Reviewerly/exHarmony huggingface dataset:
Study of population partitioning techniques on efficiency of swarm algorithms

This paper presents a study of various population partitioning techniques and their effect on the efficiency of swarm algorithms. Population partitioning techniques based on different concepts have been studied. Prominent amongst them is self-adaptive multi-population (SAMP) technique where populations are added and deleted dynamically based on their diversity. This techniques start with a single randomly initialised population, called free population. After evolution, if the distance between solutions drops below a limit, it is considered to have converged. If all existing populations have converged, a new randomly generated population is added. SAMP keeps at least one free population at all times, hence ensuring the algorithm doesn’t get trapped in local optima. Another promising population partitioning technique studied is random partitioning, where a single population is divided into many smaller sub-populations randomly. Few extensions to the studied techniques are proposed, like an adaptive hierarchical partitioning technique, seed based partitioning with fixed seeds, random partitioning with master population, SAMP with random partitioning etc. All the studied and proposed techniques are compared over a set of benchmark functions. The strongest amongst all techniques was found to be SAMPR. SAMPR is a hybrid of self-adaptive multi-population (SAMP) technique and random partitioning where after every few generations all populations are combined together and re-partitioned randomly. Efficiency of SAMPR is validated over seven well-known swarm algorithms. Extensive comparisons are conducted over multiple benchmark functions, CEC′14 function set and 800 GKLS generated functions. Results establish the efficiency of the proposed technique for improving performance of swarm algorithms.
Real-Time Tone Mapping: A Survey and Cross-Implementation Hardware Benchmark

The rising demand for high quality display has ensued active research in high dynamic range (HDR) imaging, which has the potential to replace the standard dynamic range imaging. This is due to HDR's features like accurate reproducibility of a scene with its entire spectrum of visible lighting and color depth. But this capability comes with expensive capture, display, storage and distribution resource requirements. Also, display of HDR images/video content on an ordinary display device with limited dynamic range requires some form of adaptation. Many adaptation algorithms, widely known as tone mapping operators, have been studied and proposed in the last few decades. In this state of the art report, we present a comprehensive survey of 50+ tone mapping algorithms that have been implemented on hardware for acceleration and real-time performance. These algorithms have been adapted or redesigned to make them hardware-friendly. All real-time application poses strict timing constraints which requires time exact processing of the algorithm. This design challenge require novel solution, and in this report we focus on these issues. In this we survey will discuss those tonemap algorithms which have been implemented on GPU [1-10], FPGA [11-41], and ASIC [42-53] in terms of their hardware specifications and performance. Output image quality is an important metric for tonemap algorithms. From our literature survey we found that, various objective quality metrics have been used to demonstrate the functionality of adapting the algorithm on hardware platform. We have compiled and studied all the metrics used in this survey [54-67]. Finally, in this report we demonstrate the link between hardware cost and image quality thereby illustrating the underlying trade-off which will be useful for the research community.
Triple-SGM: Stereo Processing using Semi-Global Matching with Cost Fusion

In this work, we propose an extension of the Semi-Global Matching framework for three images from a triplet-stereo rig consisting of a horizontal and vertical camera pair. After calculating the matching costs separately for both image pairs, these are merged at cost level using cubic spline interpolation. For cost values near the left/bottom image boundaries, we propose an advanced weighting strategy. Subsequently, the fused matching can be used directly for the cost aggregation and disparity estimation.The benefits of the proposed fusion strategy are demonstrated by an evaluation based on synthetic and real-world data. To encourage further comparisons on triple stereo algorithms, the dataset used for evaluation is made publicly available.
Video Quality of Experience Metric for Dynamic Adaptive Streaming Services Using DASH Standard and Deep Spatial-Temporal Representation of Video

DASH (Dynamic Adaptive Streaming over HTTP (HyperText Transfer Protocol)) as a universal unified multimedia streaming standard selects the appropriate video bitrate to improve the user’s Quality of Experience (QoE) according to network conditions, client status, etc. Considering that the quantitative expression of the user’s QoE is also a difficult point in itself, this paper researched the distortion caused due to video compression, network transmission and other aspects, and then proposes a video QoE metric for dynamic adaptive streaming services. Three-Dimensional Convolutional Neural Networks (3D CNN) and Long Short-Term Memory (LSTM) are used together to extract the deep spatial-temporal features to represent the content characteristics of the video. While accounting for the fluctuation in the quality of a video caused by bitrate switching on the QoE, other factors such as video content characteristics, video quality and video fluency, are combined to form the input feature vector. The ridge regression method is adopted to establish a QoE metric that enables to dynamically describe the relationship between the input feature vector and the value of the Mean Opinion Score (MOS). The experimental results on different datasets demonstrate that the prediction accuracy of the proposed method can achieve superior performance over the state-of-the-art methods, which proves the proposed QoE model can effectively guide the client’s bitrate selection in dynamic adaptive streaming media services.
Using Bayes' Theorem for Command Input: Principle, Models, and Applications

Entering commands on touchscreens can be noisy, but existing interfaces commonly adopt deterministic principles for deciding targets and often result in errors. Building on prior research of using Bayes' theorem to handle uncertainty in input, this paper formalized Bayes' theorem as a generic guiding principle for deciding targets in command input (referred to as "BayesianCommand"), developed three models for estimating prior and likelihood probabilities, and carried out experiments to demonstrate the effectiveness of this formalization. More specifically, we applied BayesianCommand to improve the input accuracy of (1) point-and-click and (2) word-gesture command input. Our evaluation showed that applying BayesianCommand reduced errors compared to using deterministic principles (by over 26.9% for point-and-click and by 39.9% for word-gesture command input) or applying the principle partially (by over 28.0% and 24.5%).
Global Co-occurrence Feature Learning and Active Coordinate System Conversion for Skeleton-based Action Recognition

Skeleton-based action recognition has attracted more and more attention in recent years. Besides, the rapid development of deep learning has greatly improved the performance. However, the current exploration of action co-occurrence is still not comprehensive enough. Most existing works only mine co-occurrence features from the temporal or spatial domain seperately, and it's common to combine them in the end. Different from previous works, our approach is able to learn temporal and spatial co-occurrence features integratedly and globally, which is called spatio-temporal-unit feature enhancement (STUFE). In order to better align the skeleton data, we introduce a novel method for skeleton data preprocessing called active coordinate system conversion (ACSC). A coordinate system can be learned automatically to transform skeleton samples for alignment. By the way, the proposed methods are compatible with current two types of mainstream models, the CNN-based and GCN-based models. Finally, on the two benchmarks of NTU-RGB+D and SBU Kinect Interaction, we validated our methods based on two mainstream models. The results show that our methods achieve the state-of-the-art.
A Secure and Practical Decentralized Ecosystem for Shareable Education Material

Traditionally, the university landscape is highly federated, which hinders potentials for coordinated collaborations. While the lack of a strict hierarchy on the inter-university level is critical for ensuring free research and higher education, this concurrency limits the access to high-quality education materials. Especially regarding resources such as lecture notes or exercise tasks we observe a high susceptibility to redundant work and lacking quality assessment of material created in isolation by individual university institutes. To remedy this situation, in this paper we propose CORALIS, a decentralized marketplace for offering, acquiring, discussing, and improving education resources across university borders. Our design is based on a permissioned blockchain to (a) realize accountable access control via simple on-chain license terms, (b) trace the evolution of encrypted containers accumulating bundles of shareable education resources, and (c) record user comments and ratings for further improving the quality of offered education material.
Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIs

Cognitive Application Program Interface (API) is an API of emerging artificial intelligence (AI)-based cloud services, which extracts various contextual information from non-numerical multimedia data including image and audio. Our interest is to apply image-based cognitive APIs to implement flexible and efficient context sensing services in a smart home. In the existing approach with machine learning by us, with the complexity of recognition object and the number of the defined contexts increases by users, it still requires directly manually labeling a moderate scale of data for training and continually try to calling multiple cognitive APIs for feature extraction. In this paper, we propose a novel method that uses a small scale of labeled data to evaluate the capability of cognitive APIs in advance, before training features of the APIs with machine learning, for the flexible and efficient home context sensing. In the proposed method, we exploit document similarity measures and the concepts (i.e., internal cohesion and external isolation) integrate into clustering results, to see how the capability of different cognitive APIs for recognizing each context. By selecting the cognitive APIs that relatively adapt to the defined contexts and data based on the evaluation results, we have achieved the flexible integration and efficient process of cognitive APIs for home context sensing.
Artificial Intelligence Techniques to Detect and Prevent Corruption in Procurement: A Systematic Literature Review

Transparency International estimates that the costs of corruption in public procurement reach between 20 and 25% of the contract value, sometimes reaching 40–50%. In this study, we analyzed differentness kinds of corruption like (bribery, collusion embezzlement, misappropriation, fraud, abuse of discretion, favoritism, nepotism), and six types of Artificial Intelligence techniques (classification, regression, clustering, prediction, outlier detection, and visualization). The methodology proposed by Torres-Carrion was used, and four research questions were raised, which allow knowing the types of research carried out, the characteristics of the organizations in which the investigations are carried out, the technological tools, and data mining methodologies and techniques. The search was done in the Scopus and Web of Science databases, getting 102 articles published between 2015 and 2019. The primary data mining techniques used are logistic models, neural networks, Bayesian networks, supported vector machines, and decision trees.
Text Similarity Calculation Method Based on Hybrid Model of LDA and TF-IDF

The traditional TF-IDF-based text similarity calculation model uses statistical methods to map text to the keyword vector space and convert the similarity of text into the distance between text vectors. Such methods have problems such as high computational dimensions, sparse data, and inability to take advantage of the semantic information contained in the text itself, so the results obtained are not as similar as the physical text. The text similarity model based on the topic model changes the traditional spatial similarity of keyword vectors, and can fully utilize the semantic information contained in the text itself. But this approach ignores the effect of words on text semantic representations with different weights. In the process of converting text into topic feature space, valuable information is lost. In view of the above problems, this paper proposes a text similarity hybrid model (L-THM) integrating LDA and TF-IDF for calculating text similarity. The model uses the semantic information contained in the text itself and the keyword information reflecting the text to comprehensively analyses and calculates the similarity between the texts. The experimental results show that the hybrid model can better represent the text information than the single model, and obtain a good F value in the cluster, which effectively improves the text similarity calculation effect.
Visual Question Answering on 360° Images

In this work, we introduce VQA 360°, a novel task of visual question answering on 360° images. Unlike a normal field-of-view image, a 360° image captures the entire visual content around the optical center of a camera, demanding more sophisticated spatial understanding and reasoning. To address this problem, we collect the first VQA 360° dataset, containing around 17,000 real-world image-question-answer triplets for a variety of question types. We then study two different VQA models on VQA 360°, including one conventional model that takes an equirectangular image (with intrinsic distortion) as input and one dedicated model that first projects a 360° image onto cubemaps and subsequently aggregates the information from multiple spatial resolutions. We demonstrate that the cubemap-based model with multi-level fusion and attention diffusion performs favorably against other variants and the equirectangular-based models. Nevertheless, the gap between the humans' and machines' performance reveals the need for more advanced VQA 360° algorithms. We, therefore, expect our dataset and studies to serve as the benchmark for future development in this challenging task. Dataset, code, and pre-trained models are available online.
Digital k-Contractibility of an n-Times Iterated Connected Sum of Simple Closed k-Surfaces and Almost Fixed Point Property

The paper firstly establishes the so-called n-times iterated connected sum of a simple closed k-surface in Z 3 , denoted by C k n , k ∈ { 6 , 18 , 26 } . Secondly, for a simple closed 18-surface M S S 18 , we prove that there are only two types of connected sums of it up to 18-isomorphism. Besides, given a simple closed 6-surface M S S 6 , we prove that only one type of M S S 6 ♯ M S S 6 exists up to 6-isomorphism, where ♯ means the digital connected sum operator. Thirdly, we prove the digital k-contractibility of C k n : = M S S k ♯ ⋯ ♯ M S S k ︷ n - times , k ∈ { 18 , 26 } , which leads to the simply k-connectedness of C k n , k ∈ { 18 , 26 } , n ∈ N . Fourthly, we prove that C 6 2 and C k n do not have the almost fixed point property (AFPP, for short), k ∈ { 18 , 26 } . Finally, assume a closed k-surface S k ( ⊂ Z 3 ) which is ( k , k ¯ ) -isomorphic to ( X , k ) in the picture ( Z 3 , k , k ¯ , X ) and the set X is symmetric according to each of x y -, y z -, and x z -planes of R 3 . Then we prove that S k does not have the AFPP. In this paper given a digital image ( X , k ) is assumed to be k-connected and its cardinality | X | ≥ 2 .
Secure Similar Image Search and Copyright Protection over Encrypted Medical Image Databases

The recent advances in digital medical imaging and storage in cloud are bringing about more demands for efficient and secure image retrieval and management. Typically, medical images are very sensitive to changes where any modifications in its content may bring about an erroneous medical diagnosis. Therefore, securing medical images is a very essential process and the major task is, the medical image must maintain their sensitive contents at the time of reconstruction. The proposed methodology executes a secure image encryption and search of medical images proficiently over encrypted image database without leaking any sensitive data. It also ensures medical data integrity by introducing an efficient recovery mechanism on ROI of the image. The proposed scheme obtains recovery information about the image from the ROI of the medical data and embeds it in the RONI region using IWT transform which act as a reversible watermarking. If any alterations or tampers are caused to ROI at the third-party end, then it can be identified and recovered from the obtained recovery data. Besides, the model also executes a Copyright protection scheme to locate the authorized users, who illegally duplicate and distribute the retrieved image to unauthorized entities.
Profit maximization based task scheduling in hybrid clouds using whale optimization technique

In cloud computing system, task scheduling plays an important key role. The tasks provided by the user to allocate in cloud have to pay for the share of resources that are used by them. The requirement of task scheduling in the cloud environment has become more and more complex, and the amount of resources and tasks is growing rapidly. Therefore, an efficient task-scheduling algorithm is necessary for allocating the task efficiently in the cloud, which can achieve minimum resource utilization, minimum processing time, high efficiency, and maximum profit. In hybrid clouds to maximize the profit of a private cloud while guaranteeing the service delay bound of delay-tolerant tasks is studied in this article. Here, a new metaheuristic technique inspired from the bubble-net hunting technique of humpback whales, namely whale optimization algorithm (WOA), has been applied to solve the task-scheduling problem. Then WOA algorithm is compared with existing algorithms such as artificial bee colony algorithm (ABC) and Genetic algorithm (GA). The experimental result shows that the proposed WOA algorithm greatly increases the efficiency and achieves maximum profit for the private cloud.
Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network

A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.
A Deep Neural Network Sensor for Visual Servoing in 3D Spaces

This paper describes a novel stereo vision sensor based on deep neural networks, that can be used to produce a feedback signal for visual servoing in unmanned aerial vehicles such as drones. Two deep convolutional neural networks attached to the stereo camera in the drone are trained to detect wind turbines in images and stereo triangulation is used to calculate the distance from a wind turbine to the drone. Our experimental results show that the sensor produces data accurate enough to be used for servoing, even in the presence of noise generated when the drone is not being completely stable. Our results also show that appropriate filtering of the signals is needed and that to produce correct results, it is very important to keep the wind turbine within the field of vision of both cameras, so that both deep neural networks could detect it.
Design and Implementation of Anomaly Condition Detection in Agricultural IoT Platform System

In recent years, with the rapid development of the Internet of Things (IoT), individuals, industry, and agriculture have begun to apply this technology to solve the difficulties encountered. IoT makes data collection more convenient, enabling us to monitor environmental information and control from a remote location. The demand of IoT in agriculture has also increased in Taiwan. We can use the data collected from the farm to do many analysis and application to solve the problems encountered by farmers at work. However, when the sensor data is abnormal, we can't know it instantly. This situation may be an anomaly situation in the farm environment and needs to be dealt with in a timely manner. Therefore, we implement an appropriate anomaly data detection system to find out the sensor that has an anomaly phenomenon. Compared with existing implementation, we consider not only the single data but also all kind of same type data simultaneously. When we find out the anomaly situation, our IoT platform will notify the farmer the information of the time, the situation and the possible anomaly condition to help the farmers find out the problems in the farm. By detecting anomaly data, it can help reduce farm losses due to environmental or human-induced.
It’s All About The Scale - Efficient Text Detection Using Adaptive Scaling

"Text can appear anywhere". This property requires us to carefully process all the pixels in an image in order to accurately localize all text instances. In particular, for the more difficult task of localizing small text regions, many methods use an enlarged image or even several rescaled ones as their input. This significantly increases the processing time of the entire image and needlessly enlarges background regions. If we were to have a prior telling us the coarse location of text instances in the image and their approximate scale, we could have adaptively chosen which regions to process and how to rescale them, thus significantly reducing the processing time. To estimate this prior we propose a segmentation-based network with an additional "scale predictor", an output channel that predicts the scale of each text segment. The network is applied on a scaled down image to efficiently approximate the desired prior, without processing all the pixels of the original image. The approximated prior is then used to create a compact image containing only text regions, resized to a canonical scale, which is fed again to the segmentation network for fine-grained detection. We show that our approach offers a powerful alternative to fixed scaling schemes, achieving an equivalent accuracy to larger input scales while processing far fewer pixels. Qualitative and quantitative results are presented on the ICDAR15 and ICDAR17 MLT benchmarks to validate our approach.