Reviewerly/exHarmony huggingface dataset:
Explainable Robotic Plan Execution Monitoring Under Partial Observability

Successful plan generation for autonomous systems is necessary but not sufficient to guarantee reaching a goal state by an execution of a plan. Various discrepancies between an expected state and the observed state may occur during the plan execution (e.g., due to unexpected exogenous events, changes in the goals, or failure of robot parts) and these discrepancies may lead to plan failures. For that reason, autonomous systems should be equipped with execution monitoring algorithms so that they can autonomously recover from such discrepancies. We introduce a plan execution monitoring algorithm that operates under partial observability. This algorithm relies on novel formal methods for hybrid prediction, diagnosis and explanation generation, and planning. The prediction module generates an expected state after the execution of a part of the plan from an incomplete state to check for discrepancies. The diagnostic reasoning module generates meaningful hypotheses to explain failures of robot parts. Unlike the existing diagnosis methods, the previous hypotheses can be revised, based on new partial observations, increasing the accuracy of explanations as further information becomes available. The replanning module considers these explanations while computing a new plan that would avoid such failures. All these reasoning modules are hybrid in that they combine high-level logical reasoning with low-level feasibility checks based on probabilistic methods. We experimentally show that these hybrid formal reasoning modules improve the performance of plan execution monitoring.
The Research of An Improved Blockchain Consensus Mechanism

This study introduces a consensus model of DPoS mechanism as well as its workflow, interprets characteristics of DPoS mechanism and analyzes existing problems. In the selection stage of proxy nodes, intergroup isolation was realized by grouping of nodes and fairness of proxy nodes in the selection process was improved. Moreover, safety performances and error-tolerate rate of blockchain network were improved with a fish swarm algorithm.
Detection of Road Images Containing a Counterlight Using Multilevel Analysis

In this paper, a method for detecting real-time images that include counterlight produced by the sun, is proposed. It involves applying a multistep analysis of the size, location, and distribution of bright areas in the image. In general, images containing counterlight have a symmetrically high brightness value at a specific location spread over an extremely large region. In addition, the distribution and change in brightness in that specific region have a symmetrically large difference compared with other regions. Through a multistep analysis of these symmetrical features, it is determined whether counterlight is included in the image. The proposed method presents a processing time of approximately 0.7 s and a detection accuracy of 88%, suggesting that the approach can be applied to a safe driving support system for autonomous vehicles.
Cocoa: Towards a Scalable Compute Cost-aware Data Analytics System

Recently, many data analytics systems have focused on adopting a newly emerging compute resource, serverless, which offers scalability and agility to deal with peak workloads in a timely and cost-efficient manner, i.e., serverless data analytics (SDA). Unfortunately, these systems may encounter a cost bottleneck ($) because they have ignored the per unit time cost ($) of serverless, which is more expensive by up to 5.8 times for the same compute capacity than a traditional compute resource such as a virtual machine (VM). In addition, SDA may also encounter a performance bottleneck due to serverless' worse performance than VM. In this paper, we first study and report when serverless is beneficial for data analytics. Then, we present a scalable compute cost-aware data analytics system, Cocoa, that exploits serverless and VM together to achieve composite benefits. A Cocoa prototype was implemented on Spark. Evaluation results show a richer cost-performance tradeoff space opened by exploiting heterogeneous compute resources together, and identify substantial opportunities for future serverless-enabled systems research.
Securing Relational Databases against Security Vulnerabilities: A Case of Microsoft SQL Server and PostgreSQL

This study evaluates mechanisms to secure relational databases against security vulnerabilities and utilized PostgreSQL and Microsoft SQL Server due to data breach incidences reported across the world. Emulation experiments with documentary review were employed to collect necessary study data. Identification of security features and vulnerabilities that are found to affect the data tier of the web applications were examined. The findings from the study have shown that Microsoft SQL Server is more security feature-rich in terms of Confidentiality, Integrity, and Availability compared to PostgreSQL and Microsoft SQL Server is more resilient to security attacks in its default behavior compared to PostgreSQL.
A hybrid classification-regression approach for 3D hand pose estimation using graph convolutional networks

Hand pose estimation is a crucial part of a wide range of augmented reality and human-computer interaction applications. Predicting the 3D hand pose from a single RGB image is challenging due to occlusion and depth ambiguities. GCN-based (Graph Convolutional Networks) methods exploit the structural relationship similarity between graphs and hand joints to model kinematic dependencies between joints. These techniques use predefined or global learned joint relationships, which may fail to capture pose dependent constraints. To address this problem, we propose a two-stage GCN-based framework that learns per-pose (per-image) relationship constraints. Specifically, the first phase quantizes the 2D/3D space to classify the joints into 2D/3D blocks based on their locality. This spatial dependency information guides the regression branch to estimate reliable 2D and 3D poses. The second stage further improves the 3D estimation through a GCN-based module that uses an adaptative nearest neighbor algorithm to determine joint relationships. Extensive experiments show that our multi-stage GCN approach yields an efficient model that produces accurate 2D/3D hand poses and outperforms the state-of-the-art on two public datasets. • We introduce a two-stage graph-based approach that combines classification and regression to estimate an accurate 3D hand pose from a single RGB image. • Exploiting spatial dependencies between neighboring hand joints to learn per-image relationship constraints. • An adaptative nearest neighbor algorithm that learns a different number of joint relationships. • Outperforming state-of-the-art on 2D/3D realistic and synthetic datasets and speeding up the inference time.
FIAT

The average US household currently hosts more than 10 Internet of Things (IoT) devices [2]. Many research papers [5, 8] have demonstrated critical security concerns of the IoT, often due to lack of best practices like partial usage of HTTPS, or old ciphers. Even when best security practices are implemented, the IoT is still vulnerable to many attacks. Intruders can penetrate the home WiFi and directly control some IoT devices. They can compromise the account associated with an IoT device, mostly relying on username and password, or of third-party services like IFTTT [4]. They can also compromise the devices where IoT apps run, i.e., mostly mobile phones [1].
DALU: Adaptive Learning Rate Update in Distributed Deep Learning

In the last decade, deep learning has attracted much attention from both the academia and industry. As one of the important hyperparameters, learning rate greatly affects the speed and convergence of deep learning. In the existing approaches, the learning rate is commonly adjusted with the training process, however, does not consider the model accuracy after each training epoch. This problem is even more complex in distributed deep learning approach, which involves multiple machines for learning. In this study, we propose an adaptive learning rate updating strategy (DALU) for distributed deep learning. After completing each training epoch, the classification accuracy on training data set is recorded. Learning rate is adjusted according to the accuracy of current epoch and previous epochs. We also design several special machines having more aggressive learning rate update than regular machines in the distributed system. Compared with current commonly used learning rate adjusting approaches, exponential decay learning rate and cosine decay learning rate, DALU improves the accuracy of deep learning by 3.0% to 3.9%.
Enhancement patterns in contrast mammography – A pictorial essay

Contrast-enhanced mammography (CEM) is a new technology in breast imaging and cancer detection. It has been shown to have a comparable performance to breast MRI. Currently, there is no independent BI-RADS lexicon available for CEM. This pictorial essay will demonstrate the use of breast MRI descriptors according to the BI-RADS breast MRI lexicon, to describe enhancement patterns for recombined CEM images. The authors recommend using enhancement pattern descriptors already in use for breast MRI when reporting CEM studies, to promote uniformity of interpretation and reporting.
Anti-Raider ATM System Using Mobilenetv2

Cash vending machines are ubiquitous and although their technology vouches for its security, they are erratically stormed by the raiders. Albeit the escalating crime counts, the raiders are fleeing from the justice by virtue of evidence lacking. This research work proposes a computer vision based Anti-Raider ATM system. The proposed approach models the image, acquired from the CCTVs against the raider images based on the computer vision and deduces the fact from the MobileNetv2 architecture. Once the model identifies the raider, the image is uploaded to the Google Drive, which serves as evidence for the judicial department. The proposed research is modeled against several optimizers and the result concludes that, among them Adam optimizer has excelled in both computation time and accuracy.
A new enabling variational inference model for approximating measurement likelihood in filtering nonlinear system

Abstract This article considers the filtering problem with nonlinear measurements. We propose a new enabling variational inference model for approximating measurement likelihood, which is constructed by a linear Gaussian regression process. The resulting filter is referred to as the new enabling variational inference filter (NEVIF). In variational inference framework, the NEVIF obtains the variational posterior of state by minimizing the Kullback–Leibler divergence between the variational distribution and the true posterior. Then, the accuracy improvement and robustness of the NEVIF compared with the traditional methods are analyzed. Furthermore, an evaluation rule called the filtering evidence lower bound is developed to analyze the estimation accuracy performance of filters. Finally, the efficiency and superiority of the proposed filters compared with some existing filters are shown in numerical simulations.
Technique for Evaluating the Security of Relational Databases Based on the Enhanced Clements–Hoffman Model

Obtaining convincing evidence of database security, as the basic corporate resource, is extremely important. However, in order to verify the conclusions about the degree of security, it must be measured. To solve this challenge, the authors of the paper enhanced the Clements–Hoffman model, determined the integral security metric and, on this basis, developed a technique for evaluating the security of relational databases. The essence of improving the Clements–Hoffmann model is to expand it by including a set of object vulnerabilities. Vulnerability is considered as a separate objectively existing category. This makes it possible to evaluate both the likelihood of an unwanted incident and the database security as a whole more adequately. The technique for evaluating the main components of the security barriers and the database security as a whole, proposed by the authors, is based on the theory of fuzzy sets and risk. As an integral metric of database security, the reciprocal of the total residual risk is used, the constituent components of which are presented in the form of certain linguistic variables. In accordance with the developed technique, the authors presented the results of a quantitative evaluation of the effectiveness of the protection of databases built on the basis of the schema with the universal basis of relations and designed in accordance with the traditional technology of relational databases.
Guided Soft Actor Critic: A Guided Deep Reinforcement Learning Approach for Partially Observable Markov Decision Processes

Most real-world problems are essentially partially observable, and the environmental model is unknown.Therefore, there is a significant need for reinforcement learning approaches to solve them, where the agent perceives the state of the environment partially and noisily.Guided reinforcement learning methods solve this issue by providing additional state knowledge to reinforcement learning algorithms during the learning process, allowing them to solve a partially observable Markov decision process (POMDP) more effectively.However, these guided approaches are relatively rare in the literature, and most existing approaches are model-based, meaning that they require learning an appropriate model of the environment first.In this paper, we propose a novel model-free approach that combines the soft actor-critic method and supervised learning concept to solve real-world problems, formulating them as POMDPs.In experiments performed on OpenAI Gym, an open-source simulation platform, our guided soft actor-critic approach outperformed other baseline algorithms, gaining 7∼20% more maximum average return on five partially observable tasks constructed based on continuous control problems and simulated in MuJoCo.
CSES: Customized Searchable Encryption Scheme with Efficient Key Management Over Medical Cloud Data

To outsource medical data to the cloud, several schemes have been recently proposed to enable search over encrypted data to preserve data owners’ privacy. However, most of the existing schemes are either inefficient as they suffer from high computation/communication overheads or they are designed only to support single-data-owner and they unfortunately do not take in consideration the multi-data-owner setting of the e-health applications. In medical applications, a user, e.g., a doctor or a Primary Care Provider (PCP), should be able to search over data of many patients. Also, the existing searchable encryption schemes do not allow the user to customize his search scope to a subset of documents related to his focus based on some search conditions. So, the user receives unrelated documents to his search focus and spends too much computation overhead to decrypt them which makes the search process inefficient. In this paper, we propose a customized searchable encryption scheme with efficient key management over medical cloud data. Each data owner, e.g, a patient, sends to the cloud server an encrypted index with each document and it contains the keywords of this document. Each doctor sends to the cloud server an encrypted trapdoor contains the keyword of the search. In a nutshell, the cloud server can compute the similarity score of the encrypted indices and the encrypted trapdoor without learning the keywords of the document and the query, and then it returns to the doctors the documents of his interest. By using customized search, doctor can limit his search scope to a subset of documents related to his interest based on some search conditions. Our formal analysis of the privacy preservation and performance evaluation indicate that our scheme is secure, expressive, and efficient compared to the literature’s existing approaches.
Making despite Material Constraints with Augmented Reality-Mediated Prototyping

We present a discussion on designing an Augmented Reality (AR)-based prototyping approach to help makers continue building low-fidelity physical computing projects despite material constraints and demonstrate an example, Polymorphic Cube (PMC). Lack of immediate or easy access to electronics is a roadblock to building physical computing projects. We present AR-mediated prototyping as an approach where mobile AR can be used to simulate missing I/O components in-situ during electronics prototyping. Using our suggested approach makers can build a circuit with available real-world materials, substitute the missing components using any augmented physical proxy, and continue implementation tinkering and interaction with the hybrid circuit. Evaluation of PMC demonstrated that users can leverage computing to overcome the lack of electronic components and build low-fidelity prototypes to support design thinking. Our study revealed the benefits and limitations of our current prototype system and encourages future explorations into an AR-mediated prototyping approach to making.
An Improved Anchor-Free Detection Method for Traffic Sign Detection

As a basic task in the intelligent driving system, the traffic sign detection can locate and classify the traffic signs in real time and accurately. In response to the own design limitation of anchor-based detection methods, an improved anchor-free detection method is proposed. The method adds feature reinforcement module and head reinforcement module. First, in order to solve the small sign detection problem, a feature reinforcement module based on the hybrid attention mechanism is proposed. Secondly, in order to reduce the interference of complex background information to distinguish the prospects from the background, a head reinforcement module is proposed. We perform sufficient experiments on the CCTSDB dataset, and the experimental results demonstrate the effectiveness and robustness of the proposed method.
Protection of Modular Data Centers from Cyber Attack via Electromagnetic Emanations

Both physical and cybersecurity designs are vital for Data Centers. Applying an adequate physical security measure to prevent a possible cyber-attack is essential as part of this design. Electromagnetic field interference (EMI) induced by power equipment can produce detrimental effects, including security breaches through eavesdropping, which is a significant threat to confidentiality, integrity, and availability of information. This security design measure sometimes holds a lower hierarchy when planning a data center. This paper discusses the next-generation methodological approaches to protect data stored within the Modular Data Center against the EMI effect and discusses their pros and cons.
An anti-tampering model of sensitive data in link network based on blockchain technology

In order to overcome the problems of traditional link network sensitive data anti tampering operation, such as long time-consuming and low data security, a tamper proof model of link network sensitive data based on blockchain technology is proposed. Calculate the evenly distributed random variables of sensitive node data and the difference of running distance to obtain the probability of meeting the sensitive data with other neighbor nodes, and determine the sensitive data in the link network; obtain the frequency domain of the sensitive data of the infected link network through the square difference function, and calculate the membership mean value of the infected data samples in the sensitive data; analyze the working principle of blockchain technology, Set the master key and public key of sensitive data, generate the encryption key of sensitive data of link network, and use blockchain technology to complete the design of tamper proof model of sensitive data in link network. The experimental results show that the shortest time-consuming of the proposed method is about 1 s, and the maximum tamper proof security factor is about 9.7.
Implementation of Smart Control of Wheelchair for a Disabled Person

In this proposed method, a voice-controlled mechanized wheelchair with constant impediment shirking is structured and actualized. It engages a weakened individual to move around uninhibitedly, using a voice affirmation application, which is interfaced with motors. The model of the wheelchair is amassed using a littler scope controller, picked for its insignificant exertion, despite its versatility and execution in logical assignments and correspondence with other electronic contraptions. The framework has been planned and executed in a savvy way. In this chapter, there are three sensors that measure the value from patient's body. Then these measured values are analyzed and displayed on liquid crystal display (LCD). Respiration sensor monitors chest or abdomen movement during breathing. Heartbeat sensor senses patient heartbeat rate every minute. The LM35 series are precision-integrated circuit temperature sensors, with a yield voltage straightly proportional to the centigrade temperature. The LM35 has a favored situation over direct temperature sensors adjusted in ° Kelvin, thusly the customer is not required to remove a colossal consistent voltage from the respect procure invaluable Centigrade scaling. To give normal exactnesses of ±¼° C at room, LM35 does not require any external change or slicing.
Wavelet neural networks functional approximation and application

Approximation theory constitutes a useful field that is related to quasi all other fields, in both theoretical and applied sciences. In approximation theory, the aim is generally to construct an idea about a function that is usually impossible or difficult to evaluate directly, and which is usually unknown. Such functions appear widely in PDE, probability law distributions, statistical modeling, etc. Some of the most known approximators nowadays are neural networks and wavelets, which constitute good classes of elementary functions permitting as efficiently as possible to describe functions in appropriate spaces. This paper aims to develop combined neural networks and wavelet approximators for functions, based on the involvement of wavelets as activation functions. Some necessary conditions on the activation function to approximate [Formula: see text] and [Formula: see text]-elements are relaxed as well as those on the measure [Formula: see text]. We prove that for a wavelet activation function, any element of [Formula: see text] as well as [Formula: see text] can be well approximated for arbitrary measures [Formula: see text]. The theoretical results are subject to an experimental application in order to show their effectiveness.
Choice of the efficient flow diagram of biological wastewater treatment at municipal wastewater treatment plants

The possibility of increasing the efficiency of municipal wastewater treatment plant (WWTP) operation by changing the flow diagram of biological wastewater treatment in aeration tanks at minimum expenses for their reconstruction is shown in the paper on the example of one of the regional centres of Ukraine. The technology of nitri-denitrification of wastewater according to the flow diagram of the two-stage modified Ludzak-Ettinger process is offered for the considered conditions. The distribution of wastewater flows and internal nitrate recycling between the individual stages of this flow diagram has been optimized in order to minimize the residual content of total nitrogen in the treated effluents. Computer dynamic modelling of biochemical processes has proved the high efficiency and reliability of the flow diagram proposed by the authors.