that common gas pump security indicators are not only ineffective at empowering consumers to detect tampering, but may be providing a false sense of security. Accordingly, stronger, reliable, inexpensive measures must be developed to protect consumers and merchants from fraud.
Radio Frequency Classification and Anomaly Detection using Convolutional Neural Networks

Radio frequency (RF) spectrum is a limited and critical resource for radar systems. As RF devices become smaller, cheaper and increasingly deployed, they present significant challenges with spectrum sharing and radar interference mitigation. This demands the need for reliable RF sensing and classification techniques for cognitive radars that can dynamically adapt to avoid interference. Our research investigates convolutional neural networks (CNN) trained on waveform images to classify RF spectrum modulations, and two techniques that uses the activations of the last hidden layer of the CNNs to detect anomalies. The CNNs used were AlexNet, GoogleNet, Inception V3, and ResNet50. Our first approach shows that CNNs and Principle Component Analysis (PCA) are highly accurate for classification and anomaly detection. Our second approach shows that appending N-nodes with randomly assigned weights and biases to the last hidden layer may give reasonable performance provided that the weights and biases are carefully selected.
Smart Shopping Prediction on Smart Shopping With Linear Regression Method

IoT-based shopping needs can provide convenience in shopping. But this experience can be improved by the ability to predict the needs of goods. Until now the solution to this problem is not available. This is the reason we offer a Smart Shopping System. This system will classify food ingredients based on the amount of food stock in which the amount of stock is the result of the estimated time series data. The system will forecast each food supply by studying patterns of food use in the past using Linear Regression techniques. The system is implemented using Raspberry Pi, webcams, and barcode image processing for stock counting at home and a smartphone for the application dashboard. From a limited period of research, forecasting performance results that show that linear regression provides good accuracy in predictions.
Object identification and tracking by quantum radar

Quantum Radar is a promising technology that could have a strong impact on the civilian and military realms. In this study we introduce a new concept design for implementing a Quantum Radar, based on the time and polarization correlations of the entangled photons for detection and identification and tracking of high-speed targets. The design is focused on extracting high resolution details of the target with precision timing of entangled photons that provides important operational capabilities like distinguishing a target from a decoy. The quantum entanglement properties guarantee the legitimacy of the photons captured by the search telescope. Time correlations of the photon detection events can be extracted via cross-correlation operation between two sets of photon detection time-tags for the entangled photons. The fact that the wavelengths of the entangled photons can be tuned also makes the Quantum Radar concept an enticing candidate for tracking stealth objects. We present the proof-of-principle test results of the Quantum Radar and discuss the technical challenges and limitations of the design.
Mapping hydrothermally altered minerals with AST_07XT, AST_05 and Hyperion datasets using a voting-based extreme learning machine algorithm

Detailed hydrothermally altered mineral mapping is important for mineral exploration. ASTER data are commonly combined with Hyperion data to classify the hydrothermally altered minerals. However, when machine learning algorithms are applied to the shortwave infrared (SWIR) bands of an ASTER reflectance image (AST_07XT), misclassification of Mg-OH group minerals is the major source of errors. In this study, an ASTER emissivity image (AST_05) and the AST_07XT SWIR bands are integrated to map minerals in the Duolong area, Tibetan Plateau. The results show that ASTER thermal infrared (TIR) bands can successfully identify Mg-OH group minerals. To improve the performance of classification, a novel voting-based extreme learning machine (V-ELM) algorithm is introduced to map hydrothermally altered minerals. The classification based on the ASTER SWIR-TIR data gets good identification of Mg-OH group minerals, which is better than those acquired using SWIR and TIR data alone. Moreover, these results also show that the AST_05 TIR bands cannot discriminate Al-OH group minerals. Compared with the classification applied to AST_07XT SWIR data, the classification applied to the ASTER SWIR-TIR data can achieve a higher overall accuracy (99.01%). The ASTER data results are spatially consistent with those of the Hyperion data. In accordance with the image processing results, a new deposit associated with felsic intrusions has been validated by field investigations. These schemes are promising for mineral mapping in the Tibetan Plateau.