Reviewerly/exHarmony huggingface dataset:
Dynamic prioritized motion coordination of multi-AGV systems

The motion coordination problem for a fleet of Autonomous Guided Vehicles (AGVs) in a confined industrial facility is addressed. The working scenario involves a group of AGVs that is tasked to transport without collisions to predefined locations within the industrial facility. We introduce a centralized motion coordination controller that utilizes a dynamic priority logic to resolve motion conflicts between AGVs as they appear. The controller relies on the implementation of a predefined, virtual transportation network that is comparable to a conventional right-handed bidirectional traffic system. The construction of the transportation network considers the physical and motion characteristics of the AGVs (dimensions and maximum speed). The high-level function of the controller is to detect imminent collisions and determine the right-of-way of conflicting AGVs in same-directional routes and intersection junctions of the transportation network. The priority update logic is inspired by the traffic control of conventional four-way stop-controlled intersections. Based on the updated priorities, the motion coordinator adjusts the advancement of the AGVs to eliminate collisions. The proposed formulation combines a high-level event-driven logic for collision avoidance with low-level feedback control laws for guidance and navigation. As a result, the controller relies only on real-time measurements, removing the need for computationally demanding look-ahead predictions (heuristics) of the AGVs' motion. It is shown that the proposed method ensures collision- and blockage-free motion of a large number of AGVs. Extensive numerical simulations validate the performance of the motion coordination algorithm.
Telecom Big Data Based Precise User Classification Scheme

With the development of information technology, we are living in a society of data explosion. In precise marketing field, how to classify the population and identify the target users has attracted much concern. Based on telecom big data, this paper has proposed a precise user classification scheme using decision tree. According to the classification result, potential users can be identified and focused on, which can dramatically improve the marketing efficiency and effectiveness with limited marketing expense.
Coverage Mission for UAVs Using Differential Evolution and Fast Marching Square Methods

This research presents a novel approach for missions of coverage path planning (CPP) carried out by unmanned aerial vehicles (UAVs) in a three-dimensional environment. These missions are focused on path planning to cover a certain area in an environment in order to carry out tracking, search, or rescue tasks. The methodology followed uses an optimization process based on the differential evolution (DE) algorithm in combination with the Fast Marching Square (FM2) planner. The DE algorithm evaluates a cost function to determine what the zigzag path with the minimum cost is, according to the steering angle of the zigzag bands (α). This optimization process allows achieving the most optimal zigzag path in terms of distance traveled by the UAV to cover the whole area. Then, the FM2 method is applied to generate the final path according to the steering angle of the zigzag bands resulting from the DE algorithm. The approach generates a feasible path free from obstacles, keeping a fixed altitude flight over the ground. The flight level, smoothness, and safety of the path can be modified by two adjustment parameters included in our approach. Simulated experiments carried out in this work demonstrate that the proposed approach generates the most optimal zigzag path in terms of distance, safety, and smoothness to cover a certain whole area, keeping a determined flight level with successful results.
Interactive Self-Attentive Siamese Network for Biomedical Sentence Similarity

The determination of semantic similarity between sentences is an important component in natural language processing (NLP) tasks such as text retrieval and text summarization. Many approaches have been proposed for estimating sentence similarity, and Siamese neural networks (SNN) provide a better approach. However, the sentence semantic representation, generated by sharing weights in the SNN without any attention mechanism, ignores the different contributions of different words to the overall sentence semantics. Furthermore, the attention operation within only a single sentence neglects interactive semantic influence on similarity estimation. To address these issues, an interactive self-attention (ISA) mechanism is proposed in this paper and integrated with an SNN, named an interactive self-attentive Siamese neural network (ISA-SNN) which is used to verify the effectiveness of ISA. The proposed model obtains the weights of words in a single sentence by means of self-attention and extracts inherent interactive semantic information between sentences via interactive attention to enhance sentence semantic representation. It achieves better performances without feature engineering than other existing methods on three biomedical benchmark datasets (a Pearson correlation coefficient of 0.656 and 0.713/0.658 on DBMI and CDD-ful/-ref, respectively).
Handling Imbalanced Data using Ensemble Learning in Software Defect Prediction

With the ever growing software industry, software defect prediction is one of the key ingredients in recipe of producing good quality software. Defects uncovered well in time helps in saving resources in terms of time, effort and money. However imbalanced nature of software data may hamper the resultant performance of models leading to incorrect interpretations of results. This problem has dragged attention of researchers and many solutions are proposed to overcome the effect of this problem. This paper aims to provide empirical comparison of software defect prediction models developed by using various boosting based ensemble methods on three open source JAVA projects. Four ensemble methods incorporate resampling techniques within them. Performances of models obtained are evaluated using stable metrics like Balance, G-Mean and AUC. Results show that use of resampling techniques before classifying using ensemble method has significantly improved model prediction as compared to classic boosting models. RUSBoost is the undisputed winner amongst all followed by MSMOTEBoost and SMOTEBoost.
Learning Noise Invariant Features Through Transfer Learning For Robust End-to-End Speech Recognition

End-to-end models yield impressive speech recognition results on clean datasets while having inferior performance on noisy datasets. To address this, we propose transfer learning from a clean dataset (WSJ) to a noisy dataset (CHiME4) for connectionist temporal classification models. We argue that the clean classifier (the upper layers of a neural network trained on clean data) can force the feature extractor (the lower layers) to learn the underlying noise invariant patterns in the noisy dataset. While training on the noisy dataset, the clean classifier is either frozen or trained with a small learning rate. The feature extractor is trained with no learning rate re-scaling. The proposed method gives up to 15.5% relative character error rate (CER) reduction compared to models trained only on CHiME-4. Furthermore, we use the test sets of Aurora-4 to perform evaluation on unseen noisy conditions. Our method has significantly lower CERs (11.3% relative on average) on all 14 Aurora-4 test sets compared to the conventional transfer learning method (no learning rate rescale for any layer), indicating our method enables the model to learn noise invariant features.
Toward automatic comparison of visualization techniques: Application to graph visualization

Many end-user evaluations of data visualization techniques have been run during the last decades. Their results are cornerstones to build efficient visualization systems. However, designing such an evaluation is always complex and time-consuming and may end in a lack of statistical evidence and reproducibility. We believe that modern and efficient computer vision techniques, such as deep convolutional neural networks (CNNs), may help visualization researchers to build and/or adjust their evaluation hypothesis. The basis of our idea is to train machine learning models on several visualization techniques to solve a specific task. Our assumption is that it is possible to compare the efficiency of visualization techniques based on the performance of their corresponding model. As current machine learning models are not able to strictly reflect human capabilities, including their imperfections, such results should be interpreted with caution. However, we think that using machine learning-based pre-evaluation, as a pre-process of standard user evaluations, should help researchers to perform a more exhaustive study of their design space. Thus, it should improve their final user evaluation by providing it better test cases. In this paper, we present the results of two experiments we have conducted to assess how correlated the performance of users and computer vision techniques can be. That study compares two mainstream graph visualization techniques: node-link (NL) and adjacency-matrix (AM) diagrams. Using two well-known deep convolutional neural networks, we partially reproduced user evaluations from Ghoniem et al. and from Okoe et al.. These experiments showed that some user evaluation results can be reproduced automatically.
Counting Dense Objects in Remote Sensing Images

Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in natural scene, this task is challenging in following factors: large scale variation, complex cluttered background and orientation arbitrariness. More importantly, the scarcity of data severely limits the development of research in this field. To address these issues, we first construct a large-scale object counting dataset based on remote sensing images, which contains four kinds of objects: buildings, crowded ships in harbor, large-vehicles and small-vehicles in parking lot. We then benchmark the dataset by designing a novel neural network which can generate density map of an input image. The proposed network consists of three parts namely convolution block attention module (CBAM), scale pyramid module (SPM) and deformable convolution module (DCM). Experiments on the proposed dataset and comparisons with state of the art methods demonstrate the challenging of the proposed dataset, and superiority and effectiveness of our method.
Designers Characterize Naturalness in Voice User Interfaces: Their Goals, Practices, and Challenges

This work investigates the practices and challenges of voice user interface (VUI) designers. Existing VUI design guidelines recommend that designers strive for natural human-agent conversation. However, the literature leaves a critical gap regarding how designers pursue naturalness in VUIs and what their struggles are in doing so. Bridging this gap is necessary for identifying designers' needs and supporting them. Our interviews with 20 VUI designers identified 12 ways that designers characterize and approach naturalness in VUIs. We categorized these characteristics into three groupings based on the types of conversational context that each characteristic contributes to: Social, Transactional, and Core. Our results contribute new findings on designers' challenges, such as a design dilemma in augmenting task-oriented VUIs with social conversations, difficulties in writing for spoken language, lack of proper tool support for imbuing synthesized voice with expressivity, and implications for developing design tools and guidelines.
Revisiting Fast Spectral Clustering with Anchor Graph

Many anchor-graph-based spectral clustering methods have been proposed to accelerate spectral clustering for large scale problems. In this paper, we revisit the popular large-scale spectral clustering method based on the anchor graph which is equivalent to the spectral decomposition on a similar matrix obtained using a second-order transition probability. However, due to the special structure of the bipartite graph, there is no stable distribution of the random walk process. The even-order transition probabilities may only a side view of the bipartite structure, resulting in breaking the independence of data points and leading to undesired artifacts for boundary samples. Therefore, we propose a Fast Spectral Clustering based on the Random Walk Laplacian (FRWL) method. The random walk Laplacian balances explicitly the popularity of anchors and the independence of data points, which keeps the structure of boundary samples. The experimental results demonstrate the efficiency and effectiveness of our method.
Multimodal Speaker Diarization of Real-World Meetings Using D-Vectors With Spatial Features

Deep neural network based audio embeddings (d-vectors) have demonstrated superior performance in audio-only speaker diarization compared to traditional acoustic features such as mel-frequency cepstral coefficients (MFCCs) and i-vectors. However, there has been little work on multimodal diarization systems that combine dvectors with additional sources of information. In this paper, we present a novel approach to multimodal speaker diarization that combines d-vectors with spatial information derived from performing beamforming given a multi-channel microphone array. Our system performs spectral clustering on a combination of speaker embeddings and spatial features that are computed using the Steered-Response Power Phase Transform (SRP-PHAT) algorithm. We evaluate our system on the AMI Meeting Corpus and an internal dataset of real-world conversations. By using both acoustic and spatial features for diarization, we achieve significant improvements over a dvector only baseline and show potential to achieve comparable results with other state-of-the-art multimodal diarization systems.
A Comparison of Resampling Techniques for Medical Data Using Machine Learning

Data imbalance with respect to the class labels has been recognised as a challenging problem for machine learning techniques as it has a direct impact on the classification model’s performance. In an imbalanced dataset, most of the instances belong to one class, while far fewer instances are associated with the remaining classes. Most of the machine learning algorithms tend to favour the majority class and ignore the minority classes leading to classification models being generated that cannot be generalised. This paper investigates the problem of class imbalance for a medical application related to autism spectrum disorder (ASD) screening to identify the ideal data resampling method that can stabilise classification performance. To achieve the aim, experimental analyses to measure the performance of different oversampling and under-sampling techniques have been conducted on a real imbalanced ASD dataset related to adults. The results produced by multiple classifiers on the considered datasets showed superiority in terms of specificity, sensitivity, and precision, among others, when adopting oversampling techniques in the pre-processing phase.
A Programming Framework for Differential Privacy with Accuracy Concentration Bounds

Differential privacy offers a formal framework for reasoning about privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analyses results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages aimed at helping a data analyst in programming differentially private analyses. However, most of the programming languages for differential privacy proposed so far provide support for reasoning about privacy but not for reasoning about the accuracy of data analyses. To overcome this limitation, in this work we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy and their trade-offs. The distinguishing feature of DPella is a novel component which statically tracks the accuracy of different data analyses. In order to make tighter accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We evaluate our approach by implementing several classical queries from the literature and showing how data analysts can figure out the best manner to calibrate privacy to meet the accuracy requirements.
DAKE: Document-Level Attention for Keyphrase Extraction

Keyphrases provide a concise representation of the topical content of a document and they are helpful in various downstream tasks. Previous approaches for keyphrase extraction model it as a sequence labelling task and use local contextual information to understand the semantics of the input text but they fail when the local context is ambiguous or unclear. We present a new framework to improve keyphrase extraction by utilizing additional supporting contextual information. We retrieve this additional information from other sentences within the same document. To this end, we propose Document-level Attention for Keyphrase Extraction (DAKE), which comprises Bidirectional Long Short-Term Memory networks that capture hidden semantics in text, a document-level attention mechanism to incorporate document level contextual information, gating mechanisms which help to determine the influence of additional contextual information on the fusion with local contextual information, and Conditional Random Fields which capture output label dependencies. Our experimental results on a dataset of research papers show that the proposed model outperforms previous state-of-the-art approaches for keyphrase extraction.
Adversarial Anomaly Detection for Marked Spatio-Temporal Streaming Data

Spatio-temporal event data are becoming increasingly commonplace in a wide variety of applications, such as electronic transaction records, social network data, and crime incident reports. How to efficiently detect anomalies in these dynamic systems using these streaming event data? This work proposes a novel anomaly detection framework for such event data combining the Long Short-Term Memory (LSTM) and marked spatio-temporal point processes. The detection procedure can be computed in an online and distributed fashion via feeding the streaming data through an LSTM and a neural network-based discriminator. This work studies the false-alarm-rate and detection delay using theory and simulation and shows that it can achieve weak signal detection by aggregating local statistics over time and networks. Finally, we demonstrate the good performance using real-world data sets.
Weightless Neural Networks as Memory Segmented Bloom Filters

Weightless Neural Networks (WNNs) are Artificial Neural Networks based on RAM memory broadly explored as solution for pattern recognition applications. Memory-oriented solutions for pattern recognition are typically very simple, and can be easily implemented in hardware and software. Nonetheless, the straightforward implementation of a WNN requires a large amount of memory resources making its adoption impracticable on memory constrained systems. In this paper, we establish a foundational relationship between WNN and Bloom filters, presenting a novel unified framework which encompasses the two. In particular, we indicate that a WNN can be framed as a memory segmented Bloom filter. Leveraging such finding, we propose a new model of WNNs which utilizes Bloom filters to implement RAM nodes. Bloom filters reduce memory requirements, and allow false positives when determining if a given pattern was already seen in data. We experimentally found that for pattern recognition purposes such false positives can build robustness into the system. The experimental results show that our model using Bloom filters achieves competitive accuracy, training time and testing time, consuming up to 6 orders of magnitude less memory resources when compared against the standard Weightless Neural Network model.
An Extremely Pipelined FPGA Implementation of a Lossy Hyperspectral Image Compression Algorithm

Segmented and pipelined execution has been a staple of computing for the past decades. Operations over different values can be carried out at the same time speeding up computations. Hyperspectral image compression sequentially processes samples, exploiting local redundancies to generate a predictable data stream that can be compressed. In this article, we take advantage of a low complexity predictive lossy compression algorithm which can be executed over an extremely long pipeline of hundreds of stages. We can avoid most stalls and maintain throughput close to the theoretical maximum. The different steps operate over integers with simple arithmetic operations, so they are especially well-suited for our FPGA implementation. Results on a Virtex-7 show a maximum frequency of over 300 MHz for a throughput of over 290 MB/s, with a space-qualified Virtex-5 reaching 258 MHz, being five times as fast as the previous FPGA designs. This shows that a modular pipelined approach is beneficial for these kinds of compression algorithms.
Sentiment Analysis of a Product based on User Reviews using Random Forests Algorithm

After many sentiment analysis as well as many types of methods classify the reviews that is based on test data and reviewer's ratings which uses training., after reading reviews it is seen that star rating of reviewer do not always give a precise measure of his sentiment. This paper primarily focuses on analyzing customer reviews from the e-commerce space. Upon surveying popular e-commerce websites it can be observed that in several instances the product rating given by a customer is not consistent with the product review written by him/her. The problem is made complex by the fact that there is no standard scale to measure the rating that the user gives and the rating of the product are instinctive to the customers' view. In several cases it is seen that a product is rated 4 out of 5. However, the reviews detail that the customer's experience with the product is not favourable. Indeed, text reviews are a true picture of the product. To get rid of this problem, the stated system will give a boolean result i.e. whether the product is good or bad and the user does not need to read all the reviews to analyze the product.
Support vector machine classifier with huberized pinball loss

The original support vector machine (SVM) uses the hinge loss function, which is non-differentiable and makes the problem difficult to solve in particular for regularized SVM, such as with ℓ1-regularized. On the other hand, the hinge loss is sensitive to noise. To circumvent these drawbacks, a huberized pinball loss function is proposed. It is less sensitive to noise, similar to the pinball loss which is related to the quantile distance. The proposed loss function is differentiable everywhere and this differentiability can significantly reduce the computational cost for the SVM algorithm. The elastic net penalty is applied to the SVM and the support vector machine classifier with huberized pinball loss (HPSVM) is proposed. Due to the continuous differentiability of the huberized pinball loss function, the Proximal Gradient method is used to solve the proposed model. The numerical experiments on synthetic data, real world datasets confirm the robustness and effectiveness of the proposed method. Statistical comparison is performed to show the significant difference between the proposed method and other compered ones.