Reviewerly/exHarmony huggingface dataset:
Clustering-based proxy measure for optimizing one-class classifiers

One-class classification is a type of unsupervised learning task wherein only information on the target class is available and that on other classes is not. As this corresponds to many real-world data scenarios, considerable research efforts have been focused on developing one-class classification algorithms to build well-performing one-class classifiers. The performance of one-class classifiers largely depends on their hyperparameters. However, optimizing hyperparameters is inherently difficult and challenging because conventional validation procedures are not directly applicable in the absence of non-target class information. The existing methods have focused on specific one-class classification algorithms, and therefore, could not be extended to other algorithms. In this study, we present a clustering-based proxy measure to evaluate the quality of different hyperparameter candidates for a one-class classification algorithm when only the target class information is given. The proposed proxy measure utilizes conventional measures of classification performance by introducing pseudo non-target classes that are derived based on clustering. The best hyperparameters for the algorithm can be estimated by alternatively optimizing this measure. It does not require incorporating any prior knowledge, and can be implemented with various types of one-class classification algorithms. We demonstrate the effectiveness through experiments comprising benchmark problems.
Single Seekers Society (SSS): Bringing together heuristic optimization algorithms for solving complex problems

This paper introduces a new metaheuristic, Single Seekers Society (SSS) algorithm, for solving unconstrained and constrained continuous optimization problems. The proposed algorithm aims to simulate the behaviour of a group of people living together, both individually and holistically. The SSS algorithm brings together several single-solution based search algorithms, single seekers, while realizing an information sharing mechanism based on the superposition principle and the reproduction procedure. Each single seeker tries to improve one single solution by using randomly generated parameter set until the stopping condition is reached. Then, the SSS algorithm exchanges partial information between the best solutions identified by the single seekers via the reproduction process. This characteristic generates new solutions to set as the starting point of the single seekers for their next run and provides a satisfactory level of diversification for the SSS algorithm. Additionally, the SSS algorithm determines a target point via the superposition principle at each iteration to make the single seekers to direct their discovery towards this target point. Thus, the SSS algorithm has the feature providing to share the information produced by the single seekers through the reproduction and the superposition principle. The performance of the proposed SSS algorithm is tested on the well-known unconstrained and constrained continuous optimization problems through a set of computational studies. This paper compares SSS algorithm against 27 and 17 different search algorithms on unconstrained and constrained problems, respectively. The experimental results indicate the stability and the effectiveness of the SSS algorithm in terms of quality of produced results, achieved level of convergence and the capability of coping with trapping in local optima and stagnation problems.
Textual Sentiment of Chinese Microblog Toward the Stock Market

In this paper, text mining and statistical models are deployed to explore the relationship between the Shanghai Stock Exchange Composite Index (SSECI) and the collective emotions of individual investors. The emotions of individual investors are quantified by extracting and aggregating investor online posts that contain finance-related keywords. To identify a set of finance-related keywords, three years of blogs from a famous financial blog site are segmented by an automatic text segmentation method; meanwhile, in the literature of social media, people typically select keywords manually. Posts that discuss the keywords are extracted out of all types of topics from Sina Weibo, the largest microblog platform in China. Statistical results reveal the relationship between daily posts and daily opening prices with a one-day lag, which indicates the existence of information (news) propagation lag. This study contributes to the existing literature by demonstrating that the microblog sentiment level reports can be quantitatively incorporated as a proxy to provide valuable support to portfolio decision making.
Using semi-independent variables to enhance optimization search

In this study, the concept of a semi-independent variable (SIV) problem representation is investigated that embodies a set of expected or desired relationships among the original variables, with the goal of increasing search effectiveness and efficiency. The proposed approach intends to eliminate the generation of infeasible solutions associated with the known relationships among the variables and cutting the search space, thereby potentially improving a search algorithm's convergence rate and narrowing down the search space. However, this advantage does not come for free. The issue is the multiplicity of SIV formulations and their varying degree of complexity, especially with respect to variable interaction. In this paper, we propose the use of automatic variable interaction analysis methods to compare and contrast different SIV formulations. The performance of the proposed approach is demonstrated by implementing it within a number of classical and evolutionary optimization algorithms (namely, interior-point algorithm, simulated annealing, particle swarm optimization, genetic algorithm and differential evolution) in the application to several practical engineering problems. The case study results clearly show that the population-based algorithms can significantly benefit from the proposed SIV formulation resulting in better solutions with fewer function evaluations than in the original approach. The results also indicate that an automatic variable interaction analysis is capable of estimating the difficulty of the resultant SIV formulations prior to any optimization attempt.
A Continuous Random Walk Model With Explicit Coherence Regularization for Image Segmentation

Random walk is a popular and efficient algorithm for image segmentation, especially for extracting regions of interest (ROIs). One difficulty with the random walk algorithm is the requirement for solving a huge sparse linear system when applied to large images. Another limitation is its sensitivity to seeds distribution, i.e., the segmentation result depends on the number of seeds as well as their placement, which puts a burden on users. In this paper, we first propose a continuous random walk model with explicit coherence regularization (CRWCR) for the extracted ROI, which helps to reduce the seeds sensitivity, so as to reduce the user interactions. Then, a very efficient algorithm to solve the CRWCR model will be developed, which helps to remove the difficulty of solving huge linear systems. Our algorithm consists of two stages: initialization by performing one-dimensional random walk sweeping based on user-provided seeds, followed by the alternating direction scheme, i.e., Peaceman-Rachford scheme for further correction. The first stage aims to provide a good initial guess for the ROI, and it is very fast since we just solve a limited number of one-dimensional random walk problems. Then, this initial guess is evolved to the ideal solution by applying the second stage, which should also be very efficient since it fits well for GPU computing, and 10 iterations are usually sufficient for convergence. Numerical experiments are provided to validate the proposed model as well as the efficiency of the two-stage algorithm.
Evaluating reading fluency behavior via reading rates of elementary school students reading e-books

As technology advances, paper-based text is transitioning to e-books, and this affects every aspect of educational practices. Fostering student literacy is critical to success. Reading ability is an important skill that allows young students to acquire knowledge. Several researchers have noted that fluency is highly correlated with reading comprehension ability. In the past, however, it was difficult to track students' reading behavior during a paper-based learning process. In this study, we establish an e-book-based detection system to record the reading process of students in the lower grades of elementary school in order to evaluate reading fluency. This study's goals are as follows: (1) to design and implement a suitable reading assessment tool which can better assess student's reading literacy, and (2) to compare the e-book system with the traditional paper-based manner of assessing reading fluency. The e-book-based detection system gives teachers the ability to evaluate reading fluency using real-time data during the reading process.
Single image fast deblurring algorithm based on hyper‐Laplacian model

An improved single image fast deblurring algorithm based on hyper-Laplacian constraint is proposed. The algorithm is improved in three aspects: image blur kernel estimation sub-region selection, blur kernel precise estimation, and fast non-blind deconvolution. First, image amplitude and gradient are used as the basis of blur kernel estimation. On the basis of analysing the edge amplitude and gradient of the image, the image sub-region for blur kernel estimation is selected. Then the sparsity of the blur kernel is restricted by hyper-Laplacian, and the fast solving mode of alternately solving different variables is designed. The blur kernel information is accurately estimated. In the fast non-blind deconvolution restoration phase of the image, the regularised constraint term of the hyper-Laplacian model is improved and the image gradient distribution is constrained. The blind growth trend of the regional gradient near the strong edge can be suppressed well, and the deblurred image with clear edge structure is generated. Experimental results show that the proposed algorithm can achieve better image deblurring effect and high efficiency.
Fully homomorphic encryption over the integers for non-binary plaintexts without the sparse subset sum problem

In this work, we solve the open problem of designing a fully homomorphic encryption scheme over the integers for non-binary plaintexts in ZQ for prime Q (Q-FHE-OI) without the hardness of the sparse subset sum problem (SSSP). Furthermore, we show that our Q-FHE-OI scheme is a useful optimization for evaluating arithmetic circuits on encrypted data for some primes. To that end, we provide a natural extension of the somewhat homomorphic encryption (SHE) scheme over the integers proposed by Cheon and Stehlé (Eurocrypt 2015) to support non-binary plaintexts. Then, a novel bootstrapping algorithm is proposed for this extended SHE scheme by introducing generalizations of several functions in binary arithmetic. As a result, we obtain a Q-FHE-OI scheme for any constant-sized prime Q≥3 without the hardness of the SSSP, whose bootstrapping algorithm is asymptotically as efficient as previous best results. Beyond that, we compare the efficiency of our scheme against a Q-FHE-OI scheme obtained by emulating mod-Q gates with boolean circuits as proposed by Kim and Tibouchi (CANS 2016). Our analysis indicates our proposed scheme performs better for prime Q up to 11287, which improves on the result of Kim and Tibouchi, who showed there is at most one prime, Q=3 where the Q-FHE-OI scheme by Nuida and Kurosawa (Eurocrypt 2015) is a better approach. This overturns our previous understanding that Q-FHE-OI schemes do not provide significant benefits.
Designing for playfulness through compassion: design for advanced dementia

Those who have the greatest need for excellent design are often the most vulnerable in society. These people may find it difficult or impossible to articulate their design requirements due to physical, sensory or memory impairment as a result of accident or disease. Finding ways to understand the challenges they face moment-by-moment and day-by-day is vital. Empathic and compassionate approaches that place an individual central to the design process can inform outcomes that significantly benefit the user as well as those that care for them.This paper presents research that aims to support the wellbeing of people living with advanced dementia by designing to promote pleasure and positive emotion. The LAUGH project is a recently completed international three-year UK AHRC funded design research project. Outputs from the study include a collection of playful objects, designed to provide comfort, pleasure and fun. This paper describes the research process underpinning the development of the designs and the Compassionate Design methodology that has informed the work.The design narratives behind the playful objects will be explained in relation to the three key themes of Compassionate Design, which stress the importance of personalization, sensory stimulation and maintaining connections between people and the world.
A Multi-Step Nonlinear Dimension-Reduction Approach with Applications to Big Data

In this paper, a novel dimension-reduction approach is presented to overcome challenges such as nonlinear relationships, heterogeneity, and noisy dimensions. Initially, the p attributes in the data are first organized into random groups. Next, to systematically remove redundant and noisy dimensions from the data, each group is independently mapped into a low dimensional space via a parametric mapping. The group-wise transformation parameters are estimated using a low-rank approximation of distance covariance. The transformed attributes are reorganized into groups based on the magnitude of their respective eigenvalues. The group-wise organization and reduction process is performed until a user-defined criterion on eigenvalues is satisfied. In addition, novel procedures are introduced to aggregate the transformation parameters when the data is available in batches. Overall performance is demonstrated with extensive simulation analysis on classification by employing 10 data-sets.
Data collection path planning with spatially correlated measurements using growing self-organizing array

Data collection path planning is a problem to determine a cost-efficient path to read the most valuable data from a given set of sensors. The problem can be formulated as a variant of the combinatorial optimization problems that are called the price-collecting traveling salesman problem or the orienteering problem in a case of the explicitly limited travel budget. In these problems, each location is associated with a reward characterizing the importance of the data from the particular sensor location. The used simplifying assumption is to consider the measurements at particular locations independent, which may be valid, e.g., for very distant locations. However, measurements taken from spatially close locations can be correlated, and data collected from one location may also include information about the nearby locations. Then, the particular importance of the data depends on the currently selected sensors to be visited by the data collection path, and the travel cost can be saved by avoiding visitation of the locations that do not provide added value to the collected data. This is a computationally challenging problem because of mutual dependency on the cost of data collection path and the possibly collected rewards along such a path. A novel solution based on unsupervised learning method called the Growing Self-Organizing Array (GSOA) is proposed to address computational challenges of these problems and provide a solution in tens of milliseconds using conventional computational resources. Moreover, the employed GSOA-based approach allows to exploit capability to retrieve data by wireless communication or remote sensing, and thus further save the travel cost.
Geogas prospecting for buried deposits under loess overburden: Taking Shenjiayao gold deposit as an example

The loess areas cover about 6% of China's land and they are thought to have a high likelihood of hosting valuable mineral resources. General geochemical exploration methods are restricted in this region due to the thick loess overburden. Here, we used geogas prospecting, which has been uniquely developed in China, to test its ability to delineate buried deposits at Shenjiayao gold deposit. Two different media: 5% ultrapure aqua regia and polyurethane foam, were used to sample the geogas in the soil. Based on concentrations and precision in our experiment, the polyfoam seemed to out-perform the aqua regia. To investigate how the anomalies formed, a special carrier was used to sample original substances in the geogas. Nanoscale gold-bearing particles were found both in the geogas, supporting the hypothesis that metallic nanoparticles released from orebodies and altered rocks during weathering process can be transported towards the surface. Besides the evidence of nanoparticles, correlations of elements in ores and geogas indicated the close connection between them. Therefore, our field experiment showed geogas prospecting can be effective in exploration for buried mineral resources.
A Randomized Gradient-Free Attack on ReLU Networks

It has recently been shown that neural networks but also other classifiers are vulnerable to so called adversarial attacks e.g. in object recognition an almost non-perceivable change of the image changes the decision of the classifier. Relatively fast heuristics have been proposed to produce these adversarial inputs but the problem of finding the optimal adversarial input, that is with the minimal change of the input, is NP-hard. While methods based on mixed-integer optimization which find the optimal adversarial input have been developed, they do not scale to large networks. Currently, the attack scheme proposed by Carlini and Wagner is considered to produce the best adversarial inputs. In this paper we propose a new attack scheme for the class of ReLU networks based on a direct optimization on the resulting linear regions. In our experimental validation we improve in all except one experiment out of 18 over the Carlini-Wagner attack with a relative improvement of up to 9%. As our approach is based on the geometrical structure of ReLU networks, it is less susceptible to defences targeting their functional properties.
Exploiting forced alignment of time-reversed data for improving HMM-based handwriting segmentation

The segmentation of unconstrained handwriting is an important issue for both recognition and synthesis systems. In this direction, hidden Markov model (HMM) has been the most popular method for segmentation of continuous handwriting. It has been employed in both implicit and explicit segmentation-based recognition systems. The main advantage of HMM-based force alignment method is that it can segment the handwriting to basic units (e.g. characters) without any knowledge of ground truth boundaries. However, the alignment acquired from an HMM framework often fails to segment the basic units to their actual boundaries. This paper analyzes the segmentation errors made by the HMM in details and identifies an early character boundary problem. Accordingly, an automatic boundary correction method is proposed which utilizes both the forward and the reverse direction alignments of handwritten data with its transcription. This improves the alignment of data and thus the character boundaries in cursive handwriting. The proposed method enhances the word recognition performance for both the Gaussian mixture model (GMM)-HMM and deep neural network (DNN)-HMM frameworks compared to the conventional approach, when evaluated on UNIPEN ICROW-03 and IAM-onDB databases.
Precise pose and radius estimation of circular target based on binocular vision

A novel method of estimating circle pose and radius is proposed, based on binocular vision. This method uses the special vectors that are irrelevant to the unknown radius to calculate the normal vector of the circular plane, which is the foundation for calculating the vanishing line of the circular plane in the imaging plane. The projections of the circle center on the left and right image are calculated afterwards through the vanishing line. Then, the circle center is reconstructed by the triangulation technology of binocular stereo vision. Finally, the radius of the circular target is calculated through the relative positions of points in the same coordinate system. The estimates of normal vector, center position, and radius size are unique and definite, so there is no need for a subsequent screening process. When the radius is unknown, most of the conventional circle pose estimation methods are unusable. However, the proposed method has no prerequisites about the circular target in order to estimate the pose and radius of the circular target, which improves flexibility and extends the application field. Besides estimating the pose of a circular target of unknown radius, the proposed method can also be applied to the vision measurement of the radius of a circular object. Both actual measurement experiments and noise simulation experiments are performed. The results show that the proposed method is more precise and robust than conventional methods.
Automatic Lock of Cursor Movement: Implications for an Efficient Eye-Gaze Input Method for Drag and Menu Selection

This study proposed a method-automatic lock of cursor movement (ALCM)-that locks a cursor at the center of a target at the instant the cursor enters the target. The method is intended to suppress irritating subtle cursor movements that occur when an eye-gaze input system transforms involuntary eye movement (e.g., drift) into cursor coordinates. The effectiveness of the proposed ALCM was verified using pointing performance (speed and accuracy) in two types of HCI tasks. In a drag task, we compared mouse input versus eye-gaze input with use of a backspace (BS) key or voice input. The key or voice facilitates target selection once the eye gaze was aligned with a target. In a menu selection task, we also compared mouse input with eye-gaze and the use of two voice input conditions. This task required gaze alignment with a menu and menu item by use of voice input for selection. Whether the ALCM function was added to the eye-gaze input system or not was a within-subject factor. The input method and target sizes were within-subject factors. The study concluded that the ALCM improved pointing accuracy for all eye-gaze input methods and all two tasks.
Evaluating Model-Driven Development Claims with Respect to Quality: A Family of Experiments

Context: There is a lack of empirical evidence on the differences between model-driven development (MDD), where code is automatically derived from conceptual models, and traditional software development method, where code is manually written. In our previous work, we compared both methods in a baseline experiment concluding that quality of the software developed following MDD was significantly better only for more complex problems (with more function points). Quality was measured through test cases run on a functional system. Objective: This paper reports six replications of the baseline to study the impact of problem complexity on software quality in the context of MDD. Method: We conducted replications of two types: strict replications and object replications. Strict replications were similar to the baseline, whereas we used more complex experimental objects (problems) in the object replications. Results: MDD yields better quality independently of problem complexity with a moderate effect size. This effect is bigger for problems that are more complex. Conclusions: Thanks to the bigger size of the sample after aggregating replications, we discovered an effect that the baseline had not revealed due to the small sample size. The baseline results hold, which suggests that MDD yields better quality for more complex problems.