Reviewerly/exHarmony huggingface dataset:
Low Error Efficient Computational Extractors in the CRS Model

In recent years, there has been exciting progress on building two-source extractors for sources with low min-entropy. Unfortunately, all known explicit constructions of two-source extractors in the low entropy regime suffer from non-negligible error, and building such extractors with negligible error remains an open problem. We investigate this problem in the computational setting, and obtain the following results. We construct an explicit 2-source extractor, and even an explicit non-malleable extractor, with negligible error, for sources with low min-entropy, under computational assumptions in the Common Random String (CRS) model. More specifically, we assume that a CRS is generated once and for all, and allow the min-entropy sources to depend on the CRS. We obtain our constructions by using the following transformations.
Improving Autonomy in GNSS-Challenging Environments by Multi-UAV Cooperation

Cooperative navigation is a promising strategy to enable safe autonomous flight in GNSS challenging environments. This paper discusses a cooperative strategy that guarantees bounded navigation error for an UAV flying in poor GNSS coverage conditions (“son”), exploiting the support of a cooperative vehicle (“father”) and the available GNSS observables. A customized path planning strategy is developed for the father UAV, which is aimed at minimizing son positioning uncertainty. In view of real time implementation, path planning is complemented by proper cooperative guidance strategies. Planning and guidance approaches are verified in simulations that take into account three-dimensional environment and multi-GNSS constellation coverage. Cooperative navigation potential is demonstrated in experiments carried out flying two quadrotors in formation. In these flight tests, GNSS-challenging conditions are simulated off-line by assuming a virtual three-dimensional environment and removing GNSS satellites that would not be in view.
Falcon — A Flexible Architecture For Accelerating Cryptography

Internet of Things (IoT) devices, once deployed, must remain secure for their entire lifetime, which can be as long as 20 years. Over this lifetime, devices must be able to update which ciphers they use to meet evolving security requirements. However, devices cannot rely on software updates for their cryptography because software implementations consume too much energy. At the same time, fixed function hardware accelerators such as an AES engine cannot support new ciphers. This paper presents Falcon, a hardware architecture for accelerating a broad range of cryptography on energy limited devices. Rather than accelerate a fixed set of current ciphers, Falcon provides a general execution engine that accelerates dominant and emerging ciphers, such as AES, Cha-Cha, SHA-256, RSA, ECC with Curve25519, as well as post-quantum ciphers such as R-LWE. For cryptography, Falcon provides the flexibility of software while reducing the energy consumption of cryptography by 5-60x compared to software. This reduction makes it feasible for IoT applications to upgrade the ciphers they use after deployment, allowing them to keep up to date with security best practices without reducing their deployment lifetime or reducing the application workload. In an application monitoring the temperature of sensitive medical supplies in hospitals, Falcon doubles the deployment lifetime (2.2x).
Spams classification and their diffusibility prediction on Twitter through sentiment and topic models

Alike web spam has been a major issue to almost every aspect of the current World Wide Web, social spam has led to a serious threat to the utilities of online social media, particularly in information diffusion. To combat this challenge, the significance and impact of social media and its content should be analyzed critically. To address this issue, Twitter is used as a case study, where we have modeled the contents of information through topic models and coupled it with the user-oriented features to predict spam diffusion with a good accuracy. Latent Dirichlet Allocation (LDA), a widely used topic modeling technique is applied to capture the latent topics from the tweets' documents. The major contribution of this work is twofold. Firstly, analyzing the variation of sentiment and topics in spam/non-spam information, and secondly, constructing a feature set that classifies tweets more accurately into spam and non-spam category as compared to the existing approaches. The extensive simulation reveals the variation in sentiments and topics shared by the spam and non-spam tweets.
Thyroid Nodule Ultrasonic Imaging Segmentation Based on a Deep Learning Model and Data Augmentation

The segmentation of thyroid nodule ultrasonic image is a critical step for thyroid disease diagnosis. With the advent of medical big data, deep convolutional neural networks (DCNNs) have contributed to the analysis of medical image. However, there is still room for improving the accuracy of the result. In this paper, we employ several data pre-processing algorithms to amplify the feature of the original data as well as augment the whole dataset. Moreover, we use a deep learning model, improved DeepLab v3+ segmentation DCNN to achieve better training and prediction performance on thyroid nodule dataset. The results show that the dice similarity coefficient is measured to be 94.08% and accuracy is 97.91%, which reveals the advance nature of our system.
Class-aware domain adaptation for improving adversarial robustness

Recent works have demonstrated convolutional neural networks are vulnerable to adversarial examples, i.e., inputs to machine learning models that an attacker has intentionally designed to cause the models to make a mistake. To improve the adversarial robustness of neural networks, adversarial training has been proposed to train networks by injecting adversarial examples into the training data. However, adversarial training could overfit to a specific type of adversarial attack and also lead to standard accuracy drop on clean images. To this end, we propose a novel Class-Aware Domain Adaptation (CADA) method for adversarial defense without directly applying adversarial training. Specifically, we propose to learn domain-invariant features for adversarial examples and clean images via a domain discriminator. Furthermore, we introduce a class-aware component into the discriminator to increase the discriminative power of the network for adversarial examples. We evaluate our newly proposed approach using multiple benchmark datasets. The results demonstrate that our method can significantly improve the state-of-the-art of adversarial robustness for various attacks and maintain high performances on clean images.
Exploring the Challenges of Developing and Operating Consortium Blockchains

Blockchain and smart contracts are being embraced by more and more industrial practitioners in multiple domains including agriculture, manufacturing, and healthcare. As a distributed, immutable, and partly public ledger, the consortium blockchain demonstrates its potential to enable trustworthy interoperability and collaboration between organizations. However, the mismatch between the unruled software engineering practices and the increased interest of the consortium blockchain technology may pose threats to the quality of systems implemented. To mitigate the possible threats, this study takes the angle of software engineering to systematically understand the challenges and possible solutions in terms of developing and operating a consortium blockchain-based system. For this purpose, we conducted a case study on a typical consortium blockchain-based system and exhaustively collected the data by two rounds in-depth interviews on practitioners of different roles in the case project. Based on the data analysis, eight pairs of challenges and potential solutions were identified, which cover the phases of the development and operation of consortium blockchains. Moreover, we also captured two implications after further analysis of the findings, which worth the special attention of researchers in the near future, i.e. DevOps and microservices for blockchain or smart contracts.
Ensemble Methodsof Sentiment Analysis: A Survey

Sentiment Analysis (SA) is growing and becoming more interesting area for researcher in the field of Natural Language Processing (NLP). It is the calculative process in which opinions, subjectivity of text and sentiments are calculated. This survey handles an extensive study or sketch of the last updated ensemble methods in sentiment analysis. Most of the recently proposed ensemble methods of SA are studied and presented in brief in this study. These articles are classified on the basis of their benefaction in the various SA ensemble methods. There are various related fields to SA that attract researchers like (Building resources (BR), Emotion detection (ED), and Transfer learning (TL)), their concepts are presented. The main purpose of this study is to presents approximately each and every aspect of SA, ensemble technique and the entire vision in a brief manner. This survey also includes the sophisticated classification of many current updated articles and the interpretation of the recent tendency of research in the area of sentiment analysis and its related fields.
FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams

Nowadays, most data is generated by devices that produce data continuously. These kinds of data can be categorized as data streams and valuable insights can be extracted from them. In particular, the insights extracted by emerging patterns (EPs) are interesting in a data stream context as easy, fast, and reliable decisions can be made. However, their extraction is a challenge due to the necessary response time, memory, and continuous model updates. In this article, an approach for the extraction of EPs in data streams is presented. It processes the instances by means of batches following an adaptive approach. The learning algorithm is an evolutionary fuzzy system where previous knowledge is employed in order to adapt to concept drift. A wide experimental study has been performed in order to show both the suitability of the approach in combating concept drift and the quality of the knowledge extracted. Finally, the proposal is applied to a case study related to the continuous determination of the profiles of New York City cab customers according to their fare amount, in order to show its potential.
Integration of N-GCPSO Algorithm with Spatial Particle Extension Algorithm for Multi-Robot Search

This paper considers multi-robot search problems where a group of robots must discover and allocate themselves to targets. To solve this problem, we embed the robot with an algorithm called the Neighborhood with the Guaranteed Convergence Particle Swarm Optimization (N-GCPSO). This study considers the problem in a simulation environment. To reduce collision between robots, we integrate the N-GCPSO algorithm with a spatial particle extension algorithm. Simulation results show that the integration of N-GCPSO with a spatial partial extension algorithm increases the effectiveness of N-GCPSO by reducing the number of collisions between robots without reducing its performance in discovering and allocating targets.
Minimum-error state discrimination and quantitative wave particle duality

We establish the link between minimum-error discrimination of two pure states and wave particle duality in two-path interferometers. In particular, the upper bound of the probability of success discrimination is derived directly from the corresponding duality relation. It is already known that quantum state discrimination can produce wave particle duality relations, here we show the converse is also true: Wave particle duality can be used to obtain information about state discrimination.
Coordinating the Motion of Labeled Discs with Optimality Guarantees under Extreme Density

We push the limit in planning collision-free motions for routing uniform labeled discs in two dimensions. First, from a theoretical perspective, we show that the constant-factor time-optimal routing of labeled discs can be achieved using a polynomial-time algorithm with robot density over \(50\%\) in the limit (i.e., over half of the workspace may be occupied by the discs). Second, from a more practical standpoint, we provide a high performance algorithm that computes near-optimal (e.g., 1.x) solutions under the same density setting.
Priority Based Delay Time Scheduling for Quality of Service in Cloud Computing Networks

Cloud computing is a model that avails computing, storage, and network resources on-demand with less or no service provider interaction. The scheduling of resources to maximize cloud service providers’ profit and also guarantee the quality of services (QoS) required by user requests is a key issue in the cloud. The main objective of this paper is to design a Priority Based Delay Time Scheduling(PBDTS) technique to maximize resource utilization (service provider profit), ensure QoS performance of delay-sensitive requests by minimizing makespan, response time and waiting time of user’s job request. An extensive experiment using CloudSim was conducted and results obtained proves that PBDTS provides greater performance compared to Min-Min and Max-Min.
Improving Extreme Learning Machine Algorithm Through Optimization Technique

Classification and function approximation tasks are used widely in artificial neural network (ANN), and there are a number of algorithms by which a neural network can be trained. ELM is known as extreme learning machine; a novel algorithm has come under the spotlight in the last decade, and the reason for its popularity is a rapid learning rate and a more efficient generalization capability, in comparison with orthodox gradient descent learning algorithms for (SFLN) single-hidden-layer feed-forward neural network. Even though it has many advantages, the single-hidden-layer feed-forward neural network trained using ELM requires a number of hidden neurons. Due to this, it faces poor condition problem because of the randomly created hidden biases and input weights. The solution to this problem is to use an optimization technique, using it to find a set of inputs that are optimal. We have proposed a hybrid learning scheme that uses one such optimization technique which will be used to figure out an optimal set of input-hidden node weights, and then a set of optimal input weights are used to determine the output weights analytically. Few of the techniques that are used for optimization like competitive swarm optimization (CSO) and particle swarm optimization (PSO) have been proposed in the past. The hybrid learning scheme provides (i) help in enhancing the search variety, which encourages us to explore the solution search space better and (ii) improve the structure of ELM to solve the problem of random input. The proposed scheme in addition to other multi-objective optimal algorithms requires smaller amounts of hidden neurons compared to the conventional ELM for achieving higher classification accuracy. The proposed hybrid methodology has the ability to produce good condition SLFNs with an improved accuracy with better generalization.
Feature Selection Techniques and its Importance in Machine Learning: A Survey

Feature selection is well studied research topic in the field of artificial intelligence, machine learning and pattern recognition. Feature selection it removes the redundant, irrelevant and noisy features from the original features of datasets by choosing the relevant features having the smaller subdivision of dataset. By applying various techniques of feature selection to the datasets, results in lower computational costs, higher classifier accuracy, reduced dimensionality and predictable model. This article investigates, feature selection techniques found in various literatures.
A Bangla Text-to-Speech System using Deep Neural Networks

We present a Deep Neural Network (DNN) based statistical parametric Text-to-Speech (TTS) system for Bangla (also known as Bengali). A first step in building a DNN-based TTS system is having large speech data. Since good speech dataset for Bangla TTS is not available publicly, we created our own dataset for our system. We prepared a phonetically rich studio-quality speech database containing more than 40 hours of speech. The database consists of 12,500 utterances. We also prepared a pronunciation dictionary (lexicon) of 1,35,000 words for front-end text processing, which, to our knowledge, is the largest lexicon for Bangla. Our system extracts linguistic features from input text. Then it uses deep neural networks for mapping these linguistic features to acoustic features. We developed two TTS voices using our dataset - one male and one female voice. Both objective and subjective evaluation tests show that our system performs significantly better than the traditional Bangla TTS systems and is comparable to the commercially available best Bangla TTS system.
Software Defect Prediction via Transformer

In order to enhance software reliability, software defect prediction is used to predict potential defects and to improve efficiency of software examination. Traditional defect prediction methods mainly focus on design static code metrics, and building machine learning classifiers to predict pieces of code that potentially defective. However, these manual extracted features do not contain syntactic and semantic information of programs. These information is much more important than those metrics and can improve the accuracy of defect prediction. In this paper, we propose a framework called software defect prediction via transformer (DP-Transformer) which capture syntactic and semantic features from programs and use them to improve defect prediction. Specifically, we first parse source code into ASTs and then select representative nodes from ASTs to form token vectors. Then we employ mapping and word embedding to convert token vectors into numerical vectors and send the numerical vectors to transformer. Transformer will automatically extract syntactic and semantic features and eventually feed these features into a Logistic Regression classifier. We evaluate our method on seven open-source Java projects with certain labels and take F-measure as evaluation criteria. The experimental results show that averagely, the proposed DP-Transformer improves the state-of-art method by 8%.
Most relevant input parameters selection for 10-min global solar irradiation estimation on arbitrary inclined plane using neural networks

This paper propounds the use of ‘Weights’ method to assess the relative importance of input features for estimating 10-min global solar irradiation on an arbitrary tilted plane, using artificial Neural Networks (ANNs). Two different data sources are exerted: satellite data, including different inclinations, are used for learning, while ground data, measured at Bouzaréah radiometric station (Algeria), are used for testing. ‘Weights’ method allowed to identify the relative importance of input variables and rank them according to their order of contribution, thus significantly improving the performance of the model by eliminating the parameters that are poorly correlated with the network output. The results show that the most relevant input data are in the following order: the global horizontal irradiation 35.9%, extraterrestrial irradiation 25.08%, declination 15.81%, elevation of the sun 12.60% and inclination 10.61%. The optimal configuration includes 16 neurons in the hidden layer with nRMSE = 6.37% and R2 = 0.992.
BatchLayout: A Batch-Parallel Force-Directed Graph Layout Algorithm in Shared Memory

Force-directed algorithms are widely used to generate aesthetically-pleasing layouts of graphs or networks arisen in many scientific disciplines. To visualize large-scale graphs, several parallel algorithms have been discussed in the literature. However, existing parallel algorithms do not utilize memory hierarchy efficiently and often offer limited parallelism. This paper addresses these limitations with BatchLayout, an algorithm that groups vertices into minibatches and processes them in parallel. BatchLayout also employs cache blocking techniques to utilize memory hierarchy efficiently. More parallelism and improved memory accesses coupled with force approximating techniques, better initialization, and optimized learning rate make BatchLayout significantly faster than other state-of-the-art algorithms such as ForceAtlas2 and OpenOrd. The visualization quality of layouts from BatchLayout is comparable or better than similar visualization tools. All of our source code, links to datasets, results and log files are available at https://github.com/khaled-rahman/BatchLayout.
GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning

Abstract Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain.
Large-scale semantic exploration of scientific literature using topic-based hashing algorithms

Searching for similar documents and exploring major themes covered across groups of documents are common activities when browsing collections of scientific papers. This manual knowledge-intensive task can become less tedious and even lead to unexpected relevant findings if unsupervised algorithms are applied to help researchers. Most text mining algorithms represent documents in a common feature space that abstract them away from the specific sequence of words used in them. Probabilistic Topic Models reduce that feature space by annotating documents with thematic information. Over this low-dimensional latent space some locality-sensitive hashing algorithms have been proposed to perform document similarity search. However, thematic information gets hidden behind hash codes, preventing thematic exploration and limiting the explanatory capability of topics to justify content-based similarities. This paper presents a novel hashing algorithm based on approximate nearest-neighbor techniques that uses hierarchical sets of topics as hash codes. It not only performs efficient similarity searches, but also allows extending those queries with thematic restrictions explaining the similarity score from the most relevant topics. Extensive evaluations on both scientific and industrial text datasets validate the proposed algorithm in terms of accuracy and efficiency.