Reviewerly/exHarmony huggingface dataset:
Pursuing open-source development of predictive algorithms: the case of criminal sentencing algorithms

Currently, there is uncertainty surrounding the merits of open-source versus proprietary algorithm development. Though justification in favor of each exists, we argue that open-source algorithm development should be the standard in highly consequential contexts that affect people’s lives for reasons of transparency and collaboration, which contribute to greater predictive accuracy and enjoy the additional advantage of cost-effectiveness. To make this case, we focus on criminal sentencing algorithms, as criminal sentencing is highly consequential, and impacts society and individual people. Furthermore, the popularity of this topic has surged in the wake of recent studies uncovering racial bias in proprietary sentencing algorithms among other issues of over-fitting and model complexity. We suggest that these issues are exacerbated by the proprietary and expensive nature of virtually all widely used criminal sentencing algorithms. Upon replicating a major algorithm using real criminal profiles, we fit three penalized regressions and demonstrate an increase in predictive power of these open-source and relatively computationally inexpensive options. The result is a data-driven suggestion that if judges who are making sentencing decisions want to craft appropriate sentences based on a high degree of accuracy and at low costs, then they should be pursuing open-source options.
Lightweight extension of an execution environment for safer function calls in Solidity/Ethereum Virtual Machine smart contracts

Solidity, a programming language used to write smart contracts, has been improved since its initial release, but a number of vulnerabilities remain. As smart contracts are usually related to cryptocurrency, these vulnerabilities should be avoided to prevent the risk of financial loss. In this paper, we classify common vulnerabilities of function calls of Solidity programs into three groups and suggest a method to avoid them. The proposed method makes use of Ethereum Virtual Machine as well as Solidity extension. Experimental results with real-world smart contracts show that our method will detect and avoid these vulnerabilities.
Long-Short Temporal Modeling for Efficient Action Recognition

Efficient long-short temporal modeling is key for enhancing the performance of action recognition task. In this paper, we propose a new two-stream action recognition network, termed as MENet, consisting of a Motion Enhancement (ME) module and a Video-level Aggregation (VLA) module to achieve long-short temporal modeling. Specifically, motion representations have been proved effective in capturing short-term and high-frequency action. However, current motion representations are calculated from adjacent frames, which may have poor interpretation and bring useless information (noisy or blank). Thus, for short-term motions, we design an efficient ME module to enhance the short-term motions by mingling the motion saliency among neighboring segments. As for long-term aggregations, VLA is adopted at the top of the appearance branch to integrate the long-term dependencies across all segments. The two components of MENet are complementary in temporal modeling. Extensive experiments are conducted on UCF101 and HMDB51 benchmarks, which verify the effectiveness and efficiency of our proposed MENet.
Training Multi-layer Perceptron Using Hybridization of Chaotic Gravitational Search Algorithm and Particle Swarm Optimization

A novel amalgamation strategy, namely, chaotic gravitational search algorithm (CGSA) and particle swarm optimization (PSO), has been employed for training multi-layer perceptron (MLP) neural network. It is called CGSAPSO. In CGSAPSO, exploration is carried out by CGSA, and exploitation is performed using PSO. The sigmoid activation function is utilized for training MLP. Besides, a matrix encoding strategy has been used for providing a synergy between neural biases, weights, and CGSAPSO searcher agents. To validate the effectiveness of the hybrid framework, CGSAPSO is applied to three different classification datasets, namely, XOR, Iris, and Balloon. The investigation of results is carried out through various performance metrics like average, standard deviation, median, convergence speed, execution time, and classification rate analysis. Besides, a pair-wise non-parametric signed Wilcoxon rank-sum test has also been conducted for statistical verification of simulation results. In addition, the numerical outcomes of CGSAPSO are also compared with standard GSA, PSO, and hybrid PSOGSA. The experimental results indicate that CGSAPSO provides better results in the form of recognition accuracy and global optima as compared to competing algorithms.
A Multiple UAV Path-Planning Approach to Small Object Counting with Aerial Images

UAV and object detection technology can automate object counting tasks, but low-altitude images, providing better inferences, require greater energy expenditure. Cooperative UAV teams can overcome this shortcoming, particularly when object density information is predetermined. We consider an aerial object-counting problem for a conservation use case: multiple quadrotor UAVs operating in a continuous region with a known probability distribution of objects (waterfowl). We build upon single-drone algorithms, generating density-based subregions covered by UAVs with unique roles. This approach scales with mission parameters, resulting in an F1 score improvement of more than 0.2 under various conditions compared to naive multiple-drone approaches.
Understanding the Ability of Deep Neural Networks to Count Connected Components in Images

Humans can count very fast by subitizing, but slow substantially as the number of objects increases. Previous studies have shown a trained deep neural network (DNN) detector can count the number of objects in an amount of time that increases slowly with the number of objects. Such a phenomenon suggests the subitizing ability of DNNs, and unlike humans, it works equally well for large numbers. Many existing studies have successfully applied DNNs to object counting, but few studies have studied the subitizing ability of DNNs and its interpretation. In this paper, we found DNNs do not have the ability to generally count connected components. We provided experiments to support our conclusions and explanations to understand the results and phenomena of these experiments. We proposed three ML-learnable characteristics to verify learnable problems for ML models, such as DNNs, and explain why DNNs work for specific counting problems but cannot generally count connected components.
A CNN-RNN Framework for Image Annotation from Visual Cues and Social Network Metadata

Images represent a commonly used form of visual communication among people. Nevertheless, image classification may be a challenging task when dealing with unclear or non-common images needing more context to be correctly annotated. Metadata accompanying images on social-media represent an ideal source of additional information for retrieving proper neighborhoods easing image annotation task. To this end, we blend visual features extracted from neighbors and their metadata to jointly leverage context and visual cues. Our models use multiple semantic embeddings to achieve the dual objective of being robust to vocabulary changes between train and test sets and decoupling the architecture from the low-level metadata representation. Convolutional and recurrent neural networks (CNNs-RNNs) are jointly adopted to infer similarity among neighbors and query images. We perform comprehensive experiments on the NUS-WIDE dataset showing that our models outperform state-of-the-art architectures based on images and metadata, and decrease both sensory and semantic gaps to better annotate images.
An Intelligent Geomagnetic Search Navigation Method Based on Evolutionary Gradient Strategy

Abstract Currently, most of the geomagnetic navigation methods are matching navigation algorithms, which require the geomagnetic maps retrieved in advance, and saved in a database. However, there are a great number of unknown environments exist worldwide that limit the extensive application of the geomagnetic matching method in practice. In this paper, an intelligent geomagnetic search navigation method based on the evolutionary gradient search (EGS) algorithm is proposed. The performance of the search and navigation method is analysed with both simulated and real geomagnetic data. And the conclusion is that compared with other intelligent search strategies, the proposed algorithm has better navigation performance with less average navigation error, 161m, which is equivalent to the most traditional geomagnetic matching algorithms. Thus, the EGS algorithm proposed in this paper can effectively improve the performance of geomagnetic navigation without prior geomagnetic information.
Reliably Calibrated Isotonic Regression

Using classifiers for decision making requires well-calibrated probabilities for estimation of expected utility. Furthermore, knowledge of the reliability is needed to quantify uncertainty. Outputs of most classifiers can be calibrated, typically by using isotonic regression that bins classifier outputs together to form empirical probability estimates. However, especially for highly imbalanced problems it produces bins with few samples resulting in probability estimates with very large uncertainty. We provide a formal method for quantifying the reliability of calibration and extend isotonic regression to provide reliable calibration with guarantees for width of credible intervals of the probability estimates. We demonstrate the method in calibrating purchase probabilities in e-commerce and achieve significant reduction in uncertainty without compromising accuracy.
Tensor-Centric Processor Architecture for Applications in Advanced Driver Assistance Systems

We describe our implementation of a tensor-friendly AI processor architecture for an advanced driver assistance system. We implement modern video-based lane-departure and blind-spot detection algorithms on our previously proposed architecture called VectorMesh, and demonstrate deployment of our technology using open neural network exchange compilers and FPGAs in a commercially viable and cost-efficient manner.
Approaches in Determining Software Development Methods for Organizations: A Systematic Literature Review

Harvard Business Review (HBR) reported 27% of projects were over budget, McKinsey & Company reported 66% of projects were over budget, and the Project Management Institute (PMI) reported more than 40% of projects were over budget. The causes are losing focus, execution issues, content issues, and skill issues. The failure of the software project falls into the process domain category. Undefined processes can lead to low-quality results regarding unexpected outcomes, schedule, and budget. In short, the project is poorly executed. A software development method is needed to execute the project correctly, so the quality is guaranteed, on time, and within budget. This study aims to summarize the approaches used to determine software development methods for organizations and how to use them. A systematic literature review technique was conducted and obtained 16 relevant papers. Ten approaches for determining software development methods were found. Instead of selecting a suitable software development method, the organization could create a software development method by tailoring several deemed appropriate methods to its needs.
Detection of Audio-Video Synchronization Errors Via Event Detection

We present a new method and a large-scale database to detect audio-video synchronization(A/V sync) errors in tennis videos. A deep network is trained to detect the visual signature of the tennis ball being hit by the racquet in the video stream. Another deep network is trained to detect the auditory signature of the same event in the audio stream. During evaluation, the audio stream is searched by the audio network for the audio event of the ball being hit. If the event is found in audio, the neighboring interval in video is searched for the corresponding visual signature. If the event is not found in the video stream but is found in the audio stream, A/V sync error is flagged. We developed a large-scaled database of 504,300 frames from 6 hours of videos of tennis events, simulated A/V sync errors, and found our method achieves high accuracy on the task.
Adaptive Web-Based VR Streaming of Multi-LoD 3D Scenes via Author-Provided Relevance Scores

The growing storage requirements of 3D virtual scenes, combined with the increased heterogeneity of consumption devices, trigger the need for novel, on-demand streaming techniques of textured meshes. This paper proposes a way to perform relevance-aware Adaptive Bit-Rate (ABR) scheduling using MPEG-DASH, tailored to VR consumption in the web browser. Scene authors can annotate the relative importance of scene assets to optimize scheduling decisions. Our approach outperforms the state-of-the-art (measured using the MS-SSIM metric) across different scene complexities and network configurations, and is found to be most beneficial when scene complexity is high and network conditions are relatively poor.
MFI: Multi-range Feature Interchange for Video Action Recognition

Short-range motion features and long-range dependencies are two complementary and vital cues for action recognition in videos, but it remains unclear how to efficiently and effectively extract these two features. In this paper, we propose a novel network to capture these two features in a unified 2D framework. Specifically, we first construct a Short-range Temporal Interchange (STI) block, which contains a Channels-wise Temporal Interchange (CTI) module for encoding short-range motion features. Then a Graph-based Regional Interchange (GRI) module is built to present long-range dependencies using graph convolution. Finally, we replace original bottleneck blocks in the ResNet with STI blocks and insert several GRI modules between STI blocks, to form a Multi-range Feature Interchange (MFI) Network. Practically, extensive experiments are conducted on three action recognition datasets (i.e., Something-Something V1, HMDB51, and UCF101), which demonstrate that the proposed MFI network achieves impressive results with very limited computing cost.
Deep Neural Network approach for navigation of Autonomous Vehicles

Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about `Autonomous Vehicles' amongst the major tech giants such as Google, Uber and Tesla. Numerous approaches have been adopted to solve this problem which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from comma.ai dataset. A heatmap was used to check for correlation among the features and finally four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers followed by five dense layers. Finally, the calculated values were tested against the labelled data where mean squared error was used as a performance metric.
Heuristics for Evaluation of AI Generated Music

Evaluation of generative AI is a difficult problem, especially in artistic domains in which aesthetic qualities of generated samples are to an extent subjective, such as in music. The most widely accepted method for evaluating such models is to conduct a survey of users, which is a resource intensive process. In this work we propose a framework for cheaply evaluating generative models in the symbolic music domain by utilizing tools from music theory, such as the circle of fifths, with the goal of producing quantifiable metrics which reflect the “musicality” of a written score or MIDI file.
On Elias-Fano for Rank Queries in FM-Indexes

We describe methods to support fast rank queries on the Burrows-Wheeler transform (BWT) string <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> of an input string <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> on alphabet <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Sigma$</tex> , in order to support pattern counting queries. Our starting point is an approach previously adopted by several authors, which is to represent <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vert \Sigma\vert$</tex> bitvectors, where the bitvector for symbol <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c$</tex> has a 1 at position <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c$</tex> if and only if <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S[i]=c$</tex> , with the bitvec-tors stored in Elias-Fano (EF) encodings, to enable binary rank queries. We first show that the clustering of symbols induced by the BWT makes standard implementations of EF unattractive. We then engineer several improvements to EF that go some way to alleviating this problem, and go on to describe two new EF-inspired bitvectors that have superior practical performance.
Spline estimation method in nonparametric regression using truncated spline approach

Abstract A parametric regression approach is used when the shape of the regression curve is known, and the nonparametric approach is used when the shape of the regression curve is unknown, the parametric regression model is still forced as a model of data patterns, it will cause inaccurate conclusions if the form of the function is not known. The truncated spline approach can be used to estimate curves in nonparametric regression models using a knot basis. The curve estimation method used is Weighted Least Square (WLS) for the truncated spline approach. In this study, the nonparametric regression model was implemented on fertilizer subsidy data in East Java. This study aims to examine comprehensively the factors that influence the satisfaction of the farming community. The response variable used in this study is the Courage of Field Extension Officers (Y), while the predictor variables include: Farmers National Culture (X1), Financial Reward for Field Extension Officers (X2), and Leadership Role (X3). The novelty of this research is trying to do regression modeling in which the whole relationship between the response variable and the predictor variable is non-linear. The results of the linearity assumption test show that all predictor variables show a non-linear relationship to the response variable, so it needs to be solved by nonparametric regression. The coefficient of determination shows that the variability of Courage of Field Extension Officers can be explained by 87,65% in the model, while the remaining 12,35% is explained by factors that are not included in the model.
Controllable Face Aging

Motivated by the following two observations: 1) people are aging differently under different conditions for changeable facial attributes, e.g., skin color may become darker when working outside, and 2) it needs to keep some unchanged facial attributes during the aging process, e.g., race and gender, we propose a controllable face aging method via attribute disentanglement generative adversarial network. To offer fine control over the synthesized face images, first, an individual embedding of the face is directly learned from an image that contains the desired facial attribute. Second, since the image may contain other unwanted attributes, an attribute disentanglement network is used to separate the individual embedding and learn the common embedding that contains information about the face attribute (e.g., race). With the common embedding, we can manipulate the generated face image with the desired attribute in an explicit manner. Experimental results on two common benchmarks demonstrate that our proposed generator achieves comparable performance on the aging effect with state-of-the-art baselines while gaining more flexibility for attribute control. Code is available at supplementary material.
Low-Complexity Deep No-Reference Light Field Image Quality Assessment with Discriminative EPI Patches Focused

Light field image quality assessment (LF-IQA) has attracted increasing research interests due to the fast-growing demands for immersive media experience. In this paper, we propose a low-complexity deep residual convolutional neural network (CNN) based no-reference (NR) LF-IQA approach for potential applications of light field images on consumer electronic devices. For efficient and accurate LF-IQA, discriminative Epipolar Plane Image (EPI) patches are taken as input to our CNN model that employs a dedicatedly designed multitask learning mechanism. The experimental results show that the LF-IQA metric resulted from our approach outperforms state-of-the-art metrics against representative benchmark datasets.
Established Mathematical Approaches for Synthetic Solar Irradiance Data Generation

This chapter gives an overview of established state-of-the-art mathematical approaches for generating synthetic solar irradiance data. The most important scientific studies from the last half-century are identified and discussed, and the general development of the field is characterized. The mathematical methods used for modeling both deterministic and stochastic components of solar irradiance are categorized and explained, along with examples of their application to synthetic irradiance modeling. The mathematical approaches described include methods to achieve stationarity, probability distribution models, autoregressive processes, Markov chain models, multivariate distribution modeling, and copulas. Synthesis of time series data with resolutions ranging from days to minutes is covered, including both purely temporal as well as more recent spatiotemporal approaches.
GAN-Based Out-of-Domain Detection Using Both In-Domain and Out-of-Domain Samples

In domain classification for spoken language understanding, correct detection of out-of-domain (OOD) utterances is crucial because it reduces confusion and unnecessary interaction costs between users and the systems. In the situation where both in-domain (ID) and OOD samples are available, our goal is to take advantage of OOD samples under the GAN-based framework for OOD detection. We propose a GAN-based OOD detector with OOD prior distribution and weighted loss (WOODP-GAN). The model consists of a GAN-based detector with OOD prior distribution for generating effective pseudo OOD samples, and a weighted loss function for balancing the loss of fake OOD samples against real OOD samples in the discriminator. Extensive experiments show our proposed WOODP-GAN model outperforms the existing methods in the benchmark dataset CLINC150.
Particle Swarm Optimization: The Foundation

Particle swarm optimization (PSO) is a very much popular swarm intelligence algorithm. Since its inception in the year 1995, it is being applied to solve optimization problems in many domains, including portfolio optimization. This chapter lays the basic PSO foundation and introduces existing PSO variants for researchers who want to solve the portfolio optimization problem. It starts with the introduction of PSO, describing the advantages, disadvantages, and applied areas of PSO. Later, the basic PSO procedure and its parameter selection mechanisms are presented. The chapter also presents three popular applications of PSO in finance, including portfolio optimization. Finally, the chapter ends by introducing the existing PSO variants to solve the portfolio optimization problem.