Reviewerly/exHarmony huggingface dataset:
Agile Project Management Challenges and Mapping Solutions

The Project Management Institute reported that the Agile approach is widely being used for project management practices. This approach has a significant impact on business growth and project performance. However, its implementation is challenging. Therefore, a systematic literature review (SLR) is used to reveal the challenges faced in Agile project execution. The Project Management Body of Knowledge (PMBOK) knowledge areas were adopted to classify the challenges. A total of 23 papers from 400 were identified as the result of SLR extraction. The challenges from related studies were categorized into the PMBOK knowledge areas. A mapping from the challenges to the solutions was performed using the PMBOK Guide, Prince2 Agile, Agile Practice Guide, and other related references. This study provides a list of Agile challenges and their mapped solutions. The biggest challenge arises from stakeholder management, which includes challenges related to Agile adaption, Agile transition, and Agile transformation. Other challenges include project resource management, project integration management, project scope management, and project schedule management. For academicians, this study provides a new understanding of Agile challenges and their suitable solutions from the perspective of project management. For practitioners, the findings provide potential lessons learned and recommendations to deal with the challenges.
Evaluating sentence representations for biomedical text: Methods and experimental results

Text representations ar one of the main inputs to various Natural Language Processing (NLP) methods. Given the fast developmental pace of new sentence embedding methods, we argue that there is a need for a unified methodology to assess these different techniques in the biomedical domain. This work introduces a comprehensive evaluation of novel methods across ten medical classification tasks. The tasks cover a variety of BioNLP problems such as semantic similarity, question answering, citation sentiment analysis and others with binary and multi-class datasets. Our goal is to assess the transferability of different sentence representation schemes to the medical and clinical domain. Our analysis shows that embeddings based on Language Models which account for the context-dependent nature of words, usually outperform others in terms of performance. Nonetheless, there is no single embedding model that perfectly represents biomedical and clinical texts with consistent performance across all tasks. This illustrates the need for a more suitable bio-encoder. Our MedSentEval source code, pre-trained embeddings and examples have been made available on GitHub.
Capability-Based Access Control for the Internet of Things: An Ethereum Blockchain-Based Scheme

The large-scale and trustless nature of the Internet of Things (IoT) calls for distributed and trustworthy access control schemes to prevent unauthorized resource access. This paper proposes a Capability-Based Access Control (CapBAC) scheme by applying the emerging Ethereum blockchain technology. This scheme uses Ethereum smart contracts, i.e., executable codes residing in the blockchain, to store and manage the capability tokens, i.e., special data structures that maintain the allowed actions of a user (i.e., subject) on a certain resource (i.e., object). To provide more fine-grained access control and more flexible token management, this scheme defines capability tokens in units of actions, i.e., by dividing a conventional capability token containing multiple actions into multiple ones with each being associated with a certain action. In addition, this scheme uses a delegation graph instead of the delegation tree in existing smart contract-based CapBAC schemes to store the token delegation relationship among the subjects. By storing the tokens and the delegation graph in smart contracts, this scheme allows object owners to verify the ownership and validity of the capability tokens of the subjects. To demonstrate the feasibility of the scheme, we constructed a local Ethereum blockchain network and conducted extensive experiments.
Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration With Application to Autonomous Sequential Repair Problems

In this letter we consider infinite horizon discounted dynamic programming problems with finite state and control spaces, and partial state observations. We discuss an algorithm that uses multistep lookahead, truncated rollout with a known base policy, and a terminal cost function approximation. This algorithm is also used for policy improvement in an approximate policy iteration scheme, where successive policies are approximated by using a neural network classifier. A novel feature of our approach is that it is well suited for distributed computation through an extended belief space formulation and the use of a partitioned architecture, which is trained with multiple neural networks. We apply our methods in simulation to a class of sequential repair problems where a robot inspects and repairs a pipeline with potentially several rupture sites under partial information about the state of the pipeline.
On the Convergence of Adam and Adagrad

We provide a simple proof of the convergence of the optimization algorithms Adam and Adagrad with the assumptions of smooth gradients and almost sure uniform bound on the $\ell_\infty$ norm of the gradients. This work builds on the techniques introduced by Ward et al. (2019) and extends them to the Adam optimizer. We show that in expectation, the squared norm of the objective gradient averaged over the trajectory has an upper-bound which is explicit in the constants of the problem, parameters of the optimizer and the total number of iterations N. This bound can be made arbitrarily small. In particular, Adam with a learning rate $\alpha=1/\sqrt{N}$ and a momentum parameter on squared gradients $\beta_2=1 - 1/N$ achieves the same rate of convergence $O(\ln(N)/\sqrt{N})$ as Adagrad. Thus, it is possible to use Adam as a finite horizon version of Adagrad, much like constant step size SGD can be used instead of its asymptotically converging decaying step size version.
Real-time Visual Object Tracking with Natural Language Description

In this work, we argue that conditioning on the natural language (NL) description of a target provides information for longer-term invariance, and thus helps cope with typical tracking challenges. However, deriving a formulation to combine the strengths of appearance-based tracking with the language modality is not straightforward. Therefore, we propose a novel deep tracking-by-detection formulation that can take advantage of NL descriptions. Regions that are related to the given NL description are generated by a proposal network during the detection stage of the tracker. Our LSTM based tracker then predicts the update of the target from regions proposed by the NL based detection stage. Our method runs at over 30 fps on a single GPU. In benchmarks, our method is competitive with state of the art trackers that employ bounding boxes for initialization, while it outperforms all other trackers on targets given unambiguous and precise language annotations. When conditioned on NL descriptions only, our model doubles the performance of the previous best attempt [25].
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder

Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset of the object segmentation and less handled in this field. Obviously, portrait segmentation has its unique requirements. First, because the portrait segmentation is performed in the middle of a whole process, it requires extremely lightweight models. Second, there has not been any public datasets in this domain that contain a sufficient number of images. To solve the first problem, we introduce the new extremely lightweight portrait segmentation model SINet, containing an information blocking decoder and spatial squeeze modules. The information blocking decoder uses confidence estimation to recover local spatial information without spoiling global consistency. The spatial squeeze module uses multiple receptive fields to cope with various sizes of consistency. To tackle the second problem, we propose a simple method to create additional portrait segmentation data, which can improve accuracy. In our qualitative and quantitative analysis on the EG1800 dataset, we show that our method outperforms various existing lightweight models. Our method reduces the number of parameters from 2.1M to 86.9K (around 95.9% reduction), while maintaining the accuracy under an 1% margin from the state-of-the-art method. We also show our model is successfully executed on a real mobile device with 100.6 FPS. In addition, we demonstrate that our method can be used for general semantic segmentation on the Cityscapes dataset. The code and dataset are available in https://github.com/HYOJINPARK/ExtPortraitSeg.
HE-Friendly Algorithm for Privacy-Preserving SVM Training

Support vector machine (SVM) is one of the most popular machine learning algorithms. It predicts a pre-defined output variable in real-world applications. Machine learning on encrypted data is becoming more and more important to protect both model information and data against various adversaries. While some studies have been proposed on inference or prediction phases, few have been reported on the training phase. Homomorphic encryption (HE) for the arithmetic of approximate numbers scheme enables efficient arithmetic evaluations of encrypted data of real numbers, which encourages to develop privacy-preserving machine learning training algorithm. In this study, we propose an HE-friendly algorithm for the SVM training phase which avoids inefficient operations and numerical instability on an encrypted domain. The inference phase is also implemented on the encrypted domain with fully-homomorphic encryption which enables real-time prediction. Our experiment showed that our HE-friendly algorithm outperformed the state-of-the-art logistic regression classifier with fully homomorphic encryption on toy and real-world datasets. To the best of our knowledge, this study is the first practical algorithm for training an SVM model with fully homomorphic encryption. Therefore, our result supports the development of practical applications of the privacy-preserving SVM model.
The Silent Helper: The Impact of Continuous Integration on Code Reviews

The adoption of Continuous Integration (CI) has been shown multiple benefits for software engineering practices related to build, test and dependency management. However, the impact of CI on the social aspects of software development has been overlooked so far. Specifically, we focus on studying the impact of CI on a paradigmatic socio-technical activity within the software engineering domain, namely code reviews. Indeed, one might expect that the introduction of CI allows reviewers to focus on more challenging aspects of software quality that could not be assessed using CI. To assess validity of this expectation we conduct an exploratory study of code reviews in 685 GitHub projects that have adopted Travis-CI, the most popular CI-service on GitHub. We observe that with the introduction of CI, pull requests are being discussed less. On average CI saves up to one review comment per pull request. This decrease in amount of discussion, however, cannot be explained by the decrease in the number of updates of the pull requests. This means that in presence of CI developers perform the same amount of work by communicating less, giving rise to the idea of CI as a silent helper.
Petrofacies classification using machine learning algorithms

Carbonate reservoirs represent a large portion of the world’s oil and gas reserves, exhibiting specific characteristics that pose complex challenges to the reservoirs’ characterization, production, and management. Therefore, the evaluation of the relationships between the key parameters, such as porosity, permeability, water saturation, and pore size distribution, is a complex task considering only well-log data, due to the geologic heterogeneity. Hence, the petrophysical parameters are the key to assess the original composition and postsedimentological aspects of the carbonate reservoirs. The concept of reservoir petrofacies was proposed as a tool for the characterization and prediction of the reservoir quality as it combines primary textural analysis with laboratory measurements of porosity, permeability, capillary pressure, photomicrograph descriptions, and other techniques, which contributes to understanding the postdiagenetic events. We have adopted a workflow to petrofacies classification of a carbonate reservoir from the Campos Basin in southeastern Brazil, using the following machine learning methods: decision tree, random forest, gradient boosting, K-nearest neighbors, and naïve Bayes. The data set comprised 1477 wireline data from two wells (A3 and A10) that had petrofacies classes already assigned based on core descriptions. It was divided into two subsets, one for training and one for testing the capability of the trained models to assign petrofacies. The supervised-learning models have used labeled training data to learn the relationships between the input measurements and the petrofacies to be assigned. Additionally, we have developed a comparison of the models’ performance using the testing set according to accuracy, precision, recall, and F1-score evaluation metrics. Our approach has proved to be a valuable ally in petrofacies classification, especially for analyzing a well-logging database with no prior petrophysical information.
Reinforcement Learning-Based Tracking Control of USVs in Varying Operational Conditions

We present a reinforcement learning-based (RL) control scheme for trajectory tracking of fully-actuated surface vessels. The proposed method learns online both a model-based feedforward controller, as well an optimizing feedback policy in order to follow a desired trajectory under the influence of environmental forces. The method's efficiency is evaluated via simulations and sea trials, with the unmanned surface vehicle (USV) ReVolt performing three different tracking tasks: The four corner DP test, straight-path tracking and curved-path tracking. The results demonstrate the method's ability to accomplish the control objectives and a good agreement between the performance achieved in the Revolt Digital Twin and the sea trials. Finally, we include an section with considerations about assurance for RL-based methods and where our approach stands in terms of the main challenges.
Learning Representations for High-Dynamic-Range Image Color Transfer in a Self-Supervised Way

Reference-based color transfer between images has been a fundamental function in image editing. However, existing approaches pay less attention to high-dynamic-range (HDR) images. It is worth noting that designing an appropriate representation for HDR images to achieve satisfying color transfer is challenging. In this paper, we propose an innovative high-dynamic-range image color transfer generative adversarial network (HDRCTGAN) to encode the original image into fine representations that allow transfer of the color of the reference image to the target image. We propose to learn fine representations through a generative adversarial network (GAN) in a self-supervised way. Particularly, the proposed method is self-supervised learning that requires only unlabeled HDR images instead of supervised learning that requires lots of ground truth pairs. HDRCTGAN consists of a generator to transfer the color of the reference image to the target image over the feature domain and a discriminator to suppress the artifacts caused by the generator. We also design a loss function to ensure that HDRCTGAN possesses two required properties: (a) high fidelity and (b) self-identity. The proposed approach yields a pleasing visual result. We have carried out HDR specific evaluations including both objective quantitative experiments with HDR metrics and subjective user studies operated on HDR display devices to demonstrate the effectiveness of our method. Furthermore, we have verified the applicability of the proposed approach to several applications, such as color transfer of HDR images captured by smartphones, color transfer of fabric images, and reference-based grayscale image colorization.
Cluster-based beam search for pointer-generator chatbot grounded by knowledge

We present an end-to-end approach for knowledge-grounded response generation in Dialog System Technology Challenges 7 (DSTC7). Our system is trained by a pointer generator model, so that an output token in a response can either be generated or copied from conversation history or facts according to a trainable action probability distribution. Furthermore, to minimize generating meaningless responses, we propose using K-means to dynamically cluster and prune semantically similar partial hypotheses at each decoding step under a fixed beam budget. Moreover, we employ a language model to filter meaningless responses. Official evaluation results show that our proposed system achieved the first place in all primary automatic evaluation metrics and the overall human evaluation score.
Unsupervised Neural Machine Translation With Cross-Lingual Language Representation Agreement

Unsupervised cross-lingual language representation initialization methods such as unsupervised bilingual word embedding (UBWE) pre-training and cross-lingual masked language model (CMLM) pre-training, together with mechanisms such as denoising and back-translation, have advanced unsupervised neural machine translation (UNMT), which has achieved impressive results on several language pairs, particularly French-English and German-English. Typically, UBWE focuses on initializing the word embedding layer in the encoder and decoder of UNMT, whereas the CMLM focuses on initializing the entire encoder and decoder of UNMT. However, UBWE/CMLM training and UNMT training are independent, which makes it difficult to assess how the quality of UBWE/CMLM affects the performance of UNMT during UNMT training. In this paper, we first empirically explore relationships between UNMT and UBWE/CMLM. The empirical results demonstrate that the performance of UBWE and CMLM has a significant influence on the performance of UNMT. Motivated by this, we propose a novel UNMT structure with cross-lingual language representation agreement to capture the interaction between UBWE/CMLM and UNMT during UNMT training. Experimental results on several language pairs demonstrate that the proposed UNMT models improve significantly over the corresponding state-of-the-art UNMT baselines.
An Automated Blood Cells Counting and Classification Framework using Mask R-CNN Deep Learning Model

Bioengineering is the art of applying engineering principles, techniques, and technologies to biology and medicine for general healthcare applications. Analyzing human biological samples such as blood, has become essential for physicians to diagnose and follow diseases evolution. Traditional blood cells counting techniques used in laboratories are time consuming and laborious. They can lead to inaccurate results due to the human intervention in this complicated process. In this paper, we propose an automated blood cells counting framework using convolutional neural network (CNN), instance segmentation, transfer learning, and mask R-CNN techniques. Red and white blood cells are identified, classified, and counted from microscopic blood smear images. The obtained results reveal highly detection rate of different blood cells. In addition, unlike other state-of-the-art techniques, our proposed method has the ability to identify overlapped and faded cells.
Learning feature spaces for regression with genetic programming

Genetic programming has found recent success as a tool for learning sets of features for regression and classification. Multidimensional genetic programming is a useful variant of genetic programming for this task because it represents candidate solutions as sets of programs. These sets of programs expose additional information that can be exploited for building block identification. In this work, we discuss this architecture and others in terms of their propensity for allowing heuristic search to utilize information during the evolutionary process. We investigate methods for biasing the components of programs that are promoted in order to guide search towards useful and complementary feature spaces. We study two main approaches: (1) the introduction of new objectives and (2) the use of specialized semantic variation operators. We find that a semantic crossover operator based on stagewise regression leads to significant improvements on a set of regression problems. The inclusion of semantic crossover produces state-of-the-art results in a large benchmark study of open-source regression problems in comparison to several state-of-the-art machine learning approaches and other genetic programming frameworks. Finally, we look at the collinearity and complexity of the data representations produced by different methods, in order to assess whether relevant, concise, and independent factors of variation can be produced in application.
On Primal and Dual Approaches for Distributed Stochastic Convex Optimization over Networks

We introduce primal and dual stochastic gradient oracle methods for distributed convex optimization problems over networks. We show that the proposed methods are optimal (in terms of communication steps) for primal and dual oracles. Additionally, for a dual stochastic oracle, we propose a new analysis method for the rate of convergence in terms of duality gap and probability of large deviations. This analysis is based on a new technique that allows to bound the distance between the iteration sequence and the optimal point. By the proper choice of batch size, we can guarantee that this distance equals (up to a constant) to the distance between the starting point and the solution.
Semantically Consistent Hierarchical Text to Fashion Image Synthesis with an Enhanced-Attentional Generative Adversarial Network

In this paper, we present the enhanced Attentional Generative Adversarial Network (e-AttnGAN) with improved training stability for text-to-image synthesis. e-AttnGAN's integrated attention module utilizes both sentence and word context features and performs feature-wise linear modulation (FiLM) to fuse visual and natural language representations. In addition to multimodal similarity learning for text and image features of AttnGAN, cosine and feature matching losses of real and generated images are included while employing a classification loss for "significant attributes". In order to improve the stability of the training and solve the issue of model collapse, spectral normalization and two-time scale update for the discriminator are used together with instance noise. Our experiments show that e-AttnGAN outperforms state-of-the-art methods on the FashionGen and DeepFashion-Synthesis datasets.
Methods of Deepfake Detection Based on Machine Learning

Nowadays, people faced an emerging problem of AI-synthesized face swapping videos, widely known as the DeepFakes. This kind of videos can be created to cause threats to privacy, fraudulence and so on. Sometimes good quality DeepFake videos recognition could be hard to distinguish with people eyes. That's why researchers need to develop algorithms to detect them. In this work, we present overview of indicators that can tell us about the fact that face swapping algorithms were used on photos. Main purpose of this paper is to find algorithm or technology that can decide whether photo was changed with DeepFake technology or not with good accuracy.