Reviewerly/exHarmony huggingface dataset:
Software Testing Approach for Cloud Applications (STACA) – Methodology, Techniques &amp; Tools

As earliest the software defects uncovered and fixed in STLC, the lesser the amount required to fix it. With the advent of cloud computing a lot of new opportunities for business opens, especially in the field of software testing & maintenance. The cloud testing methodology is the set of techniques, tools and process to be followed while undergoing tests for cloud service. A new methodology is proposed to test the cloud which is known as SUPerB methodology, deals with cloud security, user acceptance, performance and business requirements. Successful implementation of SUPerB methodology for cloud testing might establish a strong foundation for an organization to lead in the market. Software vulnerabilities give rise to the cyber-crime and related risk associated with it just due to lapses in security policies, which increases the security breaches in the business. Efficiency of a system can be measured in terms of performance testing. While selecting a test tool, users must keep certain characteristics of cloud testing tool in consideration, like platform compatibility, available support, flexibility and service cost. It might be possible that an organization has to deal with a number of challenges while adopting cloud computing services.
Dynamic Dictionary Matching in the Online Model

In the classic dictionary matching problem, the input is a dictionary of patterns $$\mathcal {D}=\{P_1,P_2,\ldots ,P_k\}$$ and a text T, and the goal is to report all the occurrences in T of every pattern from $$\mathcal {D}$$ . In the dynamic version of the dictionary matching problem, patterns may be either added or removed from $$\mathcal {D}$$ . In the online version of the dictionary matching problem, the characters of T arrive online, one at a time, and the goal is to establish, immediately after every new character arrival, which of the patterns in $$\mathcal {D}$$ are a suffix of the current text. In this paper, we consider the dynamic version of the online dictionary matching problem. For the case where all the patterns have the same length m, we design an algorithm that adds or removes a pattern in $$\mathcal {O}(m\log \log \Vert \mathcal {D}\Vert )$$ time and processes a text character in $$O(\log \log \Vert \mathcal {D}\Vert )$$ time, where $$\Vert \mathcal {D}\Vert = \sum _{P\in \mathcal {D}} |P|$$ . For the general case where patterns may have different lengths, the cost of adding or removing a pattern P is $$\mathcal {O}(|P|\log \log \Vert \mathcal {D}\Vert + \log d /\log \log d)$$ while the cost per text character is $$\mathcal {O}(\log \log \Vert \mathcal {D}\Vert + (1+occ)\log d /\log \log d)$$ , where $$d=|\mathcal {D}|$$ is the number of patterns in $$\mathcal {D}$$ and occ is the size of the output. These bounds improve on the state of the art for dynamic dictionary matching, while also providing online features. All our algorithms are Las-Vegas randomized and the time costs are in the worst-case with high probability. A by-product of our work is a solution for the fringed colored ancestor problem, resolving an open question of Breslauer and Italiano [J. Discrete Algorithms, 2013].
Logistic Regression based DFS for Trip Advising Software (ASCEND)

Graphs have played a pivotal role in the field of computer science and has been an efficient method for representing and modeling abstractions in various fields. They can be used to represent several real life models. Several domains in today's world use the concept of graphs extensively such as GPS Navigation systems, Computer networks, WebCrawler, Social Networking websites, peer to peer networking, medical and biological field, neural networks etc. Taking into account the numerous applications of the concept of graphs in today's world, graph searching becomes inevitably significant. In this scenario it is important to note that several graph searching algorithms that were proposed to give exhaustive searches doesn't provide the most satisfying outcome in terms of asymptotic time complexity. Through this paper we intend to highlight the significance of machine learning as a useful tool that can be incorporated in various graph searching algorithms that can reduce its complexity. We classify the existing graph searching techniques as subsets or modifications of two major conventional graph searching algorithms namely BFS(Breadth First Search) and DFS(Depth First Search) and suggest the application of logistic regression to improve their performance. It is confounding that only few research papers explore the application of machine learning to the aforementioned graph searching algorithms. Hence, it is evident that there exists scope for future research on this topic and we aim to suggest directions for the same.
Virtual Reality Based Environment for Immersive, Hands-On Learning

Within the virtual reality (VR) world, the process of creating a usable model is relatively intuitive and easy. The goal of this work was to create a learning tool for teachers to use in their classrooms that allows the students to take apart and put back together different models of the teacher's choice. This virtual model construction/deconstruction could be an affordable, more feasible alternative when hands-on learning is not possible. The product could save schools money and allow the students to explore a much more diverse range of material.
Design and Implementation of Automatic Coin Dispensing Machine

Availability of coins in exchange of currency notes has been a perennial issue in our day to day transactions. This paper presents the design and development of an automatic coin dispensing machine to resolve this issue. The proposed system constitutes three modules viz. detection of genuine currency note, determining the value of the note and dispensing of equivalent coins. The currency genuineness is evaluated using analysis of security strip with the help of color histogram of image of the currency note captured under Ultraviolet (UV) light. The denomination of the note is then detected by calculating the unique ‘width-height’ ratio of the currency for each denomination. The machine dispenses coins as per denomination of the note if and only if the note is genuine. The proposed system gives an accuracy of 92.12% for detection of the denomination of the currency note and an accuracy of 90.07% for genuineness of the currency note with FRR of 32.81%. The overall system efficiency to dispense the coins of equivalent value is 100%.
Orthogonal Flower Pollination Algorithm based Mixed Kernel Extreme Learning Machine for Analog Fault Prognositcs

In this paper, a novel analog fault prognostics model based on the mixed kernel extreme learning machine (MKELM) is proposed, MKELM can take full advantages of the nonlinear mapping characteristics of different kernel functions to enhance the learning performance of KELM. On the basis, in order to achieve the higher predictive efficiency and stability, an improved flower pollination algorithm based on orthogonal learning strategy and catfish effect mechanism (OCFPA) that our previous proposed is used to optimize the relevant parameters combination in MKELM. The simulation results prove that OCFPA-MKELM can significantly improve the predictive performance and our proposed method can establish a more efficient fault prognostics model.
Light Field Video Compression and Real Time Rendering

Abstract Light field imaging is rapidly becoming an established method for generating flexible image based description of scene appearances. Compared to classical 2D imaging techniques, the angular information included in light fields enables effects such as post‐capture refocusing and the exploration of the scene from different vantage points. In this paper, we describe a novel GPU pipeline for compression and real‐time rendering of light field videos with full parallax. To achieve this, we employ a dictionary learning approach and train an ensemble of dictionaries capable of efficiently representing light field video data using highly sparse coefficient sets. A novel, key element in our representation is that we simultaneously compress both image data (pixel colors) and the auxiliary information (depth, disparity, or optical flow) required for view interpolation. During playback, the coefficients are streamed to the GPU where the light field and the auxiliary information are reconstructed using the dictionary ensemble and view interpolation is performed. In order to realize the pipeline we present several technical contributions including a denoising scheme enhancing the sparsity in the dataset which enables higher compression ratios, and a novel pruning strategy which reduces the size of the dictionary ensemble and leads to significant reductions in computational complexity during the encoding of a light field. Our approach is independent of the light field parameterization and can be used with data from any light field video capture system. To demonstrate the usefulness of our pipeline, we utilize various publicly available light field video datasets and discuss the medical application of documenting heart surgery.
Research on Product Novelty Recommendation Based on User Demands

The recommender systems play an important role in alleviating information overload, extracting interesting commodities for users and improving user personalized experience. Today, with the increasing abundant of products and the personalized needs of users, the tradition recommendation systems fail to enhance user satisfaction because they overemphasis the accuracy of results. The recommendation researches have increasingly focused towards introducing novelty in user recommendation lists. Existing methods aim to find the right balance between the similarity and novelty of the recommended items. However, they ignore the different user demands for the accuracy and novelty. Therefore, this paper further analyzes user characteristics according to product types and quantity selected by users, constructs users' demands using the information entropy theory and proposes an adaptive random walk model based on user demands. The experimental results show that the proposed model can adaptively meet users' needs for novelty while ensuring accuracy and enrich the models of the novelty recommendation.
Video summarization using sparse representation of local descriptors

In this paper, a new method is proposed for video summarization and keyframe extraction using combined color, texture and motion information of the video as well as the sparse representation of local descriptors. To reduce the computational overhead of the algorithm, a non-uniform frame sampling st rategy is employed using a shot detection algorithm. Subsequently, Binary Robust Invariant Scalable Keypoints (BRISK) and Histogram of Oriented Gradient (HOG) around the keypoints in the sampled frames are extracted as the local descriptors. By sparse representation and spatially partitioning of local features, the frame discriminating curve is constructed. We extract initial keyframes by detecting local maxima of frame discriminating curve and removing weak maxima. In order to remove redundant keyframes, we use similarity measure and motion model between the initial keyframes to extract final keyframes. Experimental results and the comparison of the results of the proposed algorithm with those of other methods show that the proposed algorithm enhances recall and F-measure indices.
Kernel conditional clustering and kernel conditional semi-supervised learning

The results of clustering are often affected by covariates that are independent of the clusters one would like to discover. Traditionally, alternative clustering algorithms can be used to solve such clustering problems. However, these suffer from at least one of the following problems: (1) Continuous covariates or nonlinearly separable clusters cannot be handled; (2) assumptions are made about the distribution of the data; (3) one or more hyper-parameters need to be set. The presence of covariates also has an effect in a different type of problem such as semi-supervised learning. To the best of our knowledge, there is no existing method addressing the semi-supervised learning setting in the presence of covariates. Here we propose two novel algorithms, named kernel conditional clustering (KCC) and kernel conditional semi-supervised learning (KCSSL), whose objectives are derived from a kernel-based conditional dependence measure. KCC is parameter-light and makes no assumptions about the cluster structure, the covariates, or the distribution of the data, while KCSSL is fully parameter-free. On both simulated and real-world datasets, the proposed KCC and KCSSL algorithms perform better than state-of-the-art methods. The former detects the ground truth cluster structures more accurately, and the latter makes more accurate predictions.
Supervised Learning in Multi-Agent Environments Using Inverse Point of View

There are many approaches that are being used in multi-agent environment to learn agents' behaviour. Semisupervised approaches such as reinforcement learning (RL) or genetic programming (GP) are one of the most frequently used. Disadvantage of these methods is they are relatively computational resources demanding, suffers from vanishing gradient during when machine learning approach is used and has often non-convex optimization function, which makes behaviour learning challenging. This paper introduces a method for data gathering for supervised machine learning using agent's inverse point of view. Proposed method explores agent's neighboring environment and collects data also from surrounding agents instead of traditional approaches that uses only agents' sensors and knowledge. Advantage of this approach is, the collected data can be used with supervised machine learning, which is significantly less computationally demanding when compared to RL or GP. A proposed method was tested and demonstrated on Robocode game, where agents (i.e. tanks) were trained to avoid opponent tanks missiles.
Verifying the claimed sale‐ranking trustworthy: A maximum marginal relevance‐based ranking method

Summary Various online contents on Internet platforms or search engines are related to the corporate reputation. Facing the huge amount of online contents, we need a mining method that can automatically extract and analyze a large number of network‐related information and obtain the real reliability of aspect for the content claimed by companies. In this paper, we propose to generate a ranking model to verify whether the sales‐rankings claimed by companies are trustworthy. The key idea is that the company that has higher confidence score should be supported by the online media. We use a unique data set of public opinion data related with a specific company, which we supplement with data from various online news platform and retrieval webpages using a distributed and generic Web crawler. Meanwhile, basic information and open financial data of companies are also collected for auxiliary analysis. We present a Maximal Marginal Relevance‐based ranking model to compute the confidence score of each company, taking into consideration the two technologies of word embedding and KL‐Divergence to filter the irrelevant documents. Extensive experiments show that the proposed method outperforms the state‐of‐the‐art MMR‐based method, and we showcase three representative cases about the corporate reputation built by us that gives positive, neutral, and negative support respectively to the sales‐ranking claim of companies.
Design Issues in Artificial Neural Network (ANN)

Artificial Neural Network (ANN) has many variants having vast application spectrum. The important aspect of any ANN system is the optimization of various design parameters in order to get robust performance with good accuracy. However, there is a lack of generalized rules with respect to various design issues of ANN. Different problems may require different design parameters to get the accurate results. This paper attempts to throw light in this direction. It aims to provide insight and conceptual understanding of various design issues of ANN.
Playfield extraction in soccer video based on Lab color space classification

The growing growth of soccer videos and its enormous popularity around the world has caused the need of creating an automated analysis system. Recognition and extraction of playfield is considered as one of the most basic processes in the analysis of soccer video. Playfield extraction is developed to identify important parts of the shot and remove other additional parts as well as reducing the processing space in order to increase system speed. This paper presents a framework based on Lab color space for classification and extraction playfield in a soccer video. In this method, first, the image is converted to the Lab color space, then the resulting image is classified in order to separate the playfield from other parts. Finally, by using morphological operations, errors and noises are removed and the playfield is extracted as a convex-hull. Experimental results showed that the proposed method is performing very well in the Lab color space, even in the difficult light conditions such as shadows.
Improved Convolutional Neutral Network Based Model for Small Visual Object Detection in Autonomous Driving

As the killer application of artificial intelligence, autonomous driving is making fundamental transformations to the transportation industry. Computer vision based on deep learning is among the enabling technologies. However, small objects around vehicles are difficult to detect because of poor visual features within small objects as well as insufficient valid samples of small objections. In this paper, we propose an end-to-end detector model based on convolutional neutral network (CNN) to enhance visual features of small traffic signs in real scenarios. With those enhanced features, we manage to obtain an efficient inference model after training. We further make preliminary comparison with Fast R-CNN and Faster R-CNN models. Experimental results indicate that our model outperforms the others by more than 10% improvement in terms of accuracy and recall.
Selfie Video Stabilization

We propose a novel algorithm for stabilizing selfie videos. Our goal is to automatically generate stabilized video that has optimal smooth motion in the sense of both foreground and background. The key insight is that non-rigid foreground motion in selfie videos can be analyzed using a 3D face model, and background motion can be analyzed using optical flow. We use second derivative of temporal trajectory of selected pixels as the measure of smoothness. Our algorithm stabilizes selfie videos by minimizing the smoothness measure of the background, regularized by the motion of the foreground. Experiments show that our method outperforms state-of-the-art general video stabilization techniques in selfie videos.
Context-Preserving Filter Reorganization for VDSR-Based Super-resolution

This paper presents a hardware design to process a CNN for single image super-resolution (SISR). Very deep convolutional network for image super-resolution (VDSR) is a promising algorithm for SISR but it is too complex to be implemented in hardware for commercial products. The proposed design aims to implement VDSR with relatively small hardware resources while minimizing a degradation of image quality. To this end, 1D reorganization of a convolution filter is proposed to reduce the number of multipliers. In addition, the 1D vertical filter is changed to reduce the internal SRAM to store the input feature map. For the implementation with a reasonable hardware cost, the numbers of layers and channels per layer, as well as the parameter resolution, are decreased without a significant reduction of image quality which is observed from simulation results. The 1D reorganization reduces the number of multiplies to 55.6% whereas the size reduction of 1D vertical filter halves the buffer size. As a result, the proposed design processes a full-HD video in real time with 8,143.5k gates and 333.1kB SRAM while the image quality is degraded by 1.06dB when compared with VDSR.
Patch Clustering for Representation of Histopathology Images

Whole Slide Imaging (WSI) has become an important topic during the last decade. Even though significant progress in both medical image processing and computational resources has been achieved, there are still problems in WSI that need to be solved. A major challenge is the scan size. The dimensions of digitized tissue samples may exceed 100,000 by 100,000 pixels causing memory and efficiency obstacles for real-time processing. The main contribution of this work is representing a WSI by selecting a small number of patches for algorithmic processing (e.g., indexing and search). As a result, we reduced the search time and storage by various factors between (50%–90%), while losing only a few percentages in the patch retrieval accuracy. A self-organizing map (SOM) has been applied on local binary patterns (LBP) and deep features of the KimiaPath24 dataset in order to cluster patches that share the same characteristics. We used a Gaussian mixture model (GMM) to represent each class with a rather small (10%–50%) portion of patches. The results showed that LBP features can outperform deep features. By selecting only 50% of all patches after SOM clustering and GMM patch selection, we received 65% accuracy for retrieval of the best match, while the maximum accuracy (using all patches) was 69%.
Virtual Tourism Using Samsung Gear VR Headset

In past few years virtual reality has emerged to become the center of attraction for people. Virtual reality may change the manner in which many of the common things are being used today such as it has already changed the gaming world by introducing virtual games. It provides an environment that is somehow look alike that it is real-world or we can say virtual reality provides a simulated real-world environment to its users. It is very popular technology in the field of gaming, but in this paper we will focus on how virtual reality can be beneficial for traveling, it can bring the feeling of reality and immersion to the user as if he is traveling in actual, but in reality he is only experiencing the tour through VR headset. Virtual tours can be used to increase revenues from market perspective. Samsung gear VR powered by oculus is especially designed for people using android technology and it provides a strong sense of reality (i.e. real world). A headset itself helps to provide the 3D look. Using the gear for virtual tours can be a big milestone in the field of tours and travel, it can help people to have exhilarating experience without visiting that place in real and it can greatly influence the tourist due to its interactive nature. The result of this research will define how gear headset can be used to experience tours and how they can be made enchanting using virtual reality.
Research on Hadoop-based massive short text clustering algorithm

Many clustering algorithms work well on small data sets of less than 200 data objects. However, a large database may contain millions of objects, and clustering on such a large data set may lead to biased results. As data volumes and availability continue to grow, so does the need for large dataset analytics. Among the most commonly used clustering algorithms, K-means proved to be one of the most popular choices to provide acceptable results in a reasonable amount of time. In this paper, we present an improved k-means algorithm with better initial centroids. Also, we implement this modified algorithm on Hadoop platform. Experiments show that the improved k-means algorithm converges faster than the classic k-means and the average execution time is reduced compared to the traditional k-means.