Reviewerly/exHarmony huggingface dataset:
Knowledge Management in the Expert Model of the Smart Tutoring System

Several studies have identified the importance of knowledge domain in the intelligent tutoring system. However, this system required a knowledge processing especially in the expert model. In this paper, we report that the combination of the state of the activity (Implicit/ explicit) and the knowledge processing of: declarative, procedural and conditional knowledge is mandated to compare the learner's behavior with that of an expert in regard to assessing their knowledge. The implementation of the expert model problem will be executed using the Bayesian network. For that purpose, the integration of the Bayes Network will be: the probability of the types of Knowledge (Declarative, Procedural or Conditional) and the choice is based on two criteria; the first one is the type of assessment (Memorization, Administration, Expertise) and the status (Explicit/Implicit).
An ILP Model for Multi-Label MRFs With Connectivity Constraints

Integer Linear Programming (ILP) formulations of multi-label Markov random fields (MRFs) models with global connectivity priors were investigated previously in computer vision. In these works, only Linear Programming (LP) relaxations [1] or simplified versions [2] of the problem were solved. This paper investigates the ILP of MRF with exact connectivity priors via a branch-and-cut method, which provably finds globally optimal solutions. It enforces connectivity priors iteratively by a cutting plane method, and provides feasible solutions with a guarantee on sub-optimality even if we terminate it earlier. The proposed ILP can be applied as a post-processing method on top of any existing multi-label segmentation approach. As it provides globally optimal solution, it can be used off-line to serve as quality check for any fast on-line algorithm. Furthermore, the scribble based model presented in this paper could be potentially used to generate ground-truth proposals for any deep learning based segmentation. We demonstrate the power and usefulness of our model by extensive experiments on the BSDS500 and PASCAL VOC dataset. The experiments show that our proposed model achieves great performance, yielding provably global optimum in most instances and that provably good optimization solutions also provide good segmentation accuracy, even with the limited computing time of few seconds.
Data placement strategies that speed-up distributed graph query processing

We consider the problem how to optimize the data distribution to improve the query performance in distributed RDF stores running on compute node clusters. When hash-based data distribution strategies are used, the query workload tends to be equally balanced among all compute nodes whereas graph-clustering-based approaches reduce the number of transferred intermediate results. Our hypothesis is that data distribution strategies that collocate entities in small sets of closely connected data items may be able to combine the advantages of both strategies. To investigate this hypothesis, we analyze two such data distribution strategies: 1. Overpartitioned minimal edge-cut cover. 2. Our novel molecule hash cover. Our analysis substantiates our hypothesis by explaining the causes for their good performance. Both strategies reduce query execution time on our set of test queries (between 5% and 98%). While overpartitioned minimal edge-cut cover fares best, when it can be computed, it may lack scalability for large datasets. Our novel molecule hash cover combines scalability and major improvements of query execution time against various baseline strategies.
Good money drives out bad: Introduction to the featured section on “The evolution of diverse e-money: Digital-community currencies and cryptocurrencies”

This article introduces the special issue featuring “The evolution of diverse e-money: digital-community currencies and cryptocurrencies,” and argue on diversity and evolution of modern money, including community currencies and cryptocurrencies. How such new “currencies” survive through users’ choice in money and what the criteria of such decision are highly critical issues. We contrast the meanings of Gresham’s law “Bad money drives out good,” and Hayek’s principle of choice in money “Good money drives out bad” and show that there are necessary conditions for either Gresham’s law or the principle of choice in money to hold. For the principle of “choice in currency” to function well, both (1) different denominations for the distinction of money in quality, and (2) the non-fixed exchange rates are necessary. Since cryptocurrencies met these conditions, the principle of choice in money began to work. Cryptocurrencies took the test of users’ choice in money in the search for good money but failed to pass the criteria of stability of currency value. Then, digital-community currencies that involve local and community contexts are the next candidates for good money. We observe that two DCCs in Japan, Sarubobo Coin and Kisarazu Coin are currently challenging toward the realization of good money.
Gaze-guided Magnification for Individuals with Vision Impairments

Video-based eye trackers increasingly have potential to improve on-screen magnification for low-vision computer users. Yet, little is known about the viability of eye tracking hardware for gaze-guided magnification. We employed a magnification prototype to assess eye tracking quality for low-vision users as they performed reading and search tasks. We show that a high degree of tracking loss prevents current video-based eye tracking from capturing gaze input for low-vision users. Our findings show current technologies were not made with low vision users in mind, and we offer suggestions to improve gaze-tracking for diverse eye input.
Android Smells Detection Using ML Algorithms with Static Code Metrics

Mobile applications development rate is predominantly increasing in comparison with the regular applications. These mobile applications prove to be change frequently according to the user requirements. Moreover, these changes in the code base may introduce some bad design practices that are called as bad smells, which can lead to a higher maintenance cost and degrade quality of the software. A very less attention has been given in the detection of code smells in the mobile applications that are also called as android smells. This research contains the rules in combination of software metrics and their threshold values to detect the bad smells in the android applications. The proposed rules are computed using three different machine learning algorithms. This framework has been applied to 2896 instances of the android applications which are open-sourced on GitHub. The android code smells MIM, LIC, DTWC and SL have been considered for the generation of detection rules and are validated using 10-fold cross validation method. The machine learning algorithm JRip furnished the best result for the android smells up to 90% overall precision, which is quite sufficient to justify the results.
Distance Metric Learned Collaborative Representation Classifier(DML-CRC)

Any generic deep machine learning algorithm is essentially a function fitting exercise, where the network tunes its weights and parameters to learn discriminatory features by minimizing some cost function. Though the network tries to learn the optimal feature space, it seldom tries to learn an optimal distance metric in the cost function, and hence misses out on an additional layer of abstraction. We present a simple effective way of achieving this by learning a generic Mahalanabis distance in a collaborative loss function in an end-to-end fashion with any standard convolutional network as the feature learner. The proposed method DML-CRC gives state-of-the-art performance on benchmark fine-grained classification datasets CUB Birds, Oxford Flowers and Oxford-IIIT Pets using the VGG-19 deep network. The method is network agnostic and can be used for other similar classification tasks.
Image dehazing using MSRCR algorithm and morphology based algorithm : A Concise Review

Nowadays quality of the image is very important. But the images captured by cameras are affected by different atmospheric conditions such as haze, fog rain etc. These conditions adversely affect the visibility of the images and reduces the contrast. It is very much adverse in applications especially in military. Since the light arrives at the lens of the camera after reflection from the air particles, a large amount of fine details in the image are lost. So it is necessary to use dehazing techniques for hazy images. Image dehazing methods involves a number of algorithms to improves the quality of the images . The base of dehazing algorithms is the atmospheric scattering model and the dark channel of the image. By applying the different algorithms, we can recover the dehazed image. In this paper we compared two such algorithms, one using morphology operation based and other using MSRCR algorithm. Both of these algorithms uses a single image restoration technique and same metrics for quantitative analysis.
Exploring performance of thumb input for pointing and dragging tasks on mobile device

Thumb based interaction is becoming increasingly popular in mobile devices. However, the interaction still remains slow, ambiguous, and error-prone. This paper presents an exploratory user experiment results of one-thumb pointing and dragging task performance, based on three factors: mobile size, target size, and posture (sitting and walking positions). Beside obvious findings, we observed some surprising scenarios, such as the gripping style of most users was casual and did not follow any formal model or structure. We concluded our experiences into design implications with respect to mobile size, posture, and gripping styles.
“The simplest protocol for oblivious transfer” revisited

In 2015, Chou and Orlandi presented an oblivious transfer protocol that already drew a lot of attention both from theorists and practitioners due to its extreme simplicity and high efficiency. Chou and Orlandi claimed that their protocol is universally composable secure (UC-secure) in the random oracle model under dynamic corruptions. UC-security is a very strong security guarantee that assures that, not only the protocol in itself is secure, but can be also used safely in larger protocols. Unfortunately, in this work we point out a flaw in their security proof for the case of a corrupt sender. In more detail, we define a decisional problem and we prove that, if a correct security proof for the Chou and Orlandi's protocol is provided, then this problem can be solved correctly with overwhelming probability. Therefore, the protocol of Chou and Orlandi cannot be instantiated securely with groups for which our decisional problem cannot be solved correctly with overwhelming probability. Consequently, the protocol of Chou and Orlandi cannot be instantiated with all groups G in which the CDH problem is intractable, but only with groups in which both the CDH problem is intractable and our decisional problem can be solved with overwhelming probability. After the appearance of our work, Chou and Orlandi acknowledged the problems we pointed out in their security proof and subsequent works showed additional issues, removing the claims of UC security of their protocol.
Feedback evaluations to promote image captioning

Image captioning can be treated as a policy gradient problem. A retrieval model to obtain the discriminability score to distinguish between two images, given the caption for one of them, has been proposed previously; the discriminability score and one of the image captioning evaluation metrics were optimised using policy gradient. Based on this, two methods to evaluate the caption and caption-generating process, referred to as feedback evaluations, are proposed in this study. The results of the evaluations were used to improve the model. First, an auxiliary retrieval loss (ARL) is introduced to evaluate the generated caption to improve the discriminability of the model. ARL has been utilised as a feedback evaluation method because it calculates similarity between the generated caption and convolutional neural network features. With ARL, a higher similarity and better discriminability were achieved. Second, an evaluation reward is introduced to evaluate the captioning process. With ER, the overall evaluation metrics can be improved. A policy gradient was used, and a captioning model could be trained by jointly adjusting the captioning process and captioning itself. The attention long short-term memory network was trained with ARL and ER successively and it demonstrated state-of-the-art performance on the COCO database.
Scalable Multicamera Heterogeneous Processing Architecture for Phase Measuring Profilometry

Despite the rapid development of 3D imaging and measurement technology in recent years, achieving high speed and high accuracy 3D measurement is still challenging. Innovative pattern diagram coding technology, flexible and high-precision system calibration methods, and parallel computing hardware platforms are proposed to improve measurement accuracy and speed. The multi-frequency phase shifting method combined with a least-squares based calibration model can provide high-precision 3D reconstruction results at the expense of speed. A heterogeneous processing architecture based on FPGA and CPU is proposed to accelerate the algorithm. From the perspective of optimal computing resource efficiency, we innovatively separate the phase calculation of 16-pattern scheme from point cloud computing, and reasonably allocate software and hardware resources. The hardware resources for a single camera are further multiplexed and expanded into an FPGA structure that can support multi-camera parallel phase calculations. Adding a small amount of resources can get a doubling of performance. Experiments show that each camera in the multi-camera system can independently reconstruct and display images with a resolution of 640 × 480 at a rate of 50.8 frames per second. The fusion using the reconstruction results of different perspectives of multiple cameras can not only make up for occlusion points, but also improve the measurement accuracy. Standard ball measurement experiments show that the standard deviation of the fusion results can reach 0.013mm. The multi-camera heterogeneous processing architecture presented in this paper demonstrates the great applicability to the requirements of high-precision real-time 3D measurement in scientific and engineering applications.
Despeckling algorithm for reducing speckle noise in images generated from active sensors

Synthetic aperture radar (SAR) images can be utilised in various fields because they are not affected by the time of day or weather conditions. However, in the process of employing active sensors to obtain a SAR image, speckle noise is generated in the image. Speckle noise degrades the ability of computer vision to observe the Earth. To improve imaging performance, an algorithm for removing speckle noise is necessary. For this purpose, the authors propose a speckle-noise removal algorithm. A speckle reducing anisotropic diffusion filter is employed as a pre-processing filter, in which multiplicative speckle noise can be converted into additive noise using a logarithmic transformation. To remove the additive noise, they use a weighted guided image filter. Experimental results indicate that the proposed method exhibits improved speckle noise suppression and edge preservation results compared with those of existing methods.
Solar Power Estimation Using GIS Considering Shadow Effects for Distribution System Planning

During the last decades, photovoltaic (PV) panels have been widely installed in Japanese residential areas to achieve clean and renewable energy. This work employs DSM (Digital Surface Model) layers to extract important elevations, such as trees and buildings, in conjunction with assumptions of cloud movement processes from GIS (Geographic Information System) to obtain realistic solar power affected by solar radiation shading on the PV panel. With GIS technology, the solar radiation shading effects are combined with distribution system modeling to observe the significant impact of shadows in the overall distribution system. Moreover, this research can facilitate a more effective distribution system planning by evaluating the influence of PV power variations, assuming a large number of installations.
Adaptive iterative global image denoising method based on SVD

Based on the image self-similarity and singular value decomposition (SVD) techniques, the authors propose an iterative adaptive global denoising method. For the structural differences between image patches, they adaptively determine the size of the search window. In each window, a similar image patch matrix is constructed based on the multi-scale similarity measure. In order to ensure the speed of the method, the adaptive step size and the number of image patches are introduced, and all image patches are denoised in different iterations. This not only ensures the speed of the method, suppresses residual noise, but also reduces the artefacts caused by the fixed step size and the number of image patches. Therefore, the problem of image denoising is converted to the estimation of low-rank matrix. New singular values are estimated according to the noise level, and similar image patch matrices without noise are estimated using them and corresponding singular vectors. Experimental results show that compared with the state-of-the-art denoising algorithms, this method has a higher PSNR and FSIM, and has a good visual effect. The new method can be applied to image and video restoration, target recognition and image classification.
Hierarchical Diagnostic Classification Modeling of Reading Comprehension

The Hierarchical Diagnostic Classification Model (HDCM) reflects on the sequences of the presentation of the essential materials and attributes to answer the items of a test correctly. In this study, a foreign language reading comprehension test was analyzed employing HDCM and the generalized deterministic-input, noisy and gate (G-DINA) model to determine and compare respondents’ mastery profiles in the test’s predefined skills and to illustrate the relationships among the attributes involved in the test to capture the influence of sequential teaching of materials on increasing the probability of getting an item a correct answer. Furthermore, Differential Item Functioning (DIF) analysis was applied to detect whether the test functions as a reason for the gender gap in participants’ achievement. Finally, classiﬁcation consistency and accuracy indices are studied. The results showed that the G-DINA and one of the HDCMs fit the data well. However, although the results of HDCM showed the existence of attribute dependencies in the reading comprehension test, the relative fit indices highlight a significant difference between the G-DINA and HDCM, favoring G-DINA. Moreover, results indicate that there is a significant difference between males and females in six items in favor of females. Besides, classiﬁcation consistency and accuracy indices specify that the Iranian University Entrance Examination holds a 71% chance of categorizing a randomly selected test taker consistently on two distinct test settings and a 78% likelihood of accurately classifying any randomly selected student into the true latent classes. As a result, it can be concluded that the Iranian University Entrance Examination can be considered as a valid and reliable test.
Imageability Estimation using Visual and Language Features

Imageability is a concept from Psycholinguistics quantizing the human perception of words. However, existing datasets are created through subjective experiments and are thus very small. Therefore, methods to automatically estimate the imageability can be helpful. For an accurate automatic imageability estimation, we extend the idea of a psychological hypothesis called Dual-Coding Theory, that discusses the connection of our perception towards visual information and language information, and also focus on the relationship between the pronunciation of a word and its imageability. In this research, we propose a method to estimate imageability of words using both visual and language features extracted from corresponding data. For the estimation, we use visual features extracted from low- and high-level image features, and language features extracted from textual features and phonetic features of words. Evaluations show that our proposed method can estimate imageability more accurately than comparative methods, implying the contribution of each feature to the imageability.
Applying Fourier Inspired Windows for Concept Drift Detection in Data Stream

Detecting concept drift by mining data stream is essentially based on two parameters - (a) the window length for observing the data, and (b) the threshold for the difference in certain properties of the data observed to confirm the drift. Efforts are mostly focused on the evaluation the statistical properties or building models using the data in the window. Less focus has been given in arriving at the size of the window length. This paper presents a Fourier analysis based mechanism to determine the window length and also presents a simple but novel algorithm for drift detection. The work is termed as Fourier Inspired Windows for Concept Drift detection (FIWCD). Fourier-inspired window slides over the stream till no drift are suspected and model parameters are gradually updated as and when required. Once a drift is suspected, the trend is observed and new model parameters are computed for the transitory phase. The transitory model and previous concept model are compared to confirm or reject the drift. Performance of proposed drift detection technique has also been compared with two popular drift detection techniques namely, Change Point Detection and Hypothesis Testing. Experiment with three commonly used public data sets reflects that FIWCD exhibits better resemblance to the ground truth.
iSparse: Output Informed Sparsification of Neural Network

Deep neural networks have demonstrated unprecedented success in various multimedia applications. However, the networks created are often very complex, with large numbers of trainable edges that require extensive computational resources. We note that many successful networks nevertheless often contain large numbers of redundant edges. Moreover, many of these edges may have negligible contributions towards the overall network performance. In this paper, we propose a novel iSparse framework and experimentally show, that we can sparsify the network without impacting the network performance. iSparse leverages a novel edge significance score, E, to determine the importance of an edge with respect to the final network output. Furthermore, iSparse can be applied both while training a model or on top of a pre-trained model, making it a retraining-free approach - leading to a minimal computational overhead. Comparisons of iSparse against Dropout, L1, DropConnect, Retraining-Free, and Lottery-Ticket Hypothesis on benchmark datasets show that iSparse leads to effective network sparsifications.