Reviewerly/exHarmony huggingface dataset:
Accurate phase retrieval algorithm based on linear correlation in self-calibration phase-shifting interferometry with blind phase shifts

The self-calibration algorithm is a powerful phase map reconstruction technique for phase-shifting interferometry with random phase shifts. The existing phase reconstruction algorithms has some restrictions on preconditions including the uniformity of background and the well distributed phase shifts. In this paper, a simple, applicable and accurate phase retrieval algorithm based on linear correlation (LCA) with non-iterative characteristic is proposed to improve this issue. Without pre-filtering, the algorithm searches for optimal linear combination coefficients of the difference intensity map by maximizing the linear correlation, and then phase shifts can be solved by these searched coefficients. Finally, according to the obtained phase shifts, the phase map is readily retrieved by using the matrix form of the linear equations. The whole phase retrieval process has no complicated mathematical transformation, hence it is simply and easily implemented. Good performance of the proposed LCA are verified by reconstructed phase maps from multiple kinds of simulation and experiment interferograms with blind phase shifts, along with the comparisons to that of the popular algorithms. Especially, the proposed algorithm has an excellent accuracy of phase reconstruction when the phase shift distributes unevenly.
Processes of changes in the educational environment under the influence of digital technologies

The transformation processes taking place in the field of education under the influence of digital technologies pose the problem of studying its new adaptive properties. The key direction of engineering of the educational process is the formation and maintaining a developing educational environment, which is characterized by dynamism, flexibility and resilience. Structural changes reflect shifts in communicative environments and information fields. The article analyzes the data of a sociological survey of Peter the Great SPbPU students, revealing a positive attitude of students to the process of digitalization. The problem of identifying the accompanying contradictory moments of transformation of the educational space is noted. As a baseline vulnerability, based on surveys, there was a lack of student readiness to increase the share of independent learning and to change the role of the teacher. The conclusion is made in favor of the development of nonlinear educational models and trends that implement a flexible dynamic approach to the educational environment in response to the action of factors - rising students' expectations and high market demands.
A parameter adaptive DE algorithm on real-parameter optimization

Differential Evolution (DE) algorithm generates a population of individuals by encoding with a floating point vector, and it is a simple and effective population-based stochastic optimization algorithm for global optimization of continuous space. Because of its excellent performance, DE variants c an be applied in a wide range of applications in science and engineering. However, the performance of DE is sensitive to the choice of trial vector generation strategy and the associated control parameters. Therefore, it is necessary to choose appropriate mutation strategy and control parameters when tackling optimization applications. In this paper, an adaptive update mechanism is proposed to update control parameters F and Cr. The experimental results are verified on the CEC 2013 test suite which contains 28 benchmark functions for the evaluation of single objective real parameter optimization. The proposed algorithm is compared with jDE, iwPSO and ccPSO, and experiment results show its good performance.
Is buttercup a kind of cup? Hyponymy and semantic transparency in compound words

Hyponymy is a semantic relation of class inclusion (e.g., a cat is an animal; a wildcat is a cat). Compound words are often, but not always, in a hyponymic relationship with the second constituent of the compound, as in wildcat and cat. This paper introduces a set of human ratings of hyponymy for over 2500 English compound words, which will facilitate research on the role of hyponymy in compound word processing and related areas. In the compound word processing literature, the semantic transparency of the compound (i.e., the extent to which the meaning of the constituents is maintained in the compound) is a critical theoretical construct. We investigate the role that hyponymy plays in the semantic transparency of compound words, as well as in the processing of compound words. We find that hyponymy is a critical component of the semantic transparency of both the second constituent (e.g., cat) and the compound as a whole (e.g., wildcat) and somewhat surprisingly also of the first constituent (e.g., wild). Hyponymy also affects the speed of processing of compound words in both lexical decision and naming.
Performance Effectiveness of Multimedia Information Search Using the Epsilon-Greedy Algorithm

In the search and retrieval of multimedia objects, it is impractical to either manually or automatically extract the contents for indexing since most of the multimedia contents are not machine extractable, while manual extraction tends to be highly laborious and time-consuming. However, by systematically capturing and analyzing the feedback patterns of human users, vital information concerning the multimedia contents can be harvested for effective indexing and subsequent search. By learning from the human judgment and mental evaluation of users, effective search indices can be gradually developed and built up, and subsequently be exploited to find the most relevant multimedia objects. To avoid hovering around a local maximum, we apply the -greedy method to systematically explore the search space. Through such methodic exploration, we show that the proposed approach is able to guarantee that the most relevant objects can always be discovered, even though initially it may have been overlooked or not regarded as relevant. The search behavior of the present approach is quantitatively analyzed, and closed-form expressions are obtained for the performance of two variants of the -greedy algorithm, namely EGSE-A and EGSE-B. Simulations and experiments on real data set have been performed which show good agreement with the theoretical findings. The present method is able to leverage exploration in an effective way to significantly raise the performance of multimedia information search, and enables the certain discovery of relevant objects which may be otherwise undiscoverable.
UIPA: User authentication method based on user interface preferences for account recovery process

Because of the global increment of various computer implementation, the usage of the internet has increased significantly. This phenomenon has mainly contributed to the growing usage of the World Wide Web, which is considered as one of the inventions in computer technology that changed the modes of human communication and exchange of information. As new work tools, an increasing number of people are using online systems. Accordingly, user authentication has become a significant issue in the online environment. The traditional password is still the most common means to verify user identity. However, strong passwords are sometimes difficult to remember, and thus a recovery procedure is needed. It can be said that the majority of online systems apply different methods of recovery, such as using challenging questions besides the out-of-band communication (i.e., email or SMS). Nevertheless, these approaches are still suffering from security and usability problems. This study therefore proposes a novel authentication method based on user interface (UI) preferences, called the User Interface Preferences Authentication (UIPA). This method can be applied with online systems that offer UI designs since it provides users with the chance to choose a preferred design of the interface on the basis of their personal characteristics. Hence, the user identity is then verified based on that interface. The performance, security, and usability of UIPA have been evaluated. The results of the experiments show that UIPA has a false positive rate of 0.416% and a false negative rate of 0%. Moreover, a user acceptance questionnaire, which has been designed based on the technology acceptance model, shows that users are pleased and willing to accept the proposed technique. To conclude, UIPA can be applied as an efficient account recovery method compared with the currently used methods. It could also help in addressing the security–usability trade-off problem.
Comprehensive Learning Particle Swarm Optimisation with Limited Local Search for UAV Path Planning

Unmanned Aerial Vehicles (UAVs) have been widely used due to their autonomy, maneuverability, low cost, and accessibility to areas inaccessible by humans or ground vehicles. Path planning is an essential part for UAVs. In this paper, we propose a new Comprehensive Learning Particle Swarm Optimisation with Limited Local Search (CLPSOLLS) algorithm to optimise UAV path planning problems. The algorithm improves accuracy with an order of magnitude less computational costs in difficult scenarios with limited degrees of freedom. The performance of the proposed CLPSOLLS is compared with the state of art variants of PSO on five scenarios of increasing complexity. The superiority of this algorithm is particularly apparent in difficult scenarios.
PUFchain: Hardware-Assisted Scalable Blockchain

This is an extended abstract for the research demo of a novel hardware-assisted scalable blockchain called PUFChain. This work presents a scalable energy-efficient private/permissioned blockchain (integrated with Physical Unclonable Functions or PUFs) which can be deployed in the IoT. PUFs have a multiple of roles in the blockchain: higher security, lower latency, and reduced energy consumption. Experimental validations of PUFChain show a transaction time of 198ms. To the best of authors knowledge this is the first ever work that presents a comprehensive framework integrating PUFs in a blockchain.
VR‐Rides: An object‐oriented application framework for immersive virtual reality exergames

SUMMARY Exercise can improve health and well‐being. With this in mind, immersive virtual reality (VR) games are being developed to promote physical activity, and are generally evaluated through user studies. However, building such applications is time consuming and expensive. This paper introduces VR‐Rides, an object‐oriented application framework focused on the development of experiment‐oriented VR exergames. Following the modular programming pattern, this framework facilitates the integration of different hardware (such as VR devices, sensors, and physical activity devices) within immersive VR experiences that overlay game narratives on Google Street View panoramas. Combining software engineering and interaction patterns, modules of VR‐Rides can be easily added and managed in the Unity game engine. We evaluate the code efficiency and development effort across our VR exergames developed using VR‐Rides. The reliability, maintainability, and usability of our framework are also demonstrated via code metrics analysis and user studies. The results show that investing in a systematic approach to reusing code and design can be a worthwhile effort for researchers beyond software engineering.
Social Media Copyright Management using Semantic Web and Blockchain

Solutions based on distributed ledgers require sophisticated tools for data modelling and integration that can be overcome using semantic and Linked Data technologies. One example is copyright management, where we attempt to adapt the Copyright Ontology so it can be used to build applications that benefit from both worlds, rich information modelling and reasoning together with immutable and accountable information storage that provides trust and confidence on the modelled rights statements. This approach has been applied in the context of an application for the management of social media re-use for journalistic purposes.
On the Hamming Distances of Constacyclic Codes of Length 5<i>p<sup>S</sup> </i>

Let p be a prime, s, m be positive integers, and λ be a nonzero element of the finite field F p m .In this paper, the algebraic structures of constacyclic codes of length 5 p s (p = 5) are obtained, which provide all self-dual, self-orthogonal and dual containing codes.Moreover, the exact values of the Hamming distances of all such codes are completely determined.Among other results, we obtain the degrees of the generator polynomials of all MDS repeated-root constacyclic codes of arbitrary length.As applications, several new and optimal codes are provided.
Efficient Key Generation Techniques for Securing IoT Communication Protocols

Internet of things (IoT) is getting bigger as communication infrastructure and device capabilities are getting improved day-by-day, creating a huge market for IoT devices for commercial as well as personal usage. This paper sheds light on the compromises done by vendors on the grounds of security, resources and flexibility to lure customers with low prices, smaller and fancier designs of IoT device and its consequences. Most of the IoT devices are resource-constrained and hence standardization bodies such as IEEE and IETF gave many protocols for IoT devices which are defined over IoT protocol stack. The degree of security in each layer of IoT protocol stack is discussed and it is found that NFC protocol which operates on physical-data link layer and 6LoWPAN, a network layer protocol, lack proper key management while encrypting the data. A most important part of key management in cryptographic algorithms is key generation and storage (if needed). Different types and methods of key generation techniques are also discussed which might give a solution over a variety of situations combining the level of security requirement and resources available in strengthening the abovementioned communication protocols.
A verified proof checker for higher-order logic

We present a computer program for checking proofs in higher-order logic (HOL) that is verified to accept only valid proofs. The proof checker is defined as functions in HOL and synthesized to CakeML code, and uses the Candle theorem prover kernel to check logical inferences. The checker reads proofs in the OpenTheory article format, which means proofs produced by various HOL proof assistants are supported. The proof checker is implemented and verified using the HOL4 theorem prover, and comes with a proof of soundness.
Local bit plane adjacent neighborhood dissimilarity pattern for medical CT image retrieval

In this article, new feature descriptor local bit plane adjacent neighborhood dissimilarity pattern (LBPANDP) is introduced for CT image retrieval. In the proposed method, the input image is first decomposed into eight binary bit planes. Then in each of the first four most significant bit planes, for each center pixel, the dissimilarity information of its binary neighbors are encoded into a single value. The encoded values of each bit plane is then compared with the intensity of the center pixel and this relationship is finally encoded to devise the LBPANDP descriptor. The proposed descriptor is highly discriminative and considers only the first four most significant bit planes for encoding, which greatly reduces the dimension of the feature vector. Retrieval performance of LBPANDP is investigated on two CT image databases and the results show a significant improvement in retrieval efficiency by LBPANDPover many recent techniques.
Web User Authentication Using Chosen Word Keystroke Dynamics

Keystroke dynamics has been used as a form of one-time user authentication and continuous verification especially when it comes to securing the cyberspace. In this paper, we present the idea of using keystroke dynamics as a form of second layer authentication in web applications. We showed that this method can authenticate a user with high accuracy and can be used as an alternate to CAPTCHA tests, security questions and image selections that are being used today. We have developed a working web-based platform in a browser environment that enforces the proposed second-layer security. We performed penetration test experiments by launching a total of 598,500 impostor and genuine authentication attempts and found the Equal Error Rate (EER) as 10.5%.
Time Series Anomaly Detection from a Markov Chain Perspective

This paper introduces a new method for the pattern-wise anomaly detection problem, which aims to find segments whose behaviors are different from the rest of the segments in the time series (as opposed to finding a single data-point in classic anomaly detection problems). An important motivation for studying this problem is to find anomalies whose data-points are within the normal range but they create an unusual pattern. To this end, normal characteristics of the data are found by clustering the overlapping subsequences of the training dataset and analyzing their orders by Markov chains. The trained model is used to assess how well the testing dataset suits the baseline behavior. The designed anomaly detection framework is capable of discovering unusual patterns in both streaming data (online) and stored data (offline). The performance of the methodology is evaluated by applying it to three datasets from different fields: a medical dataset (electrocardiogram), a utility usage dataset, and a New York City taxi demand dataset. The detected anomaly in the medical data agrees with the results of the studies in the literature. A domain expert confirmed the accuracy of the results for the utility usage data, and the anomalies of the New York City taxi demand data referred to major US holidays.
Annete: An Intelligent Tutoring Companion Embedded into the Eclipse IDE

With Computer Science (CS) class sizes that are often large, it is challenging to provide effective personalized feedback to students. Intelligent Tutoring Companions can provide such feedback and improve CS students' experience. This work describes the construction of a Tutoring Companion, Annete, designed to support students in a university Java programming course by providing them with intelligent feedback generated by a neural network. Annete is embedded into the Eclipse Integrated Development Environment (IDE), which is an environment that is already familiar to students in programming courses. Embedding Annete into Eclipse improves her effectiveness, as the students do not need to learn how to use an additional tool. While the student works in Eclipse, Annete collects 21 pieces of data from the student's code, including whether certain key words are used, error messages from the compiler, and cyclomatic complexity. When a run attempt, debug attempt, or a request for help occurs in Eclipse, Annete uses the data available to infer a feedback message to show to the student. Our approach is evaluated among 28 CS students completing a programming assignment while Annete assists them. Results suggest that students feel supported while working with Annete and show potential for using neural network modeling with embedded tutoring companions in the future. Challenges are discussed, as well as opportunities for future work.
Edge-Guided Depth Image Super-Resolution Based on KSVD

This paper proposes an edge-guided super-resolution algorithm for single frame depth images based on K singular value decomposition (KSVD).Compared with conventional algorithms, the proposed algorithm has two key contributions.Firstly it suppresses the jagged edge effect of up-sampled depth images by KSVD, which learns a complete dictionary to describe the mapping between the jagged edges and corresponding smooth ones.Secondly it improves the joint bilateral filter based on connectivity.The improved filter can not only preserve the sharpness of the edges during the interpolation, but also suppress noise.The proposed algorithm has been extensively tested on the Middlebury dataset and compared with some existing state-of-the-art methods.Both quantitative and qualitative experimental results show its performance superiority.
Conversational AI for Corporate e-Learning

Natural language tutors have been an active research topic for decades and the widespread use of chat interfaces lead to a high level of acceptance of chatbots. Despite that, conversational AI has not found its way into the practice of corporate e-learning yet. In this paper we present a novel approach to leverage the advances in the field of conversational AI for corporate e-learning. Following a design science approach, we identify the pivotal stakeholders and design objectives. We propose a service architecture and demonstrate its feasibility with a prototypical implementation. Finally, we conclude that the proposed approach has the potential to lower entry barriers for conversational AI for the practice of corporate e-learning.
Recovering facial reflectance and geometry from multi-view images

While the problem of estimating shapes and diffuse reflectances of human faces from images has been extensively studied, there is relatively less work done on recovering the specular albedo. This paper presents a lightweight solution for inferring photorealistic facial reflectance and geometry. Our system processes video streams from two views of a subject, and outputs two reflectance maps for diffuse and specular albedos, as well as a vector map of surface normals. A model-based optimization approach is used, consisting of the three stages of multi-view face model fitting, facial reflectance inference and facial geometry refinement. Our approach is based on a novel formulation built upon the 3D morphable model (3DMM) for representing 3D textured faces in conjunction with the Blinn-Phong reflection model. It has the advantage of requiring only a simple setup with two video streams, and is able to exploit the interaction between the diffuse and specular reflections across multiple views as well as time frames. As a result, the method is able to reliably recover high-fidelity facial reflectance and geometry, which facilitates various applications such as generating photorealistic facial images under new viewpoints or illumination conditions.