Reviewerly/exHarmony huggingface dataset:
Video Analytics for Face Detection and Tracking

Machine Learning has substantially grown over a period of decade. Deep Learning the new field of Machine Learning is gaining ever-increasing interest in research due to its implicit capability appropriate for successful applications in the field of computer vision, speech processing, image processing, object detection, human and face sub-attribute detection, and many more analytical systems. Intelligent Video Systems and Video Analytics is managed by a wide collection in transportation, security, health care and customer analytics. A Deep-Learning- based approach helps researchers control the massive, complex and diverse dataset to improve the performance by proficiently extracting only the vital features from a large video dataset. In this paper we present a system to autonomously detect facial images from a video surveillance and extract feature points in the frame. We use Viola-Jones algorithm and the KLT algorithm in our system to track faces in real-time. We also present a brief survey on Deep Learning (DL) models deployed in the field of Video Analytics. We cover architectures, tasks and related analytical methods and demonstrate the importance of Video Systems.
Design of Cloud Computing for Educational Centers Using Private Cloud Computing: A Case Study

Abstract Cloud computing is becoming a popular approach to solve many serious problematic issues since its introduction in 2000. Cloud computing includes data storage in addition to software and hardware sharing via using network infrastructure, computers, and other resources. However, the educational centers are still considered conventional and limited in terms of their existing hardware infrastructure. A case study of cloud computing deployment in the University of Diyala-ICC (Internet and Computer Center)-will be used to illustrate the situation. This study aims to develop a basic cloud-based design for improving the efficiency of ICC laboratories, as an educational center in the University of Diyala to make it a study and research center of cloud computing for computer science students in the university. The proposed design is built via using “Software as a Service” (SaaS) model. Cloud computing in ICC laboratories is designed using Private Cloud Computing. The proposed design provided flexibility to ICC and allows to improve the capabilities of computer network and helps in managing their resources easily.
Microscopic Blood Smear RBC Classification using PCA and SVM based Machine Learning

The therapeutic analysis f microscopic blood smear begins with recognizing blood cells f various categories as well as estimating cell count in blood sample. The distinctive blood cell grading and counting furnish priceless knowledge to the pathologist about sundry infections. This exercise can be easily concluded if the shapes f blood cells are pinpointed first and using the shapes, we classify the blood cells. In this research work we build and test an automatic microscopic blood smear red blood cell (RBC) classification by using Principal Component Analysis (PCA) and Support Vector Machine (SVM) based machine learning. We train and test the statistical data models based n probabilistic pattern recognition to classify the blood smear RBC into Normal Cells, Echinocytes, Elliptocytes and Sickle cells. The H-minimum Transform (HmT) and Watershed Transform (WT) are used in pre-processing f images to increase the accuracy if segmentation shape extraction f the blood cells. Then the Bag f Features (BoF) created considering the 500 strongest features f each type f blood cell after K-Means clustering. Training takes place through Image Category Classifier (ICC) whose performance measured by using Mean Average Precision (mAP) justifies that SVM based classifiers provide audacious results.
M<sup>2</sup>-Net: A Multi-scale Multi-level Feature Enhanced Network for Object Detection in Optical Remote Sensing Images

Object detection in remote sensing images is a challenging task due to diversified orientation, complex background, dense distribution and scale variation of objects. In this paper, we tackle this problem by proposing a novel multi-scale multi-level feature enhanced network ( M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net) that integrates a Feature Map Enhancement (FME) module and a Feature Fusion Block (FFB) into Rotational RetinaNet. The FME module aims to enhance the weak features by factorizing the convolutional operation into two similar branches instead of one single branch, which helps to broaden receptive field with less parameters. This module is embedded into different layers in the backbone network to capture multi-scale semantics and location information for detection. The FFB module is used to shorten the information propagation path between low-level high-resolution features in shallow layers and high-level semantic features in deep layers, facilitating more effective feature fusion and object detection especially those with small sizes. Experimental results on three benchmark datasets show that our method not only outperforms many one-stage detectors but also achieves competitive accuracy with lower time cost than two-stage detectors.
BraIN: A Bidirectional Generative Adversarial Networks for image captions

Although progress has been made in image captioning, machine-generated captions and human-generated captions are still quite distinct. Machine-generated captions perform well based on automated metrics. However, they lack naturalness, an essential characteristic of human language, because they maximize the likelihood of training samples. We propose a novel model to generate more human-like captions than has been accomplished with prior methods. Our model includes an attention mechanism, a bidirectional language generation model, and a conditional generative adversarial network. Specifically, the attention mechanism captures image details by segmenting important information into smaller pieces. The bidirectional language generation model produces human-like sentences by considering multiple perspectives. Simultaneously, the conditional generative adversarial network increases sentence quality by comparing a set of captions. To evaluate the performance of our model, we compare human preferences for BraIN-generated captions with baseline methods. We also compare results with actual human-generated captions using automated metrics. Results show our model is capable of producing more human-like captions than baseline methods.
A Novel Edge Computing Offloading and Privacy-preserving Scheme for Energy Internet

With the rapid development of Internet of things technology, “massive” data generated by Internet of things terminals, which leads to the problem of excessive cloud computing pressure and network delay. Therefore, edge computing emerges as the time requires. At the same time, the privacy security of edge computing is particularly important. In this paper, ZSS short signature algorithm and Paillier homomorphic cryptosystem are used to propose a secure and efficient edge computing privacy-preserving in energy Internet. The scheme can achieve data confidentiality and privacy-preserving. Finally, the model of unloading is established to solve the problem quickly. Experimental results show that our scheme has less computation and communication overhead.
Multi-source Seq2seq guided by knowledge for Chinese healthcare consultation

Online healthcare consultation offers people a convenient way to consult doctors. In this paper, we aim at building a generative dialog system for Chinese healthcare consultation. As the original Seq2seq architecture tends to suffer the issue of generating low-quality responses, the multi-source Seq2seq architecture generating more informative responses is much more preferred in this task. The multi-source Seq2seq architecture takes advantage of retrieval techniques to obtain responses from the database, and then takes these responses alongside the user-issued question as input. However, some of the retrieved responses might be not much related to the user-issued question, resulting in the generation of unsatisfying responses that are not correct in diagnosis or instead provide inappropriate advice on prevention or treatment. Therefore, this paper proposes multi-source Seq2seq guided by knowledge (MSSGK) to handle this problem. MSSGK differs from the multi-source Seq2seq architecture in that domain knowledge, including disease labels and topic labels about prevention and treatment, is introduced into the response generation via a multi-task learning framework. To better exploit the domain knowledge, we propose three attention mechanisms to provide more appropriate guidance for response generation. Experimental results on a dataset of real-world healthcare consultation show the effectiveness of the proposed method.
SaVioR: Thwarting Stack-Based Memory Safety Violations by Randomizing Stack Layout

Stack-based memory corruption vulnerabilities have been exploited, allowing attackers to execute arbitrary code and read/write arbitrary memory. Although several solutions have been proposed to prevent memory errors on the stack, they are either limited to a specific type of attack (either spatial or temporal attacks) or cause significant performance degradation. In this article, we introduce SaVioR, an efficient and comprehensive stack protection mechanism. The key technique involves randomization of the stack layout to reduce its predictability and exploitability. SaVioR isolates an individual object from spatially and temporally adjacent vulnerable objects and randomizes each object's location, which prevents attackers from predicting the stack layout and thus reduces the likelihood of memory errors being exploited. We implemented SaVioR based on the LLVM compiler framework and applied it to the SPEC CPU2006 benchmarks and real-world applications. Our security evaluation showed that SaVioR provides a high degree of randomness in the stack layout and thus reduces the likelihood of successful exploitation of spatial and temporal memory errors on the stack. Our performance evaluation also demonstrated that it incurs a modest performance overhead (14 percent) with the SPEC CPU2006 benchmark suite, which improves performance compared to the state-of-the-art stack protection while achieving a comparable security level.
Resource Creation for Sentiment Analysis of Under-Resourced Language: Marathi

With the hike of social networking sites like Facebook, Twitter, Instagram, the Marathi web data are increasing day by day. Mining these data towards the corporate and government has become a broad area of research under Natural Language Processing. Sentiment Analysis (SA), identification of the public’s attitude, using machine learning or subjective lexicon, is easier for resource-rich languages like English. Still, for Marathi being poor resource language, it’s a difficult task. In this research, the three approaches have experimented – Corpus-based, SentiWordNet3.0-based, and Hindi SentiWordNet (HSWN)-based to create the Marathi sentiment lexicon (adjective, adverb). The first two approaches use Google Translator to make use of English resource-SWN3.0. The third approach uses HSWN and Marathi WordNet, which minimizes translation errors. The word coverage of the SWN3.0-based lexicon is noteworthy. This paper attempts the Marathi subjective lexicon creation for the first time, which would aid for SA chore precise to the Marathi data.
Hashing modulo alpha-equivalence

In many applications one wants to identify identical subtrees of a program syntax tree. This identification should ideally be robust to alpha-renaming of the program, but no existing technique has been shown to achieve this with good efficiency (better than O(n2) in expression size). We present a new, asymptotically efficient way to hash modulo alpha-equivalence. A key insight of our method is to use a weak (commutative) hash combiner at exactly one point in the construction, which admits an algorithm with O(n (logn)2) time complexity. We prove that the use of the commutative combiner nevertheless yields a strong hash with low collision probability. Numerical benchmarks attest to the asymptotic behaviour of the method.
Benchmarking Blockchain Interactions in Mobile Edge Cloud Software Systems

As blockchain becomes an essential part of many software systems in the edge and cloud, the developer starts to treat blockchain features like commodity software components that can be integrated into edge and cloud software systems. For the developer it is quite challenging to determine, customize, and evaluate suitable blockchain features for software systems in the edge and cloud environments. In this paper, we conceptualize important blockchain interactions in mobile edge computing software systems (MECSS) and present generic techniques for evaluating these interactions. We determine different interaction patterns for different deployments of compute resources and networks. We abstract and represent application-level mobile edge computing (MEC) features and blockchain features to create MECSS deployment models to be coupled with testbed deployments for benchmarking application-level interactions within application contexts. Based on that, we develop a generic framework for building and executing benchmarks of application-level blockchain interactions within MECSS. We will demonstrate our framework for vehicle-to-everything communication scenarios with two main blockchain technologies, Hyperledger Fabric and Ethereum, using various types of compute resources in edge and cloud infrastructures.
"@alex, this fixes #9": Analysis of Referencing Patterns in Pull Request Discussions

Pull Requests (PRs) are a frequently used method for proposing changes to source code repositories. When discussing proposed changes in a PR discussion, stakeholders often reference a wide variety of information objects for establishing shared awareness and common ground. Previous work has not considered how the referential behavior impacts collaborative software development via PRs. This knowledge gap is the major barrier in evaluating the current support for referencing in PRs and improving them. We conducted an explorative analysis of \textasciitilde7K references, collected from 450 public PRs on GitHub, and constructed taxonomies of referent types and expressions. Using our annotated dataset, we identified several patterns in the use of references. Referencing source code elements was prevalent but the authoring interface lacks support for it. Three classes of contextual factors influence referencing behaviors: referent type, discussion thread, and project attributes. Referencing patterns may indicate PR outcomes (e.g., merged PRs frequently reference issues, users, and tests). We conclude with design implications to support more effective referencing in PR discussion interfaces.
Real-Time Path Planning With Virtual Magnetic Fields

Humans and animals have learned or evolved to use magnetic fields for navigation. Knowing how to model and estimate these fields can be used for motion planning. However, computing the propagation of electromagnetic fields in a given environment requires solving complex differential equations with advanced numerical methods, and therefore it is not suitable for real-time decision making. In this latter, we present a real-time approximator for Maxwell's equations based on deep neural networks that predicts the distribution of a virtual magnetic field. We show how our approximator can be used to perform autonomous 2D navigation tasks, outperforming state-of-the-art navigation algorithms, ensuring completeness, and providing a near-optimal path up to 200 times per second without any post processing stage. We demonstrate the effectiveness of our method with physics-based simulations of an unmanned aerial vehicle, an autonomous car, as well as real-world experiments using a small off-road autonomous racing vehicle. Furthermore, we show how the approach can be applied to multi-robot systems, video game technology, and can be extended to 3D problems.
Locating freedom of speech in an era of global white nationalism

The chief spokespersons for this more refined sentiment against persons and voices that are new and unfamiliar to the campus and intellectual discourse are not the purveyors of gutter hate speech. ...
Fast Confidence Prediction of Uncertainty based on Knowledge Graph Embedding

The uncertainty is an inherent feature of Knowledge Graph (KG), which is often modelled as confidence scores of relation facts. Although Knowledge Graph Embedding (KGE) has been a great success recently, it is still a big challenge to predict confidence of unseen facts in KG in the continuous vector space. There are several reasons for this situation. First, the current KGE is often concerned with the deterministic knowledge, in which unseen facts’ confidence are treated as zero, otherwise as one. Second, in the embedding space, uncertainty features are not well preserved. Third, approximate reasoning in embedding spaces is often unexplainable and not intuitive. Furthermore, the time and space cost of obtaining embedding spaces with uncertainty preserved are always very high. To address these issues, considering Uncertain Knowledge Graph (UKG), we propose a fast and effective embedding method, UKGsE, in which approximate reasoning and calculation can be quickly performed after generating an Uncertain Knowledge Graph Embedding (UKGE) space in a high speed and reasonable accuracy. The idea is that treating relation facts as short sentences and pre-handling are benefit to the learning and training confidence scores of them. The experiment shows that the method is suitable for the downstream task, confidence prediction of relation facts, whether they are seen in UKG or not. It achieves the best tradeoff between efficiency and accuracy of predicting uncertain confidence of knowledge. Further, we found that the model outperforms state-of-the-art uncertain link prediction baselines on CN15k dataset.
Research on Detection Algorithm of Wheel Position based on CenterNet

Abstract In order to accurately determine the stopping state of a vehicle, a wheel location detection method combining enhanced images with CenterNet network was proposed.Image enhancement network was used to process the detected images to improve the contrast and sharpness of images. Then, CenterNet network was used to detect the wheel position in the enhanced image.The algorithm proposed in this paper can efficiently and accurately detect the vehicle wheel location, thus determining a variety of vehicle stopping states, so as to avoid the scene complexity limit, camera Angle and other factors that lead to the wrong judgment of stopping states, and greatly improve the efficiency of roadside parking management.
Research on Real-time Object Detection Algorithm in Traffic Monitoring Scene

Aiming at the detection precision and detection speed of Mobilenetv2-YOLOv3 in the process of object detection, the feature extraction module is improved to improve the network feature extraction capability; the FPN multi-feature fusion module and multi-scale aggregation module are used to enhance information between multi-scale feature maps Fusion; Introduce the dilated convolution to build the receptive field module, Improve the ability to extract features of different scale object, and improve detection precision; According to the characteristics of the KITTI data set, K-means algorithm is adopted for dimension clustering to obtain the new anchor box parameter values. Test the algorithm performance on the KITTI dataset, Experimental results show that compared with Mobilenetv2-YOLOv3, the improved algorithm improves the mAP by 8.99%, and the detection speed reaches 14FPS on the embedded hardware TX2 development board.
Context-aware RNNLM Rescoring for Conversational Speech Recognition

Conversational speech recognition is regarded as a challenging task due to its free-style speaking and long-term contextual dependencies. Prior work has explored the modeling of long-range context through RNNLM rescoring with improved performance. To further take advantage of the persisted nature during a conversation, such as topics or speaker turn, we extend the rescoring procedure to a new context-aware manner. For RNNLM training, we capture the contextual dependencies by concatenating adjacent sentences with various tag words, such as speaker or intention information. For lattice rescoring, the lattice of adjacent sentences are also connected with the first-pass decoded result by tag words. Besides, we also adopt a selective concatenation strategy based on tf-idf, making the best use of contextual similarity to improve transcription performance. Results on four different conversation test sets show that our approach yields up to 13.1% and 6% relative char-error-rate (CER) reduction compared with 1st-pass decoding and common lattice-rescoring, respectively.
An Elderly Fall Detection System using Depth Images

In this proposed conceptual technique, we have collected the Microsoft Kinect depth images of elderly fall event. After collecting the necessary depth images the background subtraction algorithm has been used to subtract the background and retain the subject. Segmentation and feature selection process have been applied on various daily activity to train the fall detection model. The model has been train using decision tree. To ensure the fall confidence, ground truthing technique has been used.
Extractive Summarization using Cohesion Network Analysis and Submodular Set Functions

Numerous approaches have been introduced to automate the process of text summarization, but only few can be easily adapted to multiple languages. This paper introduces a multilingual text processing pipeline integrated in the open-source ReaderBench framework, which can be retrofit to cover more than 50 languages. While considering the extensibility of the approach and the problem of missing labeled data for training in various languages besides English, an unsupervised algorithm was preferred to perform extractive summarization (i.e., select the most representative sentences from the original document). Specifically, two different approaches relying on text cohesion were implemented: a) a graph-based text representation derived from Cohesion Network Analysis that extends TextRank, and b) a class of submodular set functions. Evaluations were performed on the DUC dataset and use as baseline the implementation of TextRank from Gensim. Our results using the submodular set functions outperform the baseline. In addition, two use cases on English and Romanian languages are presented, with corresponding graphical representations for the two methods.
Relational Database Watermarking Techniques: A Survey

Abstract While data is used in cooperative milieus for information extraction; Thus, it is vulnerable to security threats concerning ownership rights and data abusing. Due to unauthorized access to the data that may alter the originality, it results in significant losses of the organization. The relational databases which are free on-hand are used by research society for mining new information regarding their research works. These databases are vulnerable to security issues. The reliability of the data source must be authenticated before using it for any application purpose. Thus, to check the ownership and reliability of data, watermarking is applied to the data. Watermarking is used for the protection of the possession rights of shared Relational Data and for providing the solution for manipulating and tampering of data.