Reviewerly/exHarmony huggingface dataset:
A formal methods-based Rule Verification Framework for end-user programming in campus Building Automation Systems

Building Automation Systems have recently exposed programming interfaces for occupants to dynamically control and personalize indoor workplace environments. To this end, the If-This-Then-That paradigm has been proposed as a user-friendly programming approach. However, conflicts, both within the occupant comfort rules and between the rules in open environment and the overall system policy, often result in reduced energy efficiency and other undesired outcomes. In this paper, we propose a software framework based on formal methods to detect and resolve rule conflicts called Rule Verification Framework. The proposed framework builds upon state-of-the-art Satisfiability Modulo Theories and utilizes a suite of algorithms to ensure system correctness and safety. The proposed framework was experimentally evaluated using a prototype implementation developed using Java and the Web Services technology. The proposed framework detected more conflicts and was faster than the previous solutions. Conflict detection algorithms had to work iteratively because conflict resolution resulted in new conflicts.
Design of stabilized polynomial-based ensemble fuzzy neural networks based on heterogeneous neurons and synergy of multiple techniques

In this study, a novel category of polynomial-based ensemble fuzzy neural networks (PEFNNs) are proposed. The study is focused on the development of advanced design methodologies to improve the performance (prediction accuracy) of the model when dealing with nonlinear regression problems. In contrast to the conventional fuzzy polynomial-based models, we adopt a hybrid network structure composed of heterogeneous neurons. The first layer of PEFNNs consists of fuzzy regular polynomial neurons optimized by clustering method. In the consecutive layers, we engage two types of polynomial neurons, which are selected and optimized by evolutionary algorithms. Moreover, an enhanced topology based on fuzzy module and enhanced interconnection (FM&EI) is designed to strengthen the characteristics of fuzzy feature information as well as increase the number and diversity of neurons. Multiple techniques are used synergistically to reinforce the performance of PEFNNs. First, a coefficient-based performance compromise algorithm (CPC) is designed to select neurons by considering the performance and complexity of the neuron. Second, L2-norm regularization is considered to improve the performance of the model. Third, evolutionary algorithm is employed to adjust the structural parameters of PEFNNs. Furthermore, FM&EI and hybrid network structure which consist of heterogeneous neurons are considered as one of the multiple approaches to construct the ensemble model. The performance and stability of PEFNNs are evaluated with a diversity of datasets. A thorough comparative analysis also is covered.
Extreme Algorithm Selection with Dyadic Feature Representation

Algorithm selection (AS) deals with selecting an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem, e.g., choosing solvers for SAT problems. Benchmark suites for AS usually comprise candidate sets consisting of at most tens of algorithms, whereas in combined algorithm selection and hyperparameter optimization problems the number of candidates becomes intractable, impeding to learn effective meta-models and thus requiring costly online performance evaluations. Therefore, here we propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms, facilitating meta learning. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation in which both problem instances and algorithms are described. We find the latter to improve significantly over the current state of the art in various metrics.
Blockchain Based E-Healthcare Record System

Electronic Healthcare Record (EHR) is an electronic variant of patient’s health history, which is sustained by the system over a period, and can incorporate the entirety of the key authoritative clinical information which is significant to the users care under a particular framework supplier, contains socioeconomics, progress notes, hitches, medications, essential signs, past clinical history, vaccinations, and radiology reports. The use of an EHR is significant to make progress, just as to ensure an elevated level of sheltered and compelling degree of patient consideration framework. Patient records are fully monitored by hospitals rather than patients, which becomes complicated to seek clinical advice from various medical clinics. Patients face stringent need to concentrate on the subtleties of their human services and reestablish the executives of their clinical information This advanced system will account for interactions with this medical data in an auditable, transparent and secure way on EHR’s distributed ledger. By using a decentralized patient-centered approach users will be able to leverage their medical data. This system will use to improve care for individuals by setting the patient at the focal point of the advanced change of healthcare. The rapid advancement of blockchain innovation with smart contracts and IPFS storage improves the existing medicinal services, including clinical records just as patient-associated evidence. This advanced model provides high flexibility and EHRs availability for healthcare services.
High-density single shot 3D sensing using adaptable speckle projection system with varying preprocessing

The speckle pattern is highly prevalent in stereo-photogrammetry systems. The speckles generated by the laser and the diffuser are influenced by the subjective noise and change the appearance with the distance. In addition to this, the density of the 3D point cloud is a major stumbling block when only one pattern for reconstruction is allowed. In this work, we propose a system to counter the aforementioned problems. When the distance of the object is changed, the proposed system adapts itself to change the speckle size back to the required optimal size for the algorithm. The speckle size is varied by changing the position of the diffuser using a motorized linear stage. We propose a preprocessing method to reduce the subjective speckle-noise based on histogram equalization and edge preserving contrast enhancement. The amount of preprocessing varies with the distance of the object for the optimization of the 3D reconstructions. Experiments are performed to realize the effect of new preprocessing and variable preprocessing methods. The results indicate that the density of the 3D point is better than the Kinect and previous single shot method and comparable to the multiple shot professional 3D scanner.
Time optimal control of two-level quantum systems

The objective of time-optimal control that helps to minimize relaxation losses, is the evolution of a quantum state from a given initial mixed state to a final target mixed state in minimum time. In this paper, we study a time-optimal control problem of the dynamic of a pure two-level system with unbounded control using Pontryagin's minimum principle and obtain the minimal time for some initial and final states. The results will apply to basically all qubit systems that one can consider such as NMR spectroscopy, trapped ions, superconducting qubits, etc. We also show that these results hold for pure states, and only the direction nˆ is important in the evolution of a quantum state. In this work, the problem of computing minimum time to produce any unitary transformation Uf∈SU(2) is reduced to finding the minimum time to steer the system from an initial to a final state.
Taking Artificial Intelligence Beyond the Turing Test

Patentability under the 35 U.S.C. § 103 obviousness standard relies onthe ability of a person having ordinary skill in the art; however, theinvolvement of artificial intelligence in the inventive process challenges thatstandard. A misconception of the current capabilities of AI leads to argumentsof universal obviousness where an independent and creative AI dominates theinventive process. Fortunately, AI of such a caliber does not yet exist. Thus,the difficulty of defining the inventive entity threatens patent protection’sincentive to innovate but need not extinguish the right to patentabilityoutright. Redefining the standard of obviousness and distinguishing the user’scontributions enable the patentability of inventions resulting from the use ofAI as an innovative tool.The proposed standard for the obviousness inquiry—a person havingordinary skill in AI—accounts for the inventor’s objectives, access to big anddeep data, and knowledge of the existing datasets to control the form andoperation of the machine learning resulting in AI-assisted inventions.Preexisting common law for patenting chemical compounds and the evolutionof the obviousness test permit shifting the standard to overcome the statutoryhurdles facing the patentability of AI-assisted inventions. However, shiftingthe level of ordinary skill in the art to the user does not permit the patentabilityof independent and autonomous inventions by inventive AI systems.The standard proposed here turns on the effort exerted by the inventorin comparison to the AI system. The inventor’s control and design of theinventive process help to resolve the level of ordinary skill in the art for AI-assisted inventions by looking at the user’s starting point, motivation basedon the prior art and data, reasonable expectation of success, and control overthe inventive process. Thus, the user’s control defines the level of ordinaryskill in the art and enables the trier of fact to refer to its established inquiriesin determining obviousness.
Semantic Integration of Sensor Knowledge on Artificial Internet of Things

Artificial Internet of Things (AIoT) integrates Artificial Intelligence (AI) with the Internet of Things (IoT) to create the sensor network that can communicate and process data. To implement the communications and co-operations among intelligent systems on AIoT, it is necessary to annotate sensor data with the semantic meanings to overcome heterogeneity problem among different sensors, which requires the utilization of sensor ontology. Sensor ontology formally models the knowledge on AIoT by defining the concepts, the properties describing a concept, and the relationships between two concepts. Due to human’s subjectivity, a concept in different sensor ontologies could be defined with different terminologies and contexts, yielding the ontology heterogeneity problem. Thus, before using these ontologies, it is necessary to integrate their knowledge by finding the correspondences between their concepts, i.e., the so-called ontology matching. In this work, a novel sensor ontology matching framework is proposed, which aggregates three kinds of Concept Similarity Measures (CSMs) and an alignment extraction approach to determine the sensor ontology alignment. To ensure the quality of the alignments, we further propose a compact Particle Swarm Optimization algorithm (cPSO) to optimize the aggregating weights for the CSMs and a threshold for filtering the alignment. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s conference track and two pairs of real sensor ontologies to test cPSO’s performance. The experimental results show that the quality of the alignments obtained by cPSO statistically outperforms other state-of-the-art sensor ontology matching techniques.
Signal-domain Kalman filtering: An approach for maneuvering target surveillance with wideband radar

As unmanned aerial vehicles (UAVs), owing to their low cost and wide variety of applications, become increasingly indispensable to modern society, and monitoring their illegal use through multichannel radar tracking has drawn widespread attention. Unfortunately, traditional tracking methods face challenges due to the UAV target with high-maneuverability and low-observability. In addition, the traditional measurements suffer some significant information loss because of their treatments like thresholding, clustering and peak sampling, which decreases tracking performance. In order to track UAV precisely, we propose a novel UAV tracking method based on the joint estimation of range and direction of arrival (DOA) in this paper. A complex-valued reference signal is introduced by coherently integrating in sliding windows to obtain SNR gain and preserve the complete structure and motion information of UAV. Besides motion state, the complex-valued reference signal is also utilized to predict the current return signal based on dynamic equation, and then a Bayes based method is derived to jointly estimate the range and DOA errors by comparing reference signal and return signal. In order to adapt to the maneuverable feature, a precise measurement model for motion variables is constructed to realize tracking combined with Kalman filter. Due to the utilization of the complex-valued reference signal and the precise measurement model, the proposed method has outstanding performance in the scene of low SNR and high maneuverability. In addition, there is no any information loss when the raw data is used to realize estimating and tracking. Finally, simulated and real-measured experiments confirm its remarkable performance.
3D Point Cloud Feature Explanations Using Gradient-Based Methods

Explainability is an important factor to drive user trust in the use of neural networks for tasks with material impact. However, most of the work done in this area focuses on image analysis and does not take into account 3D data. We extend the saliency methods that have been shown to work on image data to deal with 3D data. We analyse the features in point clouds and voxel spaces and show that edges and corners in 3D data are deemed as important features while planar surfaces are deemed less important. The approach is model-agnostic and can provide useful information about learnt features. Driven by the insight that 3D data is inherently sparse, we visualise the features learnt by a voxel-based classification network and show that these features are also sparse and can be pruned relatively easily, leading to more efficient neural networks. Our results show that the Voxception-ResNet model can be pruned down to 5% of its parameters with negligible loss in accuracy.
Visual Perception Framework for an Intelligent Mobile Robot

Visual perception is a fundamental capability necessary for intelligent mobile robots to interact properly and safely with the humans in the real-world. Recently, the world has seen revolutionary advances in deep learning has led to some incredible breakthroughs in vision technology. However, research integrating diverse visual perception methods into robotic systems is still in its infancy and lacks validation in real-world scenarios. In this paper, we present a visual perception framework for an intelligent mobile robot. Based on the robot operating system middleware, our framework integrates a broad set of advanced algorithms capable of recognising people, objects and human poses, as well as describing observed scenes. In several challenge scenarios of international robotics competitions using two mobile service robots, the performance and acceptability of the proposed framework are evaluated.
Video-assisted smart health monitoring for affliction determination based on fog analytics

Satisfying the expectations of quality living is essential for smart healthcare. Therefore, the determination of health afflictions in real-time has been considered as one of the most necessary parts of medical or assistive-care domain. In this article, a novel fog analytic-assisted deep learning-enabled physical stance-based irregularity recognition framework is presented to enhance personal living satisfaction of an individual. To increase the utility of the proposed framework for assistive-care, an attempt has been made to record predicted activity scores on cloud by following the continuous time series policy to provide future health references to authorized medical specialist. Furthermore, a smart two-phased decision generation mechanism is proposed to intimate medical specialist and caretakers about the current physical status of an individual in real-time. The generation of the alert is directly proportional to the predicted physical irregularity and the scale of health severity. The experimental results highlight the advantages of fog analytics that helps to increase the recognition rate up to 46.45% for 40 FPS and 45.72% for 30 FPS against cloud-based monitoring solutions. The calculated outcomes justify the superiority of the proposed fog analytics monitoring solution over the conventional cloud-based monitoring solutions by achieving high activity prediction accuracy and less latency rate in decision making.
BLOCKCHAIN FORKS: A FORMAL CLASSIFICATION FRAMEWORK AND PERSISTENCY ANALYSIS

Blockchain forks can have severe economic implications, sow uncertainty and undermine trust. In this paper, we introduce a formal framework to study the emergence, persistency and economic consequences of blockchain forks. We argue that blockchain forks can be process- or protocol-based and emerge unintentionally or deliberately. We then proceed with a sub-classification of protocol-based forks and study the circumstances under which a chain split may become permanent. It can be shown that the persistency of a fork depends on the nature of the change to the consensus rules and on the relative allocation of the consensus-relevant resources. Lastly, we discuss business implications as well as potential consequences for policy makers and practitioners.
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning in Fading Channels

To enable communication-efficient federated learning, fast model aggregation can be designed using over-the-air computation (AirComp). In order to implement a reliable and high-performance AirComp over fading channels, power control at edge devices is crucial. Existing works assume that the signal to be aggregated from each device is identically distributed, and normalized with zero mean and unit variance. This assumption, however, does not hold for gradient aggregation in machine learning because gradient statistics are unknown for each device. In this paper, we study the optimal power control problem for efficient over-the-air FL by taking gradient statistics into account. Our goal is to minimize the model aggregation error measured by mean square error (MSE) by jointly optimizing the transmit power of each device and the denoising factor at the edge server. We first derive the optimal solution in closed form where the gradient first-order and second-order statistics are known. We then propose a method to estimate the gradient statistics based on the historical aggregated gradients and then dynamically adjust the transmit power on devices over each training iteration. Experiment results show that our proposed power control is better than existing full power transmission and threshold-based power control in both model accuracy and convergence rate.
Organic Fanatic: A Quiz-Based Mobile Application Game to Support Learning the Structure and Reactivity of Organic Compounds

Organic Fanatic is a free-of-charge quiz-based mobile application game that encourages students to test their abilities and enhance their learning of organic structure and reactivity through a fun, arcade-style interface. Students can attempt a total of 270 multiple choice questions, choosing from a possible 1080 associated answers that can be selected from 9 different functional group categories at varying difficulties. The game can be played in single player mode as part of independent learning or in multiplayer mode, facilitating student interaction. Organic Fanatic was provided to first-year undergraduate students studying chemistry, biochemistry, or natural sciences degree programs as a resource following all organic chemistry teaching but ahead of the examination period. Students found Organic Fanatic to be an innovative and enjoyable resource where they could learn organic chemistry through play and via the convenience of using their smart phones. Such an interactive resource has shown to be a useful component to facilitate a blended learning environment in organic chemistry instruction.
IoT Healthcare Monitoring Systems Overview for Elderly Population

The aging population and the growing requirement of healthcare services in clinics encourage the arrival of remote elderly patient monitoring. Development in the physiologic sensory systems and the advancement of stable wireless network low energy mechanics have allowed remote health monitoring systems to be developed. Smart homes, which have wearable sensors and medical actuators, as well as up-to-date communication and information technology, have the ability to monitor remotely the elderly health and well-being in a simple fashion with low cost. Therefore, the elderly will stay in their comfortable homes rather than going to hospitals where sometimes they face expensive and limited facilities. In a real-time, medical staff can also keep track of the overall health conditions of the elderly people and provide proper feedback and support at a distance. In this paper, we introduce a summary of elderly people monitoring applications using IoT frameworks. In addition, the paper discusses the patient data security and integrity.
Improving the detection of robot anomalies by handling data irregularities

The ever-increasing complexity of robots causes failures of them as a side effect. Successful detection of anomalies in robotic systems is a key issue in order to improve their maintenance and consequently reducing economic costs and downtime. Going one step further in the detection of anomalies in robots, different mechanisms to deal with data irregularities are proposed and validated in present paper in order to increase detection rates. More precisely, strategies to overcome missing values and class imbalance are considered as complementary tools to get better one-class classification results. The effect of such strategies is evaluated through cross-validation when applying a standard supervised learning model, the Support Vector Machine. Experiments are run on an up-to-date and public dataset that contains some examples of different software anomalies that the middleware of the robot under analysis may experience.
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks

This paper discusses the prediction of hierarchical time series, where each upper-level time series is calculated by summing appropriate lower-level time series. Forecasts for such hierarchical time series should be coherent, meaning that the forecast for an upper-level time series equals the sum of forecasts for corresponding lower-level time series. Previous methods for making coherent forecasts consist of two phases: first computing base (incoherent) forecasts and then reconciling those forecasts based on their inherent hierarchical structure. With the aim of improving time series predictions, we propose a structured regularization method for completing both phases simultaneously. The proposed method is based on a prediction model for bottom-level time series and uses a structured regularization term to incorporate upper-level forecasts into the prediction model. We also develop a backpropagation algorithm specialized for application of our method to artificial neural networks for time series prediction. Experimental results using synthetic and real-world datasets demonstrate the superiority of our method in terms of prediction accuracy and computational efficiency.