Reviewerly/exHarmony huggingface dataset:
Algorithms and Lower Bounds for De Morgan Formulas of Low-Communication Leaf Gates

The class FORMULA[s]∘G consists of Boolean functions computable by size- s De Morgan formulas whose leaves are any Boolean functions from a class G. We give lower bounds and (SAT, Learning, and pseudorandom generators ( PRG s )) algorithms for FORMULA[n 1.99 ]∘G, for classes G of functions with low communication complexity . Let R (k) G be the maximum k -party number-on-forehead randomized communication complexity of a function in G. Among other results, we show the following: • The Generalized Inner Product function GIP k n cannot be computed in FORMULA[s]° G on more than 1/2+ε fraction of inputs for s=o(n 2 /k⋅4 k ⋅R (k) (G)⋅log⁡(n/ε)⋅log⁡(1/ε)) 2 ). This significantly extends the lower bounds against bipartite formulas obtained by [62]. As a corollary, we get an average-case lower bound for GIP k n against FORMULA[n 1.99 ]∘PTF k −1 , i.e., sub-quadratic-size De Morgan formulas with degree-k-1) PTF ( polynomial threshold function ) gates at the bottom. Previously, it was open whether a super-linear lower bound holds for AND of PTFs. • There is a PRG of seed length n/2+O(s⋅R (2) (G)⋅log⁡(s/ε)⋅log⁡(1/ε)) that ε-fools FORMULA[s]∘G. For the special case of FORMULA[s]∘LTF, i.e., size- s formulas with LTF ( linear threshold function ) gates at the bottom, we get the better seed length O(n 1/2 ⋅s 1/4 ⋅log⁡(n)⋅log⁡(n/ε)). In particular, this provides the first non-trivial PRG (with seed length o(n)) for intersections of n halfspaces in the regime where ε≤1/n, complementing a recent result of [45]. • There exists a randomized 2 n-t #SAT algorithm for FORMULA[s]∘G, where t=Ω(n\√s⋅log 2 ⁡(s)⋅R (2) (G))/1/2. In particular, this implies a nontrivial #SAT algorithm for FORMULA[n 1.99 ]∘LTF. • The Minimum Circuit Size Problem is not in FORMULA[n 1.99 ]∘XOR; thereby making progress on hardness magnification, in connection with results from [14, 46]. On the algorithmic side, we show that the concept class FORMULA[n 1.99 ]∘XOR can be PAC-learned in time 2 O(n/log n) .
Modelling a complex gold deposit with multiple-point statistics

This paper presents an innovative approach to the geological modelling and stochastic estimation of a Mineral Resource in a structurally complex gold deposit. A Direct Sampling multiple-point statistics algorithm is adopted to produce stochastic models of the lithology and gold grade distributions while using sparse geological and grade data. A multivariate modelling framework shows potential for modelling finely intercalated folded lithologies, which are problematic with classical methods. The method performs well with sparse data or even in the absence of data, provided auxiliary variables are available. Geological input is limited to an elementary training image and structural measurements of the lithological contacts to create a representative potential field of ductile deformation. A new modelling framework is adopted where a portion of the 'true' model is reserved to be used as a training image, while the remaining part serves as a validation tool prior to arriving at a satisfactory ensemble of realisations. This ensemble is carried forward as a stochastic model for the poorly informed part of the deposit. This approach lends itself to the adaptation of modern developments in artificial intelligence and machine learning. The geologist's involvement is confined to incorporating an understanding of the deposit and its genesis into the explicit 'prior' model.
EHUCM: An Efficient Algorithm for Mining High Utility Co-location Patterns from Spatial Datasets with Feature-specific Utilities

High utility co-location pattern mining is still computationally expensive in terms of both runtime and memory consumption. In this paper, an efficient high utility co-location pattern mining algorithm, named EHUCM, is proposed to address this problem, which introduces the ideas of neighborhood materialization, participating objects of features and filtering unpromising candidate patterns to discover high utility co-location patterns more efficiently. To reduce the cost of dataset scanning, EHUCM pre-storing spatial relationships in a data structure to facilitate the search for potential candidate patterns. In addition, two effective pruning strategies are proposed in the EHUCM algorithm to improve the running overhead due to the utility measure not satisfying the downward closure property. Extensive experiments show that the EHUCM algorithm is 10 times or even 100 times faster than the traditional high utility co-location pattern mining algorithm.
Color-Grayscale-Pair Image Sentiment Dataset and Its Application to Sentiment-Driven Image Color Conversion

In this study, we focused on the fact that the color information of an image has a significant effect on the emotion recalled, and we created a dataset with discrete and continuous emotion labels for color and grayscale image pairs, which are not present in existing emotion image datasets. As a result of the analysis of the continuous-valued emotion labels, we found that the color images evoked a wider range of positive and negative emotions. We also conducted experiments to transform the color of the image to the target emotion using the emotion label and the image as input and succeeded in changing the color of the entire image according to the input emotion value. Another experiment is conducted to convert the emotion category and we confirmed that the image was generated in such a way that the evoked emotion was changed compared to the original image.
Soudain: Online Adaptive Profile Configuration for Real-time Video Analytics

Since the real-time video analytics with high accuracy requirement is resource-consuming, the profiles regarding such resource-accuracy trade-off are needed before the analytics for better resource allocation at resource-constrained edges. With the inner changes of the video contents, outdated profiles fail to capture the trade-off dynamically over time, which requires the profiles to be updated periodically and incurs an overwhelming resource overhead. Thus, we present Soudain, which dynamically adjusts the configurations in profiles and corresponding profiling intervals to capture the inner changes of multiple video streams at edges. Upon the fine-grained decisions for profiles, we propose an integer program to maximize the accuracy of video analytics in a long-term scope with resource constraint, and then design an algorithm to adjust the profiles in an online manner. We implement Soudain upon the server with GPU. Our testbed evaluations confirm that, by using the live video streams derived from real-world traffic cameras, Soudain ensures the real-time requirement and achieves up to 25% improvement on the detection accuracy, compared with multiple state-of-the-art alternatives.
Interpretable Run-Time Prediction and Planning in Co-Robotic Environments

Mobile robots are traditionally developed to be reactive and avoid collisions with surrounding humans, often moving in unnatural ways without following social protocols, forcing people to behave very differently from human-human interaction rules. Humans, on the other hand, are seamlessly able to understand why they may interfere with surrounding humans and change their behavior based on their reasoning, resulting in smooth, intuitive avoiding behaviors. In this paper, we propose an approach for a mobile robot to avoid interfering with the desired paths of surrounding humans. We leverage a library of previously observed trajectories to design a decision-tree based interpretable monitor that: i) predicts whether the robot is interfering with surrounding humans, ii) explains what behaviors are causing either prediction, and iii) plans corrective behaviors if interference is predicted. We also propose a validation scheme to improve the predictive model at run-time. The proposed approach is validated with simulations and experiments involving an unmanned ground vehicle (UGV) performing go-to-goal operations in the presence of humans, demonstrating non-interfering behaviors and run-time learning.
An IRT-based approach to assess the learning gain of a virtual reality lab students’ experience

The current trends in education for distance and personalized learning, calls for further investigation on the educational benefits of a Virtual Reality (VR) software, regarding laboratory science courses. In this study, we investigated the teaching effectiveness of a VR-oriented innovative method vs a more traditional pedagogical approach, by measuring the Pre-Post change, commonly called as Learning Gain (LG). An Item Response Theory model, The Rasch Model (RM), was used to assess the LG as the difference between the students’ ability before and after the educational treatment. The participants, (N= 54) graduate students enrolled in the Department of Primary Education, at the University of Patras, in Greece, were divided into two groups and followed two different scenarios to be educated on the topic of microscopy. Our findings provided evidence in favor of using simulations as a supplementary tool to the learning procedure. According to the LG analysis, the students that interacted with the VR software showed a higher change in ability, compared to their fellow students, who followed a more classic teaching methodology and obtained no LG between the Pre and Posttesting situation.
Method for Inspecting High-voltage Power Lines Using UAV Based on the RRT Algorithm

Diagnostics of high-voltage power lines using unmanned aerial vehicles enables to identify problems at the early stages. This method of inspection of power transmission lines allows for aerial imaging in hard-to-reach places in order to further analyze the images obtained. The construction of the optimal route in terms of avoiding obstacles and the time spent on calculating the trajectory itself and flying along it to study power lines directly affects the quality, speed, and results of examining the towers and elements of power lines. The paper proposes a method for examining power lines by detection of damage to the structure and elements of power transmission lines. The method consists of two algorithms: an algorithm for detecting operational altitudes and an algorithm for UAV movement along the power transmission line. In our proposed solution, the developed method of inspection of power lines is not limited to one straight section of power lines. The peculiarity of the method is that the path planning using the RRT algorithm along the power transmission line occurs in two-dimensional space at a given working height. To test the efficiency of the proposed survey method, simulation was performed in the Gazebo software environment. The path search time in our proposed survey method does not exceed 0.3 μs for two sections of power transmission lines with an angular tower and a total path length of 200 m. Moreover, this algorithm allows the UAV to fly around the towers at a safe distance, subject to a predetermined terrain map.
When is Psychology Research Useful in Artificial Intelligence? A Case for Reducing Computational Complexity in Problem Solving

A problem is a situation in which an agent seeks to attain a given goal without knowing how to achieve it. Human problem solving is typically studied as a search in a problem space composed of states (information about the environment) and operators (to move between states). A problem such as playing a game of chess has 10120$10^{120}$ possible states, and a traveling salesperson problem with as little as 82 cities already has more than 10120$10^{120}$ different tours (similar to chess). Biological neurons are slower than the digital switches in computers. An exhaustive search of the problem space exceeds the capacity of current computers for most interesting problems, and it is fairly clear that humans cannot in their lifetime exhaustively search even small fractions of these problem spaces. Yet, humans play chess and solve logistical problems of similar complexity on a daily basis. Even for simple problems humans do not typically engage in exploring even a small fraction of the problem space. This begs the question: How do humans solve problems on a daily basis in a fast and efficient way? Recent work suggests that humans build a problem representation and solve the represented problem-not the problem that is out there. The problem representation that is built and the process used to solve it are constrained by limits of cognitive capacity and a cost-benefit analysis discounting effort and reward. In this article, we argue that better understanding the way humans represent and solve problems using heuristics can help inform how simpler algorithms and representations can be used in artificial intelligence to lower computational complexity, reduce computation time, and facilitate real-time computation in complex problem solving.
Spatial light path analysis and calibration of four-mirror-based monocular stereo vision

The feasibility and accuracy of four-mirror-based monocular stereo vision (FMSV) are related to the system layout and calibration accuracy, respectively. In this study, a spatial light path analysis method and a calibration method are proposed for an FMSV system. As two-dimensional light path analysis cannot fully characterize the imaging parameters, a spatial light path model is proposed, which allows refinement of the system design. Then, considering the relationship between the lens distortion and the imaging depth of field (DoF), a DoF-distortion equal-partition-based model is established. In the traditional calibration method, the optical axis must be perpendicular to the chessboard. Here, an accurate and practical FMSV calibration method without this constraint is proposed based on the above model. Using the proposed spatial light path analysis technique, a high-accuracy, high-portability FMSV system is constructed and calibrated, for which the average error of the vision-reconstructed distance is 0.0298 mm. In addition, robot path accuracy is evaluated by the system and compared to laser-tracker measurement results. Hence, high accuracy of 0.031 mm is determined for the proposed vision system.
Neural Network-based Estimation of the MMSE

The minimum mean-square error (MMSE) achievable by optimal estimation of a random variable <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> given another random variable <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> is of much interest in a variety of statistical contexts. Motivated by a growing interest in auditing machine learning models for unintended information leakage, we propose a neural network-based estimator of this MMSE. We derive a lower bound for the MMSE based on the proposed estimator and the Barron constant associated with the conditional expectation of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> given <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> . Since the latter is typically unknown in practice, we derive a general bound for the Barron constant that produces order optimal estimates for canonical distribution models.
Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene

This paper contributes a new high-quality dataset for hand gesture recognition in hand hygiene systems, named "MFH". Generally, current datasets are not focused on: (i) fine-grained actions; and (ii) data mismatch between different viewpoints, which are available under realistic settings. To address the aforementioned issues, the MFH dataset is proposed to contain a total of 731147 samples obtained by different camera views in 6 non-overlapping locations. Additionally, each sample belongs to one of seven steps introduced by the World Health Organization (WHO). As a minor contribution, inspired by advances in fine-grained image recognition and distribution adaptation, this paper recommends using the self-supervised learning method to handle these preceding problems. The extensive experiments on the benchmarking MFH dataset show that the introduced method yields competitive performance in both the Accuracy and the Macro F1-score.
Emitter identification based on improved Variational modal decomposition at low SNR

Individual identification of radiation source is a technique based on extracting RF signal features. A new RF fingerprint extraction method is proposed for low SNR environment and low individual identification rate of radiation sources. The VMD algorithm is used to decompose the signal, and the autocorrelation function is used to optimize it to strengthen the suppression of the modal aliasing phenomenon after the signal decomposition. The decomposed modes were analyzed by the Teager energy operator, and the energy and box dimension were extracted as fingerprint features, which were input into the support vector machine (SVM) for classification and recognition. The simulation results show the effectiveness of using VMD to extract fingerprint features under low SNR environment. And the proposed emitter identification scheme can improve the recognition rate of emitter in the environment with low signal-tonoise ratio (SNR) -5~0dB.
Dynamic Receptive Field Adaptation for Attention-Based Text Recognition

Existing attention-based recognition methods generally assume that the character scale and spacing in the same text instance are basically consistent. However, this hypothesis not always hold in the context of complex scene images. In this study, we propose an innovative dynamic receptive field adaption (DRA) mechanism for recognizing scene text robustly. Our DRA introduces different levels of receptive field features for classifying character and designs a novel way to explore historical attention information when calculating attention map. In this way, our method can adaptively adjust receptive field according to the variations of character scale and spacing in a scene text. Hence, our DRA mechanism can generate more informative features for recognition than traditional attention-based mechanisms. Notablely, our DRA mechanism can be easily generalized to off-the-shelf attention-based methods in text recognition to improve their performances. Extensive experiments on various public available benchmarks, including the IIIT-5K, SVT, SVTP, CUTE80, and ICDAR datasets, indicate the effectiveness and robustness of our method against the state-of-the art methods.
Demographics and Personality Discovery on Social Media: A Machine Learning Approach

This research proposes a new feature extraction algorithm using aggregated user engagements on social media in order to achieve demographics and personality discovery tasks. Our proposed framework can discover seven essential attributes, including gender identity, age group, residential area, education level, political affiliation, religious belief, and personality type. Multiple feature sets are developed, including comment text, community activity, and hybrid features. Various machine learning algorithms are explored, such as support vector machines, random forest, multi-layer perceptron, and naïve Bayes. An empirical analysis is performed on various aspects, including correctness, robustness, training time, and the class imbalance problem. We obtained the highest prediction performance by using our proposed feature extraction algorithm. The result on personality type prediction was 87.18%. For the demographic attribute prediction task, our feature sets also outperformed the baseline at 98.1% for residential area, 94.7% for education level, 92.1% for gender identity, 91.5% for political affiliation, 60.6% for religious belief, and 52.0% for the age group. Moreover, this paper provides the guideline for the choice of classifiers with appropriate feature sets.
Optimal components selection based on fuzzy-intra coupling density for component-based software systems under build-or-buy scheme

Abstract Component-Based Software Engineering (CBSE) is an approach to building and developing software systems based on software components. In component-based software systems, there are various software components, including Commercial off the Shelf (COTS) and in-house components. Software developers can build their desired software component as in-house or COTS. The problem of deciding optimally between COTS and in-house components is one of the major challenges of software developers, which is known as the component selection problem. This can be resolved by evaluating the criteria for optimality in component selection and then solving the component selection problem by optimization techniques. In this paper, an attempt was made to optimize the component selection problem through the multi-objective optimization by maximizing the Fuzzy-Intra Coupling Density (Fuzzy-ICD) and functionality as objective functions, and also taking into account budget, delivery time, reliability, and Fuzzy-ICD as constraints of multi-objective problems. Fuzzy ICD is a more accurate criterion to calculate the relationship between Cohesion and Coupling of components, which is obtained through the fuzzy computing of each of them, based on the Meyers classification. Thus, after a two-criterion optimization model formulation, this optimization problem was solved by fuzzy multi objectives approach. Finally, the proposed method was evaluated by performing the case study of financial-accounting system. Comparison of the results showed that the proposed method could select optimal components with maximum functionality and Fuzzy-ICD and fewer rates of time and Budget (0.29, 0.43, 1.1 s, and 88$ were the improved rates of functionality, Fuzzy-ICD, time, and budget, respectively).
Quality of 8K Ultra-High-Definition Television Viewing Experience in Practical Viewing Conditions

In this study, we examine the psychological effects of 8K ultra-high-definition television (UHDTV) under daily TV viewing conditions.The design of 8K-UHDTV targets the realization of the "ultimate 2D image."8K-UHDTV is already being used practically and the audience have reported experiencing a feeling of reality and presence.However, the benefits offered by 8K for daily TV viewing and the degree to which the design goals have been achieved have not been sufficiently verified.Hence, in this study, we verify them by investigating the popular impressions evoked by familiar images and their modulation with the viewing distance through subjective evaluation experiments and statistical analysis.The obtained results indicate that the impressions are enhanced by increasing the resolution from 4K to 8K, even at viewing distances beyond the design viewing distance, which are conventionally considered less effective.Thus, they confirm that 8K provides strong impressions over a wide viewing range.Furthermore, the relationship between higher-order impressions, such as "being-there" and "reality," and lower-order impressions is derived through multiple regression analysis.This provides insight into the enhancement of higher-order impressions in production and post-processing.