Reviewerly/exHarmony huggingface dataset:
CHERI Macaroons: Efficient, host-based access control for cyber-physical systems

Cyber-Physical Systems (CPS) often rely on network boundary defence as a primary means of access control; therefore, the compromise of one device threatens the security of all devices within the boundary. Resource and real-time constraints, tight hardware/software coupling, and decades-long service lifetimes complicate efforts for more robust, host-based access control mechanisms. Distributed capability systems provide opportunities for restoring access control to resource-owning devices; however, such a protection model requires a capability-based architecture for CPS devices as well as task compartmentalisation to be effective.This paper demonstrates hardware enforcement of network bearer tokens using an efficient translation between CHERI (Capability Hardware Enhanced RISC Instructions) architectural capabilities and Macaroon network tokens. While this method appears to generalise to any network-based access control problem, we specifically consider CPS, as our method is well-suited for controlling resources in the physical domain. We demonstrate the method in a distributed robotics application and in a hierarchical industrial control application, and discuss our plans to evaluate and extend the method.
Privacy-Preserving Data Aggregation with Probabilistic Range Validation

Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption of these protocols less likely. We propose a privacy-preserving data aggregation protocol for calculating the sum of user inputs. Our protocol gives the aggregator confidence that all inputs are within a desired range. Instead of zero-knowledge proofs, our protocol relies on a probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Furthermore, our protocol is robust to user dropouts and, apart from the setup phase, it is non-interactive.
Integrating Machine-Learning-Based Operators in Visual Effects Toolsets

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Post-production workflows rely heavily on image processing and computer vision algorithms for the implementation of their visual effects (VFX) tools. In these fields, machine learning has been shown to be disruptive. By integrating the domain statistics from a given training data set, machine-learning-based operators may be more efficient at solving existing problems and may enable new problems to be solved, expanding the VFX toolset. In this study, we share our experience developing and integrating several machine-learning-based operators into software for the post-production industry. We will present a sky segmentation operator, a depth map estimation operator, and an operator to compute face geometric maps (UVs,<xref ref-type="fn" rid="fn1"><sup>*</sup></xref> depth, and normals) from sequences of images. More specifically, these operators consist of trained deep convolutional neural networks (DCNNs) taking as input an RGB color image and outputting the associated maps, that is, a matte, depth, normals, and/or UV maps. Such maps permit the application of many different effects to the input image during color grading, including beautification or even relighting of faces. These studies will serve as a case study to review and discuss the multiple challenges posed by the implementation, integration, and deployment of machine-learning-based operators into a VFX toolset</i> .
Stereoscopic Image Quality Assessment Based on Deep Convolutional Neural Models

The deep convolutional neural network (CNN) has achieved great success in image process areas in recent years. Many image quality assessment methods directly use CNN for quality prediction. Optimizing deep convolutional neural network with high generalization ability needs a huge amount of data, however, the most popular IQA databases are usually too small. Therefore, transfer learning and patch-wise strategy are developed to realize data enhancement. On the basis of alleviating the insufficient training data, some methods improve the CNN framework to better simulate HVS, and the implementation details are described in this chapter. Finally, some necessary related knowledges about CNN-based IQA methods are introduced.
Quantum Computational Electromagnetics

We investigate quantum information preserving computational electromagnetics. To do so, we propose two different methods which are (1) numerical canonical quantization and (2) quantum finite-difference time-domain scheme. The proposed algorithms find numerical solutions of quantum Maxwell's equations involved in arbitrary inhomogeneous dielectric media based on the macroscopic theory on quantum electrodynamics; hence, they are useful to study the quantum nature of electromagnetic fields in arbitrary passive and lossless quantum optical instruments. We provide a numerical example associated with two photon interference occurring in a quantum beam splitter, which is the underlying principle of recent quantum technologies.
Two Strong Simple Proofs That All Doppler Shifts (including the Axial) Impact Observed Time, and All the Transverse Does

The first proof (Sections 2 -4), applies Doppler shifts to the Fourier time sine series.It shows if K = f'/f (frequency ratio of the shift, f' the shifted frequency), then the value of the series at time = t occurs in the shifted series at time = t/K.That is because in each harmonic the sin(f't) and cos(f't) became sin(Kft) and cos(Kft).The original series could be the number of photons in area of a beam with encoded information.Therefore the number of observed photons and information has the same resultant Doppler shift as frequency.Resultant is total effect of axial, transverse and gravitational shifts.Mass and energy of light do not have the same Doppler shift which may indicate missing parts that Doppler shift and that mass traveling at the speed of light is different from other mass and energy.The second proof (Section 5) the vector equations of space time require observed time to have 3 dimensions if the speed of light is constant in all directions, but the Doppler shift in each direction is not the same.The blue shift (compression) of time has paradoxes.If time has many dimensions, that would solve the paradoxes, but break conservation laws.No solution of that is given here.It is not expected that radical a solution will have any followers.
Design of Quad Plane UAV with Carrier Gripper for First Aid of Victims from Aircraft Accident

Accidents can happen in many kind of public transportation to all people. This main of this kind of accident is evacuate process of transportation accident like plane and ship, meanwhile the location of accident can not reach, in case like aircraft accident at mountain forest and shinking of ship in the middle of the sea. Exploration in the accident area will be difficult with terrain to access it from rescue team. Therefore UAV is needed, UAV must have capability to hover at deep forest and can cruise in the middle of ocean, so the rescue team can easily find it. The author design a quad plane with an estimation of the plane's total weight is 4400 gram and have ability to hover using four vertical motor and cruise using one horizontal motor that placed in the behind of UAV. Whereas to make transition from hover mode to cruise mode it requires an airspeed sensor as the parameter to make transition so than the plane will not lose the lift force. From the results of flight tests, quad plane can make the transition from QHOVER mode to FBWA at 10 m/s, with an altitude at 34 meters, a flying distance of quad plane at 188 meters, and a maximum speed of quad plane is 25 m/s. The quad plane also able to release the payload in the form of first aid which is useful for providing first aid to survivors.
A Framework for e-Recruitment Recommender Systems

e-Recruitment Recommender Systems have been attracting attention over the last few years. It is an economically relevant field and can potentially revolutionize how organizations execute talent search and acquisition. This paper briefly discusses the e-Recruitment problem and presents a framework together with three recommendation models aiming to overcome the particular challenges presented in this field.
Research of Adaptive Multi-Swarm Dynamic Multi-Objective Particle Swarm Optimization Arithmetic in Power System

In order to realize the coordinated control of multi-objective system in dynamic environment, this paper proposes an adaptive multi-swarm dynamic multi-objective particle swarm optimization arithmetic based on sparse distance and ε domination method. The arithmetic adopts an improved ε domination strategy to update the external archive, adjust the ε value in real time, and ensure the fast distribution of particle swarm in the early stage and accurate search in the later stage. Global optimal solution is selected based on the sparse distance of external archive to ensure the distribution of non-inferior solution. Through the test of distribution and convergence, it is proved that the selection of learning samples based on sparse distance helps to keep the distribution of non-inferior solution and the improved ε control external file maintenance method not only improves the distribution, but also increases the external archive scale with the increase of iterations, so that the non-inferior solution can better cover the Pareto optimal frontier.
AgentDevLaw: A Middleware Architecture for Integrating Legal Ontologies and Multi-agent Systems

In social simulation, agents play different roles with different behaviors. Each has its goals and can cooperate or not with others and consume resources. Sometimes it is necessary to regulate these agents with mechanisms like norms that explain action limitations and how the society works for the agents. To explain how the world works for the agents, in different domains, ontologies have been used to provide human knowledge. In this sense, a more special kind of ontology is built with the legal information of a society: legal ontologies. These legal ontologies operate with concepts and data collected from human society and this can be provided to the agents. With this type of information, the agents can be assisted in monitoring and evaluating actions, comparing their beliefs with existing laws. Platforms into programming multi-agent systems provide an environment to develop the social simulations, but not always provided a connection with ontologies resources. This study proposes the AgentDevLaw middleware that integrates platforms of agents’ simulation with an existent legal ontology. The application of the software component is exemplified and tested with simulation scenarios in two agent’s environments: JaCaMo and JADE.
Dynamic Hybrid Secured Environment (DHSE) to Provide Confidentiality in Cloud Computing Environment

With help of internet technology today people can process, store and share data easily moreover, cloud computing model has provided various application services to users such as storage because cloud provide pay as you go environment. Storage helps data owner to share their data among users or employees of organization. But there is a need for data owners to provide flexible access control such that unauthorized users cannot have access to the data and confidentiality can be maintained. In addition, there is a need that only encrypted data is shared over the cloud. Encryption schemes have a major role in security domain of cloud computing model. In this article, DHSE scheme which is a hybrid technique and uses AES, ABE and IBE for key and data encryption is proposed. Furthermore, to validate and to do the security investigation of our plan we have verified our results using AVISPA tools.
Research on the Construction of Tourism English Autonomous Learning Resources under the Network Environment

The rapid development of modern electronic information technology has made rapid progress in wireless network communication technology. The construction of independent learning resources is an important part of tourism English learning under the network environment. The rapid development of mobile network technology has provided rich learning resources for tourism English teaching. At the same time, the rapid development of mobile network technology also provides students with a broad space for learning, which is of great significance to promoting students' autonomous learning and using fragmented time to study anytime and anywhere.
Introduction of Boolean Operation into Context Search Engine

This paper proposes to implement boolean operations on a query of Context search engine (CSE), and shows experimental results to present its advantages on information retrieval tasks. Although web search engines have shown lots of advantages for us to search on the internet, its basic usage is remaining in only entering a few keywords. Its simplicity enables many people to use it easily, however, combining keywords needs well-experienced ability, therefore, it cannot be applied to another usage such as searching trends. CSE has been established to deal with temporal data, especially, trends among contents. However, it does not support combined queries like that of using boolean operations. This paper proposes to introduce boolean operations into CSE, and shows its advantages through experiments conducted with help of participants of students.
Intelligent Management System: Deep Convolutional Neural Networks for Automatic Attribute Recognition in IP Surveillance Networks

In recent years, IP surveillance networks are expected to enable various practical applications, such as finding suspects, monitoring pedestrians, and securing societies (e.g., securing a city, a company and a data center). With these applications, IP surveillance network is regarded as one of the potential technologies toward developing smart cities. To support the concept of IP surveillance networks, automatic attribute recognition systems have emerged as a promising intelligent management system. To automatically recognize attributes of pedestrians (e.g., gender and clothing), we apply deep convolutional neural networks (CNNs), and the main contributions of this paper are threefold: (1) we proposed a practical system architecture for intelligent management of surveillance networks; (2) we implemented different deep CNNs, and an ensemble-learning method that leverages these multiple deep-learning models; (3) we evaluated the models using the real data of IP surveillance networks.
A Resolution for Scalability Problem of Record Datatype Based Formal Memory Models in Coq

The formal memory model is one of the most critical components for program verification tools development. Theoretically, the record datatype based formal memory model is a type of intuitive and efficient formalization scheme. However, practical applications of record datatype based formal memory models in Coq are limited because of their scalability problem. Present work addresses this issue by defining a new multi-level table framework with modular polymorphism in Coq. First, we present polymorphic meta specifications of formal memory model and corresponding operations. Second, through the combination of polymorphic instantiation and recursive nesting of modules, we simulate the linear and tail recursion principles. With these principles, a new record type based abstract specifications of formal memory model with multi-level table structure and corresponding memory operations are constructed. Finally, we define a logic-physical memory conversion ratio to quantify the optimization of scalability, and we present a series experiments based on the ratio to certify the improvement of scalability of the optimized framework in Coq compared with conventional definitions.
What Rankers Can be Statistically Distinguished in Multileaved Comparisons?

This paper presents findings from an empirical study of multileaved comparisons, an efficient online evaluation methodology, in a commercial Web service. The most important difference from the previous studies is the number of rankers involved in the online evaluation: we compared 30 rankers for around 90 days by multileaved comparisons. A relatively large number of rankers answered several questions that could not be addressed in the previous work due to a small number of rankers: How much ranking difference is required for rankers to be statistically distinguished? How many impressions are necessary for finding statistically significant differences for correlated rankers? How large difference in offline evaluation can predict significant differences in a multileaved comparison? We answer these questions with the results of the multileaved comparisons, and generalized some of the findings by simulation-based experiments.
Automatic Playtesting for Yahtzee

Yahtzee is a dice game with elements of skill and chance. There are many numeric parameters that govern the game's scoring rules, and varying those parameters will affect many aspects of game play, including strategic depth. We take advantage of the ease of computing the optimal policy for solitaire Yahtzee to use supervised learning to develop near-optimal agents based on neural networks. With the aim of automatically selecting scoring parameters that increase strategic depth, we measure the agent's skill as we vary the resources available to the neural networks and use metrics derived from the resulting learning curve as an indicator of strategic depth.
Hyper UP-algebras

In this paper, the concept of hyper UP-algebras is introduced and some related properties are investigated.Also, the concepts of hyper UP-ideals have been introduced and analyzed. In addition, the concept of homomorphisms between hyper UP-algebras is also considered.
Applying <scp>BIBFRAME</scp> in large‐scale digital libraries: The <scp>H</scp>athi<scp>T</scp>rust <scp>R</scp>esearch <scp>C</scp>enter's experience

Abstract In 2018 the HathiTrust Research Center built an experimental triplestore of BIBFRAME RDF from the HathiTrust Digital Library's MARC metadata catalog. This poster describes processes used to convert from MARC to BIBFRAME, to enrich descriptions with links, and to visualize worksets using RDF created.
A robust traffic scene recognition algorithm based on deep learning and Markov localization

This paper designs a traffic scene recognition module for the agent’s perception system. First, we enabled the output features of the convolutional neural network to be the descriptor of the traffic scene and adapted to the cost function of the image sequence to construct the observation module of the agent. Second, we assumed that the movement of the agent would be recursively updated and wouldn’t jump dramatically, which simultaneously possesses the Markov property, so the Markov localization algorithm was used to improve overall robustness. Third, the Kalman filter method was adopted to represent the probability distribution of the entire system using the first and second moments of the Gaussian distribution, so that the loop iteration in the state estimation can be transformed into a linear operation, and the penalty term in the standard variance of the observation probability can also be added to describe the reliability of the observation. Experimental results show that the agent can efficiently remove unreliable observations and achieve robust recognition accuracy of the traffic scene in all weather conditions.
Similarity–Difference Analysis and Matrix Fault Diagnosis of SoC-components

Universal metric of data search in cyberspace is proposed; it is based on the use of similarity - difference parameters and matrix structure in binary form. Method for analyzing matrix data structures using the similarity-difference metric for fault detection in digital systems is described. The difference diagnostic method is characterized by the execution of three logical operations on the binary states of the matrix vector-rows and is focused on single and multiple faults in digital systems and software applications. The qubit-difference method for fault detection is characterized by the use of vector parallel logical operations to form a diagnosis of a multivalued technical state, according to the principle of "divide and unite, eliminating contradictions." The advantage of the qubit-vector representation of data in matrix cells is shown, which makes it possible to perform logical operations in parallel in three automatic cycles in order to obtain the required result. Method for detecting single and multiple faults in digital software and hardware systems is described, focused on the hardware implementation of parallel logical register operations, which provide a significant increase in performance compared to existing analogues. Method and hardware implementation of a sequencer for determining similarity-difference-inclusion is proposed, which is characterized by obtaining a more accurate structured assessment of the interaction of two objects.
A Quantum Approach Using Stochastic Simulation and Schrodinger with Multi-Layer Learning for Finding Features from Dataset

Quantum computing relies on the quantity of the mechanical phenomenon, such as interference and overlap. It aims to solve issues which are not realistically possible on computers. The research work introduces the new quantum-based model from a provided dataset for forecasting the infection. This technique is beneficial in describing the association among different statistical models. Our study has resulted in highest precision than ever applied technique, which was differentiated and calculated from the defined dataset and results. Such suggested strategies were evaluated and reviewed against multiple state-of-the-art methods to demonstrate efficacy. The qualitative and graphical results are provided for the verification of the current approach. The suggested model is more robust than existing mathematical models due to the findings.
Sole Arch Width Extraction Using Greedy Algorithm

In footwear production, the size of sole should be checked to match the corresponding upper before combining them. At present, manual recognition method and traditional machine vision method is not able to recognize unlabelled sole. In this paper, we propose a novel method to extract the feature parameter of unlabelled sole to recognize its size. The feature parameter is the arch width of the sole which has a quality with shape invariance. An innovative greedy algorithm was proposed to get the arch width. The algorithm adopts successive approximation method to extracted the shortest width of sole. Experimental results show that the arch width extraction method proposed in this paper can be effectively used to recognize the size of sole.
Analysis of the Development Trend of Water Supply in Chinese Civil Catering Building

This article focuses on the characteristics of the water supply design of civil catering buildings, and analyzes and studies from the aspects of water supply safety and water supply development. It is pointed out that the quality of water supply is the premise of solving the water supply safety. At the same time, through the analysis of the measured data, the development law of catering buildings is found : With the continuous improvement of water-saving equipment and technology, the unit water consumption has been decreasing; with the development of the national economy, the aviation business volume continues to increase, and the total water volume increases with the increase of production. The above two aspects should be fully considered in the design of the water supply system. At the same time of meeting the needs of energy conservation and development, a set of water supply system meeting the expected target year is designed, which is also a set of water-saving water supply system.