Reviewerly/exHarmony huggingface dataset:
Low‐resolution face recognition and feature selection based on multidimensional scaling joint <i>L</i> <sub>2,1</sub> ‐norm regularisation

Face recognition is confronted with situations wherein the captured face images are not as clear as the registered ones; this is known as low-resolution face recognition. To solve this problem, the authors propose a new sparse coupled projection method using multidimensional scaling joint L2,1-norm regularisation (MDSL21). The MDSL21 maps the low-resolution faces and high-resolution faces into a common sparse subspace in which feature selection and coupled transformation can be simultaneously achieved. In their proposed method, the authors first learn the common responds using the multidimensional scaling model. Specifically, the distance between the responds is approximated to the distance between the high-resolution samples, and the local manifolds are preserved. Then, the seeking coupled projections are formulated as a regression model. Inspired by the sparse constraint utilised in the classical subspace learning methods, the authors add an L2,1-norm regularisation term to the regression model to realise the sparsity and present its optimisation method. Experimental results validate the effectiveness of their proposed method on the low-resolution face recognition task.
Parallel hybrid chicken swarm optimization for solving the quadratic assignment problem

&lt;p&gt;In this research, we intend to suggest a new method based on a parallel hybrid chicken swarm optimization (PHCSO) by integrating the constructive procedure of GRASP and an effective modified version of Tabu search. In this vein, the goal of this adaptation is straightforward about the fact of preventing the stagnation of the research. Furthermore, the proposed contribution looks at providing an optimal trade-off between the two key components of bio-inspired metaheuristics: local intensification and global diversification, which affect the efficiency of our proposed algorithm and the choice of the dependent parameters. Moreover, the pragmatic results of exhaustive experiments were promising while applying our algorithm on diverse QAPLIB instances . Finally, we briefly highlight perspectives for further research.&lt;/p&gt;
Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting

Automatic and intelligent object sorting is an important task that can sort different objects without human intervention, using the robot arm to carry each object from one location to another.These objects vary in colours, shapes, sizes and orientations.Many applications, such as fruit and vegetable grading, flower grading, and biopsy image grading depend on sorting for a structural arrangement.Traditional machine learning methods, with extracting handcrafted features, are used for this task.Sometimes, these features are not discriminative because of the environmental factors, such as light change.In this study, Hierarchical Extreme Learning Machine (HELM) is utilized as an unsupervised feature learning to learn the object observation directly, and HELM was found to be robust against external change.Reinforcement learning (RL) is used to find the optimal sorting policy that maps each object image to the object's location.The reason for utilizing RL is lack of output labels in this automatic task.The learning is done sequentially in many episodes.At each episode, the accuracy of sorting is increased to reach the maximum level at the end of learning.The experimental results demonstrated that the proposed HELM-RL sorting can provide the same accuracy as the labelled supervised HELM method after many episodes.
Study on Thumbnail Images and Titles Selected by Viewers in YouTuber’s Videos

In order to increase the number of videos playback, we researched on the thumbnail images and the titles which were selected by the viewers. We built the samples and conducted the questionnaires in order to reveal the factors that make a great influence on the evaluation and be clear about the positive levels. We revealed the viewers decide the videos under the influence of the images rather than sentences and words. Also, Images factors are particularly effective for males. On the other hand, females are more affected by sentences and words than males. This research revealed the guidelines that YouTuber creates thumbnail images and titles and the elements to incorporate according to the target the males or the females. As a starting point of this research, we will be able to conduct more detailed research on thumbnail images and titles.
Achieving Change Requirements of Feature Models by an Evolutionary Approach

Software Product Lines (SPLs) are families of products that share some common features, and differ on some others. The variability of SPLs is usually described at design time by using variability models; one of the main used variability models are feature models (FMs).
Trichomonas Detection in Leucorrhea Based on VIBE Method

Trichomonas examination is one of the important items in the leucorrhea routine detection. And it cannot be recognized by still images because of the unstable morphology and unfixed focal location caused by motion characteristic. We proposed an improved VIBE algorithm. 6 videos (totally 1414 frames) are collected for testing. In order to compare the effects of the algorithms, we segment each frame artificially as ground truth. Experiments show that percentage of correct classification (PCC) achieves 88%. The proposed improved method can effectively suppress the false detection caused by the formed components such as epithelial cells in the leucorrhea microscopic image and the missed detection caused by the background model update during the movement. At the same time, improvements can effectively suppress smear and ghost areas. The algorithm proposed in this paper can be integrated into the leucorrhea automatic detection system.
Research on Information Integration Method of Agricultural Products Producing and Managing Based on Knowledge Graph

In order to improve the integration and access efficiency of agricultural information, this paper propose an agricultural information integration framework based on knowledge graph. A knowledge graph of agricultural products producing and managing was constructed, covering the basic process of “Planting - farming - processing - quality inspection - warehousing - Transportation - Sales” and realizing the storage, mapping and inquiry of knowledge graph. Improves the method of mapping data linkage based on database mapping relation, and realizes the transformation of elements from database to knowledge graph elements. Map data link method of database based on mapping relations, realize the conversion of database elements to the knowledge graph elements, the iterative discovery of relation and pattern in text information is realized by means of weak supervised machine learning method. This method integrates the application in the Green-Cloud-Grid platform, and improves the efficiency of information source integration, correlation analysis and mining utilization under the platform.
Conditional Domain Adaptation GANs for Biomedical Image Segmentation.

Due to visual differences in biomedical image datasets acquired using distinct digitization techniques, Transfer Learning is an important step for improving the generalization capabilities of Neural Networks in this area. Despite succeeding in classification tasks, most Domain Adaptation strategies face serious limitations in segmentation. Therefore, improving on previous Image Translation networks, we propose a Domain Adaptation method for biomedical image segmentation based on adversarial networks that can learn from both unlabeled and labeled data. Our experimental procedure compares our method using several domains, datasets, segmentation tasks and baselines, performing quantitative and qualitative comparisons of the proposed method with baselines. The proposed method shows consistently better results than the baselines in scarce label scenarios, often achieving Jaccard values greater than 0.9 and adequate segmentation quality in most tasks and datasets.
Indoor Scene and Position Recognition Based on Visual Landmarks Obtained from Visual Saliency without Human Effect

Numerous autonomous robots are used not only for factory automation as labor saving devices, but also for interaction and communication with humans in our daily life. Although superior compatibility for semantic recognition of generic objects provides wide applications in a practical use, it is still a challenging task to create an extraction method that includes robustness and stability against environmental changes. This paper proposes a novel method of scene and position recognition based on visual landmarks (VLs) used for an autonomous mobile robot in an environment living with humans. The proposed method provides a mask image of human regions using histograms of oriented gradients (HOG). The VL features are described with accelerated KAZE (AKAZE) after extracting conspicuous regions obtained using saliency maps (SMs). The experimentally obtained results using leave-one-out cross validation (LOOCV) revealed that recognition accuracy of high-saliency feature points was higher than that of low-saliency feature points. We created our original benchmark datasets using a mobile robot. The recognition accuracy evaluated using LOOCV reveals 49.9% for our method, which is 3.2 percentage points higher than the accuracy of the comparison method without HOG detectors. The analysis of false recognition using a confusion matrix examines false recognition occurring in neighboring zones. This trend is reduced according to zone separations.
Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis.

The adoption of intelligent systems creates opportunities as well as challenges for medical work. On the positive side, intelligent systems have the potential to compute complex data from patients and generate automated diagnosis recommendations for doctors. However, medical professionals often perceive such systems as black boxes and, therefore, feel concerned about relying on system generated results to make decisions. In this paper, we contribute to the ongoing discussion of explainable artificial intelligence (XAI) by exploring the concept of explanation from a human-centered perspective. We hypothesize that medical professionals would perceive a system as explainable if the system was designed to think and act like doctors. We report a preliminary interview study that collected six medical professionals' reflection of how they interact with data for diagnosis and treatment purposes. Our data reveals when and how doctors prioritize among various types of data as a central part of their diagnosis process. Based on these findings, we outline future directions regarding the design of XAI systems in the medical context.
Petrochemistry and Manganese Mineralization Potentials of the Calc-alkaline Granitoids from Northeastern Hawal Massif, North-Eastern Nigeria

The High-K calc-alkaline granitoids in southern part of Hawal massif is an integral part of the well-exposed syn-to post collisional plutons in eastern Nigeria. The evolution of the rocks can be linked to the thermo-tectonic period of faulting, uplift, cooling, fractionation and high-level magmatism, which led to the intrusion of the granites and related rocks during the Palaeoproterozoic, about 580± 10 Ma. A total of nine granitic rocks, constituting seven biotite granites of varying grain sizes and two porphyritic granites were sampled in Mubi area to shed new light regarding their petrochemistry. Detailed field and laboratory investigations show that the area is characterized majorly by Pan African granitoids. The massif has S-type strongly peraluminous geochemical signature, high-K calc-alkaline, magnesian, granodiorite, exemplified by high ASI (averagely 0.81 to 1.59), low Na2O (average 2.09 wt. %), and CaO (average 2.37 wt. %). Moreso, they exhibit low FeOt/(FeOt + MgO) ratios (0.64  0.80 wt%), low to moderate Na2O+K2O-CaO ratio (2.41  5.41 wt%), high Na2O+K2O (average 6.42 wt%) and K2O/Na2O ratios (average 2.11 wt%), as well as high Mg# (30.49  50.22), low molar CaO/(MgO + FeOt) (0.19  0.63 wt%) and molar Al2O3/(MgO +FeOt) ratio (2.10  4.31 wt%). Discrimination diagram shows that the granitoids are products of sedimentary protolith (metagreywacke) formed within a syn-collision and supra-crustal setting. The constituent mineral assemblages in the granitoids (quartz, plagioclase, alkali feldspar, biotite, hornblende, titanite, iron oxides) from the northern Hawal massif are obviously related to the condition of evolution and compositions of the protolith.
Designing a Usibility Assessment Process for Adaptive Intelligent Tutoring Systems: A Case Study

This study aims at examining the usability evaluation process of ArtiBos, an adaptive intelligent tutoring system specifically designed for teaching of problem solving, and proposing usability evaluation recommendations accordingly. The study group consists of 90 students selected from 3 different secondary education institutions and 30 experts from the fields of Computer Education and Instructional Technology and Mathematics Teaching. The usability of ArtiBos is evaluated according to the criteria of effectiveness, efficiency, and satisfaction included in the definition of usability by the International Standards Organization (ISO). In this study, quantitative and qualitative data were used for triangulation. Study data included users’ screenshots during performance of the instructed tasks in ArtiBos, sound recordings taken from the think aloud technique, interviews with students, and opinions of experts. The study elaborates the procedure of usability tests of ArtiBos and makes suggestions for usability evaluation processes of similar systems. The results are expected to guide researchers planning to carry out similar systems’ usability studies
The subject of ROAR in the mind and in the corpus: What divergent results can teach us

The use of different research methods in linguistics invariably leads to questions about the convergence and divergence of research findings. Aiming for convergence, while understandable, may distort our understanding of language phenomena, if convergence is seen as the only publishable result. We suggest a place for diverging results in furthering our understanding of the data techniques used to investigate linguistic phenomena. We illustrate this point through an experimental and corpus-based investigation of the preferred syntactic subjects of the English verb ROAR and discuss how deeper reflection on these diverging results leads to a better understanding of the different data types.
A Compression-Inspired Framework for Macro Discovery

We consider the problem of how a reinforcement learning agent, tasked with solving a set of related Markov decision processes, can use knowledge acquired early on in its lifetime to improve its ability to more rapidly solve novel tasks. We propose a three-step framework that generates a diverse set of macros that lead to high rewards when solving a set of related tasks. Our experiments show that augmenting the original action-set of the agent with the identified macros allows it to more rapidly learn optimal policies in novel MDPs.
FPQA implementation of modified Elliptic Curve Digital Signature Algorithm

With rapid deployment of Internet-of-Things (IoT) devices, security issues related to data transmitted between the devices increases. Thus the integrity of perceptual layer devices is of utmost importance to secure the information being transmitted between the devices. In a secured information system, digital signature generation and verification processes are entirely different from data encryption and decryption processes. Digital signatures are rapidly emerging due to the problems related to data integrity thus playing a crucial role in the authentication process by enabling the sender to attach a signature to the encrypted message. Based on the devices it is beneficial to select an algorithm showing favorable behavior, therefore Keccak-f [1600] algorithm is best suited for devices having area and cost constraints. In this paper, implementation of the original Elliptic Curve Digital Signature Algorithm and its variants are considered and evaluated in terms of the security level and computational cost. Here the modified ECDSA scheme concepts related to signature generation and verification are similar to the original ECDSA scheme. The computational cost of the Modified ECDSA is reduced by removing inverse operation in key generation and signing phase, also problems related to signature being forged are resolved using hidden generator point concept. Hence the Modified ECDSA is more secure with less computational cost when implemented on FPGA using Verilog HDL. Therefore, this algorithm can be applied for the devices being connected in perceptual layer of the IoT.
Exploring the Importance of Entities in Semantic Ranking

In recent years, entity-based ranking models have led to exciting breakthroughs in the research of information retrieval. Compared with traditional retrieval models, entity-based representation enables a better understanding of queries and documents. However, the existing entity-based models neglect the importance of entities in a document. This paper attempts to explore the effects of the importance of entities in a document. Specifically, the dataset analysis is conducted which verifies the correlation between the importance of entities in a document and document ranking. Then, this paper enhances two entity-based models—toy model and Explicit Semantic Ranking model (ESR)—by considering the importance of entities. In contrast to the existing models, the enhanced models assign the weights of entities according to their importance. Experimental results show that the enhanced toy model and ESR can outperform the two baselines by as much as 4.57% and 2.74% on NDCG@20 respectively, and further experiments reveal that the strength of the enhanced models is more evident on long queries and the queries where ESR fails, confirming the effectiveness of taking the importance of entities into account.
Development of a Fuzzy Controller Applied to the Velocity of a DC Motor

Control system designs are becoming increasingly complex due to the operational requirements of current processes.An alternative way to this fact is the application of intelligent systems.This work presents a design of a fuzzy controller tuned by genetic algorithm and applied to the velocity control of a DC motor.For comparative performance purposes, it is designed and applied a conventional PI controller.In general, the fuzzy controller presented a better result compared with the conventional implementation.Results from computer simulations are presented.
Analysis of Mobile Robot Path Planning with Artificial Potential Fields

This paper presents an analysis of a mobile robot path planning using potential field technique. The mobile robot has four wheels which can be driven and steered independently. For simplicity, this paper assumes Ackermann steering such that the vehicle can be modelled as a two-wheel system for path planning purposes. The potential field method which emphasizes on attractive potential and repulsive potential fields for path planning are proposed for analysis. For simulation settings, the mobile robot priorly does not pose any information on the environment and then moves until it reaches its goal using the calculated attractive and repulsive potential fields. The control gains which represents the attractive and repulsive force are studied to determine the effectiveness of the proposed method. Based on the simulation results, the robot is able to avoid obstacle and at the same time successfully arrived at the goal. Different cases of the potential fields and landmarks positions are also presented to determine the effectiveness of the technique.
Two Factor Authentication for e-Government Services using Hardware-Like One Time Password Generators

A safe and accessible authentication technique is a prerequisite for any modern e-government application.Two-factor authentication is currently widely adopted, since it alleviates many vulnerabilities of password-based authentication.The majority of e-government systems currently make use of text messages to deliver the second authentication factor, but these messages do not constitute an adequate (secure and reliable) solution.In this paper we show how to use One-Time Passwords (OTP) generated by a per-user, ad-hoc built application installed on a smartphone to support a two-factor authentication scheme specifically targeted to e-government tasks.In particular, we develop a process for the request, generation and distribution of such an application that achieves the same security of OTP hardware devices but avoids the related distribution and management costs, requiring no dedicated hardware and relying on the pre-existing administrative infrastructure.The process is designed to be accessible by any citizen who is able to perform very basic operations on a smartphone.
Blockchains for Accelerating Open Innovation Systems for Sustainability Transitions

Challenges related to climate change, inequality, environmental degradation, and resource scarcity threaten our ability to sustain ourselves while ensuring an equal and prosperous future. The inherent complexity and interconnectedness of these challenges demand a rethinking of our traditional approach toward organizing our production and consumption systems. Transitions toward sustainability require systemic changes, implying a wide network of actors coordinating for new forms of organizing. This chapter proposes that blockchains could offer opportunities for such organizing by leveraging the various combinations of skills, capabilities, and knowledge across open innovation networks to facilitate transitions. The characteristics of open innovation networks and their significance to blockchains are discussed, and their relevance in sustainability transitions. Democratizing access to knowledge and coding trust and consensus through smart contracts make new and innovative economic spaces and opportunities possible. Firms may have to rethink traditional modes of organizing to seize the multiple opportunities that blockchain-enabled open innovation systems present.
A framework for improving error messages in dependently-typed languages

Abstract Dependently-typed programming languages provide a powerful tool for establishing code correctness. However, it can be hard for newcomers to learn how to employ the advanced type system of such languages effectively. For simply-typed languages, several techniques have been devised to generate helpful error messages and suggestions for the programmer. We adapt these techniques to dependently-typed languages, to facilitate their more widespread adoption. In particular, we modify a higher-order unification algorithm that is used to resolve and type-check implicit arguments. We augment this algorithm with replay graphs, allowing for a global heuristic analysis of a unification problem-set, error-tolerant typing, which allows type-checking to continue after errors are found, and counter-factual unification, which makes error messages less affected by the order in which types are checked. A formalization of our algorithm is presented with an outline of its correctness. We implement replay graphs, and compare the generated error messages to those from existing languages, highlighting the improvements we achieved.