Reviewerly/exHarmony huggingface dataset:
Neural Network Based Approaches for Prediction of the Indian Summer Monsoon Rainfall

This paper investigated the application of Single Layer Feed Forward Neural Network (SLFN), Extreme Learning Machine (ELM) and Regularized Online Sequential - Random Vector Functional Link (ROS-RVFL) neural network for prediction of the Indian Summer Monsoon Rainfall (ISMR) using Sea Surface Temperature (SST), Sea Level Pressure (SLP) and the combination of SST and SLP (SST+SLP) as input predictors. Nine sets of predictions are made using the SLFN, ELM, and ROS-RVFL techniques by applying SST, SLP, and the combination of SST and SLP (SST+SLP) predictors. It is found that when the combination of SST and SLP (SST+SLP) is chosen as the input predictor, the aforesaid neural networks based approaches produce minimal error scores and provide better accuracy. The ROS-RVFL technique outperformed in all the trials used in this study and the ELM technique produced more accurate outcomes than the SLFN technique.
Blockchain for tracking serial numbers in money exchanges

Summary Money exchange is one of the most common day‐to‐day activities performed by humans in the daily market. This paper presents an approach to money tracking through a blockchain. The proposed approach consists of three main components: serial number localization, serial number recognition, and a blockchain to store all transactions and ownership transfers. The approach was tested with a total of 110 banknotes of different currency types and achieved an average accuracy of 91.17%. We conducted a user study in real‐time with 21 users, and the mean accuracy across all users was 86.42%. Each user gave us feedback on the proposed approach, and most of them welcomed the idea.
The Grimm Brothers: A stylometric network analysis

Abstract Stylometric methods can be used to reveal similarities between texts and, combined with network analysis, to depict the stylistic relations between those texts. The research conducted here focuses on a corpus of letters written by Jacob and Wilhelm Grimm. Using stylometric analysis, we model the writing styles of the brothers depending on the addressees and chronology. The brothers have individual styles: Wilhelm has a more friendly and personal tone independent on addresses, while Jacob has a more impersonal style, unless he was writing to Wilhelm. Their styles merge at the interactions of their career or personal development.
Automatic Generation of Program Comments Based on Problem Statements for Computational Thinking

To support the understanding of programs and understanding of procedures, we think need to automatically generate comments from source code. As a method, we learn the source code and comment pair by Encoder-Decoder translation model using LSTM, thereby generating comments of the source code that was the target of learning. Though, since it is difficult to increase the amount of learning data when generating comment for problem description, generated comments have some incorrect word for problem sentence. We use program problem sentence to generate comments for source code more suitable for users.
Emoji Prediction: A Transfer Learning Approach

We present a transfer learning model for the Emoji Prediction task described at SemEval-2018 Task 2. Given a text of tweet, the task aims to predict the most likely emoji to be used within such tweet. The proposed method used a pre-training and fine-tuning strategy, which applies the pre-learned knowledge from several upstream tasks to downstream Emoji Prediction task, solving the data scarcity issue suffered by most of the SemEval-2018 participants using supervised learning strategy. Our transfer learning-based model can outperform state-of-the-art system (best performer at SemEval-2018) by 2.53% in macro F-score. Except from providing details of our system, this paper also intends to provide a comparison between supervised learning models and transfer learning models in solving Emoji Prediction task.
Word Sense Disambiguation in Bengali Using Sense Induction

In this paper an algorithm is proposed for Word Sense Disambiguation in Bengali language using Sense Induction technique. The overall work is carried out in two phases. In the first phase, different sense clusters are created using Sense Induction technique and in the second phase, Word Sense Disambiguation is developed using Semantic Similarity Measure. The data sets are prepared from the corpus, developed under the TDIL (Technology Development for Indian Languages) project of the Government of India. The developed model is tested on 10 commonly used Bengali ambiguous words, each of which is having approximately 200 sentences. The overall accuracy is achieved as 63.71% in Word Sense Disambiguation task. The challenges and the pitfalls of this work are explained in detail at the end of this paper.
Content-Based Superpixel Matching Using Spatially Constrained Student’s-t Mixture Model and Scale-Invariant Key-Superpixels

This paper addresses an image matching methodology designed for correspondence problem in computer vision. Firstly, a novel superpixel segmentation model driven by spatially constrained Student's-t mixture model (SMM) is proposed. The tails of Student's t-distribution are heavier than that of traditional Gaussian distribution, therefore, SMM is more insensitive to outliers and noise. In this model, a spatially constraint term based on Markov random field (MRF) is designed, so that good boundary adherence and intensity homogeneity would be achieved. Next, by constructing an adaptive superpixel Gaussian filter and a superpixel salient detector, this paper establishes an innovative key-superpixel detection method by building a superpixel scale-space pyramid. Different from conventional keypoint based detection, two images could then be matched directly in a superpixel-to-superpixel manner. During the matching process, a combinatorial feature descriptor that merges color, shape, gradient and texture features is set up to distinguish each considered key-superpixel. One main advantage of this approach is that implementation time would be largely reduced by less matching demand for key-superpixels and few corresponding local features. Some experiments on datasets at the end would demonstrate a relatively better performance of our model.
A New Approach to Prevent Reentrant Attack in Solidity Smart Contracts

Currently, Solidity is a high-level language for smart contracts that need to run on Ethereum virtual machines, it is being promoted with the widespread use of Ethereum. However, the Solidity has a feature of fallback function, makes it easier for attackers to use fallback function to launch reentrant attack, which may cause huge economic losses about the user. Therefore, a new method based on Solidity and Condition-Orientated programming is proposed to prevent reentrant attack. This method separates conditional branches and major logical state changes, encapsulates the separated conditional branches into multiple modifiers and defines a global state variable, packages the state of the state variable in the modifier, and finally uses the modifier as a precondition for the transfer function in the smart contract. When an attacker reenters the transfer function in the smart contract, the reentrant attack can be prevented by controlling state variable. The experimental results show that this method not only makes the logic of the contract code more reasonable, but also effective.
On Fault Localization Using Machine Learning Techniques

Early prediction of faulty modules provides a way to support software quality assurance activities through improved scheduling and effectiveness of process control. Different fault prediction models have been proposed by researchers for early prediction of faults. However, despite predicting faulty classes, these techniques don't provide information about identifying the location of occurrence of faults. In this paper, we aim to propose a methodology not only to predict faults but also for fault localization as well. We first make use of various datasets to build a software fault prediction model based. We make use of random forest machine learning technique to train our model. We also extract CK-metrics from different modules and we then make use of these metrics for fault localization. In order to do validation, we make use of case studies where we do fault prediction, fault localization and then run test cases to see if our prediction was correct.
On-GPU thread-data remapping for nested branch divergence

Nested branches are common in applications with decision trees. The more layers in the branch nest, the larger slowdown is caused by nested branch divergence on GPU. Since inner branches are impractical to evaluate on host end, thread-data remapping via GPU shared memory is so far the most suitable solution. However, existing solution cannot handle inner branches directly due to undefined behavior of GPU barrier function when executed within branch statements. Race condition needs to be prevented without using barrier function. Targeting nested divergence, we propose NeX as a nested extension scheme featuring an inter-thread protocol that supports sub-workgroup synchronization. We further exploit the on-the-fly nature of Head-or-Tail (HoT) algorithm and propose HoT2 with enhanced flexibility of wavefront scheduling. Evaluated on four GPU models including NVIDIA Volta and Turing, HoT2 confirms to be more efficient. For benchmarks with branch nests up to five-layer-deep, NeX further boosts performance by up to 1.56x.
Technical and Legal Challenges for Healthcare Blockchains and Smart Contracts

The paper considers the technical and legal challenges impacting recent proposals for healthcare applications of blockchain and smart contracts. Healthcare blockchain data and actors are rather different to cryptocurrency data and actors, resulting in a different emphasis on blockchain features. Technical issues with healthcare blockchain implementation and trust are considered, as well as a variety of potential legal issues. Conclusions and recommendations are proposed for open source and standardization efforts to reduce technical and legal risks for healthcare blockchains and smart contracts.
“I Don’t Know Too Much About It”: On the Security Mindsets of Computer Science Students

The security attitudes and approaches of software developers have a large impact on the software they produce, yet we know very little about how and when these views are constructed. This paper investigates the security and privacy (S&P) perceptions, experiences, and practices of current Computer Science students at the graduate and undergraduate level using semi-structured interviews. We find that the attitudes of students already match many of those that have been observed in professional level developers. Students have a range of hacker and attack mindsets, lack of experience with security APIs, a mixed view of who is in charge of S&P in the software life cycle, and a tendency to trust other peoples' code as a convenient approach to rapidly build software. We discuss the impact of our results on both curriculum development and support for professional developers.
Categories of Information Need Expressed by Parents of Individuals with Rare Genetic Disorders in a Facebook Community Group: A Case Study with Implications for Information Professionals

Costello syndrome is a rare genetic disorder that affects virtually all aspects of an individual’s life. Few specialists have been trained to understand the condition and provide guidance to caregivers and virtually no traditional information sources (e.g. books, websites) exist to provide information to caregivers. In this case, the community becomes a major source of information for caregivers of individuals with this condition. This study examines the types of information that need to be expressed in one of these communities: an online Facebook group. The categories of information need to be identified in the study to demonstrate areas of concern to caregivers and offer areas for further research or publication for researchers of rare disorders and health conditions. The role of information professionals in satisfying these information needs of caregivers of individuals with Costello syndrome and other rare disorders.
An Adaptive Authentication Based on Reinforcement Learning

Different authentication confidence probabilities are required when doing different amount of online commercial transfer. We should use different combinations of authentication methods according to different level of authentication confidence requirements. This paper proposes a new multi-factor authentication based on reinforcement learning to realize adaptive authentication. Numerical results show our scheme can reduce the authentication cost while satisfying authentication confidence.
Outlier Detection with Supervised Learning Method

Outliers are data points that can affect the quality of data and the results of analysis from data mining. Outlier detection can also be seen as a pre-processing step to find data points that do not properly placed in the data set. Previously outlier detection methods are unsupervised. Today, ODDS provide data set with outlier information as a ground truth for supervised learning. However, outliers are commonly minor in any data set. For supervised learning this will lead to an imbalanced data classification problem. This paper presents the result of popular classification method, k-Nearest neighbor, Centroid Classifier, and Naive Bayes to handle outlier detection task. Even the mentioned methods were not designed to detect outlier, they proved by achieving 81% average sensitivity which is good for further research.
Learning Linear Programs from Data

Linear Programming lies at the core of mathematical modelling and optimization. Designing linear programs (LPs) is a difficult and expensive process, as it requires both mathematical programming and domain expertise, and it involves both designing an objective function and feasibility constraints. To support this design process, we propose INCALP, an algorithm for inducing linear programs from examples. Since the objective can often be learned with standard techniques (e.g. regression), INCALP learns the hard constraints only. It does so by encoding constraint learning as a mixed integer linear program. INCALP achieves significant efficiency gains by considering gradually larger subsets of examples, and terminating as soon as a suitable program is found. In addition, INCALP encourages both compactness and sparsity of the learned program. Our empirical analysis on synthetic data and textbook problems highlights the promise of the approach.
Secure Mechanism for Communication Using Image Steganography

In this paper, we introduce a tactic to ensure secure data transmission over a network which has reformed the mode of transmitting data. A new method is recommended that combines the techniques of Steganography along with Cryptography considering a 24 bit color image. This is achieved using the Advanced Encryption Standard algorithm and Bit Plane Complexity Segmentation Steganography techniques. The process involves encrypting the secret message into a cipher text using AES algorithm, then embedding the encrypted text onto a cover image, then splitting the cover image into 2 images and encrypting the two images using the same AES algorithm to get two separate encrypted images. Finally these encrypted images are further embedded on the initial carrier image separately in order to get two stego images. The privacy of the message is achieved by making it almost impossible for a third party to detect and retrieve the hidden data.
Statistical Modeling of Solar Irradiance for Northeast Brazil

In this work it is proposed a methodology to obtain statistical models based on probabilistic density functions (PDF) and Clear Sky Index (CSI) to represent the solar irradiance variability. This methodology consists on four steps. First, the available irradiance data set is processed to obtain the CSI. Once it is done, the occurrence of their values is quantified using annual and monthly histograms. By analysing these histograms, it is determined the PDF which better represent their shapes. The obtained functions are validated by two criteria: the Mean Square Error (MSE) and the Chi-Square Test. Once the models are validated, they can be used to generate synthetic series for the solar irradiation in a typical day. As a study case, it was used a 20-year irradiation data from a Brazilian northeastern city. Results shown that Weibull and Beta functions provided better results to describe the solar variability in the studied city. Moreover, the obtained synthetic series generated values within the historical data range. Hence, these series can be used for making informed decisions on bidding strategies for energy auctions by the Brazilian solar energy sector.
Recognizing The Components of Inflectional Word Gestures in Indonesian Sign System known as SIBI (Sistem Isyarat Bahasa Indonesia) by using Lip Motion

Sign language is a method of communication used by people who suffer from hearing impairment. Several countries have invented their own sign language including Indonesia's Sistem Isyarat Bahasa Indonesia (SIBI). The uniqueness of SIBI is that it applies the Indonesian language grammatical structure to the hand motions used and uses facial expressions as the replacement of intonation and lip motions to pronounce a word. From previous studies, to increase the accuracy of sign language recognition it is necessary to supplement the hand tracking with lip reading. This study proposes a model to recognize inflectional word gestures through lip motions. From the proposed model, this research found that the sequence of lip motions are very similar to each other, which resulted in very low accuracy in determining prefix (21,64%), root words (23,87%), and suffixes (22,61%), in SIBI just by analyzing the sequence of lip motions. Using similarity-groupings determined in this study, the accuracy of the model proposed can be increased significantly with the accuracy increasing to prefix (73,94%), root words (83,98%), and suffixes (82,10%). Additionally, another cause for the low accuracy of the model is that the labeling was done according to the hand gestures, this resulted in the frame by frame sequence that was captured to not be representative of the entire lip motions.
Data Expansion for Foreign Object Detection in Power Grid

There have been some researches on the use of deep learning algorithm in the work of power patrol inspection. With some current object detection algorithms as the core, a power grid image recognition system can be built to detect abnormal operation or foreign invasion in the power grid, which could save manpower and material resources and improve the security of the power grid. Deep learning requires a number of effective samples to work. There are few real images of hidden dangers, which cannot meet the demand. This paper aims to explore a feasible data expansion scheme. One possible way is to merge the target with the context background image according to some rules. Another method is to generate samples via GAN (Generative adversarial network). Experiments results show that the performance of the power grid image recognition system is improved by using the extended training set.
From Digital Commons to Scholar Profiles: Implementing a New System to Raise College Academic Distinction

Institutional repositories, or digital archives, are a fundamental part of an academic institution in higher education. Saint Mary’s institutional repository plays an integral role in supporting one of the goals in the College’s Strategic Plan: “Raise the Academic Profile and Distinction.” This case study describes the benefits and challenges of institutional repositories for small institutions, the launch of Saint Mary’s Digital Commons, and the implementation of its new scholar profiles system. The Library is proud to be recognized for its expertise as the manager of the College’s institutional repository and its new research information management system.
Path Planning For Autonomous Car

The autonomous cars will transform the lifestyle of people by creating an efficient way of driving on roads. The ability to detect and track the stationary or the moving objects is said to be one of the major challenging tasks for decades. The research on object detection seems to be looking for a longterm vision in autonomous cars so that there is a significant enhancement in safety. The object detection is necessary for both day and night facilitating smooth transportation. In this paper, we are analyzing the images. The images classification algorithms are used such as a convolutional neural network (CNN). The algorithms will later be implemented on the graphical processing unit which will lead to the steering of the automobile based on the study of image recognition.
A Novel Collision Avoidance Method for Multiple Fixed-wing Unmanned Aerial Vehicles

In this paper, we study the collision avoidance problem with fixed-wing unmanned aerial vehicles (UAVs). According to the relative spatial position and speed between the drones, the non-conflicting sufficient conditions for conflict detection are proposed. Then an emergency collison avoidance control law is designed based on the proposed conditions to ensure that the UAVs can be driven back to safe state from danger when emergent conflict are detected. Furthermore, a conflict buffer is additionally introduced to prevent frequent switching of control outputs between the “safe mode” and “danger mode”. The proposed control strategy cannot only tackle two-UAV conflict scenarios, but has also been extended to multi-UAV conflict scenarios with several additional motion rules. Meanwhile, control input saturation is explicitly considered in the emergency maneuver. Simulation results are presented to illustrate the effectiveness of the proposed methodology.
Editing-Enabled Signatures: A New Tool for Editing Authenticated Data

Data authentication primarily serves as a tool to achieve data integrity and source authentication. However, traditional data authentication does not fit well where an intermediate entity (editor) is required to modify the authenticated data provided by the source/data owner before sending the data to other recipients. To ask the data owner for authenticating each modified data can lead to higher communication overhead. In this article, we introduce the notion of editing-enabled signatures where the data owner can choose any set of modification operations applicable on the data and still can restrict any possibly untrusted editor to authenticate the data modified using an operation from this set only. Moreover, the editor does not need to interact with the data owner in order to authenticate the data every time it is modified. We construct an editing-enabled signature (EES) scheme that derives its efficiency from mostly lightweight cryptographic primitives. We formalize the security model for editing-enabled signatures and analyze the security of our EES scheme. Editing-enabled signatures can find numerous applications that involve generic editing tasks and privacy-preserving operations. We demonstrate how our EES scheme can be applied in two privacy-preserving applications.