Reviewerly/exHarmony huggingface dataset:
Practices of Motivators in Adopting Agile Software Development at Large Scale Development Team from Management Perspective

Agile software development methodologies have become the most popular software development methods in the last few years. These methodologies facilitate rapid development. The low cost and prioritized user satisfaction make these methodologies more attractive. These methodologies were also intended for small scale developmental teams. Therefore, challenges were encountered when these methodologies were used in large-scale development teams. This study was based on the identification of factors which were discovered in our previous study. Some of the factors included “leadership strong commitment and team autonomy”, “cooperative organizational culture”, and “team competency—agile development expertise”. A total of 147 practices were identified in this study via a systematic literature review. These practices will help practitioners and project managers to adopt agile software methodologies and encourage them to the enhance them.
Generative Memory-Guided Semantic Reasoning Model for Image Inpainting

The critical challenge of single image inpainting stems from accurate semantic inference via limited information while maintaining image quality. Typical methods for semantic image inpainting train an encoder-decoder network by learning a one-to-one mapping from the corrupted image to the inpainted version. While such methods perform well on images with small corrupted regions, it is challenging for these methods to deal with images with large corrupted area due to two potential limitations. 1) Such one-to-one mapping paradigm tends to overfit each single training pair of images; 2) The inter-image prior knowledge about the general distribution patterns of visual semantics, which can be transferred across images sharing similar semantics, is not explicitly exploited. In this paper, we propose the Generative Memory-guided Semantic Reasoning Model (GM-SRM), which infers the content of corrupted regions based on not only the known regions of the corrupted image, but also the learned inter-image reasoning priors characterizing the generalizable semantic distribution patterns between similar images. In particular, the proposed GM-SRM first pre-learns a generative memory from the whole training data to explicitly learn the distribution of different semantic patterns. Then the learned memory are leveraged to retrieve the matching semantics for the current corrupted image to perform semantic reasoning during image inpainting. While the encoder-decoder network is used for guaranteeing the pixel-level content consistency, our generative priors are favorable for performing high-level semantic reasoning, which is particularly effective for inferring semantic content for large corrupted area. Extensive experiments on Paris Street View, CelebA-HQ, and Places2 benchmarks demonstrate that our GM-SRM outperforms the state-of-the-art methods for image inpainting in terms of both visual quality and quantitative metrics.
An Enhanced Handwritten Digit Recognition Using Convolutional Neural Network

Handwritten digit recognition have great impact in the applications of deep learning. Convolutional Neural Network in the deep learning has become one of the major methods and one of the important factors in the various success in recent times and deep learning is used majorly in the area of object recognition. In the paper work, the speech output feature is integrated along with the text output. Convolutional Neural Network model is applied in the image classification. The dataset used to train and test is the MNIST dataset. There are various applications of handwritten digit recognition in the real time. It is applied in detection of vehicle number, reading of bank cheques, the arrangement of letters in the post office.
Performance Analysis of Sigmoid and Relu Activation Functions in Deep Neural Network

Now in the modern era of technology, everyone wants to get accurate and relevant results within a minimum period from a system. At the time of deciding to develop a deep neural network for generating the desired result, the model must be robust and efficient. This manuscript contains an original research work where two activations, namely sigmoid and Relu, are critically analyzed and studied for understanding the comparative performance of these two activations-based models. There are three deep learning libraries used for loading and preprocessing datasets such as Keras, NumPy, and Scikit-learn. The most popular MNIST dataset is used in this model. Further, the loaded dataset is divided into two parts for training and testing, and it has been done with a total number of ten iterations. The defined model is a multiclass network model having ten output classes, so that softmax activation is used in the output layer, which performs well with multiclass classification.
An Efficient Full Hardware Implementation of Extended Merkle Signature Scheme

This paper presents a full hardware implementation of the eXtended Merkle Signature Scheme (XMSS), a NIST approved and IETF RFC specified post-quantum cryptography (PQC) algorithm. An optimized node traversal is proposed to enable efficient memory utilization without compromising the computational latency of the L-tree and Merkle tree construction, which are two key components used for the compression of the Winternitz One-Time Signature (WOTS) public key in XMSS. The computation of the authentication path during signature generation has also been significantly sped up by our proposed hardware implementation of the Buchmann, Dahmen, and Schneider (BDS) algorithm. Our implementation has completely avoided the use of block random-access memory, which is known to be vulnerable to side-channel attacks. The memory requirement has been highly optimized for implementation with small flip-flop chains and register counters as pointers for fast data access. To the best of our knowledge, this is the first full hardware implementation of all three <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">key generation</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signing</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">verification</i> operations of XMSS. The design has been prototyped and evaluated on a 28 nm FPGA platform to demonstrate its performance improvements over the most efficient software and hardware/software co-design methods reported to date. Specifically, it increases the computational efficiency of the best reported XMSS implementation for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">key generation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signature generation</i> by about 20% and 50%, respectively. It can also run at 10% higher clock speed than the fastest hardware implementation of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signature verification</i> in FPGA with 8% lower hardware resource utilization.
Robotic Lever Manipulation using Hindsight Experience Replay and Shapley Additive Explanations

This paper deals with robotic lever control using Explainable Deep Reinforcement Learning. First, we train a policy by using the Deep Deterministic Policy Gradient algorithm and the Hindsight Experience Replay technique, where the goal is to control a robotic manipulator to manipulate a lever. This enables us both to use continuous states and actions and to learn with sparse rewards. Being able to learn from sparse rewards is especially desirable for Deep Reinforcement Learning because designing a reward function for complex tasks such as this is challenging. We first train in the PyBullet simulator, which accelerates the training procedure, but is not accurate on this task compared to the real-world environment. After completing the training in PyBullet, we further train in the Gazebo simulator, which runs more slowly than PyBullet, but is more accurate on this task. We then transfer the policy to the real-world environment, where it achieves comparable performance to the simulated environments for most episodes. To explain the decisions of the policy we use the SHAP method to create an explanation model based on the episodes done in the real-world environment. This gives us some results that agree with intuition, and some that do not. We also question whether the independence assumption made when approximating the SHAP values influences the accuracy of these values for a system such as this, where there are some correlations between the states.
A novel and efficient index based web service discovery approach

Nowadays, with the expansion of the number of web services, the discovery and the selection of the latter are becoming complex tasks. To address this issue, different methods (indexing, clustering, database etc.) have been proposed in the literature so far. In this paper, we deal with index based methods to manage web service repositories. Indeed, using indexes perspective in web services discovery can help to promote the web service composition. Within this context, we propose a novel index method named Multi Level Sorted Index (MLSI) to improve the run time of the discovery process. The implementation and the simulation of our method have shown the efficiency of our approach in comparison with the inverted index and Multilevel Index Model (MLIM) in term of retrieval and addition times.
Learning Visual Models Using a Knowledge Graph as a Trainer

Traditional computer vision approaches, based on neural networks (NN), are typically trained on a large amount of image data. By minimizing the cross-entropy loss between a prediction and a given class label, the NN and its visual embedding space are learned to fulfill a given task. However, due to the sole dependence on the image data distribution of the training domain, these models tend to fail when applied to a target domain that differs from their source domain. To learn a more robust NN to domain shifts, we propose the knowledge graph neural network (KG-NN), a neuro-symbolic approach that supervises the training using image-data-invariant auxiliary knowledge. The auxiliary knowledge is first encoded in a knowledge graph with respective concepts and their relationships, which is then transformed into a dense vector representation via an embedding method. Using a contrastive loss function, KG-NN learns to adapt its visual embedding space and thus its weights according to the image-data invariant knowledge graph embedding space. We evaluate KG-NN on visual transfer learning tasks for classification using the mini-ImageNet dataset and its derivatives, as well as road sign recognition datasets from Germany and China. The results show that a visual model trained with a knowledge graph as a trainer outperforms a model trained with cross-entropy in all experiments, in particular when the domain gap increases. Besides better performance and stronger robustness to domain shifts, these KG-NN adapts to multiple datasets and classes without suffering heavily from catastrophic forgetting.
GeoCol: A Geo-distributed Cloud Storage System with Low Cost and Latency using Reinforcement Learning

More and more web applications are deployed on the cloud storage services that store data objects of the web applications in the geo-distributed datacenters belonging to Cloud Service Providers (CSPs). In order to provide low request latency to the web application users, in the previous work, the web application developers need to store more data object replicas in a large number of datacenters or send redundant requests to multiple datacenters (e.g., closest datacenters), both of which increase monetary cost. In this paper, we conducted request latency measurement from a GENI server (as a client) to AWS S3 datacenters for one month, and our observations lay the foundation for our proposed system called GeoCol, a geo-distributed cloud storage system with low cost and latency using reinforcement learning (RL). To achieve the optimal tradeoff between the monetary cost and the request latency, GeoCol encompasses a request split method and a storage planning method. The request split method uses the SARIMA machine learning (ML) technique to predict the request latency as an input to an RL model to determine the number of sub-requests and the datacenter for each sub-request for a request in order to enable the parallel transmissions for a data object. In the storage planning method, each datacenter uses RL to determine whether each data object should be stored and the storage type of each stored data object. Our trace-driven experiment on AWS S3 and GENI platform shows that GeoCol outperforms other comparison methods in monetary cost with 32 % reduction and data object request latency with 51 % reduction.
Temporal Group Fusion Network for Deep Video Inpainting

Video inpainting is a task of synthesizing spatio-temporal coherent content in missing regions of the given video sequence, which has recently drawn increasing attention. To utilize the temporal information across frames, most recent deep learning-based methods align reference frames to target frame firstly with explicit or implicit motion estimation and then integrate the information from the aligned frames. However, their performance relies heavily on the accuracy of frame-to-frame alignment. To alleviate the above problem, in this paper, a novel Temporal Group Fusion Network (TGF-Net) is proposed to effectively integrate temporal information through a two-stage fusion strategy. Specifically, the input frames are reorganized into different groups, where each group is followed by an intra-group fusion module to integrate information within the group. Different groups provide complementary information for the missing region. A temporal attention model is further designed to adaptively integrate the information across groups. Such a temporal information fusion way gets rid of the dependence on alignment operations, greatly improving the visual quality and temporal consistency of the inpainted results. In addition, a coarse alignment model is introduced at the beginning of the network to handle videos with large motion. Extensive experiments on DAVIS and Youtube-VOS datasets demonstrate the superiority of our proposed method in terms of PSNR/SSIM values, visual quality and temporal consistency, respectively.
Sampling Subgraph Network With Application to Graph Classification

Graphs are naturally used to describe the structures of various real-world systems in biology, society, computer science etc., where subgraphs or motifs as basic blocks play an important role in function expression and information processing. However, existing research focuses on the basic statistics of certain motifs, largely ignoring the connection patterns among them. Recently, a subgraph network (SGN) model is proposed to study the potential structure among motifs, and it was found that the integration of SGN can enhance a series of graph classification methods. However, SGN model lacks diversity and is of quite high time complexity, making it difficult to widely apply in practice. In this paper, we introduce sampling strategies into SGN, and design a novel sampling subgraph network model, which is scale-controllable and of higher diversity. We also present a structural feature fusion framework to integrate the structural features of diverse sampling SGNs, so as to improve the performance of graph classification. Extensive experiments demonstrate that, by comparing with the SGN model, our new model indeed has much lower time complexity (reduced by two orders of magnitude) and can enhance a series of graph classification methods.
Multi-Type-TD-TSR – Extracting Tables from Document Images Using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: From OCR to Structured Table Representations

As global trends are shifting towards data-driven industries, the demand for automated algorithms that can convert images of scanned documents into machine readable information is rapidly growing. In addition to digitization there is an improvement toward process automation that used to require manual inspection of documents. Although optical character recognition (OCR) technologies mostly solved the task of converting human-readable characters from images, the task of extracting tables has been less focused on. This recognition consists of two sub-tasks: table detection and table structure recognition. Most prior work on this problem focuses on either task without offering an end-to-end solution or paying attention to real application conditions like rotated images or noise artefacts. Recent work shows a clear trend towards deep learning using transfer learning for table structure recognition due to the lack of sufficiently large datasets. We present a multistage pipeline named Multi-Type-TD-TSR, which offers an end-to-end solution for table recognition. It utilizes state-of-the-art deep learning models and differentiates between three types of tables based on their borders. For the table structure recognition we use a deterministic non-data driven algorithm, which works on all three types. In addition, we present an algorithm for non-bordered tables and one for bordered ones as the basis of our table structure detection algorithm. We evaluate Multi-Type-TD-TSR on a self annotated subset of the ICDAR 2019 table structure recognition dataset [] and achieve a new state-of-the-art. Source code is available under https://github.com/Psarpei/Multi-Type-TD-TSR.
Utilization and Energy Consumption Optimization for Cloud Computing Environment

In a cloud environment, the workload that has to be maintained using visualization is limited by the available hardware resources of virtual machines (VMs). So utilization of VMs becomes significant to do more work with lesser infrastructure. Thus, in recent times, major thrust was shown by researchers in the field of task allocation algorithms on VMs. There are many techniques discussed in the literature, which uses different allocation methods, which can improve the performance by changing the working of cloud environment. In this research work, analysis, implementation and performance comparison of the existing allocation techniques have been performed using CloudSim. So performance tuning is being done analytically and practically for the task allocation algorithm. VMs and cloudlets are configured for experimental purposes and parameter results are obtained. Parameters recorded are execution time, makespan, utilization ratio and power consumption. A new algorithm is proposed for task allocation algorithm (Tiwari et al in Int J Adv Intell Syst Comput, 2016 (Tiwari and Kumar in Telecommun. Syst. 62:149–165, 2016)). These parameters are calculated for FCFS, SJF, Hungarian and the proposed algorithm. Then, result analysis is done and majorly got a speedup in utilization ratio of the proposed algorithm w.r.t. to FCFS as 53.20%, 18.08% w.r.t. to SJF and 10.52% w.r.t. to Hungarian. For power consumption, the algorithm has shown a significant decrease in power consumption from 37.21, 16.52 and 10.52% w.r.t. to FCFS, SJF and Hungarian algorithms.
Detrital gold as an indicator mineral

Abstract Detrital gold fulfils the criteria of chemical inertia and physical durability required by indicator minerals but it has not found wide application in this role because it may be formed in different deposit types. This problem is soluble, because the generic compositional features of hydrothermal gold differ according to mineralization environment. The wide distribution of gold as a minor component of mineralization where other commodities are the principal exploration target extends the potential of an indicator methodology based on detrital gold to beyond the search for gold itself. Here we highlight how distinctive gold compositional signatures derived from alloy composition and deposit- specific suites of mineral inclusions could contribute to exploration for Cu–Au porphyries, redox-controlled uranium mineralization and ultramafic-hosted PGE mineralization. Future refinement this approach will focus on establishing the spatial distribution of elements at trace levels within gold particle sections using ToF-LA-ICP-MS and application of Exploratory Data Analysis to the resulting datasets. This approach is in its infancy, but aims to develop a classification algorithm useful to researchers irrespective of their previous experience. A pilot study has shown that random forests provide the best approach to establishing gold particle origins.
Positive-Unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations

Cell detection in histopathology images is of great value in clinical practice. Convolutional neural networks (CNNs) have been applied to cell detection to improve the detection accuracy, where cell annotations are required for network training. However, due to the variety and large number of cells, complete annotations that include every cell of interest in the training images can be challenging. Usually, incomplete annotations can be achieved, where positive labeling results are carefully examined to ensure their reliability but there can be other positive instances, i.e., cells of interest, that are not included in the annotations. This annotation strategy leads to a lack of knowledge about true negative samples. Most existing methods simply treat instances that are not labeled as positive as truly negative during network training, which can adversely affect the network performance. In this work, to address the problem of incomplete annotations, we formulate the training of detection networks as a positive-unlabeled learning problem. Specifically, the classification loss in network training is revised to take into account incomplete annotations, where the terms corresponding to negative samples are approximated with the true positive samples and the other samples of which the labels are unknown. To evaluate the proposed method, experiments were performed on a publicly available dataset for mitosis detection in breast cancer cells, and the experimental results show that our method improves the performance of cell detection given incomplete annotations for training.