Reviewerly/exHarmony huggingface dataset:
Bond Dissociation Energy and Pattern Spectrum for Shape Classification

The paper represents an effective, robust and shape-preserving, pattern spectrum based on sigma-pi model. A new idea behind the translation, rotation and scale invariant features are introduced called bond-dissociation energy of sigma and pi bond in a sigma-pi model. In this model, we map the structure of pixels in a shape to the structure of atoms in a crystalline solid-state substance. The model compute the energy required to break the atom from a lattice. The sigma-pi model corresponding to the bond-dissociation energy pattern spectrum provides excellent power, which is demonstrated by excellent retrieval performance on several popular shapes benchmarks, including MPEG-7 CE-Shape-l data set, Myth dataset, Tool dataset and Kimia’s data set. Experimental results obtained from popular databases demonstrate that the proposed model can achieve comparably better results than existing algorithms.
How does (C)CAPM digest anomalies?

In a pioneering effort, we re-evaluate the performance of (C)CAPM (joint CAPM and consumption CAPM) in digesting a large number of anomalies. Our key contribution illustrates that the performance of (C)CAPM appears to be quite sensitive to the choice of weighting matrix. OLS cross-sectional regression reveals the poor performance of (C)CAPM. In contrast, the CAPM model actually explains a large portion of anomalies quite well when using an efficient weighting matrix (GLS), indicating that the prior expectation that the CAPM exhibits poor empirical performance should fundamentally be reversed.
Pre-Service Language Teachers' Development of Augmented Reality Applications

This qualitative study explored pre-service English language teachers' attitudes, subjective norms, and perceived behavioural control beliefs regarding their intentions to use augmented reality (AR) technology for their future language classes. The data were collected through semi-structured interviews (n=15) and retrospective reflection papers (n=55) from two groups of pre-service English language teachers in a state and foundation university in Istanbul, Turkey. In development of the interview questions and analysis of the data, the authors utilized decomposed theory of planned behavior as their research framework. Findings revealed that participants intend to use AR in their future classes due to its perceived usefulness, student influence, and perceived ease of use. Additionally, they also reported their views on affordances and drawbacks of augmented reality technology for language learning and teaching purposes. Furthermore, the findings portrayed that new technologies such as AR can be adopted by pre-service teachers once they are given an opportunity to experience.
TranScent in Stillness: Exploring the Feasibility of Using Incense Art with Virtual Reality for Meditation

The need of supporting meditation through digital technology has been increased especially after COVID-19. By combining the olfactory experience provided by the ambient incense connected with virtual reality technology, we propose to bring the aesthetic and affective aspects of smell to the users for meditation in the digital era. TranScent aims to provide users a hybrid composition of sensory experiences that transcends the spatial and temporal characteristics in their surroundings. It lets the users meditate with the incense burnt in the real world while immersing in the audiovisual virtual environment. Rather than emphasizing on the mobility in fast pace, it focuses on giving users the stillness atmosphere for meditation practice through olfactory art with virtual reality.
A Comprehensive Review on Blockchain-Based Internet of Things (BIoT)

The internet of things (IoT) represents rapid development in research and industry that enables both virtual and physical objects to be linked and transfers information in order to produce various services that enhance our excellence of life. Traditional security and privacy methods are not applicable for IoT, mostly due to their topological constraints and versatility of IoT devices. Blockchain technology has started to fascinate younger generations because it works especially well in the digital world. Blockchain is suitable for internet of things applications. Advancements in IoT have propelled distributed systems. The blockchain concept demands a method for exchanging and storing data that is managed by a decentralized network. The rise of IoT applications is hindered by these obstacles. One option to fix these problems is to use a distributed ledger technology using blockchain technology. This chapter gives a comprehensive overview of blockchain's strengths and weaknesses with its applications.
Pro-CCaps: Progressively Teaching Colourisation to Capsules

Automatic image colourisation studies how to colourise greyscale images. Existing approaches exploit convolutional layers that extract image-level features learning the colourisation on the entire image, but miss entities-level ones due to pooling strategies. We believe that entity-level features are of paramount importance to deal with the intrinsic multimodality of the problem (i.e., the same object can have different colours, and the same colour can have different properties). Models based on capsule layers aim to identify entity-level features in the image from different points of view, but they do not keep track of global features.Our network architecture integrates entity-level features into the image-level features to generate a plausible image colourisation. We observed that results obtained with direct integration of such two representations are largely dominated by the image-level features, thus resulting in unsaturated colours for the entities. To limit such an issue, we propose a gradual growth of the reconstruction phase of the model while training. By advantaging of prior knowledge from each growing step, we obtain a stable collaboration between image-level and entity-level features that ultimately generates stable and vibrant colourisations. Experimental results on three benchmark datasets, and a user study, demonstrate that our approach has competitive performance with respect to the state-of-the-art and provides more consistent colourisation.
Transformation of Asset Management Systems Through Blockchain

The concept of blockchain is prevalent globally in today's times. It has shown remarkable growth and has shown a lot of achievement by executing systems of peer-to-peer cryptocurrency. The cryptocurrency was introduced in 2009 but created hype about digital currency around the world in 2017. Blockchain works on the concept of a “distributed ledger/database.” The transactions are recorded and replicated to all the participating parties chronologically. Blockchain has verified to be immutable and provides accountability, integrity, and quite a lot of confidentiality through a pair of private and public keys. Various sectors have started using blockchain due to its salient features. Asset management is also one of the areas where blockchain can reduce transaction costs, approval waiting time, and increase transparency. The complicated processes of asset management can be automated by unifying permissioned and permissionless blockchain. This chapter discusses how asset management firms can use blockchain opportunities to harness its benefits.
An efficient chaotic system based hybrid radiation heat transfer sunflower optimization algorithm for securing digital image transmission

Abstract At the pixel level, several chaos‐based image encryption and decryption models have been developed throughout the years. However, due to long‐haul, privacy, and security considerations, it has a number of drawbacks, including increased network complexity and low scalability, and actual text cracking avoidance is more challenging. In this study, we have proposed a novel approach for image encryption and decryption. The confusion and diffusion process is performed using fractional‐order chaotic maps with the hybrid radiation heat transfer algorithm based sunflower optimization (HRHT‐SO) algorithm. The proposed method works for both color and grayscale images. The HRHT‐SO algorithm is the combination of both heat transfer search (HTS) and sunflower optimization (SO) algorithms. During the search process, the SO algorithm improves the radiation stage of the HTS model. Further, the DNA sequence improves the performance of image encryption and decryption using fractional‐order chaotic maps with the HRHT‐SO algorithm. The experimental investigations are carried out by using various parameters such as key sensitivity analysis, keyspace analysis, histogram analysis, correlation coefficient analysis, entropy analysis, PSNR, NPCR, and UACI analysis thereby analyzing the efficiency and complexity of the encryption scheme. Hence, the proposed method outperforms superior performances in all the experiments.
Learning hidden Markov models with unknown number of states

This paper proposes a novel method for learning hidden Markov models (HMMs) with an unknown number of states based on a valuable feature set. The feature set is built using the hitting times of HMMs. Based on the feature set, we obtain a parameter estimation for HMMs by SVD and the clustering algorithm. The advantages of the proposed method are that it can accurately and automatically identify the number of hidden states, it is robust to misspecified emission distributions, it is less sensitive to initialization, and is asymptotically consistent. Numerical experiments show that the proposed method performs better than other methods when the observed time series is long enough.
On elementary logics for quantitative dependencies

We define and study logics in the framework of probabilistic team semantics and over metafinite structures. Our work is paralleled by the recent development of novel axiomatizable and tractable logics in team semantics that are closed under the Boolean negation. Our logics employ new probabilistic atoms that resemble so-called extended atoms from the team semantics literature. We also define counterparts of our logics over metafinite structures and show that all of our logics can be translated into functional fixed point logic implying a polynomial time upper bound for data complexity with respect to BSS-computations.
Detection and Recognition of Traffic Lights in Complex Environment in Unmanned System

With the development of the social economy and the progress of science and technology, cars have been gradually popularized in life, traffic congestion, frequent accidents and other phenomena are increasing, traffic safety and road traffic efficiency and other problems has become more and more serious, and become the focus of attention. In this context, the safety of driverless smart cars has become one of the key technologies and difficulties. The research of traffic signal detection and recognition algorithm based on computer vision has become an important research field of intelligent traffic, including two modules: signal light detection and recognition. Although there are many detection and recognition algorithms at home and abroad, due to the variability of the color and environmental light of the signal light itself, the complex environment, and the real-time requirements of the traffic intersection, the existing algorithms will have some limitations in their application. They can't handle the above problems well. In this paper, the widely used circular and arrowhead traffic lights are detected and identified.
Multi‐party smart contract for an AI services ecosystem: An application to smart construction

Abstract Various smart applications, such as in the domain of smart construction, require the use of artificial intelligence (AI) based services. In order to support such environments, various AI/knowledge service provider and consumer ecosystems have started to emerge. Within such ecosystems, the goals are to improve the quality, reliability, dependability in terms of governance and trust in the data which is exchanged among the various actors, which must be supported by specific business models. This work introduces a novel multi‐party smart contract (SC) that is designed to address the above mentioned goals. Specific service level agreements that illustrate the interactions among the AI service providers and consumers are also presented. The developed multi‐party SC supports different pricing schemes, which are analyzed in detail against the goals of the study.
Pizza and Poop: Using Playful Probes to Investigate Community in Semi-Public Restrooms on a University Campus

This exploratory paper aims to discuss how community is fostered in semi-public restrooms on a college campus. While previous research has been undertaken in similar semi-private environments, this paper differs by simultaneously offering the researchers’ reflective insights in tandem with participants’ input on the research question. We begin by unpacking the challenges around Participatory Design (PD) activities that are undertaken in sensitive and private interior environments. Gathering perceptions of these sensitive spaces required methods that allowed for both anonymity and a communal approach through the use of provocative and evocative probes such as comment boxes and graffiti wall posters. This paper not only catalogues the findings of this research but also documents the difficulties in utilizing a participant-led approach, gaining access to sites and participants, and countering our own biases throughout the study’s construction. Through researcher accounts and participatory data analysis, the researchers offer a focused reflection on a possible new frontier for advancing PD methods in sensitive environments through playful probes. The contribution of this paper offers six lessons on the efficacy of using probes in semi-private environments, with playfulness as a primary driver of engaging participants.
Research on Multi-Objective Optimization Method of Edge Cloud Computing Virtual Machine Placement

Abstract With the emergence of demand for massive computing tasks in the edge cloud of the city, the disordered computing in the edge cloud leads to the high energy consumption of CPU computing and the problem of too long time delay caused by the blockage of computing tasks. This has become the first technical difficulty to solve in the construction of edge cloud. The virtual machine placement method is optimized by computing energy consumption and computing delay. First, the LRR physical host screening model is constructed to detect the status of the physical hosts and form a list of migrated physical hosts; secondly, the MMT time scale model is constructed to generate the list of migrated virtual machines; finally, the GA algorithm is used to place the virtual machines. The simulation results show that this algorithm can reduce the CPU energy consumption of the edge cloud center by 20. 21% and the time delay by 16. 11%. This optimization method has a good theoretical guidance effect on the construction of cloud computing.
Leveraging execution traces to enhance traceability links recovery in BPMN models

Traceability Links Recovery has been a topic of interest for many years, resulting in techniques that perform traceability based on the linguistic clues of the software artifacts under study. However, BPMN models tend to present an overall lack of linguistic clues when compared to code-based artifacts or code generation models. Hence, TLR becomes a harder task when performed among requirements and BPMN models.
Avatar: Reinforcing Fault Attack Countermeasures in EDA with Fault Transformations

Cryptography hardware are highly vulnerable to a class of side-channel attacks known as Differential Fault Analysis (DFA). These attacks exploit fault induced errors to compromise secret keys from ciphers within a few seconds. A bias in the error probabilities strengthens the attack considerably. It abets in bypassing countermeasures and is also the basis of powerful attack variants like the Differential Fault Intensity Analysis (DFIA) and Statistical Ineffective Fault Analysis (SIFA). In this paper, we make two significant contributions. First, we identify the correlation between fault induced errors and gatelevel parameters like the threshold voltage, gate size, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${V_{\text{DD}}}$</tex> . We show how these parameters can influence the bias in the error probabilities. Then, we propose an algorithm, called Avatar, that carefully tunes gate-level parameters to strengthen the redundancy countermeasures against DFA, DFIA, and SIFA attacks with no additional logic needed. The central idea of Avatar is to reconfigure gates in the redundant circuits so that each circuit has a unique behavior to faults, making fault detection much more efficient. In AES for instance, fault attack resistance improves by 40% for DFA and DFIA, and 99% in the case of SIFA. Avatar incurs negligible area overheads and can be quickly adopted in any cipher design. It can be incorporated in commercial EDA flows and provides users with tunable knobs to trade-off performance and power consumption, for fault attack security.
Manifold Guided Graph Neural Networks for Skeleton-based Action Recognition in Human Computer Interaction Videos

As the key application in video analysis for human computer interaction (HCI), the problem of skeleton-based action recognition has been solved by some researchers with graph neural networks, but it remains an unsolved issue on complex variations of spatiotemporal dependence across skeleton joints flow. A newly dynamic spatio-temporal graph structure learning method, manifold guided graph neural networks (MGNN), was proposed to solve this problem. In MGNN, a novel manifold guided graph updating mechanism is built based on the baseline graph neural network to further describe the spatio-temporal dependence. With the manifold guided multi-scale skeleton graph, the proposed MGNN is further trained with two streams of joint and bone to improve the efficiency, which forms a single network seamlessly and enables it be trained in a same umbrella. Comparing with the existing methods, MGNN has been proved that it yields better performance on challenging datasets: NTU RGB+D 60 and Kinetics 400.
Attention-based 3D convolutional networks

Being simple and portable, the three-dimensional (3D) convolution network has achieved great success in action recognition. However, its applicability in spatiotemporal feature learning is not evident. This study aims to improve the 3D convolution model and propose a flexible and significant attention module for the extraction of spatiotemporal information. Our first contribution is a self-additive attention module and a feature-based attention module, which is a simple yet effective method for measuring the spatiotemporal importance of a video. In self-additive attention, the spatiotemporal fusion between the frames is defined intuitively, where we set equivalent weights between the video frames manually. Further, the feature-based attention that is trained adaptively by the 3D convolution process combines the spatiotemporal information from the feature map. This study also focuses on attention fusion in learning the spatiotemporal characteristics for 3D convolution. The proposed attention fusion method exhibits outstanding performance in comparison to the recently developed attention modules and the latest 3D networks when applied to the data from the UCF101 and HMDB51 datasets. The experiments show consistent improvements, affirming the robustness of the method in extracting spatiotemporal attention.
Control method of stepping motor for spaceborne solar irradiance spectrometer

The encoder is not included in the hardware design of the Spaceborne Solar Irradiance Spectrometer, so in this paper, a high precision control method of stepping motor is proposed, which can position accurately without encoder. When the wavelength scanning mechanism is controlled, the validity of the hall signal is judged by controlling the forward and reverse rotation of the motor and the fixed steps of the motor. When the hall signal is valid, the operation of returning to the starting position is performed normally. The motor drive is subdivided into 16 substeps, and each substep is only 0.1125°. The control method of turntable mechanism is to quickly return to the starting position, after overshoot, one-step reverse rotation with appropriate delay to move out of hall effective area, and then identify the starting position. The experimental results show that the method can meet the requirements of wavelength repeatability less than 0.01 nm, wavelength calibration accuracy less than 0.05 nm, and calibration accuracy stability less than 0.2%. At the same time, the volume and weight of the system are reduced, and the miniaturization of the system is realized.
Cyber Information Retrieval Through Pragmatics Understanding and Visualization

The amount of cybersecurity-related information is extraordinarily increasing, given the fast-growing number of cybersecurity attacks and the significant influence brought by them. How to efficiently obtain and precisely understand the relevant knowledge in the sea of information on cybersecurity becomes a challenge. In this article, we propose an innovative cybersecurity retrieval scheme that supports automatic indexing and searching of cybersecurity information based on semantic contents and hidden metadata. The proposed scheme leverages a customized neural model that incorporates new linguistic features and word embedding by identifying the entities related to cybersecurity incidents from the text. We implement a novel cybersecurity search engine to demonstrate effective, understandable and pragmatic cybersecurity information retrieval based on the proposed schema. Comprehensive performance evaluation over real-world datasets has been conducted to validate the new algorithms and techniques developed for cybersecurity information retrieval. The new engine makes it possible to conduct augmented search, cybersecurity analytics, and visualization, with the ultimate goal of providing direct and efficient results to help people obtain and truly understand cybersecurity information.