Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis for Sinhala Language using Deep Learning Techniques

Due to the high impact of the fast-evolving fields of machine learning and deep learning, Natural Language Processing (NLP) tasks have further obtained comprehensive performances for highly resourced languages such as English and Chinese. However Sinhala, which is an under-resourced language with a rich morphology, has not experienced these advancements. For sentiment analysis, there exists only two previous research with deep learning approaches, which focused only on document-level sentiment analysis for the binary case. They experimented with only three types of deep learning models. In contrast, this paper presents a much comprehensive study on the use of standard sequence models such as RNN, LSTM, Bi-LSTM, as well as more recent state-of-the-art models such as hierarchical attention hybrid neural networks, and capsule networks. Classification is done at document-level but with more granularity by considering POSITIVE, NEGATIVE, NEUTRAL, and CONFLICT classes. A data set of 15059 Sinhala news comments, annotated with these four classes and a corpus consists of 9.48 million tokens are publicly released. This is the largest sentiment annotated data set for Sinhala so far.
DROP TEST RESISTANCE ON RAMIE FIBER BULLETPROOF PANELS BASED ON HARVEST TIME AND FIBER TREATMENT OF RAMIE

Absorption of energy in bulletproof panels by fibers and resins is essential to reduce or stop the movement of the bullets. The discussion on energy absorption of ramie fiber bullet panels was not conducted. The study was aimed at determining the drop test resistance on bulletproof panels made of ramie fiber. Ramie fiber was used as a bulletproof panel material through a certain treatment. First, the fiber was harvested at the age of 60 days, and then it was processed through a decortication using a waterless machine. To find out the absorbed energy from the panel, a static test was performed. Next, a drop test was carried out by taking several layers of ramie fiber. The number of layers varied from 3, 6, 9, to 12. The drop test result shows that the bulletproof absorption energy is increasing along with the added lamina. Bulletproof panel resistance was also enhanced with the addition of ramie fiber lamina. Improved bulletproof panel resistance is shown by the increasing energy per unit thickness.
PrivRec: User-Centric Differentially Private Collaborative Filtering Using LSH and KD

The collaborative filtering (CF)-based recommender systems provide recommendations by collecting users’ historical ratings and predicting their preferences on new items. However, this inevitably brings privacy concerns since the collected data might reveal sensitive information of users, when training a recommendation model and applying the trained model (i.e., testing the model). Existing differential privacy (DP)-based approaches generally have non-negligible trade-offs in recommendation utility, and often serve as centralized server-side approaches that overlook the privacy during testing when applying the trained models in practice. In this paper, we propose PrivRec, a user-centric differential private collaborative filtering approach, that provides privacy guarantees both intuitively and theoretically while preserving recommendation utility. PrivRec is based on the locality sensitive hashing (LSH) and the teacher-student knowledge distillation (KD) techniques. A teacher model is trained on the original user data without privacy constraints, and a student model learns from the hidden layers of the teacher model. The published student model is trained without access to the original user data and takes the locally processed data as input for privacy. The experimental results on real-world datasets show that our approach provides promising utility with privacy guarantees compared to the commonly used approaches.
Analysis of the Application of Customer Purchase Mining Data on Paint Sales Using Apriori Algorithm (Case Study: PT Indowarna Cemerlang Indonesia)

Sales transaction data is one thing that can be used for making business decisions. Most sales transaction data is not reused, and is only stored as an archive and only used for making a sales report. Paint sales data is one science that can be applied in cases like this. Sales transactions that are not utilized properly can be extracted and reprocessed into useful information using data mining techniques. Using one of the data mining methods, namely the a priori algorithm, sales transaction data can be reprocessed so that it can produce a consumer buying pattern. This consumer buying pattern will later help companies make business decisions. PT Indowarna Cemerlang Indonesia is a company engaged in the paint trade, where the main activity is selling various wall paints, oil / wood paints, NC paints (car paints), epoxy paints (floor paints), depo-proof (anti leaked). PT Indowarna Cemerlang Indonesia does not reuse sales transaction data resulting from its sales activities. This data is only used as a reference for making sales reports and as an archive only, causing accumulation of data and unknown paint brands that are often sold or those that are of interest to customers. Therefore, the author takes the title application of data mining analysis of customer purchase patterns in paint sales using a priori algorithm. By doing this research, it is expected to provide results in the form of information that can be useful for related parties and can design sales strategies to increase company turnover.
Retrieving and mining professional experience of software practice from grey literature: an exploratory review

Retrieving and mining practitioners’ self-reports of their professional experience of software practice could provide valuable evidence for research. The authors are, however, unaware of any existing reviews of research conducted in this area. The authors reviewed and classified previous research, and identified insights into the challenges research confronts when retrieving and mining practitioners’ self-reports of their experience of software practice. They conducted an exploratory review to identify and classify 42 studies. They analysed a selection of those studies for insights on challenges to mining professional experience. They identified only one directly relevant study. Even then this study concerns the software professional&apos;s emotional experiences rather than the professional&apos;s reporting of behaviour and events occurring during software practice. They discussed the challenges concerning: the prevalence of professional experience; definitions, models and theories; the sparseness of data; units of discourse analysis; annotator agreement; evaluation of the performance of algorithms; and the lack of replications. No directly relevant prior research appears to have been conducted in this area. They discussed the value of reporting negative results in secondary studies. There are a range of research opportunities but also considerable challenges. They formulated a set of guiding questions for further research in this area.
Dynamic hard pruning of Neural Networks at the edge of the internet

Neural Networks (NN), although successfully applied to several Artificial Intelligence tasks, are often unnecessarily over-parametrized. In edge/fog computing, this might make their training prohibitive on resource-constrained devices, contrasting with the current trend of decentralizing intelligence from remote data centres to local constrained devices. Therefore, we investigate the problem of training effective NN models on constrained devices having a fixed, potentially small, memory budget. We target techniques that are both resource-efficient and performance effective while enabling significant network compression. Our Dynamic Hard Pruning (DynHP) technique incrementally prunes the network during training, identifying neurons that marginally contribute to the model accuracy. DynHP enables a tunable size reduction of the final neural network and reduces the NN memory occupancy during training. Freed memory is reused by a dynamic batch sizing approach to counterbalance the accuracy degradation caused by the hard pruning strategy, improving its convergence and effectiveness. We assess the performance of DynHP through reproducible experiments on three public datasets, comparing them against reference competitors. Results show that DynHP compresses a NN up to 10 times without significant performance drops (up to 3.5% additional error w.r.t. the competitors), reducing up to 80% the training memory occupancy.
Bayesian nonparametric monotone regression

In many applications there is interest in estimating the relation between a predictor and an outcome when the relation is known to be monotone or otherwise constrained due to the physical processes involved. We consider one such application--inferring time-resolved aerosol concentration from a low-cost differential pressure sensor. The objective is to estimate a monotone function and make inference on the scaled first derivative of the function. We proposed Bayesian nonparametric monotone regression which uses a Bernstein polynomial basis to construct the regression function and puts a Dirichlet process prior on the regression coefficients. The base measure of the Dirichlet process is a finite mixture of a mass point at zero and a truncated normal. This construction imposes monotonicity while clustering the basis functions. Clustering the basis functions reduces the parameter space and allows the estimated regression function to be linear. With the proposed approach we can make closed-formed inference on the derivative of the estimated function including full quantification of uncertainty. In a simulation study the proposed method performs similar to other monotone regression approaches when the true function is wavy but performs better when the true function is linear. We apply the method to estimate time-resolved aerosol concentration with a newly-developed portable aerosol monitor. The R package bnmr is made available to implement the method.
Continuously Adaptive Mean-shift Tracking Algorithm Based on Improved Gaussian Model

In complex scenes with light changes, deformations, and occlusions, target tracking easily contains a large amount of background color information when building a target color model. Thus, the tracking effect is reduced. To improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios, a target tracking algorithm based on an improved Gaussian mixture model was proposed. Using the Gaussian mixture model, the tracking image was divided into the foreground and background superposition. The histograms of the hue component were respectively established in the foreground and background of the target area. By suppressing the same hue as the background color in the tracking image, the target color model was established. The target position was iteratively obtained by implementing the CAMShift algorithm using the enhanced target color model. The Bhattacharyya distance between the candidate target and the target template was used as basis for updating the target model. Simulation analysis under benchmark data sets and actual monitoring scenarios verified the accuracy of the proposed algorithm. Results show that the distance precision and overlap success rate of the proposed algorithm are 0.88 and 0.625, respectively. The proposed algorithm effectively solves long-term target tracking problems with complex scenes, such as occlusion, background clutters, and illumination variation. This study eliminates the problem of target recognition caused by environmental changes and provides references for real-time monitoring of abnormal traffic conditions.
Stochastic Distance Transform

The distance transform (DT) and its many variations are ubiquitous tools for image processing and analysis. In many imaging scenarios, the images of interest are corrupted by noise. This has a strong negative impact on the accuracy of the DT, which is highly sensitive to spurious noise points. In this study, we consider images represented as discrete random sets and observe statistics of DT computed on such representations. We, thus, define a stochastic distance transform (SDT), which has an adjustable robustness to noise. Both a stochastic Monte Carlo method and a deterministic method for computing the SDT are proposed and compared. Through a series of empirical tests, we demonstrate that the SDT is effective not only in improving the accuracy of the computed distances in the presence of noise, but also in improving the performance of template matching and watershed segmentation of partially overlapping objects, which are examples of typical applications where DTs are utilized.
Prediction of neural network performance by phenotypic modeling

Surrogate models are used to reduce the burden of expensive-to-evaluate objective functions in optimization. By creating models which map genomes to objective values, these models can estimate the performance of unknown inputs, and so be used in place of expensive objective functions. Evolutionary techniques such as genetic programming or neuroevolution commonly alter the structure of the genome itself. A lack of consistency in the genotype is a fatal blow to data-driven modeling techniques: interpolation between points is impossible without a common input space. However, while the dimensionality of genotypes may differ across individuals, in many domains, such as controllers or classifiers, the dimensionality of the input and output remains constant. In this work we leverage this insight to embed differing neural networks into the same input space. To judge the difference between the behavior of two neural networks, we give them both the same input sequence, and examine the difference in output. This difference, the phenotypic distance, can then be used to situate these networks into a common input space, allowing us to produce surrogate models which can predict the performance of neural networks regardless of topology. In a robotic navigation task, we show that models trained using this phenotypic embedding perform as well or better as those trained on the weight values of a fixed topology neural network. We establish such phenotypic surrogate models as a promising and flexible approach which enables surrogate modeling even for representations that undergo structural changes.
Locality-Sensitive Hashing for Efficient Web Application Security Testing

Web application security has become a major concern in recent years, as more and more content and services are available online. A useful method for identifying security vulnerabilities is black-box testing, which relies on an automated crawling of web applications. However, crawling Rich Internet Applications (RIAs) is a very challenging task. One of the key obstacles crawlers face is the state similarity problem: how to determine if two client-side states are equivalent. As current methods do not completely solve this problem, a successful scan of many real-world RIAs is still not possible. We present a novel approach to detect redundant content for security testing purposes. The algorithm applies locality-sensitive hashing using MinHash sketches in order to analyze the Document Object Model (DOM) structure of web pages, and to efficiently estimate similarity between them. Our experimental results show that this approach allows a successful scan of RIAs that cannot be crawled otherwise.
DATAMAFIA at WNUT-2020 Task 2: A Study of Pre-trained Language Models along with Regularization Techniques for Downstream Tasks

This document describes the system description developed by team datamafia at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets.This paper contains a thorough study of pre-trained language models on downstream binary classification task over noisy user generated Twitter data.The solution submitted to final test leaderboard is a fine tuned RoBERTa model which achieves F1 score of 90.8% and 89.4% on the dev and test data respectively.In the later part, we explore several techniques for injecting regularization explicitly into language models to generalize predictions over noisy data.Our experiments show that adding regularizations to RoBERTa pre-trained model can be very robust to data and annotation noises and can improve overall performance by more than 1.2%.
Synthesis of Data Word Transducers

In reactive synthesis, the goal is to automatically generate an implementation from a specification of the reactive and non-terminating input/output behaviours of a system. Specifications are usually modelled as logical formulae or automata over infinite sequences of signals ($ω$-words), while implementations are represented as transducers. In the classical setting, the set of signals is assumed to be finite. In this paper, we consider data $ω$-words instead, i.e., words over an infinite alphabet. In this context, we study specifications and implementations respectively given as automata and transducers extended with a finite set of registers. We consider different instances, depending on whether the specification is nondeterministic, universal or deterministic, and depending on whether the number of registers of the implementation is given or not. In the unbounded setting, we show undecidability for both universal and nondeterministic specifications, while decidability is recovered in the deterministic case. In the bounded setting, undecidability still holds for nondeterministic specifications, but can be recovered by disallowing tests over input data. The generic technique we use to show the latter result allows us to reprove some known result, namely decidability of bounded synthesis for universal specifications.
Regularization of Distinct Strategies for Unsupervised Question Generation

Unsupervised question answering (UQA) has been proposed to avoid the high cost of creating high-quality datasets for QA.One approach to UQA is to train a QA model with questions generated automatically.However, the generated questions are either too similar to a word sequence in the context or too drifted from the semantics of the context, thereby making it difficult to train a robust QA model.We propose a novel regularization method based on teacher-student architecture to avoid bias toward a particular question generation strategy and modulate the process of generating individual words when a question is generated.Our experiments demonstrate that we have achieved the goal of generating higher-quality questions for UQA across diverse QA datasets and tasks.We also show that this method can be useful for creating a QA model with few-shot learning.
On nested code pairs from the Hermitian curve

Nested code pairs play a crucial role in the construction of ramp secret sharing schemes [Kurihara et al. 2012] and in the CSS construction of quantum codes [Ketkar et al. 2006]. The important parameters are (1) the codimension, (2) the relative minimum distance of the codes, and (3) the relative minimum distance of the dual set of codes. Given values for two of them, one aims at finding a set of nested codes having parameters with these values and with the remaining parameter being as large as possible. In this work we study nested codes from the Hermitian curve. For not too small codimension, we present improved constructions and provide closed formula estimates on their performance. For small codimension we show how to choose pairs of one-point algebraic geometric codes in such a way that one of the relative minimum distances is larger than the corresponding non-relative minimum distance.
Faster Dynamic Compressed d-ary Relations

The $k^2$-tree is a successful compact representation of binary relations that exhibit sparseness and/or clustering properties. It can be extended to $d$ dimensions, where it is called a $k^d$-tree. The representation boils down to a long bitvector. We show that interpreting the $k^d$-tree as a dynamic trie on the Morton codes of the points, instead of as a dynamic representation of the bitvector as done in previous work, yields operation times that are below the lower bound of dynamic bitvectors and offers improved time performance in practice.
Personalized Query Rewriting in Conversational AI Agents

Spoken language understanding (SLU) systems in conversational AI agents often experience errors in the form of misrecognitions by automatic speech recognition (ASR) or semantic gaps in natural language understanding (NLU). These errors easily translate to user frustrations, particularly so in recurrent events e.g. regularly toggling an appliance, calling a frequent contact, etc. In this work, we propose a query rewriting approach by leveraging users' historically successful interactions as a form of memory. We present a neural retrieval model and a pointer-generator network with hierarchical attention and show that they perform significantly better at the query rewriting task with the aforementioned user memories than without. We also highlight how our approach with the proposed models leverages the structural and semantic diversity in ASR's output towards recovering users' intents.
Adaptive Neuro-Fuzzy control of Autonomous Ground Vehicle (AGV) based on Machine Vision

Development of self-autonomous robot that has the capability to deal with dynamic obstacles as well as static obstacles and keep finding its optimal path represent big challenge to many of the researchers nowadays. Utilization of artificial intelligence (AI) in developing a robust algorithm for nonlinear system was proved in some applications. The present work focus on excessive analysis and investigation of the optimal AI algorithm technique for controlling the path planning of four -wheel autonomous robot to achieve its set target through both static and dynamic obstacles with the optimal path without human interference. Fuzzy logic, Neural Network, and Adaptive Neuro-Fuzzy Inference System (ANFIS) were tested and evaluated as controlling algorithm for this navigation task. The performance evaluation was done both theoretically and experimentally using vision-based feedback sensor, Kinect camera, on a real four-wheel mobile robot. Results prove that ANFIS successfully control the automatic path planning of the robot with the optimal path by reaching its target with
Discrimination of quantum states under locality constraints in the many-copy setting

We study quantum hypothesis testing between orthogonal states under restricted local measurements in the many-copy scenario. For testing arbitrary multipartite entangled pure state against its orthogonal complement state via the local operation and classical communication (LOCC) operation, we prove that the optimal average error probability always decays exponentially in the number of copies. Second, we provide a sufficient condition for the LOCC operations to achieve the same performance as the positive-partial-transpose (PPT) operations. We further show that testing a maximally entangled state against its orthogonal complement and testing extremal Werner states both fulfill the above-mentioned condition. Hence, we determine the explicit expressions for the optimal average error probability, the optimal trade-off between the type-I and type-II errors, and the associated Chernoff, Stein, Hoeffding, and strong converse exponents. Then, we show an infinite asymptotic separation between the separable (SEP) and PPT operations by providing a pair of states constructed from an unextendible product basis (UPB). The quantum states can be distinguished perfectly by PPT operations, while the optimal error probability, with SEP operations, admits an exponential lower bound. On the technical side, we prove this result by providing a quantitative version of the well-known statement that the tensor product of UPBs is a UPB.