Reviewerly/exHarmony huggingface dataset:
A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study

This article deals with the design and implementation of a prototype of an efficient Low-Cost, Low-Power, Low Complexity–hereinafter (L-CPC) an image recognition system for person detection. The developed and presented methods for processing, analyzing and recognition are designed exactly for inbuilt devices (e.g., motion sensor, identification of property and other specific applications), which will comply with the requirements of intelligent building technologies. The paper describes detection methods using a static background, where, during the search for people, the background image field being compared does not change, and a dynamic background, where the background image field is continually adjusted or complemented by objects merging into the background. The results are compared with the output of the Horn-Schunck algorithm applied using the principle of optical flow. The possible objects detected are subsequently stored and evaluated in the actual algorithm described. The detection results, using the change detection methods, are then evaluated using the Saaty method in order to determine the most successful configuration of the entire detection system. Each of the configurations used was also tested on a video sequence divided into a total of 12 story sections, in which the normal activities of people inside the intelligent building were simulated.
Development of an Instructional Model with Augmented Reality Technology for Vocational Certificate Students

The purposes for this research were to design and develop an instructional model using augmented reality technology for vocational certificate students, then evaluate the efficiency of the model, and finally evaluate the samples' satisfaction toward the model.The sample group were thirty third-year students from a vocational college in the northeast region in Thailand, who enrolled in microcontroller foundation course in the second semester of academic year 2019.The sample group were selected by simple random sampling method and then divided into two groups as experiment group and control group, consisted of fifteen students in each group.The research tools comprised of 1) the instructional model with augmented reality technology, and 2) the learning achievement test.The research results found that the instructional model consisted of six steps called ISDEEE; Information, Searching, Discussion, Explanation, Experiment and Evolution.The efficiency of the model was 84.22/83.86,which was higher than the expected standard 80/80.The learning achievements of the students in the experiment group after using the model were significant higher than the control group at .05 level.The students' satisfaction toward the instructional model was also at a high level.Therefore, it could be concluded that the instructional model using augmented reality technology had efficiency and suitable to apply to the microcontroller foundation course for vocational certificate students.
Robust registration for ultra-field infrared and visible binocular images

The ultra-field infrared and visible image registration is a challenging task due to its nonlinear imaging and multi-modal image features. In this paper, a robust registration method is proposed for the ultra-field infrared and visible images. First, control points are extracted utilizing phase congruency and optimized based on the guidance map, which is proposed according to significant structures information. Second, ROI pair matching is accomplished based on epipolar curve. Its effect is equivalent to a search window that is popular in methods with the standard field of view, and it can overcome the content differences in the search window caused by nonlinear imaging and vision disparity. Third, a descriptor, named multiple phase congruency directional pattern (MPCDP), is established and composed of distribution information and main direction. The phase congruency amplitudes are encoded as binary patterns, and then they are represented as weighted histogram for distribution information. Six pairs of ultra-field infrared and visible images are employed for registration experiments, and the results demonstrate that the performance of the proposed is robust and accurate in five types of ultra-field scenes and two different camera relationships.
Node Overlap Removal Algorithms: an Extended Comparative Study

In the context of graph layout, many algorithms have been designed to remove node overlapping, and many quality criteria and associated metrics have been proposed to evaluate those algorithms.Unfortunately, a complete comparison of the algorithms based on some metrics that evaluate their quality has never been provided and it is thus difficult for a visualisation designer to select the algorithm that best suits their needs.In this paper, we review 22 metrics available in the literature, classify them according to the quality criteria they try to capture, and select a representative one for each class.Based on the selected metrics,we compare 9 node overlap removal algorithms.Our experiment involves 854 synthetic and real-world graphs.Finally, we propose a JavaScript library containing both the algorithms and the criteria, and we provide a Web platform, AGORA, in which one can upload graphs, apply the algorithms and compare/download the results.JGAA, 0(0) 0-0 (0) 1 1 An interesting discussion about the concept of mental map preservation is available in [1]. 2 https://github.com/agorajs/agorajs.github.io(accessed
How to Evaluate Single-Round Dialogues Like Humans: An Information-Oriented Metric

Developing a dialogue response generation system is one of important topics in natural language processing, but many obstacles are yet to be overcome before autogenerated dialogues with a human-like quality can become possible. A good evaluation method will help narrow the gap between machines and humans in dialogue generation. Unfortunately, the existing automatic evaluation methods are biased and correlate very poorly with human judgments of response quality. Such methods are incapable of assessing whether a dialogue response generation system can produce high-quality, knowledge-related and informative dialogues. In response to this challenge, we design an information-oriented framework to simulate human subjective evaluation. Using this framework, we implement a learning-based metric to evaluate the quality of a dialogue. An experimental validation demonstrates our proposed metric's effectiveness in dialogue selection and model evaluation on a Twitter dataset (in English) and a Weibo dataset (in Chinese). In addition, the metric is more relevant than the existing methods of dialogue evaluation to human subjective judgment.
Quantum annealing speedup of embedded problems via suppression of Griffiths singularities

Optimal parameter settings for application problems embedded into hardware graphs is key to practical quantum annealers (QAs). Embedding chains typically crop up as harmful Griffiths phases but can be used as a resource as we show here: to balance out singularities in the logical problem, changing its universality class. A smart choice of embedding parameters reduces annealing times for random Ising chain from $O(exp[c\sqrt{N}])$ to $O({N}^{2})$. Dramatic reduction in time-to-solution for QAs is confirmed by numerics, for which we developed a custom integrator to overcome convergence issues.
Deep Features Homography Transformation Fusion Network—A Universal Foreground Segmentation Algorithm for PTZ Cameras and a Comparative Study

The foreground segmentation method is a crucial first step for many video analysis methods such as action recognition and object tracking. In the past five years, convolutional neural network based foreground segmentation methods have made a great breakthrough. However, most of them pay more attention to stationary cameras and have constrained performance on the pan–tilt–zoom (PTZ) cameras. In this paper, an end-to-end deep features homography transformation and fusion network based foreground segmentation method (HTFnetSeg) is proposed for surveillance videos recorded by PTZ cameras. In the kernel of HTFnetSeg, there is the combination of an unsupervised semantic attention homography estimation network (SAHnet) for frames alignment and a spatial transformed deep features fusion network (STDFFnet) for segmentation. The semantic attention mask in SAHnet reinforces the network to focus on background alignment by reducing the noise that comes from the foreground. STDFFnet is designed to reuse the deep features extracted during the semantic attention mask generation step by aligning the features rather than only the frames, with a spatial transformation technique in order to reduce the algorithm complexity. Additionally, a conservative strategy is proposed for the motion map based post-processing step to further reduce the false positives that are brought by semantic noise. The experiments on both CDnet2014 and Lasiesta show that our method outperforms many state-of-the-art methods, quantitively and qualitatively.
Fast Multi-View Outlier Detection via Deep Encoder

Multi-view outlier detection has a wide range of applications and has been well investigated in recent years. However, 1) most existing state-of-the-art methods cannot efficiently handle outlier detection problem for large-scale multi-view data, since exploring pairwise constraints among different views causes highly-computational cost; 2) the data collected from original heterogeneous feature spaces further increases the consistent difficulty of multi-view outlier detection. To address these issues, we present a fast multi-view outlier detection model via learning a low-rank latent subspace representation with deep encoder architecture, which can not only efficiently identify the outliers for large-scale data even with numerous data views, but also exploit a discriminative common latent subspace shared by all the views. First, we learn a set of orthogonal bases as view-specific dictionaries from a small dataset, which is randomly sampled from the original dataset. Benefitting from view-specific dictionaries, the sampled data is projected and decomposed as a shared and discriminative latent subspace representations, which correspond to the view-consistent and view-specific components across multiple views, respectively. Then, the obtained discriminative latent representations are applied to train the view-specific deep encoders, which can efficiently compute the abnormal score for the remaining instances. Our proposed model can cost-effectively identify the outliers in large-scale datasets from numerous data views with less computational complexity. Experiments conducted on eight real datasets and a synthesis dataset show that our proposed model outperforms the existing ones on effectiveness and efficiency.
Volume preserving image segmentation with entropy regularized optimal transport and its applications in deep learning

Image segmentation with a volume constraint is an important prior for many real applications. In this work, we present a novel volume preserving image segmentation algorithm, which is based on the entropy and Total Variation (TV) regularized optimal transport theory. The volume and classification constraints can be regarded as two measures preserving constraints in the optimal transport. By studying the dual problem, we develop a simple but efficient dual algorithm for our model. Moreover, to be different from many variational based image segmentation algorithms, the proposed algorithm can be directly unrolled to a new Volume Preserving and TV regularized softmax (VPTV-softmax) layer for semantic segmentation in the popular Deep Convolution Neural Network (DCNN). The experiment results show that our proposed model is very competitive and can improve the performance of many semantic segmentation networks such as the popular U-net and DeepLabv3+.
NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

Neural Architecture Search (NAS) is an open and challenging problem in machine learning. While NAS offers great promise, the prohibitive computational demand of most of the existing NAS methods makes it difficult to directly search the architectures on large-scale tasks. The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset. Despite a number of recent results that show the promise of transfer from proxy datasets, a comprehensive evaluation of different NAS methods studying the impact of different source datasets has not yet been addressed. In this work, we propose to analyze the architecture transferability of different NAS methods by performing a series of experiments on large scale benchmarks such as ImageNet1K and ImageNet22K. We find that: (i) The size and domain of the proxy set does not seem to influence architecture performance on the target dataset. On average, transfer performance of architectures searched using completely different small datasets (e.g., CIFAR10) perform similarly to the architectures searched directly on proxy target datasets. However, design of proxy sets has considerable impact on rankings of different NAS methods. (ii) While different NAS methods show similar performance on a source dataset (e.g., CIFAR10), they significantly differ on the transfer performance to a large dataset (e.g., ImageNet1K). (iii) Even on large datasets, random sampling baseline is very competitive, but the choice of the appropriate combination of proxy set and search strategy can provide significant improvement over it. We believe that our extensive empirical analysis will prove useful for future design of NAS algorithms.
Automated Screenplay Annotation for Extracting Storytelling Knowledge

Narrative screenplays follow a standardized format fortheir parts (e.g., stage direction, dialogue, etc.) including short descriptions for what, where, when, and howto film the events in the story (shot headings). We created a grammar based on the syntax of shot headings toextract this and other discourse elements for automatic screenplay annotation. We test our annotator on over a thousand raw screenplays from the IMSDb screenplay corpus and make the output available for narrative intelligence research.
A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction

Fact triples are a common form of structured knowledge used within the biomedical domain.As the amount of unstructured scientific texts continues to grow, manual annotation of these texts for the task of relation extraction becomes increasingly expensive.Distant supervision offers a viable approach to combat this by quickly producing large amounts of labeled, but considerably noisy, data.We aim to reduce such noise by extending an entity-enriched relation classification BERT model to the problem of multiple instance learning, and defining a simple data encoding scheme that significantly reduces noise, reaching state-of-the-art performance for distantly-supervised biomedical relation extraction.Our approach further encodes knowledge about the direction of relation triples, allowing for increased focus on relation learning by reducing noise and alleviating the need for joint learning with knowledge graph completion.
Averaging versus voting: A comparative study of strategies for distributed classification

In this paper we proposed two strategies, averaging and voting, to implement distributed classification via the divide and conquer approach. When a data set is too big to be processed by one processor or is naturally stored in different locations, the method partitions the whole data into multiple subsets randomly or according to their locations. Then a base classification algorithm is applied to each subset to produce a local classification model. Finally, averaging or voting is used to couple the local models together to produce the final classification model. We performed thorough empirical studies to compare the two strategies. The results show that averaging is more effective in most scenarios. Note: The second author Honglan Xu’s affiliation is added online.
Hamming Distance Encoding Multihop Relation Knowledge Graph Completion

Knowledge graphs (KGs) play an important role in many real-world applications like information retrieval, question answering, relation extraction, etc.To reveal implicit knowledge from a knowledge graph (KG), viz.knowledge graph completion (KGC), is a crucial task for the downstream applications based on KG.For this purpose various embedding-based approaches have been proposed recently.This paper proposes a new approach named HRESCAL to KGC.It extends the well-known embedding-based approach RESCAL by introducing Hamming distance-based encoder to capture implicit multihop and partial inverse relation features in a KG.Experimental results on widely used KGC benchmarks show that the new approach achieves state-of-the-art or is competitive AUC performance.
An approach for enhancing data confidentiality in Hadoop

&lt;p class="Abstract"&gt;The amount of data processed and stored in the cloud is growing dramatically. The traditional storage devices at both hardware and software levels cannot meet the requirement of the cloud. This fact motivates the need for a platform which can handle this problem. Hadoop is a deployed platform proposed to overcome this big data problem which often uses MapReduce architecture to process vast amounts of data of the cloud system. Hadoop has no strategy to assure the safety and confidentiality of the files saved inside the Hadoop distributed File system(HDFS). In the cloud, the protection of sensitive data is a critical issue in which data encryption schemes plays avital rule. This research proposes a hybrid system between two well-known asymmetric key cryptosystems (RSA, and Paillier) to encrypt the files stored in HDFS. Thus before saving data in HDFS, the proposed cryptosystem is utilized for encrypting the data. Each user of the cloud might upload files in two ways, non-safe or secure. The hybrid system shows higher computational complexity and less latency in comparison to the RSA cryptosystem alone.&lt;/p&gt;
ACAIOT: A Framework for Adaptable Context-Aware IoT applications

Recently, the Internet of Things (IoT) and context-aware IoT applications are involved in various domains such as healthcare, traffic, and smart homes.The main challenge, while developing context-aware IoT applications, is managing the massive amounts of data and events to get relevant context information.This paper proposes a semantic-based and domain-independent framework called ACAIOT.It hides the details of context management from the high-level services.It proposes a programming abstraction for adapting services' execution to various domains easily.We demonstrate the features and effectiveness of ACAIOT by its execution on a real smart home dataset.We evaluated ACAIOT against previous work in developing a set of services such as monitoring, prediction, and notification.Results show that ACAIOT achieves an average F1-score of 0.82, which is comparable with the current state-of-the-art methods.Moreover, ACAIOT is able to incorporate important compound activities that cannot be handled by previous work.
EMSContExt: EMS Protocol-Driven Concept Extraction for Cognitive Assistance in Emergency Response

This paper presents a technique for automated curation of a domain-specific knowledge base or lexicon for resource-constrained domains, such as Emergency Medical Services (EMS) and its application to real-time concept extraction and cognitive assistance in emergency response. The EMS responders often verbalize critical information describing the situations at an incident scene, including patients' physical condition and medical history. Automated extraction of EMS protocol-specific concepts from responders' speech data can facilitate cognitive support through the selection and execution of the proper EMS protocols for patient treatment. Although this task is similar to the traditional NLP task of concept extraction, the underlying application domain poses major challenges, including low training resources availability (e.g., no existing EMS ontology, lexicon, or annotated EMS corpus) and domain mismatch. Hence, we develop EMSContExt, a weakly-supervised concept extraction approach for EMS concepts. It utilizes different knowledge bases and a semantic concept model based on a corpus of over 9400 EMS narratives for lexicon expansion. The expanded EMS lexicon is then used to automatically extract critical EMS protocol-specific concepts from real-time EMS speech narratives. Our experimental results show that EMSContExt achieves 0.85 recall and 0.82 F1-score for EMS concept extraction and significantly outperforms MetaMap, a state-of-the-art medical concept extraction tool. We also demonstrate the application of EMSContExt to EMS protocol selection and execution and real-time recommendation of protocol-specific interventions to the EMS responders. Here, EMSContExt outperforms MetaMap with a 6% increase and six times speedup in weighted recall and execution time, respectively.
Optimizing adiabatic quantum pathways via a learning algorithm

Designing proper time-dependent control fields for slowly varying the system to the ground state that encodes the problem solution is crucial for adiabatic quantum computation. However, inevitable perturbations in real applications demand us to accelerate the evolution so that the adiabatic errors can be prevented from accumulation. Here, by treating this trade-off task as a multiobjective optimization problem, we propose a gradient-free learning algorithm with pulse smoothing technique to search optimal adiabatic quantum pathways and apply it to the Landau-Zener Hamiltonian and Grover search Hamiltonian. Numerical comparisons with a linear schedule, local adiabatic theorem induced schedule, and gradient-based algorithm searched schedule reveal that the proposed method can achieve significant performance improvements in terms of the adiabatic time and the instantaneous ground-state population maintenance. The proposed method can be used to solve more complex and real adiabatic quantum computation problems.
Stylized Text Generation: Approaches and Applications

Text generation has played an important role in various applications of natural language processing (NLP), and kn recent studies, researchers are paying increasing attention to modeling and manipulating the style of the generation text, which we call stylized text generation. In this tutorial, we will provide a comprehensive literature review in this direction. We start from the definition of style and different settings of stylized text generation, illustrated with various applications. Then, we present different settings of stylized generation, such as style-conditioned generation, style-transfer generation, and style-adversarial generation. In each setting, we delve deep into machine learning methods, including embedding learning techniques to represent style, adversarial learning, and reinforcement learning with cycle consistency to match content but to distinguish different styles. We also introduce current approaches to evaluating stylized text generation systems. We conclude our tutorial by presenting the challenges of stylized text generation and discussing future directions, such as small-data training, non-categorical style modeling, and a generalized scope of style transfer (e.g., controlling the syntax as a style).