Reviewerly/exHarmony huggingface dataset:
Practical True Random Number Generator Using CMOS Image Sensor Dark Noise

We present a true random number generator (TRNG) using dark noise of a CMOS image sensor.Because the proposed TRNG is based on the dark characteristics of the CMOS image sensor, it does not require any additional hardware, such as light source and optics, for providing true randomness.Therefore, it can be a promising solution for compact and low-cost mobile application.By using NIST SP 800-90B entropy assessment suite, we evaluate the min-entropy for the raw outputs of our original noise source and the final random numbers including post-processing as well.We also adopt NIST SP 800-22 statistical randomness test suite for the evaluation of the random numbers.The test results demonstrate that the generated random numbers pass all the statistical tests and have high entropy.
Absumption to complement subsumption in learning classifier systems

Learning Classifier Systems (LCSs), a 40-year-old technique, evolve interrogatable production rules. XCSs are the most popular reinforcement learning based LCSs. It is well established that the subsumption method in XCSs removes overly detailed rules. However, the technique still suffers from overly general rules that reduce accuracy and clarity in the discovered patterns. This adverse impact is especially true for domains that are containing accurate solutions that overlap, i.e. one data instance is covered by two plausible, but competing rules. A novel method, termed absumption, is introduced to counter over-general rules. Complex Boolean problems that contain epistasis, heterogeneity and overlap are used to test the absumption method. Results show that absumption successfully improves the training performance of XCSs by counteracting over-general rules. Moreover, absumption enables the rule-set to be compacted, such that underlying patterns can be precisely visualized successfully. Additionally, the equations for the optimal size of solutions for a problem domain can now be determined.
What are natural concepts? A design perspective

Abstract Conceptual spaces have become an increasingly popular modeling tool in cognitive psychology. The core idea of the conceptual spaces approach is that concepts can be represented as regions in similarity spaces. While it is generally acknowledged that not every region in such a space represents a natural concept, it is still an open question what distinguishes those regions that represent natural concepts from those that do not. The central claim of this paper is that natural concepts are represented by the cells of an optimally designed similarity space.
Multimodal Emotion and Sentiment Modeling From Unstructured Big Data: Challenges, Architecture, &amp; Techniques

The exponential growth of multimodal content in today's competitive business environment leads to a huge volume of unstructured data.Unstructured big data has no particular format or structure and can be in any form, such as text, audio, images, and video.In this paper, we address the challenges of emotion and sentiment modeling due to unstructured big data with different modalities.We first include an up-to-date review on emotion and sentiment modeling including the state-of-the-art techniques.We then propose a new architecture of multimodal emotion and sentiment modeling for big data.The proposed architecture consists of five essential modules: data collection module, multimodal data aggregation module, multimodal data feature extraction module, fusion and decision module, and application module.Novel feature extraction techniques called the divide-and-conquer principal component analysis (Div-ConPCA) and the divide-andconquer linear discriminant analysis (Div-ConLDA) are proposed for the multimodal data feature extraction module in the architecture.The experiments on a multicore machine architecture are performed to validate the performance of the proposed techniques.
A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation

Accurate face segmentation strongly benefits the human face image analysis problem. In this paper we propose a unified framework for face image analysis through end-to-end semantic face segmentation. The proposed framework contains a set of stack components for face understanding, which includes head pose estimation, age classification, and gender recognition. A manually labeled face data-set is used for training the Conditional Random Fields (CRFs) based segmentation model. A multi-class face segmentation framework developed through CRFs segments a facial image into six parts. The probabilistic classification strategy is used, and probability maps are generated for each class. The probability maps are used as features descriptors and a Random Decision Forest (RDF) classifier is modeled for each task (head pose, age, and gender). We assess the performance of the proposed framework on several data-sets and report better results as compared to the previously reported results.
Strategies for Reflexive Ethnography in the Smart Home: Autoethnography of Silence and Emotion

Smart technologies in the home promise efficiency and control, but this simplistic story obscures their potential to reconfigure relationships and introduce new tensions into domestic contexts. This article explores ethnography as a method to facilitate sociological analysis of smart technologies in the home and develop a grounded understanding of their role in lived experience. The article assembles insights from ethnography of silence, ethnography of infrastructure and autoethnography. While much sociological commentary stresses the dataveillance capacities of such technologies, for ethnographers it is important to remember that our role is to do justice to members’ understandings whether they relate to dataveillance or not. Ethnographers need to address the common tendency for facilitating technologies of this kind to become unspoken aspects of everyday life. Autoethnography offers a route into exploring the nuanced meaning of the silences that the use of smart technologies entails and engaging with emotional dimensions of their use.
Intrusion recognition method based on echo state network for optical fiber perimeter security systems

In order to accurately and efficiently identify different types of intrusion signals of optical fiber perimeter security systems, this paper proposes a novel intrusion signal recognition method based on an echo state network (ESN). A perimeter security system based on an in-line Sagnac interferometer is employed to simulate in lab two laying situations of the sensing fiber: attached to a physical fence or buried under ground and acquire various event signals. The preprocessed signals are input into a trained well ESN to identify different types of events. The final recognition result of an event signal segment is determined according to the most dominant classification label corresponding to the signal segment. As a result, the average identification rates are 98.75% and 100% for the two laying situations, respectively. The proposed method has no need of extracting signal features and a large number of samples to train the classifier model. Therefore, more accurate and more effective intrusion identification can be achieved by the method than by others. The method is expected to satisfy the requirements of the practical application in the security field.
Rota-Net: Rotation Equivariant Network for Simultaneous Gland and Lumen Segmentation in Colon Histology Images

Analysis of the shape of glands and their lumen in digitised images of Haematoxylin & Eosin stained colon histology slides can provide insight into the degree of malignancy. Segmenting each glandular component is an essential prerequisite step for subsequent automatic morphological analysis. Current automated segmentation approaches typically do not take into account the inherent rotational symmetry within histology images. We incorporate this rotational symmetry into an encoder-decoder based network by utilising group equivariant convolutions, specifically using the symmetry group of rotations by multiples of 90 $$^\circ $$ . Our rotation equivariant network splits into two separate branches after the final up-sampling operation, where the output of a given branch achieves either gland or lumen segmentation. In addition, at the output of the gland branch, we use a multi-class strategy to assist with the separation of touching instances. We show that our proposed approach achieves the state-of-the-art performance on the GlaS challenge dataset.
Employee Perspective on Information Security Related Human Error in Healthcare: Proactive Use of IS-CHEC in Questionnaire Form

The objective of the research was to establish data relating to underlying causes of human error which are the most common cause of information security incidents within a private sector healthcare organization. A survey questionnaire was designed to proactively apply the IS-CHEC information security human reliability analysis (HRA) technique. The IS-CHEC technique questionnaire identified the most likely core human error causes that could result in incidents, their likelihood, the most likely tasks that could be affected, suggested remedial and preventative measures, systems or processes that would be likely to be affected by human error and established the levels of risk exposure. The survey was operational from 15th November 2018 to 15th December 2018. It achieved a response rate of 65% which equated to 485 of 749 people targeted by the research. The research found that, in the case of this particular participating organization, the application of the IS-CHEC technique through a questionnaire added beneficial value as an enhancement to a standard approach of holistic risk assessment. The research confirmed that the IS-CHEC in questionnaire form can be successfully applied within a private sector healthcare organization and also that a distributed approach for information security human error assessment can be successfully undertaken in order to add beneficial value. The results of this paper indicate, from the questionnaire responses supplied by employees, that organizational focus on its people and their working environment can improve information security posture and reduce the likelihood of associated information security incidents through a reduction in human error.
A study of phatic emoji use in WhatsApp communication

Abstract Mobile messaging is considered as a prominent site for phatic communication, where interpersonal connection is often foregrounded over information transaction. Though frequently overlooked, a large amount of this interpersonal work is done nonverbally through regular and meaningful emoji use. This exploratory study deals with emoji use within Laver’s (1975) phatic token framework, showing that different relationship structures (e.g., status-differential vs. solidary) correspond to distinct phatic token norms. The article analyzes phatic emoji use in a small-scale corpus of WhatsApp interactions between (a) a teacher and her L2-English students and (b) a teacher and her friends/family. Qualitative and quantitative analyses reveal patterns which widely corroborate Laver’s account of socially marked and unmarked token options: the teacher, the students, and the friends/family members tend towards addressee-specific use of neutral, other-oriented, and self-oriented phatic emojis.
An improved background segmentation algorithm for fringe projection profilometry based on Otsu method

In fringe projection profilometry, there always exist background and shadow areas in the captured fringe images. To improve the data quality, these areas should be recognized and deleted before the point cloud is reconstructed. For the existing algorithms, it is a nontrivial task to obtain a reasonable threshold to recognize the background and shadow areas from the fringe images. An inappropriate threshold usually leads to a misclassification of the object and background/shadow areas. In this paper, an improved adaptive thresholding method is built to address this problem. Instead of the gray level histogram, the modulation level histogram is utilized to compute the threshold. The modulation image is computed from all the captured images in the projection sequence. A novel objective function with a Gaussian weighted factor is proposed to improve Otsu’s method. With this weighted factor, the between-class variance between the object and background/shadow is intensified. The new built objective function also makes the objective function plot much smoother, which makes the proposed method more suitable for noise image segmentation. Additionally, the weighted factor changes adaptively with the input images. Experiment results illustrate that the proposed scheme performs better than existing methods. The scan for an area on a car body rear demonstrates the effectiveness and feasibility of the proposed method in structured light measurement of real metal components.
Effective moving object tracking using modified flower pollination algorithm for visible image sequences under complicated background

In real time moving object tracking, various unconstraint factors such as severe occlusion and background clutter remain a challenge for developing an effective yet robust tracking method. In this study, a novel moving object tracking algorithm based on the modified flower pollination algorithm (MFPA) is proposed. A search window with the features of centroid coordinates and width of the search window is used to locate the position of the moving object. The sub-image within the search window is then extracted. The hue, saturation and value (HSV) histogram of the extracted region is calculated in order to model the object appearance representation. Subsequently, the Bhattacharyya distance between the HSV histograms from two consecutive frames is formulated as the fitness function of MFPA, in which maximizing the similarity of both histograms is sought after. The comparative experimental results show that the developed algorithm outperforms others in terms of efficiency and accuracy.
Active contour model based on local bias field estimation for image segmentation

The active contour model is a commonly used image segmentation method. When applied to complex images, such as images with grayscale inhomogeneity, most existing active contour models produce poor segmentation results. In order to solve this problem, we propose an active contour model based on local bias field estimation (LBFE), which makes the improved model better able to segment complex images. Firstly, we propose a new function to compute the value of bias field with fuzzy c-means clustering algorithm. This computation is completed before the iteration, which greatly improves the compute speed. Secondly, compute minimization with the energy function in the bias correction model (BC) proposed by Li et al. Thirdly, a new variational level set function is proposed to limit the segmentation range and greatly improve the robustness. Experiment results have proved that the proposed model not only segments images with intensity inhomogeneity effectively and shortens time spent, but also shows a better robustness to initialization and a higher segmentation accuracy than other classic models.
Tuning of reinforcement learning parameters applied to SOP using the Scott–Knott method

In this paper, we present a technique to tune the reinforcement learning (RL) parameters applied to the sequential ordering problem (SOP) using the Scott–Knott method. The RL has been widely recognized as a powerful tool for combinatorial optimization problems, such as travelling salesman and multidimensional knapsack problems. It seems, however, that less attention has been paid to solve the SOP. Here, we have developed a RL structure to solve the SOP that can partially fill that gap. Two traditional RL algorithms, Q-learning and SARSA, have been employed. Three learning specifications have been adopted to analyze the performance of the RL: algorithm type, reinforcement learning function, and $$\epsilon $$ parameter. A complete factorial experiment and the Scott–Knott method are used to find the best combination of factor levels, when the source of variation is statistically different in analysis of variance. The performance of the proposed RL has been tested using benchmarks from the TSPLIB library. In general, the selected parameters indicate that SARSA overwhelms the performance of Q-learning.
Cost-aware active learning for named entity recognition in clinical text

Active Learning (AL) attempts to reduce annotation cost (ie, time) by selecting the most informative examples for annotation. Most approaches tacitly (and unrealistically) assume that the cost for annotating each sample is identical. This study introduces a cost-aware AL method, which simultaneously models both the annotation cost and the informativeness of the samples and evaluates both via simulation and user studies.We designed a novel, cost-aware AL algorithm (Cost-CAUSE) for annotating clinical named entities; we first utilized lexical and syntactic features to estimate annotation cost, then we incorporated this cost measure into an existing AL algorithm. Using the 2010 i2b2/VA data set, we then conducted a simulation study comparing Cost-CAUSE with noncost-aware AL methods, and a user study comparing Cost-CAUSE with passive learning.Our cost model fit empirical annotation data well, and Cost-CAUSE increased the simulation area under the learning curve (ALC) scores by up to 5.6% and 4.9%, compared with random sampling and alternate AL methods. Moreover, in a user annotation task, Cost-CAUSE outperformed passive learning on the ALC score and reduced annotation time by 20.5%-30.2%.Although AL has proven effective in simulations, our user study shows that a real-world environment is far more complex. Other factors have a noticeable effect on the AL method, such as the annotation accuracy of users, the tiredness of users, and even the physical and mental condition of users.Cost-CAUSE saves significant annotation cost compared to random sampling.
An interactive human centered data science approach towards crime pattern analysis

The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making. In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme. The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph. This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime. We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis.
GaTO: An Ontological Model to Apply Gamification in Intelligent Tutoring Systems

Intelligent Tutoring Systems (ITSs) are concerned with the use of artificial intelligence techniques for performing adaptive tutoring to learners' according to what they know about the domain. Researchers are increasingly interested in applying gamification in e-learning systems to engage students and to drive desired learning behaviors. However, little attention has been drawn to the effective application of gamification in ITS, and how to connect theories of both concepts in a standard and formal way. Moreover, gamified ITS should manipulate a huge amount of knowledge regarding several models, i.e., gamification, domain, student and pedagogical models. Formally connecting such theories as well as representing system's knowledge relies on the use of ontologies. In this paper, we present an ontological model that connects gamification and ITS concepts. Our model takes advantage of ontologies to allow automated reasoning (e.g., on the domain, student, pedagogical or gamification models), to enable interoperability, and create awareness about theories and good practices for the designers of gamified ITS. To evaluate our model, we use an ontology evaluation method based on five knowledge representation roles. We also illustrate how it could support the development of an intelligent authoring tool to design gamified ITS.
Real‐world scene perception in infants: What factors guide attention allocation?

Abstract The foci of visual attention were modeled as a function of perceptual salience, adult fixation locations, and attentional control mechanisms (measured in separate tasks) in infants ( N = 45, 3‐ to 15‐month‐olds) as they viewed static real‐world scenes. After controlling for the center bias, the results showed that low‐level perceptual salience predicts where infants look. In addition, high‐level factors also played a role: Infants fixated parts of the scenes frequently fixated by adults and this effect was stronger for older than younger infants. In line with this finding, infant fixation durations were longer on regions more frequently fixated by adults, implying longer time taken to process the available information. Fixation durations decreased with age, and this decline interacted with orienting skills such that fixation durations decreased faster with age for infants with high orienting skills, relative to infants with low orienting skills. There was a further interaction between fixation durations and selective attention abilities: Infants with low selective attention skills showed a decrease in fixation durations with age, whereas infants with higher selective attention skills showed a slight increase in fixation durations with age. These findings imply that infants' visual processing of static real‐world stimuli develops in accord with attentional control.