Reviewerly/exHarmony huggingface dataset:
Stability analysis of the human behavior-based particle swarm optimization without stagnation assumption

Human behaviour-based particle swarm optimization (HPSO) is an improved particle swarm optimization (PSO) algorithm, which can effectively increase the diversity of population, convergence speed and precision by changing particles' flight schema. In this paper, the first- and second-order stabilities of HPSO on the assumption that the personal optimum, the global optimum and the global worst particle are mutual independence during the whole evolutionary process, while the expectations and variances of particles' positions vary with time so that our calculations do not require the stagnation assumption. The first- and second-order stabilities of HPSO are expressed by stochastic recurrence relation. The convergence boundaries of HPSO are obtained, and which show that the stabilities of HPSO are independent of the expectations and variances of the personal best and the global best. In addition, the reason for choosing the coefficient of the impelled/penalized term in HPSO is analysed, and the results of simulation experiments further confirm the theoretical analysis. Finally, HPSO is used to solve four real-world engineering design problems. Experimental results further confirm the theoretic results and illustrate that the performance of HPSO is promising and competitive.
An Adaptive Deep Learning Model for Smart Home Autonomous System

Aiming at the problem of enhancing the suggestion and decision-making ability of home automation system under the condition of less user intervention involved, an adaptive control system based on scene perception in smart home is proposed. Firstly, the scenario information is extracted from graphic data by reinforcement learning method, and then the adaptive decision model based on deep learning is constructed, a simulated system was tested based on its ability to predict the best interval present in day for lights and blinds in different rooms, operations of the home equipment is updated according to the user's behavior. Results shows that the proposed model is able to predict the best 15 minute interval in a day for lights and blinds in different rooms of a home without user intervention and user feedback could improve model prediction performance.
Determinant Regularization for Gradient-Efficient Graph Matching

Graph matching refers to finding vertex correspondence for a pair of graphs, which plays a fundamental role in many vision and learning related tasks. Directly applying gradient-based continuous optimization on graph matching can be attractive for its simplicity but calls for effective ways of converting the continuous solution to the discrete one under the matching constraint. In this paper, we show a novel regularization technique with the tool of determinant analysis on the matching matrix which is relaxed into continuous domain with gradient based optimization. Meanwhile we present a theoretical study on the property of our relaxation technique. Our paper strikes an attempt to understand the geometric properties of different regularization techniques and the gradient behavior during the optimization. We show that the proposed regularization is more gradient-efficient than traditional ones during early update stages. The analysis will also bring about insights for other problems under bijection constraints. The algorithm procedure is simple and empirical results on public benchmark show its effectiveness on both synthetic and real-world data.
A Comprehensive Study on Loss Functions for Cross-Factor Face Recognition

A significant progress has been made to face recognition in recent years. The progress includes the advancement of the deep learning solutions and the availability of more challenging databases. As the performance on previous benchmark databases, such as MPIE and LFW, saturates, more challenging databases are emerging and keep driving the development of face recognition technology. The loss function considered in a deep face recognition network plays a critical role for the performance. To better evaluate the state-of-the-art loss functions, we define four challenging factors, including pose, age, occlusion and resolution with specific databases and conduct an extensive experimental study on the latest loss functions. We select the IARPA Janus Benchmark-B (IJB- B) and IARPA Janus Benchmark-C (IJB-C) for pose, the FG-Net Aging Database (FG-Net) for age, the AR Face Database (AR Face) for occlusion, and the Surveillance Cameras Face Database (SCface) for low resolution. The loss functions include the Center Loss, the Marginal Loss, the SphereFace, the CosFace and the ArcFace. Although for most factors, the ArcFace outperforms others. However, the best performance against low-resolution is achieved by the SphereFace. Another attractive finding of this study is that the cross-age performance is the lowest among the four factors with a clear margin. This highlight possible directions for future research.
Learning heterogeneous information network embeddings via relational triplet network

Network embedding algorithms learn low-dimensional features from the relationships and attributes of networks. The basic principle of these algorithms is to preserve the similarities in the original networks as much as possible. However, in heterogeneous information networks, existing algorithms are not sufficiently expressive to capture detailed semantic patterns between nodes. In this paper, we propose a novel heterogeneous information network embedding algorithm called the relational triplet network (RTN). In the data sampling phase, meta-schema-based random walks are performed to extract semi-hard quadruplets based on the node type and degree. In the representation learning phase, a relational triplet loss is designed to optimize the distance of triplet embeddings on diverse heterogeneous relationships. The empirical results demonstrate that our algorithm can obtain multiple types of representations and outperform other state-of-the-art methods in node classification and link prediction.
Learning Ordered Top-k Adversarial Attacks via Adversarial Distillation

Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, especially white-box targeted attacks. This paper studies the problem of how aggressive white-box targeted attacks can be to go beyond widely used Top-1 attacks. We propose to learn ordered Top-k attacks (k ≥ 1), which enforce the Top-k predicted labels of an adversarial example to be the k (randomly) selected and ordered labels (the ground-truth label is exclusive). Two methods are presented. First, we extend the vanilla Carlini-Wagner (C&W) method and use it as a strong baseline. Second, we present an Adversarial Distillation (AD) framework consisting of two components: (i) Computing an adversarial probability distribution for a given ordered Top-k targeted labels. (ii) Learning adversarial examples by minimizing the Kullback-Leibler (KL) divergence between the adversarial distribution and the predicted distribution, together with the perturbation energy penalty. In computing adversarial distributions, we explore how to leverage label semantic similarities, leading to knowledge-oriented attacks. In experiments, we test Top-k (k = 1, 2, 5, 10) attacks in the ImageNet-1000 val dataset using three representative DNNs trained with the clean ImageNet-1000 train dataset, ResNet-50 [11], DenseNet-121 [14] and AOGNet-12M [21]. Overall, the proposed AD approach obtains the best results, especially by a large margin when computation budget is limited. It reduces the perturbation energy consistently with the same attack success rate on all the four k's, and improve the attack success rate by a large margin against the modified C&W method for k = 10.
Using Mixture of Expert Models to Gain Insights into Semantic Segmentation

Not only correct scene understanding, but also ability to understand the decision making process of neural networks is essential for safe autonomous driving. Current work mainly focuses on uncertainty measures, often based on Monte Carlo dropout, to gain at least some insight into a models confidence. We investigate a mixture of experts architecture to achieve additional interpretability while retaining comparable result quality. By being able to use both the overall model output as well as retaining the possibility to take into account individual expert outputs, the agreement or disagreement between those individual outputs can be used to gain insights into the decision process. Expert networks are trained by splitting the input data into semantic subsets, e.g. corresponding to different driving scenarios, to become experts in those domains. An additional gating network that is also trained on the same input data is consequently used to weight the output of individual experts. We evaluate this mixture of expert setup on the A2D2 dataset and achieve similar results to a baseline FRRN network trained on all available data, while getting additional information.
Pulmonary Disease Pattern Recognition on X-Ray Radiography Image Using Artificial Neural Network (ANN) Method

Abstract This research aims to recognize the pattern of pulmonary disease on x-ray radiography image using artificial neural network (ANN) method. The images, which were used such as images of healthy pulmonary, pulmonary tuberculosis, and pulmonary tumour. Pattern recognition was using an extraction feature of GLCM (Gray Level Co-occurrence Matrix) and back propagation method. Before being identified, the images were processed by median filter and adaptive histogram equalization. The GLCM features that used were homogeneity, energy, contrast, variance and correlation. The parameters were learning rate and hidden layer. Learning rate was 0.3 and hidden layer was 25. Back propagation training showed 100% accuracy, which all of 44 images were used had been successfully identified. From the result, the healthy pulmonary showed 60% accuracy, 83.3% for pulmonary tuberculosis and 100% for pulmonary tumor. Hence, the overall result showed 81.25% accuracy, which 13 of 16 images had been successfully identified. From these result, extraction feature of GLCM using back propagation method was capable to recognize the pattern of pulmonary disease. However, due to narrow range of the feature, this application may not be used optimally for comparing features in every category of images. Therefore, the further research is needed to determine the best features and parameters of training back propagation.
Virtual Sensor Creation to Replace Faulty Sensors Using Automated Machine Learning Techniques

With the introduction of the Internet of Things (IoT) into every area of life, more and more applications are created that rely on information collected from IoT sensors. These applications are developed by third-party developers and depend on a continuous information flow but do not operate the sensor network themselves. However, in case of sensor failures, the flow of information will be interrupted which will pose a problem for the user/application as well as the data provider who might not be able to replace the sensor in time. Depending on the requirements of the application, estimators which are trained through machine learning algorithms, called Virtual Sensors, can ensure the uninterrupted operation of the application. However, for a solution that can be used in practice, the deployment of such a Virtual Sensor needs to be fast enough as well as fully automated, so that no human interaction and domain knowledge is required. This paper describes a framework that is capable of finding correlating IoT sensors in the surrounding environment, selecting a machine learning algorithm, and training a fitting model to run a Virtual Sensor.
Situated Automation

This paper introduces the notion of situated automation to explore how emerging technologies of automation (such as artificial intelligence, machine learning, and algorithms) might be considered as actors in Participatory Design (PD). The growing significance of these algorithmic systems in the shaping of public and private lives of the citizens make them a timely and important site of research and advocacy. This paper presents examples of practices and projects that work with the paradoxes of automation and participation as a site where the different actors collide and form what we describe as algorithmic creatures. We map the different modes of reflection about/through/with technologies of automation to argue for a PD with algorithmic creatures focused on pluralism, justice, equity, and care.
Enhancing convolution-based sentiment extractor via dubbed N-gram embedding-related drug vocabulary

Everyday patients’ narratives on social media can reveal crucial public health issues. Mining those online narratives, which remained so far unconsidered, may mirror further hidden patient health status. Deep learning-based sentiment analysis (SA) approaches broadly focus on grammar directions such as semantic direction or only center on extract sentiment words. They provide both richer representation capabilities and better performance but do not consider the related medication concepts. As a result, the inaccurate recognition of related drug entities may seriously fail to retrieve the relevant sentiment expressed, leading to a lower recall than desired. Thus, the frequent use of informal medical language, non-standard format, wrongly spelled, and abbreviation forms, as well as typos in social media messages, has to be taken into consideration. In other words, the core of efficiently quantifying the sentimental aspects for related medication texts hardly involves a degree of medical language comprehension. In this paper, we seek to improve the importance of considering related drug entities that keep appearing in new Unicode Versions, ranging from drugs’ names, disease symptoms, drug misuse to potentially adverse effects. We propose N-gram-based convolution vocabulary scheme, which is dedicated mainly to featuring text under medical setting and clarifying related sentiment at the same level. This vectorization results in highly sentiment extraction, which produces medical concept normalization under distributed dependency. This architecture’s layers are a shared neural network between the medical featuring channel and the bidirectional sentiment information detector channel. Fewer approaches are proposed in this matter, we evaluate the effectiveness and transferability of this study across five benchmarking datasets and various online medication-related posts (Twitter posts, Parkinson’s disease forum’s discussions), which were significantly better than all other baselines.
bwSlicer: A bandwidth slicing framework for cloud data centers

Bandwidth allocation is an important and influential factor in enhancing the performance of the data centers’ nodes. In this paper we propose bwSlicer, a framework for bandwidth slicing in cloud data centers, that sheds light on the virtues of effective dynamic bandwidth allocation on improving the system performance and energy efficiency. Three algorithms are investigated to deal with this issue. In the first algorithm, called Fair Bandwidth Reallocation (FBR), two virtual machines co-hosted on the same node conditionally exchange bandwidth slices based on their requirements. The second algorithm, called Required Bandwidth Allocation (RBA), periodically monitors the co-hosted virtual machines and adds/removes bandwidth slices for each of them based on their bandwidth utilization. The third algorithm, called Divide Bandwidth Reallocation (DBR), divides the bandwidth of the virtual machine into slices once it finishes its execution, and distributes the slices among the co-hosted running virtual machines according to a specific policy. The proposed bandwidth slicing algorithms are emulated in a virtualized networking environment using the Mininet network emulator. The emulation results demonstrated a promising improvement ratio in execution time and energy consumption reaching up to 30%. These results present a call for action for further research into bandwidth slicing and reallocation as a viable complement to other energy-saving techniques for enhancing the energy consumption in cloud data centers.
Particle Swarm Optimization for robot target tracking application

Tracking is a process of finding continuous path of a selected target. Tracking is important in various civil and defense applications. For tracking applications, various adaptive filters are being used such as Kalman Filter and it's variants. In this paper, a popular meta-heuristic algorithm, namely, Particle Swarm Optimization (PSO) algorithm is applied to track a target which is a robot and it is transmitting noisy data due to installation of cheap quality sensors onboard. These noisy observations are filtered by PSO algorithm in order to estimate true path of the robot. The simulated results are presented by using MATLAB software. From the results, it is observed that the PSO algorithm is very effective algorithm in estimating the target location continuously with almost zero estimation error. Thus, Particle Swarm Optimization algorithm is a suitable algorithm for robot tracking applications and it can also be used for nonlinear data processing in autonomous robot design applications.
Initial Lessons from Building an IVR-based Automated Question-Answering System

With improvements in speech recognition and natural language processing capabilities, voicebot systems show promise to run interactive information services for less-literate populations in developing regions. In this context, we describe our initial experiences towards building an automated question-answering system in the domain of sexual and reproductive health and rights. This system is trained on data acquired from an IVR (Interactive Voice Response) platform on which users could record questions, which were then moderated and sent to an expert to get answers. Our goal is to now use this data to build an automated answer retrieval system so that questions can be answered in real time by retrieving an appropriate answer from the corpus of questions and answers available so far. Our insights are likely to be useful for several initiatives using IVR systems and looking to automate their search and retrieval functionality.
Self-Supervised Object Detection and Retrieval Using Unlabeled Videos

Learning an object detection or retrieval system requires a large data set with manual annotations. Such data are expensive and time-consuming to create and therefore difficult to obtain on a large scale. In this work, we propose using the natural correlation in narrations and the visual presence of objects in video to learn an object detector and retriever without any manual labeling involved. We pose the problem as weakly supervised learning with noisy labels, and propose a novel object detection and retrieval paradigm under these constraints. We handle the background rejection by using contrastive samples and confront the high level of label noise with a new clustering score. Our evaluation is based on a set of ten objects with manual ground truth annotation in almost 5000 frames extracted from instructional videos from the web. We demonstrate superior results compared to state-of-the-art weakly- supervised approaches and report a strongly-labeled upper bound as well. While the focus of the paper is object detection and retrieval, the proposed methodology can be applied to a broader range of noisy weakly-supervised problems.
Absolute phase measurement with four patterns based on variant shifting phases

Fringe projection profilometry has been proverbially utilized for measuring the shapes of objects. A common challenge in those systems is to accurately obtain a smooth absolute phase. Many new methods have been proposed to address this challenge. In this paper, we discuss a technique based on variant shifting phases. This approach embeds codewords into the shifting phase and only needs four patterns. However, reliable measurement results are difficult to achieve with a large number of codewords because of the phase errors. To address this shortcoming, we present a robust coding method that embeds a specific code sequence into the shifting phase and can generate more than 36 periods. The fringe order is determined using unique three-adjacent-codes combining the current period and its neighbors. An error correction algorithm is also proposed to optimize the codewords. The proposed method is experimentally verified using an established measurement system. The result shows that the proposed method is robust and efficient.
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles

Deep reinforcement learning methods have been considered and implemented for autonomous vehicle's decisionmaking in recent years. A key issue is that deep neural networks can be fragile to adversarial attacks through unseen inputs, and thus the reinforcement learning policy, that uses deep neural network would be also fragile to malicious attacks or benign but out of distribution perturbations. In this paper, we address the latter issue: we focus on generating socially acceptable perturbations (SAP), so that the autonomous vehicle (AV agent under evaluation), instead of the challenging vehicle (challenger), is primarily responsible for the crash. In our process, one challenger is added to the environment and trained by deep reinforcement learning to generate the desired perturbation. The reward is designed so that the challenger aims to fail the AV agent in a socially acceptable way. After training the challenger, the AV agent policy is evaluated in both the original naturalistic environment and the environment with one challenger. The results show that the AV agent policy which is safe in the naturalistic environment has many crashes in the perturbed environment.
Bangla Toxic Comment Classification (Machine Learning and Deep Learning Approach)

Toxic comment classification problem is a popular classification problem nowadays. There are many attempts in English but it's rare in Bangla language. We tried to build a classifier for Bangla language. We tried different approach to find the optimized classifier with better accuracy and optimized for log-loss, hamming-loss. As this is a multilevel problem, we used binary relevance methods for binary classifiers.
Remote Detection of Indoor Human Proximity using Bluetooth Low Energy Beacons

The way people interact in daily life is a challenging phenomenon to capture and to study without altering the natural rhythm of interactions. Our work investigates the possibility of automatically detecting proximity among people, the first mandatory condition before a dyad starts interacting. We present Remote Detection of Human Proximity (ReD-HuP), an algorithm based on the analysis of Bluetooth Low Energy beacons emitted by commercial wearable tags. We validate ReD-HuP with real-world indoor settings and we compare its performance with respect to detailed ground truth data collected from a number of volunteers. Experimental results show an accuracy and F-Score metric up to 95%.