Reviewerly/exHarmony huggingface dataset:
Deflectometry based inline inspection of linearly moving plastic parts

Widely used for surface slopes measurements and for three-dimensional shape reconstruction, deflectometry is a particularly powerful technique that can also be applied for defects detection on specular surfaces. In the visible domain, deflectometry is usually based on the projection of complex encoded light patterns and necessitates heavy processing that makes it not suitable for inline inspection. In this paper, A new deflectometry based approach that is more adapted for inline inspection of linearly moving parts (parts on conveyors) is proposed. Based on a more affordable and a simpler hardware setup, the new approach allows at the same time for a proper localization and a precise geometrical quantification of any defects on the scanned specular surfaces. The proposed approach uses a fast and simple processing algorithm that lends itself very well to real-time inspection. The new method is tested and validated in laboratory for the inspection of defects on specular surfaces of plastic parts.
Improving in-car emotion classification by NIR database augmentation

On-board detection of driver's emotions has become a task of high importance for car manufacturers, as negative emotions appear to be one of the major risks for car accidents. Deep neural networks have become over the last years the state of the art methods for computer vision and image classification. Yet, their success depends upon their being trained on a comprehensive database, which should cover all of the real-life situations that may arise in practice. Most of the in-car driver monitoring cameras capture images in the near infra-red (NIR) domain therefore one needs a large database with images featuring emotions in the NIR domain. As most databases featuring human emotions contain images acquired in the visible domain, we discuss in this paper two methods of transferring the NIR-like look into the "visible" images, by using a CycleGAN style-transferring neural network trained using "paired" and "unpaired" images. We show that the resulted database augmented with NIR-like images leads to a much improved performance in emotion classification for a deep neural network, when tested on real NIR images.
Chemical Reaction Multi-Objective Optimization for Cloud Task DAG Scheduling

Cloud computing systems often have two conflicting objective, maximizing service performance, and minimizing computing cost. The excellent task scheduling and resource allocation strategies can improve the cost/utility ratio efficiently. It is an NP-hard problem to optimize task scheduling of precedence-constrained parallel tasks represented by a directed acyclic graph (DAG) on the cloud system. In order to address this problem, a chemical reaction multi-objective optimization algorithm (CRMO) is proposed in this paper. The CRMO executes four chemical reaction operators (named on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis) for cloud tasks DAG scheduling. The experimental results show that CRMO can produce outstanding cloud task scheduling solutions set.
A Mobile Game for Learning English Vocabulary with Augmented Reality Block Builder

English learning is an essential part in modern education, and English vocabulary development has been shown particularly important in English learning. Yet, learners often found the process of English vocabulary learning dry and tasteless. This paper explores the possibility of using mobile game and Augmented Reality (AR) technology to improve the experience of English vocabulary learning. Our mobile game allows learners to play and learn anywhere and anytime. It is a multi-player word-guessing game, where a player can construct a virtual figure for an English word using an AR block builder and other players can guess the correct word from the virtual figure using their imagination. Preliminary user evaluation showed that our game facilitates players’ learning on English vocabulary and also fosters their creativity. We believe this game will help forming a learning community, which can serve as a stepping stone for more involved collaborative learning activities on English learning.
Image Search Algorithm in Local Data Base

This article consides visual search system for conventions detection in images collection. System contains k-means clusterisation and key points based method. This article consides existing methods comparison of visual search and their software implementation.
Refining fine fraction soil extraction methods and analysis for mineral exploration

Soils are widely used as geochemical sample media. In transported cover, the soils that host the mobile element signature are the smallest size fractions, so we should consider concentrating the clay size fraction ( Supplementary material: Spreadsheets of soils and regolith data from orientation sites and regional map sites analysed with the UltraFine+ method is available at https://doi.org/10.25919/5d1058f8b05fc
Cross-Cultural Analysis of Top Page Design Among Brazilian, Chinese, Japanese and United States Web Sites

The aim of present study was to reveal the differences of web design including cultural preference among the four countries that have different cultures. Twenty local municipal sites were randomly chosen from United States, Japan, China, and Brazil. The characteristics of these web sites was investigated from viewpoint of the following seven categories: (1) text, (2) layout, (3) navigation, (4) multimedia, (5) links, (6) items location, (7) color. When the web site had each factor, the researcher gave a “1” for that factor. Chi-square goodness-of-fit test was performed to compare the percentage of “0” or “1” of each factor among the four countries. Additionally, the correspondence analysis was performed to grasp the characteristics of each country. The characteristics of the top page design of the four countries were grasped based on these analyses.
Modularity metrics for genetic programming

With improvements in selection methods and genetic operators, Genetic Programming (GP) has been able to solve many software synthesis problems. However, so far, the primary focus of GP has been on improving success rates (fraction of the runs that succeeds in finding a solution). Less attention has been paid to other important characteristics and quality measures of human-written programs. One such quality measure is modularity. Since the introduction of Automatically Defined Functions (ADFs) by John Koza, most of efforts involving modularity in GP have been directed towards pre-programming modularity into the GP system, rather than measuring it for evolved programs. Modularity has played a central role in evolutionary biology. To study its effects on the evolution of software, however, we need a quantitative formulation of modularity. In this paper, we present two platform-independent modularity metrics, namely, reuse and repetition, that make use of the information contained in the execution traces of the programs. We describe the process of calculating these metrics for any evolved program, using problems that have been solved with the PushGP system as examples. We also discuss some mechanisms for integrating these metrics into the evolution framework itself.
Who Breathes the Smoke

While technologists have investigated environmental sustainability and conservation problems from a variety of angles, rural communities who manage natural resources have underexplored opportunities to benefit from new technologies. Many rural, Indigenous, and non-industrialized communities around the world have developed mature environmental governance structures and practiced effective resource management for thousands of years, and western environmental studies are increasingly recognizing the importance of social, cultural, and institutional factors.
Series of Semihypergroups of Time-Varying Artificial Neurons and Related Hyperstructures

Detailed analysis of the function of multilayer perceptron (MLP) and its neurons together with the use of time-varying neurons allowed the authors to find an analogy with the use of structures of linear differential operators. This procedure allowed the construction of a group and a hypergroup of artificial neurons. In this article, focusing on semihyperstructures and using the above described procedure, the authors bring new insights into structures and hyperstructures of artificial neurons and their possible symmetric relations.
Publish a Jason Agent BDI Capacity as Web Service REST and SOAP

In this paper, we discussed a new strategy to deploy the couple web service (REST and SOAP type) and BDI agent in a Java SE environment in order to obtain an Intelligent SOA. Deploying the BDI agent in a modern Web-App server or application server is a difficult task. The first reflex or first track on this deployment to create the pair web service and agent is to use the JEE server. But, there are alternative lightweight methods and simpler to build web services using Java SE, it is the use of APIs Non-Blocking Input Output (NIO API) technology Java. So our strategy is to exploit the Java NIO API framework, in particular, the NIO API Grizzly and Netty to run the BDI Agent and Web Service pairs in the same environment. Grizzly is used to take the role of a server of our application running in a Java SE environment in the one side. While Netty played the role of middleware to ensure communication between web services and BDI agents in the other side. The results of the proposed approach are BDI agents called Jason-RS and Jason-WS that are able to publish their capabilities in a web browser as a web service running on a Java SE environment. And, to prove our strategy, we conducted a case study using Jason-RS and Jason-WS. This has proven that the proposed approach provides intelligent SOA based on the pair BDI agent and the web service technology.
Cyborg Maintenance: Design, Breakdown, and Inclusion

The lived experiences of common cyborgs are ignored by those who most wish to be cyborg. I push back against the utopic, teleological imaginaries of the Transhumanist movement using a daily concern for actual cyborgs: maintenance. Scholarly work on maintenance is sparse, relatively recent, and generally focused on infrastructure and large technological systems. I merge the work done on these large technological systems with the biopolitical work by disability studies scholars and activists. I show that the common narratives of innovation that insist that technology becomes faster, more efficient, and more durable over time are false, and how upkeep rather than upgrading will be the norm for cyborg bodies. I call for a more deliberate design ethos for producing more accessible maintenance as well. The interfaces between technology and the body are sites of significant breakdown as each degrades the other. Moisture, acidity, and healing processes make it difficult for technological systems to last long within the body, and the intrusion of foreign material into the human body triggers scarification, fibroses, infection, and systemic reactions. Disabled people regularly navigate these concerns, and their description of the sorts of work they do to keep up their techno-bodyminds does not match well with the transhumanist narrative. The concerns of cyborg maintenance need to be considered as technologists continue to produce technological interventions into our bodies, and disabled folks for whom such intervention is already a reality can tell us a lot about how that needs to happen.
Towards fast and memory efficient discovery of periodic frequent patterns

Periodic frequent pattern (PFP) mining, the process of discovering frequent patterns that occur at regular periods in databases, is an important data mining task for various decision-making. Although several algorithms have been proposed for discovering PFPs, most of these algorithms often employ a two-stage approach to mining these periodic frequent patterns. That is, by firstly deriving the set of periods of a pattern from its coverset and subsequently evaluating the patterns' periodicity from the derived set of periods. This two-stage approach in discovering periodic frequent patterns as a result make existing algorithms inefficient in both runtime and memory usage. This paper presents solutions towards reducing the runtime, as well as, memory usage in discovering periodic frequent patterns. This is achieved by evaluating the periodicity of patterns without deriving the set of periods from their coversets. Experimental analysis on benchmark datasets show that the proposed solutions are efficient in reducing both the runtime and memory usage in mining periodic frequent patterns.
Reference Channels for Steganalysis of Images with Convolutional Neural Networks

When available, reference signals may dramatically improve the accuracy of steganalysis. Particularly powerful reference signals are embedding invariants that exist when the steganographic algorithm swaps values from small disjoint subsets of the cover elements' dynamic range, such as, but not limited to, embedding schemes utilizing least significant bit replacement. This paper describes a general method how to prepare such reference signals for a certain type of embedding operations, and incorporate them in detectors built as convolutional networks to improve their detection accuracy. The beneficial effect of reference signals is shown experimentally in both the spatial and especially JPEG domain, on model-based steganography and a generic LSB flipper with and without stochastic restoration of the histogram (OutGuess).
Generating Graphic Representations of Spoken Interactions from Journalistic Data

Generated distinct types of graphic patterns depicting the discourse structure of spoken journalistic texts - interviews or discussions, contribute to a user-independent evaluation of spoken Human-Human conversation and interaction. Distinct types of the graphic representation also function as visual representations of Cognitive Bias. The distinct types of generated graphic representations and values allow the identification of additional, “hidden,” Illocutionary Acts, beyond the defined framework of the spoken conversation and interaction.
Learning to Teach in a Remote School Context: Exploring the Organisation of Teachers’ Professional Development of Digital Competence Through Networked Learning

This chapter takes a school management perspective and investigates an upper secondary remote school in northern Sweden and its ambitions to create conditions for teachers’ professional development (TPD) of digital competence. More specifically, the chapter explores possibilities and challenges in how TPD of digital competence can be organised, facilitated, and sustained. By means of Cultural–Historical Activity Theory (CHAT), the results and analysis show that the development of teachers’ digital competence requires a school management that is supportive in creating a culture of change that can be sustained beyond single TPD actions and activities. Moreover, teachers need support to elaborate and negotiate on what type of tools, rules, roles, and divisions need to be added to the activity for the networked learning to take place and to proceed both in a short-term and long-term perspective. It is also shown how the school management needs to be sensitive to when and how the learning network is in need of encouragement and external support, that is, the importance of finding a balance between when the learning network can be self-organised and when it is in need of being externally directed with support from the school management.
An Automated Dual Threshold Band-Based Approach for Malaria Parasite Segmentation from Thick Blood Smear

Thick blood smear examination essentially plays an important role in rapid screening of malaria parasite. In this paper, an automatic malaria parasite detector is proposed to perceive the malaria-infected blood cells in a stained thick blood smear image. The detector hence can verify presence of malarial parasites in blood cells and count the number against each white blood corpuscle. It could precisely accelerate the process of diagnosing malaria in human blood and thus reduces the chances of human errors. Here, an automated image segmentation technique has been developed that proposes to efficiently improve the accuracy over manual techniques. The experimental results show that the proposed method can provide impressive performance in segmenting the malarial parasites from a thick blood smear image on some published malaria dataset and also taken under an origami-based paper microscope. The algorithm makes use of certain preprocessing methods for comparatively better results in the proposed intensity-based segmentation technique, followed by a dual threshold band-based technique. The experimental results are comparable to other existing and traditional malaria segmentation processes.
MABE 2.0

MABE (Modular Agent-based Evolver) is an open-source evolutionary computation (EC) research platform designed to be used by biologists, engineers, computer scientists, and other researchers. MABE's primary goal is to reduce the time between thinking up a new hypothesis and generating results. The design assumes that there are common elements in many EC research projects. MABE improves efficiency by allowing for the reuse of these common elements and standardizing of interfaces for non-common elements so that they can be used interchangeably. As of the writing of this paper, the MABE framework is five years old. Here, we reflect on the current version of MABE, including its successes and shortcomings, and propose upgrades for the next release.
Design and Implementation of a Lightweight Encryption Scheme for Wireless Sensor Nodes

For the Internet engineering society, Internet of Things (IoT) is a new prospect as it links embedded devices to the Internet Protocol (IP) based networks. One of the concept in IoT is 6LoWPAN, which enables Internet Protocol (IP) to be applied to the smallest devices. 6LoWPAN architecture is more popular and known for its architecture in the constrained environment. 6LoWPAN is used in many applications like wireless sensor networks (WSNs) where the nodes operating have limited capabilities in terms of power, memory and footprint area. Currently in the 6LoWPAN stack, the encryption algorithm used is AES-128/256 which is more power consumptive to do encryption on any given platform. Moreover, its hardware implementation as IP core will also result in higher Gate Equivalents (GE’s). This paper is all about replacing the encryption algorithm- Advanced Encryption Standard (AES-128/256) used in 6LoWPAN with a new lightweight encryption design NUCLEAR, which focuses on making the 6LoWPAN stack more lightweight. The lightweight encryption design can also be implemented as IP core as it results in very fewer gate counts as compared to AES-128/256 or any other lightweight cipher designs. We have proposed a Generalized Feistel Structure (GFS) cipher NUCLEAR which is highly efficient in terms of memory requirements, power dissipation and footprint area. The proposed design NUCLEAR satisfy all the constraints posed by the resource-constrained environment like IoT.
A real-time decentralized algorithm for task scheduling in multi-agent system with continuous damage

In this paper, a common model of task scheduling problems in agent rescue scenario is proposed, in which tasks with continuous dynamic damage are introduced to capture the emerging applications of using rescue robots and other resources to enhance human disaster rescue capability. Beyond this, we mainly focus on finding the optimal task scheduling strategy. We design a heuristic algorithm based on greedy strategy to obtain the optimal dynamic scheduling strategy of agents. Compared with solving global integer programming directly, the computational time is greatly reduced. The proof of the greedy strategy’s validity is also demonstrated under some specific damage functions. By comparing with the two strategies commonly used in real life, it is proved that our strategy is optimal. For practical application, we design an automatic negotiation framework, which realizes the real-time decentralized automated negotiation of agents. Then, using Game Description Language (GDL) as a tool, an automated negotiation algorithm is implemented, which enables agents to adjust the plan dispersedly. Experiments show that the algorithm is more efficient than the centralized algorithm in the case of limited communication.
A Survey of the Software Vulnerability Discovery Using Machine Learning Techniques

Nowadays, the study of vulnerability discovery has been attracted the widespread attention and the experts have proposed many different approaches in the past decades. To optimize the efficiency of the method, machine learning techniques are introduced into this area. In this paper, we provide an extensive review of the work in the field of software vulnerability discovery that utilize machine learning techniques. For the three key technologies of static analysis, symbolic execution and fuzzing in vulnerability discovery field, we first explain the basic principles respectively. Afterward, we review the research situation of software vulnerability discovery using machine learning techniques. Finally, we discuss both advantages and limitations of the approaches reviewed in the paper, and point out challenges and some uncharted territories in the three categories. In this paper, a brief study of the software vulnerability discovery using machine learning techniques is given, which is helpful to carry out the follow-up research work.
Gabor Filter Based Classification of Mammography Images Using LS-SVM and Random Forest Classifier

Breast cancer is formed in breast cells and it occurs primarily in women. Mammogram is x-ray of the breast that provides structural details. In the proposed work, attempt has been made to discriminate normal and abnormal mammograms belonging to fatty, fatty-glandular or dense-glandular breast tissue density. Breast x-ray images acquired in Medio-Lateral-Oblique (MLO) view (n = 197) chosen from mini-MIAS database are preprocessed using median filter to remove noise and adaptive histogram equalization has been applied to improve image contrast. The preprocessed images are subjected to Pectoral Muscle Removal (PMR) algorithm to obtain Region of Interest (RoI) comprising of only breast tissue region. For the obtained RoI, Gabor filtering has been employed at 5 various scales and 8 orientations leading to (5 $$\times $$ 8) 40 filter responses. Magnitude, phase information and statistical features such as energy, entropy, variance, kurtosis and skewness are computed for the obtained filter responses. In the next step, reduced feature set is obtained by implementing two schemes. In scheme1, the dominant scale and orientations of Gabor filter responses are determined by employing Kernel Principal Component Analysis (KPCA) and features are considered for the obtained dominant scale and orientation coefficients. Further, absolute difference value of extracted features are computed and compared to choose the significant features. In scheme2, sequential forward selection algorithm is employed to obtain significant features that discriminates normal and abnormal subjects. The derived significant features belonging to each category of breast density are fed to Least Square-Support Vector Machine (LS-SVM) and Random Forest (RF) classifier to classify subjects as normal and abnormal. The results indicate that it is possible to discriminate normal and abnormal subjects using Gabor and statistical features. LS-SVM classifier performs better with an accuracy of 93.54%, sensitivity of 95.24% and specificity of 90% for fatty-glandular tissues.