Reviewerly/exHarmony huggingface dataset:
Analysis of Computer Software Experiment Cloud System Design Based on OpenStack

Abstract Computer is widely used in school teaching. The scale of computer room seriously limits the consistency of students’ use of computer, so that the learning content and experimental content cannot be effectively preserved. In view of this situation, many teaching and teaching units are building computer software experimental cloud system based on OpenStack, in order to provide students with independent operation, safe and reliable learning space without increasing the human and financial burden of the computer room. In this paper, the design of computer software experimental cloud system based on OpenStack platform is analyzed, which can be used as a reference for developers.
Object Detection Based on Feature Scale Fusion and Feature Scale Enhancement

Currently, object detection is widely used to deal with the image analysis problem, which is the most important task in computer version. As the high image resolution leads to high computational cost and the low image resolution leads to low accuracy, the key bottleneck of CNN based object detection is the image resolution selection. In this work, we solve the problem of obtaining powerful object detection effect with feature scale fusion and feature scale enhancement. The proposed module can achieve significant accuracy by applying Feature Scale Fusion Module (FSFM) and Feature Scale Enhancement Module (FSEM) to the feature extraction layer of Faster R-CNN. In this case, the enhanced feature map become the input of RPN layer and ROI layer of Faster R-CNN. The accuracy gain of the propose module is verified via the Pascal VOC and MSCOCO datasets, which is proved to obtain significant improvements over the most advanced detection models.
Query-by-Example Word Spotting with Fuzzy Word Sizes

This paper proposed the query-by-example word spotting model for handwritten documents with image fuzzification. Fuzzy size of word images was used to size of problem. The number of classes in each set were decreased, which made it is easy to choose parameters. The Pyramid of Histogram of Oriented Gradients (PHOG) feature and Support Vector Machine (SVM) were employed to use in the model. IAM handwritten database was used for evaluating the model. The result demonstrates that the micro precision of model with image fuzzification and without image fuzzification were 35.11% and 23.54% respectively. However, the accuracies of the models were 35.11% and 40.14% respectively. Thus, the image fuzzification can be used for reducing of type one error with slightly accuracy loss.
Cell Blood Image Segmentation Based on Genetic Algorithm

There are many methods of clustering that have been used to divide images. k-means algorithm is considered the most popular method of cluster analysis. Due to disadvantages of k-means algorithm, in this paper, the image is segmented using modified genetic algorithm (GA) by k-means algorithm; where k-means is used as an initialization of GA. The proposed algorithm was applied to several of cell blood images from microscope and the results showed that the value of PSNR for the proposed algorithm is higher than other algorithms, which indicates its efficiency in image segmentation.
A Comparative Evaluation of AutoEncoder-Based Unsupervised Anomaly Detection Methods Applied on Space Payload

The anomaly detection technology is the basis for ensuring the safe and stable operation of the on-rail payload. The traditional threshold-based anomaly detection method has low accuracy and poor flexibility, and cannot detect abnormalities in real time. In addition, due to the lack of abnormal samples, the distribution of positive and negative samples is extremely imbalanced, which increases the difficulty of abnormal detection. Therefore, this paper proposes an unsupervised learning method based on AutoEncoder and its variants, the Basic AutoEncoder, Deep AutoEncoder and Sparse AutoEncoder are used to verify the algorithm on three public datasets. And using the above three algorithms to carry out the case application on the real load dataset. The experiments show whether in the public dataset or the real data of the payload, the three methods of AutoEncoder have achieved good results, proving the AutoEncoder and its variants have a good application in anomaly detection. At the same time, it is verified that the three algorithms have different effects on different datasets, which proves that the AutoEncoder with different characteristics need to be selected in different scenarios.
Pattern Search in Grammar-Compressed Graphs

A fundamental operation on large graphs is the search of occurrences of a given pattern in the graph. Like [1], we consider an original graph OG and a query pattern Q of labeled nodes and unlabeled edges and aim to compute a relation called simulation of Q in OG, which determines a superset of the occurrences of Q in OG. An algorithm in [1] computes the simulation of Q in OG in time O(n*m) for n vertices and m edges of OG. In comparison, we have developed an algorithm [2] that works on a compressed graph grammar GG of OG, instead of operating on OG itself. The speed-up of our algorithm [2] compared to the algorithm in [1] grows with the size |OG| of OG and with the compression strength |OG| / |GG| of the graph grammar GG.
Predictive Distribution Estimation for Bayesian Machine Learning using a Dirichlet Process Prior

In Bayesian treatments of machine learning, the success or failure of the estimator/classifier hinges on how well the prior distribution selected by the designer matches the actual data-generating model. This paper assumes that the model distribution is a realization of a Dirichlet process and assesses the mismatch between the true predictive distribution and the predictive distribution approximated using the training data. It is shown that highly localized Dirichlet priors can overcome the burden of a limited training set when the prior mean is well matched to the true distribution, but will degrade the approximation if the match is poor. A bias/variance trade-off will be demonstrated with illustrative examples.
Face Frontalization with Inpainting Method

Problems caused by large profile views are always holdback for face recognition and face reconstruction, such as texture missing problem. In this paper, we propose a method to generate frontal faces from faces of different poses in the wild. Different from previous works for face frontalization which only use GANs or only render a rotated 3D face model to an image, we combine face reconstruction and generative adversarial model, which can preserve the shape information from 3D space and refine the texture through learned distribution from GAN. The task of our GAN is to inpaint the incomplete frontal images which are rendered from the reconstructed 3D model. As the results show, our inpainting method has a better performance than the Poisson editing method on face verification.
A Report on the PhD Symposium Track

Innovations in Software Engineering Conference (ISEC) is the flagship annual conference of iSoft, which is an arm of ACM India and serves as the India chapter of ACM SIGSOFT. The 13th edition of ISEC is to be held at IIITDM Jabalpur during the period February 27-29 2020, and will hold a PhD Symposium. The objective of the PhD Symposium is to provide for a forum for junior as well senior level PhD students working in the field of software engineering to present their work in a friendly setting, to get feedback on their direction from experts, to present posters about their work to the ISEC attendees, and to interact and network with each other. This report summarizes the motivation for this symposium and the plan for the event, and lists the full set of accepted submissions.
Improved Performance of Visual Concept Detection in Images Using Bagging Approach with Support Vector Machines

With rapid advances in imaging devices and internet, millions of images are uploaded on the internet without much information about the image. An efficient method is necessary for detecting the concept of the desired image from this vast collection of images. In this paper, Support Vector Machine (SVM) based architecture is presented to detect concept of a given input image. To enhance the performance of proposed system, a bagging approach is implemented. Color moments, HSV Color Histogram, Grey level co-occurrence matrix, Wavelet Transform and Edge orientation histogram are used for image representation purpose. These low-level feature descriptors are used to train multiple SVM models. The final concept of the query image is obtained by voting from outputs of these multiple models. The proposed system is evaluated on Wang’s Corel 10K. Results of proposed system indicate its improved performance over existing systems.
Sequence Generation Using Unwords

Statistical context models for sequence generation provide probabilities for each event in a sequence, conditioned on the context or history of the event in the sequence. A fundamentally different type of generative method inverts this view entirely, stating only what patterns cannot occur, allowing all other possibilities. The two concepts of statistical models and unexpectedly absent words can be combined for sequence generation, by sampling from a statistical model while avoiding generation of any unwords. A desirable feature of this approach is that an efficient random walk procedure can be used even for long sequences.
Efficient Edge Detection based on Multi-scale Gabor Filters

Spatially scaled edges are ubiquitous in natural images. Therefore, to better detect edge with different scales, we propose an edge detection algorithm based on multi-scale Gabor filters. First, the imaginary parts of the Gabor filters (IPGFs) with five scales and eight directions are constructed. Second, the input image is filtered by IPGFs to extract edge features.Then, dimensionality reduction is performed by using pca. Finally, image fusion and double threshold method are used to preserve the edge continuity. Furthermore, we test our method on widely used images. The proposed detector is compared with other detectors. The experiment results illustrate that the algorithm can detect more image details while ensuring the accuracy of detectio
QoS-enabled Cache Architecture for a Private Cloud

The in-memory cache system is an important component in a cloud for the data access performance. As the tenants may have different performance goals for data access depending on the nature of their tasks, effectively managing the memory cache is a crucial concern in such a shared computing environment. Two extreme methods for managing the memory cache are unlimited sharing and complete isolation, both of which would be inefficient with the expensive storage complexity to meet the per-tenant performance requirement. In this paper, we present a new cache model that incorporates global caching (based on unlimited sharing) and static caching (offering complete isolation) for a private cloud, in which it is critical to offer the guaranteed performance while minimizing the operating cost. This paper also presents a cache insertion algorithm tailored to the proposed cache model. From an extensive set of experiments conducted on the simulation and emulation settings, the results confirm the validity of the presented cache architecture and insertion algorithm showing the optimized use of the cache space for meeting the per-tenant performance requirement.
Machine Learning based Video Hosting Site Identification Method for MVNO Networks

Zero-rating service provided by Mobile Virtual Network Operators (MVNOs) has been attracting smartphone users who frequently watch web videos that are delivered by heavily bandwidth-consuming applications. With the increase of encrypted traffic, MVNOs need to identify video hosting sites accessed by smartphone users via encrypted traffic analysis for enabling such services. If traffic from permitted sites is identified as coming from non-permitted sites due to mistaken identification of video hosting sites, unreasonable payments are inevitable for MVNOs or subscribers, and vice versa. In this paper we propose two feature sets considering multiple flow transmission and analyze the feature sets by supervised machine learning for identifying video hosting sites accurately. The first set is traffic features extracted from flows for only transmitting video contents. The second is 4-tuple distribution of established flows for transmitting various contents in a single video web page. These feature sets are based on our investigation of the characteristics of four of the most popular video hosting sites in Japan. Through video hosting site identification experiments, the identification accuracy of single flow analysis reaches 85.9%, and the accuracy of the proposed method reaches 92.0%.
Improving the Data Quality of the MovieLens Dataset Using Dimensionality Reduction Techniques

The presence of recommendation systems within the current era is not controlled or even managed like before, for providing the optimum services to users. For that reason, these services need to be in an ideal and complete state since the real-world data is often not that much perfect. That optimum state could not be achieved without the interaction of preprocessing method. Preprocessing can offer a lot of insights about the data and its structure and quality. Preprocessing techniques ensure that the dataset has complete, consistent and integrant properties for further analysis. Dimensionality reduction is this paper is discussed as one of the preprocessing stages using the most popular matrix factorization methods. We have also made an experiment to show how the dimensionality reduction algorithms can overcome any constrained computational resources and prove better performance and efficiency for such demanding recommendation systems.
Entropy-Based Feature Selection Classification Approach for Detecting Phishing Websites

In this modern era of computing, phishing is a noxious cyber-attack in which the user’s sensitive personal or financial information is obtained by an illegitimate website. The detection of phishing websites is very important to cope with phishing incidents to protect the user’s credentials on the web. The uniform resource locators (URLs) of phishing websites look almost like the URLs of legitimate ones however differ in several aspects. In this research work, we have presented an entropy-based feature selection (FS) approach with machine learning (ML) classification to differentiate phishing website patterns from the legitimate ones. The main aim of this study is to identify the set of most dominant features associated with the phishing website patterns. This research work is divided into two phases. In the first phase, we have proposed different FS methods to mine the optimal feature subset having the most dominant features representing the phishing patterns. In the second phase, we have implemented five different classification techniques namely k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and multi-layer perceptron (MLP) classifier to identify the insecure patterns on the web and classify them as phishing websites by making use of optimal feature subset. Finally, the performance of ML classifiers is evaluated using different performance evaluation metrics i.e., specificity, sensitivity, precision, accuracy, and ROC-AUC (receiver operating characteristic - area under the ROC curve) curve. The results of the paper proved that the integration of the entropy-based FS method with the ML classifiers helps to improve their efficiency and accuracy. Furthermore, the comparison of the results indicates that MLP classifiers with entropy-based FS outperformed other classifiers for detecting phishing websites and making the web more secure for the people to surf.
A Novel Multiple Object Tracking Algorithm for Autonomous Vehicles

Multiple object tracking is a vital task for autonomous vehicle environment perception. In this paper, we design a novel multi-object tracking method for autonomous vehicles. In the detection section, we utilize popular Faster-RCNN as our baseline method. Then, in data association, we combine appearance, motion, and interaction model to build a unified feature descriptor to explore the nature of tracking object. We evaluate our algorithm on a popular and standard benchmark and compare with the state-of-the-art methods. The results denote that our algorithm achieve good performance at high frame rates.
Towards Natural Language Understanding of Procedural Text Using Recipes

Procedural knowledge, or how-to knowledge, is the knowledge acquired from natural language understanding of instructions in procedural text. Procedural knowledge bases containing textual descriptions of tasks in procedures have witnessed explosive growth recently. This has facilitated a significant body of work in various natural language understanding tasks. A rich source of procedural text is in the form of recipes describing food preparation procedures. The ready availability of online recipes has enabled progress in food computing, which refers to computing tasks related to recipes, such as food perception, recipe image recognition and calorie estimation, and food-oriented retrieval of recipes. However, past work on food computing has not covered the procedural knowledge inherent in recipes and the natural language understanding tasks required to uncover that knowledge. We seek to address this by presenting an overview of recent work in natural language understanding tasks in food computing and describing how this contributes to how-to knowledge and future applications.
Reflections on Four Years of Team-Based Learning in First-Year Property

This article reflects what we have learned since the William H. Bowen School of Law’s decision in 2014 to adopt team-based learning in its two real property first-year courses. Since 2014, every Bowen student has learned property in a team-based learning system. The purpose of this article is not to provide a treatise on team-based learning; those details are readily available in excellent articles, books, websites, and even an active, enthusiastic listserv. This article is a reflection on our process of implementation, and the choices (and missteps) we have made as we have worked to make this flexible learning strategy fit the needs of our curriculum, our faculty, and most importantly our students. Our decisions about how best to use this learning strategy have been and continue to be rooted in team-based learning’s essential elements and the core components that frame the experience for our students. Our experience with team-based learning has also forced us to become more expert in learning theory, as we have worked to anticipate and manage any increased social stress team-based learning may cause students, to ensure students use their experiences in a team to improve their individual capacity, and to accurately grade the property course while still motivating students to fully participate in teams.Our own evidence that team-based learning has improved our students’ learning is preliminary, but positive. In 2017 — the first year students taught property with team-based learning were eligible to take the Arkansas Bar Exam — our students scored higher on the MBE property questions relative to the national average than they did in any other tested subject. Team-based learning has challenged our students and us, but our adoption of the learning system has energized our classrooms and reinvigorated our teaching.
Generating Context-Free Group-Level Emotion Landscapes Using Image Processing and Shallow Convolutional Neural Networks

Emotion recognition is an integral part of any Human–Machine Interaction (HMI) system. Proper emotion recognition allows for HMI systems to choose the successive appropriate responses, given context and the emotion expressed by human(s). The advent of deep learning using Deep Neural Networks (DNN) has made incredible strides in achieving and even exceeding human accuracy in image classification and face detection. Many papers have been published mentioning the successful applications of DNNs like the Convolutional Neural Networks (CNNs), which have now become the de facto algorithm for facial image classification tasks because they combine the feature extraction and classification steps into one mathematical model. They learn the desirable features by themselves from the input images and have been demonstrated to be robust to variations in facial image data. However, there is one big bottleneck for CNNs: the models with good accuracy have many hidden layers, and hence are very deep and require heavy computing power, memory space and, of course, time to train themselves. Our paper puts forward two experimental approaches that can be extremely beneficial to the domains of CNNs and HMI systems. For the first approach, we were able to achieve a very good accuracy in emotion classification using a shallow CNN with only three and four hidden layers. This was possible only because we passed the input images through a carefully designed pipeline of image preprocessing techniques before feeding them to the CNN for training. For the second approach, we developed the interpretation of emotion landscape or the distribution of emotion classes detected in static images or videos with many people’s faces visible. This is similar to the group-level emotion classification studies and publications with a distinct difference in possible applications. The rationale behind this integration is in advancing the idea of studying emotions expressed by people in a group setting and how they are mutually influential, visualize the change in the emotion distribution with time and thereby form an emotional landscape in time, and enhance the understanding of collective sentiments non-verbally expressed through facial emotions in gatherings of known social context.
Radial and tangential velocity effects of a probed scanning mirror on laser Doppler frequency shift

A laser Doppler vibrometer (LDV) is precise and fast in measuring the translational velocity of a vibrating diffuse surface. However, it could fail to measure the tangential velocity of a rotating mirror. While the specular reflection away from the mirror can be recovered by a retroreflective collector, the recovered LDV reading is found to deviate from the true tangential velocity of the probed scanning mirror. This happens because the probed spot shifts radially along the rotating mirror surface and thus introduces extra Doppler shift, while the laser beam is aimed at a constant height on the scanning mirror. Here, we derive an analytical relationship between the laser Doppler shift and the tangential velocity of the measured spot. With the input of the prescribed scan profile, we recover the true reading of tangential velocity of the scanning mirror even at a large rotational angle. This corrected LDV reading is as precise as the measurement by a high-speed camera.