Reviewerly/exHarmony huggingface dataset:
Automated demarcation of requirements in textual specifications: a machine learning-based approach

Abstract A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable writing and markup conventions, one can provide an immediate and unequivocal demarcation of requirements at the time a specification is being developed. However, neither the presence nor a fully accurate enforcement of such conventions is guaranteed. The result is that, in many practical situations, analysts end up resorting to after-the-fact reviews for sifting requirements from other material in a requirements specification. This is both tedious and time-consuming. We propose an automated approach for demarcating requirements in free-form requirements specifications. The approach, which is based on machine learning, can be applied to a wide variety of specifications in different domains and with different writing styles. We train and evaluate our approach over an independently labeled dataset comprised of 33 industrial requirements specifications. Over this dataset, our approach yields an average precision of 81.2% and an average recall of 95.7%. Compared to simple baselines that demarcate requirements based on the presence of modal verbs and identifiers, our approach leads to an average gain of 16.4% in precision and 25.5% in recall. We collect and analyze expert feedback on the demarcations produced by our approach for industrial requirements specifications. The results indicate that experts find our approach useful and efficient in practice. We developed a prototype tool, named DemaRQ, in support of our approach. To facilitate replication, we make available to the research community this prototype tool alongside the non-proprietary portion of our training data.
Attention-based interpolation network for video deblurring

Video deblurring is a challenging low-level vision task due to variant blur artifacts caused by factors such as depth variations, high-speed movements and camera shakes. Although significant efforts have been devoted to addressing this task, two challenges of capturing temporal patterns and spatial topologies still remain. In this paper, an attention-based interframe compensation scheme is proposed to address the first challenge. The proposed scheme replaces frames in blurry sequences with newly restored frames, and estimates temporal patterns among the replaced sequence to restore the whole sequence. After each replacement, an attention block is employed to exploit dependencies among restored and blurry frames to capture stable temporal patterns. To tackle the second challenge, we propose an adaptive residual block that dynamically fuses multi-level features via learning location-specific weights. Comprehensive experimental results demonstrate that the proposed method achieves state-of-the-art performance in terms of accuracy, visual effect and model size.
Item Response Theory Based Ensemble in Machine Learning

In this article, we propose a novel probabilistic framework to improve the accuracy of a weighted majority voting algorithm. In order to assign higher weights to the classifiers which can correctly classify hard-to-classify instances, we introduce the item response theory (IRT) framework to evaluate the samples' difficulty and classifiers' ability simultaneously. We assigned the weights to classifiers based on their abilities. Three models are created with different assumptions suitable for different cases. When making an inference, we keep a balance between the accuracy and complexity. In our experiment, all the base models are constructed by single trees via bootstrap. To explain the models, we illustrate how the IRT ensemble model constructs the classifying boundary. We also compare their performance with other widely used methods and show that our model performs well on 19 datasets.
Medicare Fraud Detection using CatBoost

In this study we investigate the performance of CatBoost in the task of identifying Medicare fraud. The Medicare claims data we use as input for CatBoost contain a number of categorical features. Some of these features, such as the procedure code and provider zip code, have thousands of possible values. One contribution we make in this study is to show how we use CatBoost to eliminate some data pre-processing steps that authors of related works take. A second contribution we make is to show improvements in CatBoost's performance in terms of Area Under the Receiver Operating Characteristic Curve (AUC), when we include another one of the categorical features (provider state) as input to CatBoost. We show that CatBoost attains better performance than XGBoost in the task of Medicare fraud detection with respect to the AUC metric. At a 99% confidence level (with p-value 0) our experiments show that XGBoost obtains a mean AUC value of 0.7615 while CatBoost obtains a mean AUC value of 0.7851, validating the significance of CatBoost's performance improvement over XGBoost. Moreover, when we include an additional categorical feature (healthcare provider state) in our data analysis, CatBoost yields a mean AUC value of 0.8902, which is also statistically signficant at a 99% confidence interval level (with p-value 0). Our empirical evidence clearly indicates CatBoost is a better alternative to XGBoost for Medicare fraud detection, especially when dealing with categorical features.
SAQE

A private data federation enables clients to query the union of data from multiple data providers without revealing any extra private information to the client or any other data providers. Unfortunately, this strong end-to-end privacy guarantee requires cryptographic protocols that incur a significant performance overhead as high as 1,000 x compared to executing the same query in the clear. As a result, private data federations are impractical for common database workloads. This gap reveals the following key challenge in a private data federation: offering significantly fast and accurate query answers without compromising strong end-to-end privacy. To address this challenge, we propose SAQE, the Secure Approximate Query Evaluator, a private data federation system that scales to very large datasets by combining three techniques --- differential privacy, secure computation, and approximate query processing --- in a novel and principled way. First, SAQE adds novel secure sampling algorithms into the federation's query processing pipeline to speed up query workloads and to minimize the noise the system must inject into the query results to protect the privacy of the data. Second, we introduce a query planner that jointly optimizes the noise introduced by differential privacy with the sampling rates and resulting error bounds owing to approximate query processing. Our research shows that these three techniques are synergistic: sampling within certain accuracy bounds improves both query privacy and performance, meaning that SAQE executes over less data than existing techniques without sacrificing efficiency, privacy, or accuracy. Using our optimizer, we leverage this counter-intuitive result to identify an inflection point that maximizes all three criteria prior query evaluation. Experimentally, we show that this result enables SAQE to trade-off among these three criteria to scale its query processing to very large datasets with accuracy bounds dependent only on sample size, and not the raw data size.
Hellinger Net: A Hybrid Imbalance Learning Model to Improve Software Defect Prediction

Software defect prediction (SDP) is a convenient way to identify defects in the early phases of the software development life cycle. This early warning system can help in the removal of software defects and yield a cost-effective and good quality of software products. A wide range of statistical and machine learning models have been employed to predict defects in software modules. But the imbalanced nature of this type of SDP datasets is pivotal for the successful development of a defect prediction model. Imbalanced software datasets contain nonuniform class distributions with a few instances belonging to a specific class compared to that of the other class. This article proposes a novel hybrid methodology, namely the Hellinger net model, for imbalanced learning to improve defect prediction for software modules. Hellinger net, a tree to network mapped model, is a deep feedforward neural network with a built-in hierarchy, just like decision trees. Hellinger net also utilizes the strength of a skew insensitive distance measure, namely Hellinger distance, in handling class imbalance problems. On the theoretical side, this article proves the theoretical consistency of the proposed model. A thorough experiment was conducted over ten NASA SDP datasets to show the superiority of the proposed method.
Offline recognition of handwritten Indic scripts: A state-of-the-art survey and future perspectives

The handwritten script recognition is one of the most interesting and challenging areas of pattern recognition due to numerous variations in writing styles. Extensive in-depth research work is reported on the recognition of handwritten text in scripts such as Latin, Chinese, Arabic and Japanese. However, the work reported on handwritten Indic scripts is still in its infancy, so significant research is required in this field. This paper aims to describe various advancements reported over the last few decades in the field of handwritten Indic scripts recognition by analysing several existing state-of-the-art studies. This comprehensive survey presents a transparent panorama of various feature extraction and classification techniques for the offline recognition of handwritten Indic scripts. The most important part of this survey is to systematically present the reported works on handwritten Indic scripts like Devanagari, Bengali, Gurumukhi, Kannada, Telugu, Gujarati, Oriya, Tamil and Malayalam. After exploring the reported works, an analysis is performed based on the findings. Several issues and challenges related to the recognition of Indic scripts are discussed, which indicates some future research prospects. Based on the extensive study conducted in this article, it has been contemplated that there is a need to develop hybrid feature extraction and classification approaches for achieving the most accurate results. So, a novel framework based on improved particle swarm optimization (PSO) algorithm to automatically construct optimal convolutional neural network (CNN) architecture has been proposed with an aim to outperform the existing techniques.
A survey on analysis and implementation of state-of-the-art haze removal techniques

Haze is a poor-quality state described by the opalescent appearance of the atmosphere which reduces the visibility. It is caused by high concentrations of atmospheric air pollutants, such as dust, smoke and other particles that scatter and absorb sunlight. The poor visibility can result in the failure of multiple computer vision applications such as smart transport systems, image processing, object detection, surveillance etc. One of the major issues in the field of image processing is the restoration of images that are corrupted due to different degradations. Typically, the images or videos captured in the outside environment have low contrast, colour fade and restricted visibility due to suspended particles of the atmosphere that directly influence the image quality. This can cause difficulty in identifying the objects in the captured hazy images or frames. To address this problem, several image dehazing techniques have been developed in the literature, each of which has its own advantages and limitations, but effective image restoration remains a challenging task. In recent times, various learning (Machine learning & Deep learning) based methods greatly condensed the drawbacks of manual design of haze related features and reduces the difficulty in efficient restoration of images with less computational time and cost. The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey. Moreover, this paper systematically summarizes the hardware implementations of various haze removal methods in real time. It is with the hope that this current survey acts as a reference for researchers in this scientific area and to provide a direction for future improvements based on current achievements.
Cold Start in Serverless Computing: Current Trends and Mitigation Strategies

Serverless Computing is the latest cloud computing model, which facilitates application development. By adopting and leveraging the modern paradigm of Serverless Computing, developers do not need to manage the servers. In this computational model, the executables are independent functions that are individually deployed on a Serverless platform offering instant per-request elasticity. Such elasticity typically comes at the cost of the “Cold Starts” problem. This phenomenon is associated with a delay occurring due to provision a runtime container to execute the functions. Shortly after Amazon introduced this computing model with the AWS Lambda platform in 2014, several open source and commercial platforms also started embracing and offering this technology. Each platform has its own solution to deal with Cold Starts. The evaluation of the performance of each platform under the load and factors influencing the cold start problem has received much attention over the past few years. This paper provides a comprehensive overview on the recent advancements and state-of-the-art works in mitigating the cold start delay. Moreover, several sets of experiments have been performed to study the behavior of the AWS Lambda as the base platform with respect to the cold start delay.
Adaptive Deep Code Search

Searching code in a large-scale codebase using natural language queries is a common practice during software development. Deep learning-based code search methods demonstrate superior performance if models are trained with large amount of text-code pairs. However, few deep code search models can be easily transferred from one codebase to another. It can be very costly to prepare training data for a new codebase and re-train an appropriate deep learning model. In this paper, we propose AdaCS, an adaptive deep code search method that can be trained once and transferred to new codebases. AdaCS decomposes the learning process into embedding domain-specific words and matching general syntactic patterns. Firstly, an unsupervised word embedding technique is used to construct a matching matrix to represent the lexical similarities. Then, a recurrent neural network is used to capture latent syntactic patterns from these matching matrices in a supervised way. As the supervised task learns general syntactic patterns that exist across domains, AdaCS is transferable to new codebases. Experimental results show that: when extended to new software projects never seen in the training data, AdaCS is more robust and significantly outperforms state-of-the-art deep code search methods.
Super-Resolution of Sentinel-2 Images Using Convolutional Neural Networks and Real Ground Truth Data

Earth observation data is becoming more accessible and affordable thanks to the Copernicus programme and its Sentinel missions. Every location worldwide can be freely monitored approximately every 5 days using the multi-spectral images provided by Sentinel-2. The spatial resolution of these images for RGBN (RGB + Near-infrared) bands is 10 m, which is more than enough for many tasks but falls short for many others. For this reason, if their spatial resolution could be enhanced without additional costs, any posterior analyses based on these images would be benefited. Previous works have mainly focused on increasing the resolution of lower resolution bands of Sentinel-2 (20 m and 60 m) to 10 m resolution. In these cases, super-resolution is supported by bands captured at finer resolutions (RGBN at 10 m). On the contrary, this paper focuses on the problem of increasing the spatial resolution of 10 m bands to either 5 m or 2.5 m resolutions, without having additional information available. This problem is known as single-image super-resolution. For standard images, deep learning techniques have become the de facto standard to learn the mapping from lower to higher resolution images due to their learning capacity. However, super-resolution models learned for standard images do not work well with satellite images and hence, a specific model for this problem needs to be learned. The main challenge that this paper aims to solve is how to train a super-resolution model for Sentinel-2 images when no ground truth exists (Sentinel-2 images at 5 m or 2.5 m). Our proposal consists of using a reference satellite with a high similarity in terms of spectral bands with respect to Sentinel-2, but with higher spatial resolution, to create image pairs at both the source and target resolutions. This way, we can train a state-of-the-art Convolutional Neural Network to recover details not present in the original RGBN bands. An exhaustive experimental study is carried out to validate our proposal, including a comparison with the most extended strategy for super-resolving Sentinel-2, which consists in learning a model to super-resolve from an under-sampled version at either 40 m or 20 m to the original 10 m resolution and then, applying this model to super-resolve from 10 m to 5 m or 2.5 m. Finally, we will also show that the spectral radiometry of the native bands is maintained when super-resolving images, in such a way that they can be used for any subsequent processing as if they were images acquired by Sentinel-2.
GraphAn

Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the state-of-the-art approaches have severe limitations: they either require prior domain knowledge, or become cumbersome and inefficient/ineffective in situations with recurrent anomalies of the same type. We recently proposed Series2Graph, a novel method based on a graph representation of a low-dimensionality embedding of subsequences, that detects anomalous subsequences. The experimental results, on the largest set of synthetic and real datasets used to date, demonstrate that the proposed approach correctly identifies single and recurrent anomalies of various types without any prior knowledge of the characteristics of these anomalies, outperforming by a large margin several competing approaches in accuracy, while being up to orders of magnitude faster. In this demonstration, we present GraphAn, a system based on Series2Graph, show-case the challenges of the problem, and demonstrate the advantages of the proposed system.
Supersingular Isogeny Key Encapsulation (SIKE) Round 2 on ARM Cortex-M4

We present the first practical software implementation of Supersingular Isogeny Key Encapsulation (SIKE) round 2, targeting NIST's 1, 2, 3, and 5 security levels on 32-bit ARM Cortex-M4 microcontrollers. The proposed library introduces a new speed record of all SIKE Round 2 protocols with reasonable memory consumption on the low-end target platform. We achieved this record by adopting several state-of-the-art engineering techniques as well as highly-optimized hand-crafted assembly implementation of finite field arithmetic. In particular, we carefully redesign the previous optimized implementations of finite field arithmetic on the 32-bit ARM Cortex-M4 platform and propose a set of novel techniques which are explicitly suitable for SIKE primes. The benchmark result on STM32F4 Discovery board equipped with 32-bit ARM Cortex-M4 microcontrollers shows that entire key encapsulation and decapsultation over SIKEp434 take about 184 million clock cycles (i.e., 1.09 seconds @168 MHz). In contrast to the previous optimized implementation of the isogeny-based key exchange on low-end 32-bit ARM Cortex-M4, our performance evaluation shows feasibility of using SIKE mechanism on the low-end platform. In comparison to the most of the post-quantum candidates, SIKE requires an excessive number of arithmetic operations, resulting in significantly slower timings. However, its small key size makes this scheme as a promising candidate on low-end microcontrollers in the quantum era by ensuring the lower energy consumption for key transmission than other schemes.
Sentiment Analysis based on GloVe and LSTM-GRU

Sentiment analysis technology generates linguistic understanding from the perspective of machines through the processing and analysis of large quantities of data, which is a hot research direction in the field of artificial intelligence. In this work, deep learning is used to build a sentence sentiment analysis system based on word vector space model and Recurrent Neural Network. Specifically, the GloVe word model is adopted here with the aim of making the best use of the global and local information for the training corpus,. In order to overcome the defect that traditional recurrent neural network cannot learn the long-term information of text, a neural network model combining LSTM and GRU is proposed in this work. Through the contrast experiment of RNN, LSTM and GRU, comparing the training time and test accuracy indicators, it is found that LSTM-GRU model performs best.
Multi-core sine cosine optimization: Methods and inclusive analysis

The Sine Cosine Algorithm (SCA) is a popular population-based optimization method, which has shown competitive results compared to other algorithms, and it has been utilized to tackle optimization cases in various domains. Despite popularity, the initial SCA suffers from minimalistic originality, mediocre performance, and shallow mathematical model. In fact, there is undoubtedly room for improvement in the structure of original SCA because it may face problems of lazy convergence and inertia to local optima. To relieve these drawbacks, this paper develops a new multi-core SCA named SGLSCA, which is combined with three strategies based on the patterns of Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), and Levy flight (LF). Based on introducing the updating strategy of SSA and GWO, it is proposed to strengthen the exploration aptitude of the conventional SCA. Also, the SSA updating strategy aims to further update the population based on the best solution of SCA, while the GWO updating plan helps using the top three solutions of SCA. Also, the LF strategy is embedded to achieve the random individual walk during the history of the exploration and further augment the competence of SCA to avoid local optimal solutions. To substantiate the structure and results of the proposed multi-core SCA, which is entitled SGLSCA, it is compared against nine state-of-art algorithms, six improved SCA variants, and nine successful advanced algorithms on 34 benchmark functions selected from 23 benchmark functions and 30 IEEE CEC 2014 benchmark problems. Additionally, three practical, real-world engineering problems are considered. The final experimental results expose that the multi-core SGLSCA outperforms other optimizers including LSHADE-cnEpSin and LSHADE methods in terms of both convergence and optimality of solutions. A public repository will support this research at http://aliasgharheidari.com for future works and possible guidance.