Reviewerly/exHarmony huggingface dataset:
Composing by Laypeople: A Broader Perspective Provided by Arithmetic Operation Grammar

Abstract Many existing approaches to teaching laypeople to compose are based, to a certain extent, on simply hiding the theoretical background. This is done, for example, by offering ready-made musical events that can be combined in any way to organize them in time. A different approach is taken in the work presented here. As an alternative to classical music theory, with all its complications, the generative composition method Arithmetic Operation Grammar (AOG) is used, which is much easier to learn. This approach has been taken in the conviction that the leaner theory on which it is based, in combination with the compact symbolic representation of entire compositions, can make a significant contribution to bringing forward the “everyday creativity” in the field of ubiquitous music. Furthermore, in the field of sonification, AOG offers the possibility of sonifying data that do not inherently include time as an ordering parameter. To prove practical use of this approach, AOG is combined with a user interface that is more suitable for adults as a target group and another one that is more appropriate for primary school children.
Overtaking Uncertainty With Evolutionary TORCS Controllers: Combining BLX With Decreasing $\alpha$ Operator and Grand Prix Selection

Evolution is a powerful problem-solving technique, extensively used for designing racing car controllers, but with a series of challenges: an evaluation function that can separate the best controllers from the rest, and a series of operators that can explore different possibilities in the controller search space. Within the context of the TORCS racing simulator, in this article, we introduce a selection policy based on competition called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Grand Prix Selection</i> (GPS), which will be able to increase robustness by using something more realistic than solo race scores to select individuals. Additionally, we increase the exploitative power of this kind of selection via a BLX operator with continuously decreasing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$alpha$</tex-math></inline-formula> . We compare these new selection and operator with hybrid approaches that apply GPS only part of the time, as well as other classical crossover operators. In general, experiments show that these combined improvements establish a new level of performance of evolved controllers, being able to beat both standard and previously evolved ones, as well as a high-ranked controller of TORCS competitions.
SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series

Many applications, e.g., healthcare, education, call for effective methods methods for constructing predictive models from high dimensional time series data where the relationship between variables can be complex and vary over time. In such settings, the underlying system undergoes a sequence of unobserved transitions among a finite set of hidden states. Furthermore, the relationships between the observed variables and their temporal dynamics may depend on the hidden state of the system. To further complicate matters, the hidden state sequences underlying the observed data from different individuals may not be aligned relative to a common frame of reference. Against this background, we consider the novel problem of jointly learning the state-dependent inter-variable relationships as well as the pattern of transitions between hidden states from multi-variate time series data. To solve this problem, we introduce the State-Regularized Vector Autoregressive Model (SrVARM) which combines a state-regularized recurrent neural network to learn the dynamics of transitions between discrete hidden states with an augmented autoregressive model which models the inter-variable dependencies in each state using a state-dependent directed acyclic graph (DAG). We propose an efficient algorithm for training SrVARM by leveraging a recently introduced reformulation of the combinatorial problem of optimizing the DAG structure with respect to a scoring function into a continuous optimization problem. We report results of extensive experiments with simulated data as well as a real-world benchmark that show that SrVARM outperforms state-of-the-art baselines in recovering the unobserved state transitions and discovering the state-dependent relationships among variables.
Fine-grained image classification model based on improved SqueezeNet

In order to solve the problem that some fine-grained algorithms have a large amount of calculation and low operating efficiency, This paper firstly proposes the use of SqueezeNet for fine-grained image recognition tasks, because SqueezeNet, as one of the representatives of lightweight CNN, has fewer parameters and faster running time. The experimental verification of SqueezeNet on three fine-grained datasets shows that SqueezeNet can also finely recognize images, but the classification accuracy rate is low, this paper further improves SqueezeNet: two different attention mechanism modules are embedded in the SqueezeNet in different ways, then use bilinear fusion to fuse the attention feature maps, and then the new attention feature map is bilinearly fused with the last layer of the network, a new tensor is obtained and sent to the linear layer for classification finally. Through experimental comparison and visual analysis, the improved SqueezeNet has a significant improvement in accuracy compared to the original network and other algorithms.
Angular-Driven Feedback Restoration Networks for Imperfect Sketch Recognition

Automatic hand-drawn sketch recognition is an important task in computer vision. However, the vast majority of prior works focus on exploring the power of deep learning to achieve better accuracy on complete and clean sketch images, and thus fail to achieve satisfactory performance when applied to incomplete or destroyed sketch images. To address this problem, we first develop two datasets that contain different levels of scrawl and incomplete sketches. Then, we propose an angular-driven feedback restoration network (ADFRNet), which first detects the imperfect parts of a sketch and then refines them into high quality images, to boost the performance of sketch recognition. By introducing a novel “feedback restoration loop” to deliver information between the middle stages, the proposed model can improve the quality of generated sketch images while avoiding the extra memory cost associated with popular cascading generation schemes. In addition, we also employ a novel angular-based loss function to guide the refinement of sketch images and learn a powerful discriminator in the angular space. Extensive experiments conducted on the proposed imperfect sketch datasets demonstrate that the proposed model is able to efficiently improve the quality of sketch images and achieve superior performance over the current state-of-the-art methods.
Bengali Handwritten Compound Characters Recognition by Utilizing Transfer Learning

Being the fifth most spoken language of the world, Bengali is spoken by 265 million people globally. Therefore, the recognition of handwritten Bengali characters has been an area of interest for a decade now. Previously, researchers have contributed to the recognition of Bengali numerals and basic characters e.g. vowels and consonants. However, apart from basic Bengali characters, Bengali has a wide range of compound characters as well. Not many remarkable contributions have been yet contributed to this domain. With a focus on contributing to this field, in this research, we started with 171 Bengali compound characters. We applied a modified DenseNet-201 architecture for the recognition of the compound characters. After result analysis, we achieved an overall accuracy of 96.89% which outperformed all previous studies by a remarkable margin. Hence, we concluded that our design is more capable of accurate recognition of the Bengali compound characters.
Research on Credit Strategy Based on XGBoost Algorithm and Optimization Problem

Abstract Small and medium-sized enterprises are the important foundation of national economic and social development, and play an important role in expanding employment, increasing income, improving people’s livelihood and national taxation. In this paper, the credit decision-making of small and medium-sized enterprises is deeply studied, and reasonable credit risk quantification results and targeted credit strategies are given. To solve the first problem, this paper establishes a multi-index scoring model based on XGBoost algorithm. According to the extracted features, the credit risk of 123 enterprises is quantitatively analyzed, and the enterprises are scored according to the quantitative results. Prioritize them by score and enterprise credit rating, take interest rate and loan amount as decision variables, and bank profit as objective function, establish a multivariate and single-objective credit strategy optimization model, and get the optimal credit strategy for each enterprise when the annual total credit is fixed. In view of the second question, this paper takes the data in Annex 1 as the training set based on the first question, and extracts the features of the relevant data in Annex 2. Using the enterprise multi-index classification scoring model based on XGBoost algorithm, the credit rating of 302 enterprises is predicted and their credit risks are quantitatively analyzed, and the enterprises are scored according to the quantitative results. Prioritize them by score and enterprise reputation grade, establish the same optimization model as the first one, and formulate specific credit rules to make the results meet the limit conditions of total credit, thus determining the interest rate and loan amount of each enterprise, and obtaining specific credit strategies. In view of the third question, this paper takes the epidemic situation in SARS-CoV-2 as an example, and comprehensively considers the impact of this sudden factor on different industries and different types of enterprises on the basis of the second question. Based on the impact of corporate profits, the data are updated, the indicators are extracted again, and the XGBoost enterprise multi-index classification scoring model is used to predict the credit rating of 302 enterprises again and quantitatively analyze the credit risk, and the enterprises are scored according to the quantitative results. Once again, they are prioritized according to the score and the reputation level of the enterprise. Use the optimization model in question 2 to adjust the corresponding credit strategy. The model established in this paper considers more indicators, and the quantitative and prediction results are reasonable. The algorithm and model can comprehensively consider the impact of unexpected factors and adjust the credit strategy in time. In addition to being applied to credit decision-making of small and medium-sized enterprises, it can also be extended to other types of financial fields, providing ideas for solving related problems.
Learning Early Exit Strategies for Additive Ranking Ensembles

Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees. We propose LEAR, a novel - learned - technique aimed to reduce the average number of trees traversed by documents to accumulate the scores, thus reducing the overall query response time. LEAR exploits a classifier that predicts whether a document can early exit the ensemble because it is unlikely to be ranked among the final top-k results. The early exit decision occurs at a sentinel point, i.e., after having evaluated a limited number of trees, and the partial scores are exploited to filter out non-promising documents. We evaluate LEAR by deploying it in a production-like setting, adopting a state-of-the-art algorithm for ensembles traversal. We provide a comprehensive experimental evaluation on two public datasets. The experiments show that LEAR has a significant impact on the efficiency of the query processing without hindering its ranking quality. In detail, on a first dataset, LEAR is able to achieve a speedup of 3x without any loss in [email protected], while on a second dataset the speedup is larger than 5x with a negligible [email protected] loss (< 0.05%).
A QoE-based 360<sup>°</sup> Video Adaptive Bitrate Delivery and Caching Scheme for C-RAN

With the development of Virtual Reality (VR) technology, the growing number of VR users puts tremendous pressure on network bandwidth. The tile-based scheme is proposed to reduce the transmission size of 360° video and improve bandwidth utilization. However, when the Field of View (FoV) of the user changes unexpectedly, the tile-based scheme will cause video distortion and quality switching by unacceptable delay. Therefore, many methods are proposed to cache the tiles that users are most likely to playback in Cloud/Edge to decrease delay. However, the dynamic adaptive bitrate delivery and the caching decision is a complex joint optimization problem, which will be a dimensional explosion problem when the scale of users and videos is large. In this paper, we design a QoE-based 360° video adaptive bitrate delivery and caching scheme aiming to maximize the quality of experience (QoE) of multi-user and ensure the fairness of users. To solve this optimization problem which is proved to be NP-Hard, we propose a bitrate selection and caching decision algorithm by greedy strategy. Numerical simulation results demonstrate that our algorithm significantly improves cache hit rate and QoE performance compared with other algorithms with fairness guaranteed.
Ensemble-Based Commercial Buildings Facades Photographs Classifier

We present an ensemble-based method for classifying photographs containing patches with text. In particular, the proposed solution is suitable for the task of classification the images of commercial building facades by the type of provided services. Our model is based on heterogeneous ensemble usage and analysis of textual and visual features as well as special visual descriptors for areas with English text. It should be noted that our classifier demonstrates remarkable performance (0.71 in \(F_1\) score against 0.43 baseline result). We also provide our own dataset containing 3000 images of facades with signboards in order to provide complete classification benchmark.
Image-to-Image Attire Transfer for Virtual Trial Room

Virtual trial room is a lucrative tool for online-based attire shopping. Developing such a system is very challenging as it requires robustness from a user's point of view. In this paper, we present a technique for image-to-image attire transfer using Generative Adversarial Networks (GAN) and image processing methods that can transfer the clothing from a person's image to another person's image. Our system takes two images: (1) a full-length image of the user, and (2) an image of another person with a target clothing. Our method then produces a new image of the user with the targeted outfit while keeping the shape, pose, action, and identity of the user unchanged. For this purpose, we exploited the Liquid Warping GAN for domain transfer and U-Net with Grab-cut for segmentation. We illustrate the results of our work in this paper and the outcomes show that our approach performs very satisfactorily for image-to-image attire transfer. We believe this work will contribute to the clothing sector of our e-commerce system by making the shopping smoother, as it will be able to transfer a human model's outfit to a buyer's body in the image and thus helping him/her in deciding a suitable product to purchase.
Science2Cure: A Clinical Trial Search Prototype

With the advances in precision medicine, identifying clinical trials relevant to a specific patient profile becomes more challenging. Often very specific molecular-level patient features need to be matched for the trial to be deemed relevant. Clinical trials contain strict inclusion and exclusion criteria, often written in free-text. Patients profiles are also semi-structured, with some important information hidden in clinical notes. We present a search system that given a patient profile searches over clinical trials for potential matches. It enables the users to leverage the powerful querying language that comes with Apache Lucene query syntax in combination with state-of-the-art Divergence From Randomness retrieval coupled with a BERT-based neural ranking component. This system aims to assist in clinical decision making.
Context-aware positional representation for self-attention networks

In self-attention networks (SANs), positional embeddings are used to model order dependencies between words in the input sentence and are added with word embeddings to gain an input representation, which enables the SAN-based neural model to perform (multi-head) and to stack (multi-layer) self-attentive functions in parallel to learn the representation of the input sentence. However, this input representation only involves static order dependencies based on discrete position indexes of words, that is, is independent of context information, which may be weak in modeling the input sentence. To address this issue, we proposed a novel positional representation method to model order dependencies based on n-gram context or sentence context in the input sentence, which allows SANs to learn a more effective sentence representation. To validate the effectiveness of the proposed method, it is applied to the neural machine translation model, which adopts a typical SAN-based neural model. Experimental results on two widely used translation tasks, i.e., WMT14 English-to-German and WMT17 Chinese-to-English, showed that the proposed approach can significantly improve the translation performance over the strong Transformer baseline.
Manhole Cover Detection Using Depth Information

Abstract Nowadays, the security of manhole cover has become a growing concern. In the past, manual inspection and Internet of Things were often used to ensure the safety of manhole cover. With the advent of smart cities, people look forward to using cameras as a more convenient method to detect whether manhole covers are damaged. Based on the structure of object detection network, we integrate the depth information in the picture and create a new model which improves the accuracy of detecting damaged manhole covers.
Entanglement Dynamics Induced by a Squeezed Coherent Cavity Coupled Nonlinearly with a Qubit and Filled with a Kerr-Like Medium

An analytical solution for a master equation describing the dynamics of a qubit interacting with a nonlinear Kerr-like cavity through intensity-dependent coupling is established. A superposition of squeezed coherent states is propped as the initial cavity field. The dynamics of the entangled qubit-cavity states are explored by negativity for different deformed function of the intensity-dependent coupling. We have examined the effects of the Kerr-like nonlinearity and the qubit-cavity detuning as well as the phase cavity damping on the generated entanglement. The intensity-dependent coupling increases the sensitivity of the generated entanglement to the phase-damping. The stability and the strength of the entanglement are controlled by the Kerr-like nonlinearity, the qubit-cavity detuning, and the initial cavity non-classicality. These physical parameters enhance the robustness of the qubit-cavity entanglement against the cavity phase-damping. The high initial cavity non-classicality enhances the robustness of the qubit-cavity entanglement against the phase-damping effect.
Incremental CFG patching for binary rewriting

Binary rewriting has been widely used in software security, software correctness assessment, performance analysis, and debugging. One approach for binary rewriting lifts the binary to IR and then regenerates a new one, which achieves near-to-zero runtime overhead, but relies on several limiting assumptions on binaries to achieve complete binary analysis to perform IR lifting. Another approach patches individual instructions without utilizing any binary analysis, which has great reliability as it does not make assumptions about the binary, but incurs prohibitive runtime overhead.
An MSB Prediction-Based Method with Marker Bits for Reversible Data Hiding in Encrypted Images

In this paper, we extend a high-capacity reversible data hiding method in encrypted images to fully ensure the reversibility and enhance the payload capacity. For the perfect reversibility, marker bits are inserted into a payload depending on the length of flag, which separates available pixels for data hiding from non-available pixels. Owing to modifying the MSB prediction error highlighting, the hiding capacity is enhanced in the proposed method. We further introduce the serpentine scan order in the prediction error detection, leading to the hiding capacity being further improved.
NICE: an algorithm for nearest instance counterfactual explanations

In this paper we propose a new algorithm, named NICE, to generate counterfactual explanations for tabular data that specifically takes into account algorithmic requirements that often emerge in real-life deployments: (1) the ability to provide an explanation for all predictions, (2) being able to handle any classification model (also non-differentiable ones), (3) being efficient in run time, and (4) providing multiple counterfactual explanations with different characteristics. More specifically, our approach exploits information from a nearest unlike neighbor to speed up the search process, by iteratively introducing feature values from this neighbor in the instance to be explained. We propose four versions of NICE, one without optimization and, three which optimize the explanations for one of the following properties: sparsity, proximity or plausibility. An extensive empirical comparison on 40 datasets shows that our algorithm outperforms the current state-of-the-art in terms of these criteria. Our analyses show a trade-off between on the one hand plausibility and on the other hand proximity or sparsity, with our different optimization methods offering users the choice to select the types of counterfactuals that they prefer. An open-source implementation of NICE can be found at https://github.com/ADMAntwerp/NICE.
Design of Equipment Condition Maintenance Knowledge Base in Power IoT Based on Edge Computing

The power IoT (Internet of things) realizes the holographic perception of power grid state, and drives the data sharing and application through the Internet of things platform, data center and business center, providing a more comprehensive data base for the construction of equipment state maintenance. Edge computing provides a real-time processing method with low delay for equipment condition monitoring, it also provides a more comprehensive data basis for equipment condition maintenance. The paper proposed a framework design of equipment condition based maintenance knowledge base using machine learning. The combination of production rules and Petri net model are used to realize knowledge compilation and reasoning, and complete the intelligent decision of equipment fault repair.