Reviewerly/exHarmony huggingface dataset:
SMACK: Subjective Measure of Applied Contextual Knowledge

We developed an augmented reality [AR] application in Microsoft's HoloLens platform to investigate if an AR-based assessment tool better assesses a student's real-world knowledge and skill compared to a traditional, pen-and-paper-based assessment. The AR-based assessment focused on applying knowledge to contextually subjective problems while the traditional assessment required recalling knowledge in unchanging, deterministic problems.Under our AR-based simulated construction scene, participants, 18 students enrolled in a Construction Management Safety course, were required to visually inspect the scene, correctly identify unsafe conditions, and justify their selections in a multiple-choice setting. After their AR-based assessment, they were required to complete a post-survey and a debrief. Also, they were required to complete the traditional assessment and pre-survey before the AR-based assessment. Our study demonstrated that the participants preferred the AR-based assessment over the traditional assessment. However, they had higher performance ratings in the traditional assessment than the AR-based assessment.
Towards Finding Flow in Tetris

One of the most challenging goals in the game industry is to design games which are difficult enough to be a fun challenge but not so hard to provoke frustration among a wide range of different types of players. Dynamic difficulty adjustment (DDA) is a set of techniques used to customize the difficulty of a game according to the skill level of the player so that the game can keep the player "flowing". In this paper, we present a novel DDA architecture that we implement using case-based reasoning and we integrate into a Tetris game. In particular, we dynamically change the difficulty of the game by selecting the next piece the player has to place on the board to make the current game more similar to one of the "good" games in our case base. Games are modeled using time series representing the evolution of different game features and evaluated by the players according to their level of entertainment. This way, we alter the difficulty of the game so that it evolves similarly to other previous good games and we expect the current player also experience the same flow.
Weighted Schatten P-Norm Minimization with Local and Nonlocal Constraints for Noisy Image Completion

Weighted Schatten p-norm minimization (WSNM) has been used successfully for noisy-free image completion. However, WSNM can introduce extra artifacts if the observed entries of image contain noise. In this paper, we present a novel WSNM-based method for noisy image completion, which incorporates both local smoothness and nonlocal self-similarity in a unified framework. More concretely, the analysis operator is utilized to ensure local smoothness and the nonlocal statistical modeling (NLSM) is adopted to constrain nonlocal self-similarity while WSNM is effective for completing the missing entries. To make the proposed method tractable and robust, the alternating direction method of multipliers (ADM-M) is employed to solve the above inverse problem. Experimental results show the effectiveness of the proposed method for noisy image completion.
Learning Quaternion Graph for Color Face Image Super-Resolution

Most of the existing face image super-resolution methods are designed for grayscale images. For color images, these methods just treat each color channel individually or considered the illumination part only, ignoring the relationships among different color channels. To address this concern, in this paper we present a color face image super-resolution method by learning the quaternion graph (LQG) representation. Instead of spatial domain, the color image is represented in the quaternionic domain, which encourages the proposed model to well preserve the correlations among different color channels. Besides, a graph regularization is learned in the quaternion space to ensure the smoothness of encoding feature space. More specifically, by utilizing the graph Laplacian, we present to promote the smoothness of representations by forcing similar training samples to share similar encoding coefficients. This not only helps to stabilize the linear system but also makes the model more robust to noise. Experimental results demonstrated the efficiency of the proposed method in super-resolving color face images.
Information Fusion via Multimodal Hashing With Discriminant Correlation Maximization

Due to low storage cost and fast query speed, hashing has been applied to similarity search in multimedia data widely. In this paper, an effective information fusion algorithm using multimodal hashing with discriminant correlation maximization is presented. The proposed algorithm not only finds the minimum of the semantic similarity across different modalities by multimodal hashing, but also minimizes the between-class correlation and maximizes the within-class correlation simultaneously to extract discriminant representations for information fusion. More importantly, two solutions with canonical case and non-canonical case are presented, and a novel solution to non-canonical case is proposed. Benefiting from the combination of semantic similarity across different modalities from multimodal hashing information and the discriminant representation strategy, the proposed strategy can achieve improved performance. Experimental results show that the proposed approach outperforms the related methods.
On the trade-offs of Proof-of-Work algorithms in blockchains

There are many different protocols that regulate the way in which each node on a blockchain network is able to reach consensus that a newly created block is valid. One of the protocols, Proof-of-Work (PoW) gained popularity when it was implemented in a blockchain-based cryptocurrency known as Bitcoin. However, there are inherent deficiencies in its current implementation. This paper discusses these deficiencies, as well as the parameters that directly and indirectly affect its efficacy and performance so that possible enhancements to the protocol can be investigated.
The Application of Hexagonal Grids in Mobile Robot Navigation

The paper addresses the problem of map building and collision free path planning based on hexagonal lattice and Dempster-Shafer theory. We presented the advantages of the proposed solution. It is shown that hexagonal maps of an environment can be easily build based on sensors reading and is useful in collision-free path planning and mobile robot localization.
Generalised Framework for Automated Conversational Agent Design via QFD

Automated conversational agents are often regarded as the most promising method of responding to customer queries with minimum human intervention. Focus of the existing literature is mostly on technological innovations such as artificial intelligence and learning. A generalized text based real-time conversational agent or a chatbot development framework can be both conceptually and practically appealing, as a way to develop technologies to improve the responsiveness and customer friendliness of the chatbots. Different forms of technological advancements could help the firms to deploy right chatbot technology with regard to the user requirements in their organizations. This paper explores some product design ideas such as Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD) drawn from industrial engineering literature for chatbot development.
An Adaptive Fitting Approach for the Visual Detection and Counting of Small Circular Objects in Manufacturing Applications

Detecting, localizing and counting small circular objects in machine parts is an important task in many applications for manufacturing. Existing methods of circle detection face difficulties due to the high-curvature and limited edge points of circles. As a result, in this paper we propose a novel two-stage circle detection method, which integrates bottom-up coarse detection and top-down circle fitting. First, a circle detector combining low-level feature descriptors and a linear SVM is developed. This is used to scan an input image in a sliding window mode to detect small circles with coarse estimates of locations and scales. Next, a hierarchical Bayesian model performs a top-down adaptive circle fitting, with the ability to achieve a maximum a posteriori probability to fit circles to local image features. The evaluation of our approach with manufacturing images has demonstrated to be efficient in detecting small circles in machine parts.
Constructing Bayesian Network Graphs from Labeled Arguments

Bayesian networks (BNs) are powerful tools that are well-suited for reasoning about the uncertain consequences that can be inferred from evidence. Domain experts, however, typically do not have the expertise to construct BNs and instead resort to using other tools such as argument diagrams and mind maps. Recently, we proposed a structured approach to construct a BN graph from arguments annotated with causality information. As argumentative inferences may not be causal, we generalize this approach to include other types of inferences in this paper. Moreover, we prove a number of formal properties of the generalized approach and identify assumptions under which the construction of an initial BN graph can be fully automated.
E-commerce Distributed Chatbot System

Chatbot system attract huge interest in the recent years in many different fields in an attempt to increase the efficiency and shortens the business process execution time replacing the human-human communication with a human-machine conversations and queries in natural languages. Chatbots today have varying levels of capabilities as general purpose conversational agents or task-oriented chatbots as well as open-domain and domain-specific agent systems. While basic chatbots may be adequate for most scenarios, some scenarios require more advanced chatbots. The paper presents a distributed chatbot system for supply chain. The system comprises several services: chat service, bot service, natural language processing service as well as supply chain service. It uses WebSocket communication between user interface and the bot, analyzes the user's query and provides information of the queried orders and supplies.
Breast Cancer Diagnosis using Curvelet and Regional Features

Mammography is the most customarily used prior procedures to diagnose breast cancer. The preprocessing step is essential to improve the contrast in the mammogram image, in this paper top-hat and bottom-hat transforms are employed. This paper proposes the feature extraction method using curvelet transform in digital mammogram to distinguish the normal and abnormal breast cancer. The curvelet handles curves by utilising only a small quantity of coefficients, and it can also perform the curve discontinuities. It is a multiscale directional transform that permits sparse representation of objects with edges. The pectoral muscles and labels have been removed by the selection of Region of Interest (ROI). The proposed work uses morphological transform for preprocessing, followed by application of curvelet transform to enhance and sharpen the images finally wrapping is done by using fast Fourier transform. The features were extracted from the curvelet coefficients by applying gray level co-occurrance matrix and features were also extracted from the regional properties of the preprocessed image. The experimental result gives 98% accuracy of the classification of cancer classes using SVM classifier. From the result, it indicates that a curvelet transformation is a capable tool for analysis of digital mammogram.
Phenomenology and the Challenge of Virtuality

This piece explicates some chief modes of consciousness in phenomenology in order to show that a very significant challenge of virtuality surfaces both within, as well as outside of, the discipline. This issue is of no small importance today, where the difference between perception and imagination, real and irreal, as well as presence and absence, are all becoming increasingly vague because of new technologies and the intrinsic virtualities involved therein. In this context, the question is: Where does virtuality fit in such a picture? I will argue that phenomenology can start to account for such developments, although much more explicative work will be required in the future. With this in mind, sections two to four will articulate an initial phenomenology of perception, phantasy, and image-consciousness, as found chiefly in works by Husserl and Sartre. Then, section five will question the preceding phenomenological theory through some phenomenological (Heidegger) and non-phenomenological (Bergson and Deleuze) thinkers, who all seem to have a concept of virtuality at the heart of their work. Lastly, in the final two sections I will suggest a difference between real and irreal virtualities, and briefly mention some current virtual technologies in order to show that there is a constant and complex interplay between the real, irreal, and the virtual in many of our everyday experiences—an interplay that needs to be investigated much further if we are to make sense of how it is changing how we think and behave.
Clinical Profiles of Selected Biomarkers Identifying Infection and Cancer Patients: A Gorzów Hospital Example

Many pathobiological processes that manifest in a patient's organs could be associated with biomarker levels that are detectable in different human systems. However, biomarkers that promote early disease diagnosis should not be tested only in personalized medicine but also in large-scale diagnostic evaluations of patients, such as for medical management.We aimed to create an easy algorithmic risk assessment tool that is based on obtainable "everyday" biomarkers, identifying infection and cancer patients.We obtained the study data from the electronic medical records of 517 patients (186 infection and 331 cancer episodes) hospitalized at Gorzów Hospital, Poland, over a one and a half-year period from the 1st of January 2017 to the 30th of June 2018.A set of consecutive statistical methods (cluster analysis, ANOVA, and ROC analysis) was used to predict infection and cancer. For in-hospital diagnosis, our approach showed independent clusters of patients by age, sex, MPV, and disease fractions. From the set of available "everyday" biomarkers, we established the most likely bioindicators for infection and cancer together with their classification cutoffs.Despite infection and cancer being very different diseases in their clinical characteristics, it seems possible to discriminate them using "everyday" biomarkers and popular statistical methods. The estimated cutoffs for the specified biomarkers can be used to allocate patients to appropriate risk groups for stratification purposes (medical management or epidemiological administration).
ERC5a – An Enhanced RC5 Algorithm on Bit Propagation in the Encryption Function

In this paper presents an enhancement to the encryption function of RC5 algorithm which addresses the poor bit propagation or avalanche effect in the early rounds of the algorithm. This weakness has made RC5 to use excessive rounds in its encryption function. The enhanced RC5 algorithm (ERC5a) improves bit propagation as early as first round without using expensive operations in the design of RC6. Comparative security evaluation has been conducted for the proposed ERC5a and classic RC5. Simulation in C language both for RC5-32/12/16 and ERC5a-32/12/16 was performed by applying 36 million random plaintexts (64-bit) for each round resulted in a 74.41% increase in hamming distance in the first round, 35.48% in the second round, and 9.67% in the third round which means that the proposed ERC5a algorithm has a better bit propagation in its encryption function. Thus, the resulting enhancement can reduce the number of rounds, reduce memory requirement and achieve better performance for all existing RC5 implementations especially for lightweight devices where these properties are deemed significant.
An Audit Tool for Assessing the Visuocognitive Design of Infographics

Visuocognitive design accommodates the alignment of visualization to human cognitive processes. Established theory suggests that 1) recognition is easier than recall [1], 2) spatial visualizations are less abstract than temporal ones [2], and 3) aesthetics induce cognitive ease [3]. These principles, and others, underpin our new audit tool that focusses on design for cognition.
Smile Detection: Current Trends, Challenges and Future Perspective

From past decades, Smile remains as a common expression to express happiness in our daily life. A smile has the ability to bring out the basic hidden emotions, such as satisfaction and happiness. Smile detection is used in many applications such as automatic image capturing, distance learning systems, interactive systems, video conferencing, patient monitoring, and product rating. Smile detection system is mainly divided into two phase namely, features extraction and classification. So the accuracy of smile detection depends on both the phases. Numerous researchers and scholars have pointed out various approaches regarding smile detection in the recent years. This paper surveys the techniques used, results obtained and challenges faced by such approaches. Also, it compares their merits and limitations.
An Approach To Comment Analysis In Online Social Media

Social media became prevalent in our day to day life. People express their views, feelings, emotion via comments. Those comments entered by the user is analyzed. The Naïve Bayes Algorithm is used to detect the offensive and sarcastic comment, thus preventing them from being uploaded in social media.
A Finite Mixture of Weibull-Based Statistical Model for Texture Retrieval in the Complex Wavelet Domain

This paper presents a new statistical model for texture retrieval in the complex wavelet domain. For this purpose, a finite mixture of Weibull distributions (MoWbl) is proposed to characterize the statistical distribution of magnitudes of complex wavelet coefficients. Despite the ability of the mixture model on capturing a wide range of distribution shapes, choosing an appropriate number of mixture components is a challenging task. To this end, we adopt an unsupervised learning of the model parameters based on the Figueiredo-Jain algorithm and maximum-likelihood estimates. As found in all retrieval statistical-based frameworks, the presence of a similarity measure is trivial. Generally, the failure of a retrieval mixture based system is closely related to the choice of the similarity measure that relies mainly on approximations of some divergences and distances. To overcome this limitation, we propose a canonical form of Weibull distribution which allows us to develop an analytic expression of Cauchy-Schwarz divergence (CSD) for MoWbl distributions. Experiments, conducted on three popular datasets, show that the proposed model yields better performance in terms of goodness-of-fit, retrieval, and execution time compared to some related statistical models for texture retrieval.
Evaluating the performance of different classification methods on medical X-ray images

X-ray images are broadly used for diagnosis and analysis in medical science. In a number of medical applications, such as disease detection, automatic classification systems are very beneficial. Therefore, finding an appropriate classification technique is helpful. In this paper, it is aimed to compare the performance of different classification methods on categorizing X-ray images. In this regard, we have done our experiments in two phases. In phase 1, the effect of wavelet transformation (WT) as a feature selection method is evaluated. Obtaining almost similar performance (about 78%) for probabilistic neural network (PNN) and WT-based PNN, while reducing computational cost, leads us to apply WT on datasets in phase 2. During phase 2, random forest (RF), decision tree (DT), support vector machine (SVM) and Naive Bayes (NB) are applied on obtained images from WT. The results reveal better performance of RF compared to other methods by 82% accuracy. With very close accuracy, SVM got the next place (81%). DT and NB classifiers are the next ones by about 66% accuracy.
Improvement of K-Means Performance Using a Combination of Principal Component Analysis and Rapid Centroid Estimation

Abstract K-Means is a simple clustering algorithm, this method starts with randomizing partitions and continuing to reassign samples to clusters based on similarities between data. However, the K-Means method has several disadvantages, including determining the initial cluster center value done randomly, and the distance model used in determining the similarity between data where conventional distance models have the same effect on each data attribute. In this study will try to improve the performance of K-Means by using a combination of Principal Component Analysis (PCA) and Rapid Centroid Estimation (RCE). PCA will determine the weight of each attribute data based on eigen value, and RCE is used to determine the beginning of the cluster center. To see the performance of the proposed method, this research will use 3 datasets obtained from the UCI Repository, including ionosphere, iris, and wine. Analysis of the performance of the proposed method is only measured based on MSE and SSE. The results of this study indicate that the PCA and RCE methods were able to improve the performance of K-Means, the highest performance improvement based on MSE was found in iris data, which amounted to 56.76%, while based on SSE occurred in the ionosphere data which was 86.08%.
Image classification with Local Directional Decoded Ternary Pattern

This paper presents an efficient handcrafted texture operator for texture modeling and classification. The proposed descriptor, referred to as local directional decoded ternary pattern (LDDTP), consists in encoding both directional pattern features and contrast information in a compact way based on local derivative variations. The proposed operator first computes for each pixel within its 3×3 overlapping square neighborhood, on the one hand, central edge response through the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> derivative of Gaussian filter, and on the other hand, eight directional edge responses using the eight Frei-Chen masks to capture more detailed information. Then, spatial relationships among the neighboring pixels through the generated edge responses are exploited independently with the help of the concepts of LTP and LDP operators to enhance the discriminative power. Finally, the produced LDDTP pattern is splitted into two distinct parts: local directional decoded ternary pattern lower ( LDDTPL) and local directional decoded ternary pattern upper ( LDDTPU), which are combined into hybrid distributions to form the final LDDTP feature descriptor. Experimental results on eight publicly available texture datasets showed that the proposed LDDTP descriptor achieves classification performance, which is competitive or better than several old and recent state-of-the-art LBP variants.