Reviewerly/exHarmony huggingface dataset:
A Deadline Constrained Preemptive Scheduler Using Queuing Systems for Multi-Tenancy Clouds

Scheduling on clouds is required so that service providers can meet Quality of Service (QoS) requirements of tenants. Deadline is a major criterion in judging QoS. This work presents a real-time, preemptive, constrained scheduler using queuing theory - PDSonQueue - which enables better meetinhg of QoS requirements. PDSonQueue also shortens a job's completion time and improves system's throughput. PDSon-Queue, as a dynamic priority real-time greedy scheduler, builds a queuing-based mathematical model to accurately predict a job's execution and waiting time, where jobs arrive by following a stochastic process and request resources. Our scheduler introduces a novel "Earliest Maximal Waiting Time First (EMWTF)" concept to fine tune job scheduling to guarantee the job being accomplished within the deadline. Deadline constrained jobs are scheduled preemptively from low priority jobs with the intent of maximising the number of jobs completed within the deadlines, while allowing system's resources to be shared by other regular jobs. PDSonQueue integrates an improved Dominant Resource Fairness (DRF) greedy resource allocation approach to capture the essence of tenants' resource allocation and run as many jobs as possible. Our experimental results indicate that PDSonQueue can improve by at least 20% of deadline-based QoS rate, and by at least 30% for throughput.
Modeling human cognitive behavior for system design

The use of models of human cognition to help in system design has been a targeted use case nearly since these models were created. These models can be represented as a combination of fixed mechanisms (the architecture) and task knowledge (the learned subtasks) that uses these mechanisms to generate behavior. I first describe how these models might be used in system design and their useful features. I then describe the types of models and architectures and their uses and then conclude with what appears to be necessary to use these models more routinely.
Short Text Analysis Based on Dual Semantic Extension and Deep Hashing in Microblog

Short text analysis is a challenging task as far as the sparsity and limitation of semantics. The semantic extension approach learns the meaning of a short text by introducing external knowledge. However, for the randomness of short text descriptions in microblogs, traditional extension methods cannot accurately mine the semantics suitable for the microblog theme. Therefore, we use the prominent and refined hashtag information in microblogs as well as complex social relationships to provide implicit guidance for semantic extension of short text. Specifically, we design a deep hash model based on social and conceptual semantic extension, which consists of dual semantic extension and deep hashing representation. In the extension method, the short text is first conceptualized to achieve the construction of hashtag graph under conceptual space. Then, the associated hashtags are generated by correlation calculation based on the integration of social relationships and concepts to extend the short text. In the deep hash model, we use the semantic hashing model to encode the abundant semantic features and form a compact and meaningful binary encoding. Finally, extensive experiments demonstrate that our method can learn and represent the short texts well by using more meaningful semantic signal. It can effectively enhance and guide the semantic analysis and understanding of short text in microblogs.
Evaluating the Effectiveness of Multi-level Greedy Modularity Clustering for Software Architecture Recovery

Software architecture recovery approaches mainly analyze various types of dependencies among software modules to group them and reason about the high-level structural decomposition of a system. These approaches employ a variety of clustering techniques. In this paper, we present an empirical evaluation of a modularity clustering technique used for software architecture recovery. We use five open source projects as subject systems for which the ground-truth architectures were known. This dataset was previously prepared and used in an empirical study for evaluating four state-of-the-art architecture recovery approaches and their variants as well as two baseline clustering algorithms. We used the same dataset for an evaluation of multi-level greedy modularity clustering. Results showed that MGMC outperforms all the other SAR approaches in terms of accuracy and modularization quality for most of the studied systems. In addition, it scales better to very large systems for which it runs orders-of-magnitude faster than all the other algorithms.
Deep Self-Paced Learning for Semi-Supervised Person Re-Identification Using Multi-View Self-Paced Clustering

Semi-supervised person re-identification (Re-ID) is an extension of the existing popular Re-ID research, which only uses a small portion of labeled data, while the majority of the training samples are unlabeled. This paper approaches the problem by constructing a set of heterogeneous convolutional neural networks (CNNs) fine-tuned by utilizing the labeled training samples, and then propagating the labels to the unlabeled portion for further fine-tuning the overall system in a self-paced manner. In this work, a novel self-paced multi-view clustering is presented to generate pseudo labels for unlabeled training samples, which combines multiple heterogeneous CNNs features to cluster. In our clustering method, we introduce a self-paced regularizer to select reliable samples for fine-tuning each CNNs by minimizing ranking loss and identification loss. Specifically, we select a small portion of unlabeled training data when multiple CNNs are weak. With CNNs become stronger, more and more unlabeled samples are selected. Pseudo label estimation and CNNs training are improved simultaneously, which optimize alternatively until all the unlabeled training samples are selected. In our framework, both the optimization of multiple CNNs training and multi-view clustering on unlabeled training samples are self-paced optimizing procedure. Extensive experiments have been conducted on two large-scale Re-ID datasets to demonstrate the superiority of the proposed method.
Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images

The representation of objects as 2D bounding boxes in monocular RGB images limits the faculty of current computer vision systems to 2D object detection. It fails to provide crucial information such as the orientation of other vehicles, which is vital for autonomous driving. At the same time, real-time performance is essential to qualify an approach for deployment in a productive environment. In order to tackle this problem, we present an approach that predicts several key points selected from a virtual 3D bounding box around a vehicle instead of a pure 2D bounding box. These key points can be interpreted as a bounding shape. With this novel representation we can calculate the actual 3D bounding box of the corresponding object. Thanks to the straightforward implementation of bounding shape in any current state-of-the-art 2D object detector both for singleshot frameworks like YOLO or SSD as well as for two-stage detectors like Faster-RCNN with a minimum of computational overhead, it is able to be run in real-time while providing additional useful information for vehicle detection. We exemplify the extension of SSD to Bounding Shape SSD ( BS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> D) and evaluate our approach using the challenging KITTI as well as the novel VIPER dataset.
A Highly Efficient Data Locality Aware Task Scheduler for Cloud-Based Systems

Scheduling tasks in the vicinity of stored data can significantly diminish network traffic. Scheduling optimisation can improve data locality by attempting to locate a task and its related data on the same node. Existing schedulers tend to ignore overhead and tradeoff between data transfer and task placement, and bandwidth consumption, by only emphasising data locality without considering other factors. We present a novel data locality aware scheduler for balancing time consumption and network bandwidth traffic - DLAforBT - to improve data locality for tasks and throughput, with the optimal placement policy exhibiting a threshold-based structure. DLAforBT uses bipartite graph modelling to represent data placement, adopts a judgment mechanism and a precise prediction model to determine moving data or moving computation. It integrates an improved Dominant Resource Fairness (DRF) resource allocation to capture tenants' resource allocation and run as many jobs as possible. DLAforBT improves by 16% of data locality rate, and 25% of throughput.
Continuous Cybersecurity Management Through Blockchain Technology

Traditional management practices applied to the cybersecurity realm are causing significant delays to the product release cycle for major organizations and government agencies. These delays are often seen as a necessary part of the security landscape to ensure appropriate approvals, audits, and risks are addressed prior to fielding software. However, the exact opposite outcome has been observed; delayed releases decrease overall security and functionality. This paper presents an alternative approval and review process using blockchain technology that complements agile development and continuous delivery paradigms. An example implementation is provided to demonstrate the behavior of the proposed system under hypothetical auditing and threat assessment scenarios.
Autonomous Framework for Person Identification by Analyzing Vocal Sounds and Speech Patterns

Speech processing has emerged as one of the important and crucial domain over the past decade. Many researchers have worked on voice recognition and verification. Some of the reported work has been done in the field of biometrics. However, this paper proposes an autonomous algorithm for the person identification by analyzing their vocal sounds and speech patterns. First, the input voice signal is introduced to our proposed system from which the low frequency contents are extracted using finite response low pass filter based on hamming window. Then the proposed system performs a cepstral analysis and extracts two distinct features from the signal spectrum i.e. the maximum pitch frequency and maximum cepstrum value. The 2D extracted feature set is passed on to the multi-level classification system constructed on supervised Support Vector Machine (SVM), which first discriminates between the person's gender and then classifies the person based on the gender. Total 120 samples were used to train the proposed classification system and the proposed system correctly identifies the speaker with the accuracy, specificity and sensitivity of 83.33%, 86.67% and 80% respectively.
Proposal of an Environmental Recognition Method for Automatic Parking by an Image-based CNN

In recent years, researches on autonomous mobile robots are being advanced, and among them, practical use of automatic driving cars is expected particularly. In this realization, there is a parking problem as one of the problems to be solved. Currently, although various sensors such as infrared lasers and millimeter wave radars are used for parking problems, it is desirable to use the less number of sensors and select cheaper ones when considering the cost. This research aims to recognize the surrounding environment for parking control by only images obtained from using a monocular camera. For safe parking, it needs to detect the parking position and check the space to turn the wheel. A technique called Convolutional Neural Network (CNN), which is now a mainstream in image recognition fields, is used to archive them. This paper constructs a system for judging a parking possibility using object detection and a system for classifying several patterns of the space to turn the wheel using depth images. Some simulations are conducted to verify whether the surrounding environment in parking can be integratedly judged by the proposed system.
Generation of an efficient structural evidence layer for mineral exploration targeting

Generation of enhanced and augmented evidence maps corresponding to operation of ore-forming geological processes improves mineral exploration success. Structural lineaments have a substantial impact on such processes. Therefore, precise recognition of the lineaments facilitates mineral exploration targeting toward undiscovered mineralized zones. The paper aims 1) to recognize and analyze structural lineaments, especially those which are spatially and genetically associated with orogenic gold mineral system, through analysis of a multi-disciplinary geospatial dataset including aeromagnetic geophysics, the Advanced Spaceborne Thermal Emission, and Reflection Radiometer imagery and Digital Elevation Model data and 2) to generate an efficient structural evidence layer for exploration targeting. The methods applied are edge enhancement filters with line detection algorithms. The detection algorithm, Hough Transform, and total horizontal derivative of upward continued filter and its tilt derivative) were utilized to extract structural lineaments. Evaluation of the spatial association of known Au mineralization in the study area with the recognized structural features demonstrated that abundance, density, and trend of such features strongly control the localization of Au mineralization. In addition, band ratio, principal component analysis, spectral angle mapper, and spectral feature fitting were applied to map and generate mineralization-related alteration layer. The evidence layers were then integrated through the conventional TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method to delineate reliable exploration targets.
Modern vs Diplomatic Transcripts for Historical Handwritten Text Recognition

The transcription of handwritten documents is useful to make their contents accessible to the general public. However, so far automatic transcription of historical documents has mostly focused on producing diplomatic transcripts, even if such transcripts are often only understandable by experts. Main difficulties come from the heavy use of extremely abridged and tangled abbreviations and archaic or outdated word forms. Here we study different approaches to train optical models which allow to recognize historic document images containing archaic and abbreviated handwritten text and produce modernized transcripts with expanded abbreviations. Experiments comparing the performance of the different approaches proposed are carried out on a document collection related with Spanish naval commerce during the XV–XIX centuries, which includes extremely difficult handwritten text images.
Weakly Supervised Object Discovery by Generative Adversarial &amp; Ranking Networks

The deep generative adversarial networks (GAN) recently have been shown to be promising for different computer vision applications, like image editing, synthesizing high resolution images, generating videos, etc. These networks and the corresponding learning scheme can handle various visual space mappings. We approach GANs with a novel training method and learning objective, to discover multiple object instances for three cases: 1) synthesizing a picture of a specific object within a cluttered scene; 2) localizing different categories in images for weakly supervised object detection; and 3) improving object discovery in object detection pipelines. A crucial advantage of our method is that it learns a new deep similarity metric, to distinguish multiple objects in one image. We demonstrate that the network can act as an encoder-decoder generating parts of an image which contain an object, or as a modified deep CNN to represent images for object detection in supervised and weakly supervised scheme. Our ranking GAN offers a novel way to search through images for object specific patterns. We have conducted experiments for different scenarios and demonstrate the method performance for object synthesizing and weakly supervised object detection and classification using the MS-COCO and PASCAL VOC datasets.
A Holistic Recognition Approach for Woodblock-Print Mongolian Words Based on Convolutional Neural Network

This paper proposed a holistic recognition approach for woodblock-print Mongolian words using a convolutional neural network (CNN). To be specific, the whole word image is regarded as input of CNN. Hence, all the word images should be normalized into the same size before being inputted into CNN. By comparison, an appropriate normalization size has been determined in our study. Through the above manner, the woodblock-print Mongolian word images do not need to be segmented into glyphs. Thereby, the segmentation errors can be avoided under the circumstance. Furthermore, to solve the problem of imbalance distribution on our dataset, SMOTE technique is adopted to generate samples. In this way, the training procedure can be more efficient and the obtained CNN is more robust. Experimental results demonstrate that the proposed approach outperforms the segmentation based method and other baselines.
Voice and Gesture Controlled Wheelchair

A sector of physically challenged people finds it very difficult to use traditional wheelchairs. Researchers have been working on computer-controlled chairs which utilize sensors and quick control algorithms to minimize the level of human intervention. This paper is based on a design that aids the voice activation system for physically disabled people by incorporating manual operation. Arduino microcontroller and voice recognition have been used to support the movement of the wheelchair. The wheelchair does not respond to an incorrect speech command. Depending on the direction given through voice and gesture, the Arduino controls the wheelchair directions. Ultrasonic sensors are used to detect obstacles. The prototype is designed in such a way that it can be used independently and efficiently with less effort. It saves time, reduces cost and energy of the users.
Semantic Segmentation of Road Profiles for Efficient Sensing in Autonomous Driving

In vision-based autonomous driving, understanding spatial layout of road and traffic is required at each moment. This involves the detection of road, vehicle, pedestrian, etc. in images. In driving video, the spatial positions of various patterns are further tracked for their motion. This spatial-to-temporal approach inherently demands a large computational resource. In this work, however, we take a temporal-to-spatial approach to cope with fast moving vehicles in autonomous navigation. We sample one-pixel line at each frame in driving video, and the temporal congregation of lines from consecutive frames forms a road profile image. The temporal connection of lines also provides layout information of road and surrounding environment. This method reduces the processing data to a fraction of video in order to catch up vehicle moving speed. The key issue now is to know different regions in the road profile; the road profile is divided in real time to road, roadside, lane mark, vehicle, etc. as well as motion events such as stopping and turning of ego-vehicle. We show in this paper that the road profile can be learned through Semantic Segmentation. We use RGB-F images of the road profile to implement Semantic Segmentation to grasp both individual regions and their spatial relations on road effectively. We have tested our method on naturalistic driving video and the results are promising.
Contour Covariance: A Fast Descriptor for Classification

This paper presents a novel shape descriptor to effectively and efficiently characterize the local image statistics. The proposed descriptor, termed contour covariance (CC), characterizes covariance features driven by a moving point on the shape contour at multiple scales. To calculate the covariance matrices, three basic features including texture, intensity and distance map, are extracted from the object image. Based on coefficients of the obtained covariance matrices, the proposed CC descriptor is compact yet informative, as well as invariant to rotation, translation and scale. The experimental results on two databases demonstrate the superiority and efficiency of the proposed method among the state-of-the-art methods for shape classification.
Computer Vision-Based Unobtrusive Physical Activity Monitoring in School by Room-Level Physical Activity Estimation: A Method Proposition

As sedentary lifestyles and childhood obesity are becoming more prevalent, research in the field of physical activity (PA) has gained much momentum. Monitoring the PA of children and adolescents is crucial for ascertaining and understanding the phenomena that facilitate and hinder PA in order to develop effective interventions for promoting physically active habits. Popular individual-level measures are sensitive to social desirability bias and subject reactivity. Intrusiveness of these methods, especially when studying children, also limits the possible duration of monitoring and assumes strict submission to human research ethics requirements and vigilance in personal data protection. Meanwhile, growth in computational capacity has enabled computer vision researchers to successfully use deep learning algorithms for real-time behaviour analysis such as action recognition. This work analyzes the weaknesses of existing methods used in PA research; gives an overview of relevant advances in video-based action recognition methods; and proposes the outline of a novel action intensity classifier utilizing sensor-supervised learning for estimating ambient PA. The proposed method, if applied as a distributed privacy-preserving sensor system, is argued to be useful for monitoring the spatio-temporal distribution of PA in schools over long periods and assessing the efficiency of school-based PA interventions.
Academic Libraries’ Outreach Efforts: Identifying Themes in the Literature

Outreach programs remain essential in fostering the use of library collections and services. A review of one hundred seventy-four papers on academic library outreach from 2008 to January 2019 highlighted the popularity of these programs during this period as well as the identification of five themes. These themes point to the importance of library outreach to targeted groups, the popularity of specialized library outreach programs, the need for collaboration in fostering outreach efforts, the benefits of managing these programs and the use of social media for library outreach. Foremost, these materials highlight opportunities and challenges for librarians in providing library outreach in academic institutions.
A Hybrid Approach for Short-Term PV Power Forecasting in Predictive Control Applications

The installed capacity of grid-connected photo-voltaic (PV) systems continues to grow. Due to variability in PV power production, accurate forecasts are essential to support power system operation. This paper presents a hybrid PV power forecasting method with parallel architecture, which combines a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model with an Artificial Neural Network (ANN) model using weighing factors computed periodically via a least squares problem. The method can be used to obtain short-term forecasts with prediction horizons from 15 minutes to 1 day or more, and is therefore well-suited for Model Predictive Control (MPC). We apply the method to forecast the power output of a rooftop PV system at the Lawrence Berkeley National Laboratory. Our analysis provides high-level suggestions to optimize the order and structure of SARIMA and ANN models. The results show that the hybrid method can reduce forecast error by 10% compared with using the individual models separately, while increasing resilience and redundancy due to its parallel architecture.