Reviewerly/exHarmony huggingface dataset:
An Efficient and Secure Image Encryption Algorithm Based on Non- Adjacent Coupled Maps

In this paper, an image encryption algorithm based on non-adjacent coupled map lattices is designed.Compared with coupled map lattices, non-adjacent coupled map lattices have few periodic windows in bifurcation, and larger parameter range in chaos dynamics.Combined with the proposed chaotic system, a new alternative encryption structure between permutation, diffusion and substitution is proposed to improve the security and encryption efficiency of the algorithm.In the diffusion process, multiplication operation in GF (257) and addition operation in GF (2 8 ) based on look-up table are adopted, which improves the encryption efficiency and enhances the permutation effect by choosing pixel value in Latin Square during the diffusion process.Moreover, based on the good cryptographic characteristics of the s-box, a dynamic sbox related to plaintext is constructed to substitute plaintext pixels.Experimental results and security analysis show that the proposed encryption scheme can resist all types of typical attacks and has good encryption effect.
Image-Level Structure Recognition Using Image Features, Templates, and Ensemble of Classifiers

Image-level structural recognition is an important problem for many applications of computer vision such as autonomous vehicle control, scene understanding, and 3D TV. A novel method, using image features extracted by exploiting predefined templates, each associated with individual classifier, is proposed. The template that reflects the symmetric structure consisting of a number of components represents a stage—a rough structure of an image geometry. The following image features are used: a histogram of oriented gradient (HOG) features showing the overall object shape, colors representing scene information, the parameters of the Weibull distribution features, reflecting relations between image statistics and scene structure, and local binary pattern (LBP) and entropy (E) values representing texture and scene depth information. Each of the individual classifiers learns a discriminative model and their outcomes are fused together using sum rule for recognizing the global structure of an image. The proposed method achieves an 86.25% recognition accuracy on the stage dataset and a 92.58% recognition rate on the 15-scene dataset, both of which are significantly higher than the other state-of-the-art methods.
Unsupervised quaternion model for blind colour image quality assessment

Without the limitations of labelled distorted images for training, the unsupervised blind colour image quality assessment (UBCIQA) model shows excellent generalization performance. However, existing UBCIQA methods fail to consider the correlation between different colour channels. This paper proposes an efficient UQBCIA algorithm. Placing R, G, and B channels of a colour image into the three imaginary parts of a quaternion, the proposed UQBCIA obtains the quaternion representation of a colour image, which allows UQBCIA to process the colour image as a whole while keeping its three colour channels dependent. The naturalness, structural and texture statistic features are then extracted to fit a multivariate Gaussian (MVG) model. The quality of the test colour image is calculated as the MVG distance between the fitted pristine images and the test image. The proposed method is evaluated from three aspects: (1) prediction accuracy, (2) computational complexity and (3) robustness on five colour databases. The results demonstrate that the proposed UQBCIA algorithm outperforms state-of-the-art unsupervised and classical supervised BIQA methods.
Information Extraction for Intestinal Cancer Electronic Medical Records

The data generated by the structured electronic medical records is helpful for mining and extracting medical data, and it is an effective way to make effective use of valuable data resources. However, the hospitals have accumulated a large number of unstructured data in electronic medical records, which cannot be effectively searched, resulting in serious waste of resources. In this paper, we study the problem of extracting attribute values from the unstructured text in electronic medical records. By observing intestinal cancer diagnostic texts, our attributes have two categories - discriminative attributes and extractive attributes, which use the text classification and the sequence labeling to tackle attribute values extraction problems. For discriminative attributes, we firstly divide the text into sentences/segments as instances. Secondly, we fine-tune the pre-trained word embedding to capture domain-specific semantics/knowledge. Thirdly, we also use an attention mechanism to select the most important instance for different attribute extractors. Finally, multi-tasking learning is used to share useful information to get better experimental results. For extractive attributes, we propose a novel model to get attribute values, including the BiLSTM layer, the CNN layer and the CRF layer. In particular, we use BiLSTM and CNN to learn text features and CRF as the last layer of the model. Experiments have shown that our method is superior to several competitive baseline methods.
LiD-CAT: A Lightweight Detector for Cache ATtacks

Cache attacks are one of the most wide-spread and dangerous threats to embedded computing systems' security. A promising approach to detect such attacks at runtime is to monitor the System-on-Chip (SoC) behavior. However, designing a secure SoC capable of detecting such attacks is very challenging: the monitors should be lightweight in order to avoid excessive power/energy and area costs and the attack behavior should be clearly known upfront. In this work, we present LiD-CAT, a lightweight and flexible hardware detector that is aware of leakage patterns that can be used by attackers to perform cache based attacks. LiD-CAT is a cache wrapper that implements a set of leakage properties derived from cache attacks and cache models using templates. These templates identify suspicious behavior that may lead to cache attacks. LiD-CAT is evaluated using two different cache architectures, one with a secure cache and one without. On each of them, SPEC2000 benchmarks are run together with malicious applications that execute cache attacks (i.e., Evict+Time, Prime+Probe, Flush+Reload and Flush+Flush). Results show that our lightweight detector successfully detects 99.99% of the attacks with less than 1% false-positives, has no timing penalties, and increases the area of a SoC with only 1.6%.
Blockchain-based Data Sharing System for Sensing-as-a-Service in Smart Cities

The sensing-as-a-service (SaaS) model has been explored to address the challenge of intractability of managing a large number of sensors faced by future smart cities. However, how to effectively share sensor data without compromising confidentiality, privacy protection, and fair trading without third parties is one of critical issues that must be solved in the SaaS in smart cities. While blockchain shows promise in solving these issues, the existing blockchain-based data sharing (BBDS) systems are difficult to apply to SaaS in smart cities because of many unresolved issues such as requiring a customized blockchain, huge storage, communication and computation costs, and dependence on a third party to achieve fair trading. We propose a BBDS system model with its security requirements before we present a concrete construction by combining <?TeX $\Sigma$?> -protocol, Paillier encryption scheme, and any secure symmetrical encryption and signature schemes. To demonstrate the utility of our proposed BBDS system, we present a security analysis and compare our system with other solutions. We implement the prototype in Remix to analyze the gas cost, and we conduct experiments to evaluate the communication and computation costs of the BBDS system using symmetric encryption (advanced encryption standard (AES)) and a signature scheme (elliptic curve digital signature algorithm (ECDSA)).
Self-learning based medical image representation for rigid real-time and multimodal slice-to-volume registration

Recently, the convolutional neural network (CNN) based real-time slice-to-volume registration methods show great potential in related clinical applications. Generally, these methods are mostly employed in monomodal scenarios because they essentially rely on image intensities. To extend this strategy in more general computer-aided surgery scenarios, we present a self-learning based multimodal image representation model for real-time and multimodal slice-to-volume registration. Different from usual approaches, which utilize structural descriptors or translate the image from one modality to another, the proposed method exploits the highly similar information embedded in multimodal images through the two-channel self-learning strategy based on the CNN. In this way, a universal image representation network for any modality can be achieved. Specifically, different multimodal image pairs can be simultaneously fed into two shared-weight channels in the training phase. The self-learning strategy is concretely implemented by making the paired outputs similar and retaining the edge information of the originals. Subsequently, the image representation of any modality can be realized through one channel. Experiments on different datasets have been conducted to evaluate the proposed method, demonstrating its significant advantage in providing multimodal representation for real-time and multimodal slice-to-volume registration; moreover, it is observed to be superior to the state-of-the-art representation methods.
Effects of Viewing Cute Pictures on Quiet Eye Duration and Fine Motor Task Performance

We investigated if viewing cute pictures could improve fine motor skills and prolong quiet eye (QE) duration. QE is a gaze phenomenon, and its duration (i.e., the period between fixation onset preceding a critical movement and fixation offset) is thought to represent attention control. As it has been reported that QE duration is longer for expert athletes than for novice athletes in various sports and becomes shorter even for experts who choke under pressure during games, resulting in performance deterioration, QE prolongation is important to prevent choking under pressure. Separately, several studies have confirmed that viewing cute pictures can induce focal attention, thus improving performance in fine motor tasks. We hypothesized that viewing cute pictures may modulate attention control and prolong QE duration. We also tested if the beneficial effects of viewing cute pictures could be obtained in a high-pressure situation in which participant performance was evaluated by an experimenter. We used a fine-motor task requiring participants to use a pair of tweezers to remove 12 small pieces from holes in a game board. We randomly assigned participants to either the baby-animal pictures group or the adult-animal pictures group, based on pictures viewed prior to the task. Participants executed the task in a pre-test, post-test, and pressure test. In both the post-test and the pressure test, participants viewed seven photographs of either baby animals or adult animals before execution of the task. In accordance with previous research, task precision increased after viewing pictures of baby animals in both the post-test and pressure test. Furthermore, QE duration was also prolonged after viewing cute pictures in the post-test, but not in the pressure test. Neither performance improvement nor QE prolongation was found after viewing pictures of adult animals. These results suggested that simply viewing cute pictures could prolong QE duration without pressure and might provide a beneficial effect on performance, even in a high-pressure situation.
Redefining HRD Roles and Practice in the Machine Learning Revolution

This chapter will seek to answer the question: to what extent do learning and development (LD) practitioners incorporate both the learning of humans and machines within their areas of responsibility? Initially, it considers some of the key ideas relating to the fourth industrial revolution with respect to human resource development (HRD)/LD. It reports the findings from a series of interviews with senior HRD practitioners which identified five themes (emerging awareness; responding; division between IT and HRD; the role of HRD; and ethical implications) that are shared and explored. This chapter suggests that machine learning (ML) and artificial intelligence (AI) are still something of a black box for HRD/LD and this enquiry prompted speculation and possibilities with an emerging recognition of the need to be involved and develop a more collaborative response. It argues that HRD/LD can make this happen and is important to the continuity, relevance and survival of the profession.
Robot global path planning using PSO algorithm based on the interaction mechanism between leaders and individuals

To overcome the disadvantages of low optimization accuracy and prematurity of the canonical PSO algorithm, we proposed an improved particle swarm optimization based on the interaction mechanism between leaders and individuals (PSO-IBLI), and used it to implement robot global path planning. In the PSO-IBLI algorithm, in different stages, each particle learns from the elites according to different regular. Moreover, the improved algorithm divides the execution state into two categories, where the parameters and the evaluation mechanisms are varied accordingly. In this way, the global best particles no longer walk randomly and have more learning objects. At the same time, other particles learn from not only the global best position, their historical best positions, but also the other elites. The learning strategy makes the search mode always in the adaptive adjustment, and it improves the speed of convergence and promotes this algorithm to find a more precise solution. The experimental results suggest that the precision and convergence speed of the PSO-IBLI algorithm is higher than the other three different algorithms. Additionally, some experiments are carried out to plan the robot’s entire collision-free path using the PSO-IBLI algorithm and the other three algorithms. The results show that the PSO-IBLI algorithm can obtain the shortest collision-free way in four algorithms.
QB-Gest: Qwerty Bimanual Gestural Input for Eyes-Free Smartphone Text Input

We developed QB-Gest, a bimanual text entry method based on simple gestures where users drag their thumbs in the direction of the desired letter while visualizing the Qwerty-layout. In an experiment with four sessions of testing, 20 users achieved text entry rates of 11.1 wpm eyes-free and 14.1 wpm eyes-on. An expert user achieved an eyes-free rate of 24.9 wpm after 10 rounds of entering the-quick-brown-fox phrase. The method holds potential for users with low vision and certain types of reduced motor function.
A general model to define the substitution, insertion and deletion graph edit costs based on an embedded space

The paper presents a method to learn the substitution, deletion and insertion costs on nodes and edges applied to the graph edit distance. We model the learning strategy as a general model and then we concretise it in two different architectures: the first architecture is based on a neural network and the second architecture is based on a multivariate normal distribution, which have been previously trained. The insertion, deletion and substitution costs on nodes and edges are defined as functions that depend on the output of the machine learning architecture. Other machine learning architectures have been presented in the literature to define the graph edit distance costs. Nevertheless, the main feature of our method is that the insertion, deletion and substitution costs are learned together, training the same machine learning and generating only one model. Thus, these costs are influenced one to another, achieving a higher accuracy in the pattern recognition stage than previous methods.
GPU Fast Convolution via the Overlap-and-Save Method in Shared Memory

We present an implementation of the overlap-and-save method, a method for the convolution of very long signals with short response functions, which is tailored to GPUs. We have implemented several FFT algorithms (using the CUDA programming language), which exploit GPU shared memory, allowing for GPU accelerated convolution. We compare our implementation with an implementation of the overlap-and-save algorithm utilizing the NVIDIA FFT library (cuFFT). We demonstrate that by using a shared-memory-based FFT, we can achieved significant speed-ups for certain problem sizes and lower the memory requirements of the overlap-and-save method on GPUs.
Navigating a Heavy Industry Environment Using Augmented Reality - A Comparison of Two Indoor Navigation Designs

The fourth industrial revolution seeks to enhance and optimize industrial processes through digital systems. However, such systems need to meet special criteria for usability and task support, ensuring users’ acceptance and safety. This paper presents an approach to support employees in heavy industries with augmented reality based indoor navigation and instruction systems. An experimental study examined two different user interface concepts (navigation path vs. navigation arrow) for augmented reality head-mounted-displays. In order to validate a prototypical augmented reality application that can be deployed in such production processes, a simulated industrial environment was created. Participants walked through the scenario and were instructed to work on representative tasks, while the wearable device offered assistance and guidance. Users’ perception of the system and task performance were assessed. Results indicate a superior performance of the navigation path design, as it granted participants significantly higher perceived support in the simulated working tasks. Nevertheless, the covered distance by the participants was significantly shorter in navigation arrow condition compared to the navigation path condition. Considering that the navigation path design resulted in a higher perceived Support, renders this design approach more suitable for assisting personnel working at industrial workplaces.
Some Generalized Integrals Applied in Scientometrics and Related Evaluation

In this study, some examples show that Hirsch's index (h-index) usually has some limitation in evaluations. In order to provide more choices and diversities for scientometrics evaluation index, this study discusses four types of extended indexes. We firstly use a type of variation of Cognitive Integral to serve as a new generalized Hirsch's index, called Cognitive Hirsch's index, and analyze some of its mathematical properties. Secondly, by introducing Reference Function generalized Integral, we further generalize and extend Cognitive Hirsch's index. Next, this study also proposes to transform any citation sequence into a unique 0-1 function. Subsequently, based on different shapes, i.e., systems of some special 0-1 functions, this paper then proposes a novel index which is measured by counting the extent to which a citation sequence's 0-1 function can cover some given function systems. Lastly, still based on the 0-1 functions, we present a new index by using some predetermined distributions.
Factors Affecting Security Behavior of Kenyan Students: An Integration of Protection Motivation Theory and Theory of Planned Behavior

The purpose of this study was to identify the factors that affect the security behavior of Kenyan University Students. Using Protection Motivation Theory (PMT) and Theory and Planned Behavior (TPB) as the theoretical base, data was collected from 125 Kenyan university students through an online survey. Data Analysis was carried out using structural equational modeling (SEM) in SmartPLS 3.2. The analysis showed that among PMT constructs, only self-efficacy played a significant role towards intention to take security measures, whereas, the attitude was the only construct TPB which had a meaningful relation with behavioral intention. Lastly, out of three constructs depicting social influence, only social support had a significant relationship with the behavioral intention. Constructs such as perceived vulnerability, perceived severity, response cost, response efficacy, subjective and descriptive norms did not show a significant relationship with the security intention of the students.
Centroid of polygonal fuzzy sets

In real applications, calculation of centroid, as a common defuzzification method, requires discretization of the fuzzy sets which makes the calculation on one hand time-consuming and on the other hand approximated. Particularly, centroid calculation for higher order fuzzy sets, is a bottleneck toward their real use. In this paper, we introduce polygonal fuzzy sets of type-1, interval type-2, and general type-2. Then we propose a closed form formula for exact and fast calculation of the centroid of polygonal type-1 fuzzy sets defined on continuous domain without the need of discretization. With the integration of the proposed method with EKM algorithm – that is a well-known algorithm for calculating the centroid of interval type-2 fuzzy sets – a fast and precise method for calculating the centroid of polygonal interval type-2 fuzzy sets and polygonal type-2 fuzzy sets on discrete and continuous domain are presented. It is shown that the proposed algorithms calculate the centroids of polygonal fuzzy sets faster and more accurately.
Hermeneutic Relations in VR: Immersion, Embodiment, Presence and HCI in VR Gaming

The emergence of Virtual Reality (VR) as a viable consumer medium for gaming offers an opportunity to reconceptualise understandings of immersion, embodiment and presence in gaming. However, many of the discourses and attempts to conceptualise experience in VR games conflate these terms rather than understanding each as a state of engagement with a VR environment or game. This results in a lack of understanding of the importance of design and intentionality in the VR game with regards to immersion, embodiment and presence. Using a post-phenomenological approach, this paper differentiates immersion, embodiment and presence as three kinds of relation utilising the I – technology – world schema. This approach allows for an understanding of these states of engagement as layered and hierarchical rather than instantly emergent on the part of the technology. The hermeneutic relation between the user and VR game [I → (technology – world)] that indicates presence can be understood as a feeling of place or placehood in VR and is intentionally the state aimed for as optional in VR games. The importance of technological intentionality as a co-constructor of embodiment and presence is exemplified through an analysis of user reviews of VR games either built-for VR or ported to VR. Built-for VR games create the possibility of a sense of place for the games by incorporating the possibility of embodiment and presence into the design of control and movement while ported VR games fail to immerse because of a lack of technological intentionality towards these goals.
Optimal Resource Allocation for Elastic and Inelastic Jobs

Modern data centers are tasked with processing heterogeneous workloads consisting of various classes of jobs. These classes differ in their arrival rates, size distributions, and job parallelizability. With respect to parallelizability, some jobs are elastic, meaning they can parallelize linearly across any number of servers. Other jobs are inelastic, meaning they can only run on a single server. Although job classes can differ drastically, they are typically forced to share a single cluster. When sharing a cluster among heterogeneous jobs, one must decide how to allocate servers to each job at every moment in time. In this paper, we design and analyze allocation policies which aim to minimize the mean response time across jobs, where a job's response time is the time from when it arrives until it completes.