Reviewerly/exHarmony huggingface dataset:
Content-Based Image Retrieval using SIFT and CNN

The graphical data is increasing rapidly and most of the data is in the form of images. The reason for this rapid increase in images is the digitalization of data in sectors like medical, education, commerce, government, etc. For searching through a pile of images most of the sectors still use primitive methods like searching manually or assigning keywords to the images. In this paper, we are trying to solve this problem by using CBIR (Content-Based Image Retrieval) technique. In CBIR we extract features from images and using those features we can match similar images which will be helpful in finding a particular image or set of images from the dataset. We are also using CNN for object detection which will help us in finding labels of objects and using those labels we can classify and store images for accelerating search. The main algorithm used for keypoints extraction is SIFT (Scale Invariant Feature Transform). We've also tested our project with Face Images dataset.
Instance-Aware Feature Alignment for Cross-Domain Cell Nuclei Detection in Histopathology Images

Robust nuclei detection is crucial prerequisite for histologic characteristics of nuclei that can assist various clinical tasks such as disease diagnosis and cancer grading. Despite of their success, most existing nuclei detection methods ignore the case where the testing (target) domain has different data distribution with the training (source) domain, which is known as the problem of domain shift. In fact, the domain shift problem is prevalent in histopathology images due to various reasons such as different staining procedures and organ specific nuclear morphology. Thus, the performance of a nuclei detection model in the source domain will be hurt if it is directly applied to the target domain. To address this problem, we propose a novel instance-aware domain adaption framework for nuclei detection in histopathology images, which includes both image-level alignment (IMA) and instance-level alignment (INA) components to minimize the domain shift. Especially, INA component extracts instance-level features by using nuclei locations as the guidance and effectively aligns the instance-level features via adversarial training. Furthermore, to facilitate instance-level feature alignment, a Temporal Ensembling based Nuclei Localization (TENL) module is introduced in INA component to automatically generate candidate nuclei locations in the target domain. We evaluate the proposed method on different benchmark settings and obtain remarkable improvements compared to existing methods on the challenging problem of cross-domain cell nuclei detection.
The Megopolis resampler: Memory coalesced resampling on GPUs

The resampling process employed in widely used methods such as Importance Sampling (IS), with its adaptive extension (AIS), are used to solve challenging problems requiring approximate inference; for example, non-linear, non-Gaussian state estimation problems. However, the re-sampling process can be computationally prohibitive for practical problems with real-time requirements. We consider the problem of developing highly parallelisable resampling algorithms for massively parallel hardware architectures of modern graphics processing units (GPUs) to accomplish real-time performance. We develop a new variant of the Metropolis algorithm—Megopolis—that improves performance without requiring a tuning parameter or reducing resampling quality. The Megopolis algorithm is built upon exploiting the memory access patterns of modern GPU units to reduce the number of memory transactions without the need for tuning parameters. Extensive numerical experiments on GPU hardware demonstrate that the proposed Megopolis algorithm is numerically stable and outperforms the original Metropolis algorithm and its variants—Metropolis-C1 and Metropolis-C2–in speed and quality metrics. Further, given the absence of open tools in this domain and facilitating fair comparisons in the future and supporting the signal processing community, we also open source the complete project, including a repository of source code with Megopolis and all other comparison methods.
Semantic Services Catalog for Multiagent Systems Society

Agent-based simulation tools have found many applications in the field of Power and Energy Systems, as they can model and analyze the complex synergies of dynamic and continuously evolving systems. While some studies have been done w.r.t. simulation and decision support for electricity markets and smart grids, there is still a generalized limitation referring to the significant lack of interoperability between independently developed systems, hindering the task of addressing all the relevant existing interrelationships. This work presents the Semantic Services Catalog (SSC), developed and implemented for the automatic registry, discovery, composition, and invocation of web and agent-based services. By adding a semantic layer to the description of different types of services, this tool supports the interaction between heterogeneous multiagent systems and web services with distinct capabilities that complement each other. The case study confirms the applicability of the developed work, wherein multiple simulation and decision-support tools work together managing a microgrid of residential and office buildings. Using SSC, besides discovering each other, agents also learn about the ontologies and languages to use to communicate with each other effectively.
RBA-CenterNet: Feature Enhancement by Rotated Border Alignment for Oriented Object Detection

Generic object detection has achieved significant progress in recent years. However, oriented object detection in aerial images is still a challenging task due to arbitrary orientation, complex backgrounds and large scale variation. Currently, the majority of oriented object detectors are anchor-based, achieving promising performance yet suffering from complicated anchor designs and imbalance between the positive and negative anchor boxes. In this work, we propose a new anchor-free model called RBA-CenterNet for oriented object detection. Specifically, we first detect the center point of each object after extracting feature of the input image. Then, we regress the rotated box parameters in other prediction branches. Considering that using only the center point of the object may hurt the detection performance, we introduce a refinement module called rotated border alignment (RBA) to integrate the border feature into the center point feature. Our experiments show that the proposed model RBA-CenterNet can achieve comparable detection performance to state-of-the-art methods.
Comparison Performance of Prostate Cell Images Classification using Pretrained Convolutional Neural Network Models

Prostate cancer is the most common cancer in men in 2019. In that year, in the United States 174,650 men (20%) had prostate cancer and the remaining 696.32 men (80%) had other cancers (lung, bronchus) etc). In cancer diagnosis, there are several problems such as errors in reporting the diagnosis and the need for a long time. Artificial intelligence has long been known to facilitate the detection process, but a comparison analysis of the model is needed to get more optimal results. This study aims to compare the performance of two pretrained models (i.e. AlexNet and GoogLeNet). The data used is the image of prostate cells taken from a light microscope at the Universitas Indonesia (UI) Hospital. This study uses k-fold cross-validation to validate the accuracy of a model used. Performance evaluation of pretrained models is based on performance metrics: accuracy, precision, recall (sensitivity), specificity and f-score and running time in the testing process. The best accuracy is obtained by GoogLeNet with 99.63% and 97.74% and the lowest accuracy is obtained by AlexNet with 99.13% and 94.11%. During the training, AlexNet had a shorter time with 47 seconds than GoogLeNet with 112 seconds. In testing times, AlexNet was also faster with 0.307 seconds than GoogLeNet with 0.372. This research is expected to assist researchers (pathologists, physician assistants, etc.) in choosing the right architecture for the classification of prostate cancer images in terms of time and accuracy.
A New Variant of the GQR Algorithm for Feedforward Neural Networks Training

This paper presents an application of the scaled Givens rotations in the process of feedforward artificial neural networks training. This method bases on the QR decomposition. The paper describes mathematical background that needs to be considered during the application of the scaled Givens rotations in neural networks training. The paper concludes with sample simulation results.
Multi-Task Voice Activated Framework Using Self-Supervised Learning

Self-supervised learning methods such as wav2vec 2.0 have shown promising results in learning speech representations from unlabelled and untranscribed speech data that are useful for speech recognition. Since these representations are learned without any task-specific supervision, they can also be useful for other voice activated tasks like speaker verification, keyword spotting, emotion classification etc. In our work, we propose a general purpose framework for adapting a pre-trained wav2vec 2.0 model for different voice activated tasks. We develop downstream network architectures that operate on the contextualized speech representations of wav2vec 2.0 to adapt the representations for solving a given task. Finally, we extend our framework to perform multi-task learning by jointly optimizing the network parameters on multiple voice activated tasks using a shared transformer backbone. Both of our single and multi-task frameworks achieve state-of-the-art results in speaker verification and keyword spotting benchmarks. Our best performing models achieve 1.98% and 3.15% EER on VoxCeleb1 test set when trained on Vox-Celeb2 and VoxCeleb1 respectively, and 98.23% accuracy on Google Speech Commands v1.0 keyword spotting dataset.
Real-time localization measure and perception detection using multi-sensor fusion for Automated Guided Vehicles

Automated Guided Vehicles (AGVs) need to localize themselves reliably and perceive environment accurately to perform their tasks efficiently. To that end, they rely on noisy sensor measurements that potentially provide erroneous estimates if they are used directly. To prevent this issue, measurements from different kinds of sensors are generally used together. This paper presents a hybrid multi-sensor fusion pipeline configuration that can function with asynchronous measurements from camera, LiDAR, odometry and Inertial Measurement Units (IMUs). The hybrid multi-sensor fusion algorithm consists of two parts that run in parallel, one of them is the combination of camera and LiDAR with a Fully Convolutional Neural Network (FCNx) architecture for performing classification and segmentation, the other one is to deal with the task of detecting objects and tracking states by using Kalman filter to fusion odometry and IMUs sensors. The developed algorithm was tested with an open-source multi-sensor navigation dataset and real-time experiments with an AGV. It was found that using sensor fusion resulted in a smaller deviation from the actual trajectory compared to using only a laser scanner. Furthermore, in each experiment, using sensor fusion decreased the localization error in the periods where the laser was unavailable, although the amount of improvement depended on the duration of unavailability and motion characteristics.
Human-Aware Dynamic Path Planning

This study is related to human-aware navigation (HAN) of a robot moving in a 2D plane. During the navigation process, the robot first finds a global path given start and goal points and it also dynamically scans the environment for possible new obstacles and rearranges its path plan as to those encountered ones. It is well-known fact that treating humans as an ordinary object by robots while passing them leads to humans feeling stressed since the actions of robots still are not explicit for them. For this reason, besides planning strategy secondly, it is also considered whether obstacles are human or non-human ones via the improved algorithm. By doing so, it is aimed for the robot to navigate in an environment considering human comfort. To examine this intention, two different path planning algorithms improved by dynamically classical and human obstacles aware scheme are tested on a home-like environment. According to the results, it has been observed that the robot dynamically plans its navigation framework given target points and new obstacles. Furthermore, it successfully achieves to keep distance from human obstacles based on the proxemics theory distance requirements.
Optimized Weighted Samples Based Semi-supervised Learning

Through semi-supervised learning with graphs, the machine learning community has achieved many advantages in extracting information from a large volume of data under inadequate initial label information. Recent research has shown the benefit of weighing the samples that are labelled can give improved accuracy. Instead of providing similar consideration for labelled samples, sample weighting establishes higher weights for samples occupied at the border of multiple classes than labelled samples occupied so far from the boundary. This article proposes a faster way to calculate the sample weights by reducing the multiple clustering methods to single clustering. The new method of sample weighting is verified using a 2D feature set so that sample weighting can be easily visualized. The proposed method does not reduce the time complexity but it can reduce the number of steps required for weighting the samples. The obtained results have shown that this method can improve the speed with acceptable accuracy.
Classification of Birds Based on Weighted Fusion Model

Fine-grained image recognition is one of the research highlights of deep learning, and bird recognition is a classic application of fine-grained image recognition. Because different kinds of birds are very similar in appearance, so it is a common method to verify the fine-grained recognition model through the bird recognition effect. This paper chose CUB-200-2011 bird data set and VGG16 and ResNet152 were selected as the basic models. And by calculating the fusion between different weights and different models to obtain optimal classification weighted fusion model for data sets. Model weights can be adjusted dynamically, which makes transfer learning more flexible in weight allocation and stronger model generalization. For the constructed model, Adam optimizer and SGD optimizer are respectively used to analyze and compare the four different learning rates of 0.1, 0.01, 0.001 and 0.0001, to obtain the weight parameters with the best classification effect.
ComPreEND: Computation Pruning through Predictive Early Negative Detection for ReLU in a Deep Neural Network Accelerator

A vast amount of activation values of DNNs are zeros due to ReLU (Rectified Linear Unit), which is one of the most common activation functions used in modern neural networks. Since ReLU outputs zero for all negative inputs, the inputs to ReLU do not need to be determined exactly as long as they are negative. However, many accelerators usually do not consider such aspects of DNNs, losing a huge amount of opportunities for speedups and energy savings. To exploit such opportunities, we propose early negative detection (END), a computation pruning technique that detects the negative results at an early stage. The key to the early negative detection is the adoption of inverted two's complement representation for filter parameters. This ensures that as soon as the intermediate results become negative, the final results are guaranteed to be negative. Upon detection, the remaining computation can be skipped and the following ReLU output can be simply set to zero. We also propose a DNN accelerator architecture (ComPreEND) that takes advantage of such skipping. ComPreEND with END significantly improves both the energy efficiency and the performance according to the evaluation. Compared to the baseline, we obtain 20.5 and 29.3 percent speedup with accurate mode and predictive mode, and energy savings by 28.4 and 41.4 percent, respectively.
Risks and risk mitigation in global software development: An update

Abstract Context: Interest in global software development (GSD) has led to the publication of numerous studies. Over time, these studies should be updated to verify if their findings and conclusions remain valid. Objective: To update a tertiary study, published in 2014, focused on investigating risks and risk mitigation advice in the context of GSD. Method: We conducted a systematic literature study based on forward snowballing, out of which we identified and selected 25 unique studies. Results: We extracted: (a) 118 risks (75 of which already identified in the original work), and (b) 167 mitigation advice (44 of which were identified in the original work). Out of 43 new risks identified, just 10 are specific to GSD settings; the others are risks of the software process described in the context of GSD. The evidence supporting these risks and mitigation were extracted from 619 and 389 primary sources, respectively. Given the amount of evidence found, we have reported only those with higher empirical support. The raw data is available online as complementary material. Conclusions: Interest in GSD, its potential risks, and mitigation strategies, remains high. This update has allowed increasing the level of empirical support provided by the findings of the original work.
On Duality Gap as a Measure for Monitoring GAN Training

Generative adversarial networks (GANs) are among the most popular deep learning models for learning complex data distributions. However, training a GAN is known to be a challenging task. This is often attributed to the lack of correlation between the training progress and the trajectory of the generator and discriminator losses and the need for the GAN's subjective evaluation. A recently proposed measure inspired by game theory - the duality gap, aims to bridge this gap. However, as we demonstrate, the duality gap's capability remains constrained due to limitations posed by its estimation process. This paper presents a theoretical understanding of this limitation and proposes a more dependable estimation process for the duality gap. At the crux of our approach is the idea that local perturbations can help agents in a zero-sum game escape non-Nash saddle points efficiently. Through exhaustive experimentation across GAN models and datasets, we establish the efficacy of our approach in capturing the GAN training progress with minimal increase to the computational complexity. Further, we show that our estimate, with its ability to identify model convergence/divergence, is a potential performance measure that can be used to tune the hyperparameters of a GAN.
Comparison of Strategies for Optimizing Bi-RRT* on Mobile Robots

Aiming at problems such as poor target orientation, redundant path inflection points and collision risk in sampling-based planning algorithm such as RRT and RRT*. Strategies for solving those problems are presented in recent work of papers which based on improving Bi-RRT that is an extension of RRT with faster convergence. This paper provides a comparison and analytical review of those strategies correspond to those problems which the performance of the strategies in terms of path length, processing time and total number of nodes in tree are presented through MATLAB simulation. Moreover, the optimal strategies are selected and implemented in Bi-RRT* which has faster convergence speed as compared to its predecessor of Bi-RRT. Further, certain aspects of improved Bi-RRT* based on selected strategies are found to be improved by comparing to traditional Bi-RRT*.
Grammar Index by Induced Suffix Sorting

We propose a new compressed text index built upon a grammar compression based on induced suffix sorting [Nunes et al., DCC'18]. We show that this grammar exhibits a locality sensitive parsing property, which allows us to specify, given a pattern P, certain substrings of P, called cores, that are similarly parsed in the text grammar whenever these occurrences are extensible to occurrences of P. Supported by the cores, given a pattern of length m, we can locate all its \(\text {occ}\) occurrences in a text T of length n within \(\mathop {}\mathopen {}{\mathcal {O}}\mathopen {}(m \lg |{\mathcal {S}}| + \text {occ}_C\lg |{\mathcal {S}}| \lg n + \text {occ})\) time, where \({\mathcal {S}}\) is the set of all characters and non-terminals, \(\text {occ}\) is the number of occurrences, and \(\text {occ}_C\) is the number of occurrences of a chosen core C of P in the right hand side of all production rules of the grammar of T. Our grammar index requires \(\mathop {}\mathopen {}{\mathcal {O}}\mathopen {}(g)\) words of space and can be built in \(\mathop {}\mathopen {}{\mathcal {O}}\mathopen {}(n)\) time using \(\mathop {}\mathopen {}{\mathcal {O}}\mathopen {}(g)\) working space, where g is the sum of the lengths of the right hand sides of all production rules. We practically evaluate that our proposed index excels at locating long patterns in highly-repetitive texts. Our implementation is available at https://github.com/TooruAkagi/GCIS_Index.
Visio-Temporal Attention for Multi-Camera Multi-Target Association

We address the task of Re-Identification (Re-ID) in multi-target multi-camera (MTMC) tracking where we track multiple pedestrians using multiple overlapping uncalibrated (unknown pose) cameras. Since the videos are temporally synchronized and spatially overlapping, we can see a person from multiple views and associate their trajectory across cameras. In order to find the correct association between pedestrians visible from multiple views during the same time window, we extract a visual feature from a tracklet (sequence of pedestrian images) that encodes its similarity and dissimilarity to all other candidate tracklets. We propose a inter-tracklet (person to person) attention mechanism that learns a representation for a target tracklet while taking into account other tracklets across multiple views. Furthermore, to encode the gait and motion of a person, we introduce second intra-tracklet (person-specific) attention module with position embeddings. This second module employs a transformer encoder to learn a feature from a sequence of features over one tracklet. Experimental results on WILDTRACK and our new dataset ‘ConstructSite’ confirm the superiority of our model over state-of-the-art ReID methods (5% and 10% performance gain respectively) in the context of uncalibrated MTMC tracking. While our model is designed for overlapping cameras, we also obtain state-of-the-art results on two other benchmark datasets (MARS and DukeMTMC) with non-overlapping cameras.
MLFF: A Object Detector based on a Multi-Layer Feature Fusion

A major challenge in the field of object detection is to design a model with high precision and high speed, which is necessary in real applications. In this paper, we study the problem of object detection, and propose a multi-layer feature fusion model for object detection, called MLFF. MLFF not only takes into account the rich location information and detail information of low-level features, but also considers the rich spatial information of high-level features. In MLFF, we develop three different components, Backbone, Neck and Head, to gradually extract and fuse low-level feature maps and high-level feature maps. In addition, CBAM attention mechanism is also used in feature fusion, which enables us to achieve more accurate feature extraction. Extensive experiments on PASCAL VOC2007 and VOC2012 datasets show that our MLFF model not only has the speed of one-stage object detection model but also has the detection precision of two-stage object detection model. Considering both precision and speed, MLFF outperforms state-of-the-art detection models significantly.