Reviewerly/exHarmony huggingface dataset:
Noncontractive Classical Logic

One of the most fruitful applications of substructural logics stems from their capacity to deal with self-referential paradoxes, especially truth-theoretic paradoxes. Both the structural rules of contraction and the rule of cut play a crucial role in typical paradoxical arguments. In this paper I address a number of difficulties affecting noncontractive approaches to paradox that have been discussed in the recent literature. The situation was roughly this: if you decide to go substructural, the nontransitive approach to truth offers a lot of benefits that are not available in the noncontractive account. I sketch a noncontractive theory of truth that has these benefits. In particular, it has both a proof- and a model-theoretic presentation, it can be extended to a first-order language, and it retains every classically valid inference.
Sublinear Algorithms for Gap Edit Distance

The edit distance is a way of quantifying how similar two strings are to one another by counting the minimum number of character insertions, deletions, and substitutions required to transform one string into the other. A simple dynamic programming computes the edit distance between two strings of length n in O(n2) time, and a more sophisticated algorithm runs in time O(n + t2) when the edit distance is t [Landau, Myers and Schmidt, SICOMP 1998]. In pursuit of obtaining faster running time, the last couple of decades have seen a flurry of research on approximating edit distance, including polylogarithmic approximation in near-linear time [Andoni, Krauthgamer and Onak, FOCS 2010], and a constant-factor approximation in subquadratic time [Chakrabarty, Das, Goldenberg, Kouck´y and Saks, FOCS 2018]. We study sublinear-time algorithms for small edit distance, which was investigated extensively because of its numerous applications. Our main result is an algorithm for distinguishing whether the edit distance is at most t or at least t^2 (the quadratic gap problem) in time Õ(n/t+t^3). This time bound is sublinear roughly for all t in [ω(1), o(n^1/3)], which was not known before. The best previous algorithms solve this problem in sublinear time only for t=ω(n^1/3) [Andoni and Onak, STOC 2009]. Our algorithm is based on a new approach that adaptively switches between uniform sampling and reading contiguous blocks of the input strings. In contrast, all previous algorithms choose which coordinates to query non-adaptively. Moreover, it can be extended to solve the t vs t^2-ε gap problem in time Õ(n/t^1-ε+t^3).
FusionEye: Perception Sharing for Connected Vehicles and its Bandwidth-Accuracy Trade-offs

Automated driving and advanced driver assistance systems benefit from complete understandings of traffic scenes around vehicles. Existing systems gather such data through cameras and other sensors in vehicles but scene understanding can be limited due to the sensing range of sensors or occlusion from other objects. To gather information beyond the view of one vehicle, we propose and explore FusionEye - a connected vehicle system that allows multiple vehicles to share perception data over vehicle-to-vehicle communications and collaboratively merge this data into a more complete traffic scene. FusionEye uses a self-adaptive topology merging algorithm based on bipartite graph. We explore its network bandwidth requirements and the trade-off with merging accuracy. Experimental results show that FusionEye creates more complete scenes and achieves a merging accuracy of 88% with 5% packet drop rate and transmission latency around 200ms. We show that richer vehicle descriptors offer only marginal accuracy improvements compared to lower communication overhead options.
Stochastic Gradient Descent on Modern Hardware: Multi-core CPU or GPU? Synchronous or Asynchronous?

There is an increased interest in building data analytics frameworks with advanced algebraic capabilities both in industry and academia. Many of these frameworks, e.g., TensorFlow, implement their compute-intensive primitives in two flavors-as multi-thread routines for multi-core CPUs and as highly-parallel kernels executed on GPU. Stochastic gradient descent (SGD) is the most popular optimization method for model training implemented extensively on modern data analytics platforms. While the data-intensive properties of SGD are well-known, there is an intense debate on which of the many SGD variants is better in practice. In this paper, we perform a comprehensive experimental study of parallel SGD for training machine learning models. We consider the impact of three factors - computing architecture (multi-core CPU or GPU), synchronous or asynchronous model updates, and data sparsity - on three measures-hardware efficiency, statistical efficiency, and time to convergence. We draw several interesting findings from our experiments with logistic regression (LR), support vector machines (SVM), and deep neural nets (MLP) on five real datasets. As expected, GPU always outperforms parallel CPU for synchronous SGD. The gap is, however, only 2-5X for simple models, and below 7X even for fully-connected deep nets. For asynchronous SGD, CPU is undoubtedly the optimal solution, outperforming GPU in time to convergence even when the GPU has a speedup of 10X or more. The choice between synchronous GPU and asynchronous CPU is not straightforward and depends on the task and the characteristics of the data. Thus, CPU should not be easily discarded for machine learning workloads. We hope that our insights provide a useful guide for applying parallel SGD in practice and - more importantly - choosing the appropriate computing architecture
User experiences of garment-based dynamic compression for novel haptic applications

Compression is a haptic stimulus used in medical interventions (e.g., compression stockings) and has the potential to be integrated into new research areas (e.g., immersive VR experiences, distributed notification mechanisms), yet remains largely understudied. This work investigates the user experience of compression garment technologies that are dynamic, remotely controllable, and low mass, to better address this research gap. Shape memory alloy-based compression garments, capable of creating spatially- and temporally- dynamic on-body compression, were designed and deployed in a user study (n=17, 8M/9F) to understand the effects of compression, and to draw insights for future compression-based applications. The major takeaways are: (1) importance of and sizing/fit, (2) individual/gender preferences and need for customizability, and (3) the relationship between context-specific stimulation and perception.
Improving the calibration of phase measuring deflectometry by a polynomial representation of the display shape

Phase measuring deflectometry is a highly precise and full field metrology technique for specular surfaces based on the distortion of known reference patterns observed as a reflection at the surface under test. Typically, liquid crystal displays are employed to provide the required patterns. Due to a lack of research, these displays are used without sufficient calibration. In this work, we present an enhanced calibration for phase measuring deflectometry, taking flatness deviations of the display surface into account. The display shape is modelled as a polynomial surface whose coefficients are determined by minimizing the retrace error in a global optimization procedure during calibration. This approach does not require any additional measurements or hardware. Improvements due to the enhanced calibration model are qualified experimentally using a flat and a spherical concave mirror. The model-based parameterization of the display surface yields significant improvement on both samples. The peak to valley (PV) of measured deviations on the plane mirror are reduced by 67% to 0.55 μm. Measuring the spherical sample without the display parameterization leads to a rather large shape deviation of 33.40 μm PV which is reduced by 94% to 1.98 μm. The viability of our approach confirms the dominant role of flatness deviations of the display surface as an error source in absolute shape measurement using phase measuring deflectometry.
A novel and accurate chess pattern for automated texture classification

In this study, a novel chess based local image descriptor is presented for textural image recognition. The proposed descriptor is inspired by chess game and the main objective of it is to extract distinctive textural features using chess game rules. Patterns of the proposed method are created by using the movements of the knight, rook and bishop chessmen and six feature images are constructed using the proposed chess-based textural image descriptor. Therefore, this method is called as chess pattern (chess-pat) consisting of 4 phases. These four phases are block division, binary features calculation using chess patterns, histogram extraction, feature reduction with maximum pooling and classification. In the first phase, the image is divided into 5 x5 overlapping blocks. To extract the features, the proposed chess patterns are used. In the proposed chess-pat, 6 varied patterns are used based on chessmen moves and 6 feature images are created based on these patterns. Then, the histograms of these images are extracted and they are combined to create feature set of 1536 dimensions (D). In addition, maximum pooling is used to reduce this feature set with 256D and two versions of the chess-pat are obtained during the feature extraction. K-nearest neighbor (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) and are utilized for classification. To evaluate the performance of the proposed chess-pat, Outex TC 00013, Outex TC 00001, Outex TC 00000, Kylberg and 2D Hela texture datasets are used. We have obtained the best accuracy rates of 75.5%, 100.0%, 99.7%, 88.9% and 100.0% for 2D Hela, Outex TC00000, Outex TC00001, Outex TC00013, and Kylberg respectively. Also, the proposed chess-pat achieved 100.0% classification rate (perfect classification performance) for 2 datasets (Outex TC00000, Kylberg). These results confirm that our proposed chess-pat method is highly accurate.
E-Commerce Recommendation System Using Mahout

The last two decades has witnessed the speedy growth of e-commerce, most conspicuously in online shopping websites such as Amazon, eBay or Alibaba. The popularity of e-commerce increases the users' lifetime value and makes online market more competitive. The strategy for platform owners is to make highly customized suggestions to customers meaning that they should know users individually by learning from their habits. Different from a traditional brick-and-mortar store where there is only one store for all users, in e-commerce, if you have 3 million customers on the Web, you should have 3 million stores on the Web (Jeff Bezos, CEO of Amazon). Therefore a need for deeply understanding users to create personalized recommendations is formed.The basic idea of recommendation is linking one product (item) to other products, one user (client) to users, as clusters, and then create connections between them. Initially, it meant just suggesting the same purchased item to other buyers. The state of affairs has been gradually improved by the technology and effort of researchers. Nowadays, Web recommendation system has become an essential part of all online e-commerce systems. In this paper, a novel process for constructing a recommendation system is proposed. And we explore the recommendation algorithm through a case study of Web log mining a real-world online shopping site.
KLEESpectre

Spectre-style attacks disclosed in early 2018 expose data leakage scenarios via cache side channels. Specifically, speculatively executed paths due to branch mis-prediction may bring secret data into the cache, which are then exposed via cache side channels even after the speculative execution is squashed. Symbolic execution is a well-known test generation method to cover program paths at the level of the application software. In this article, we extend symbolic execution with modeling of cache and speculative execution. Our tool KLEE SPECTRE , built on top of the KLEE symbolic execution engine, can thus provide a testing engine to check for data leakage through the cache side channel as shown via Spectre attacks. Our symbolic cache model can verify whether the sensitive data leakage due to speculative execution can be observed by an attacker at a given program point. Our experiments show that KLEE SPECTRE can effectively detect data leakage along speculatively executed paths and our cache model can make the leakage detection more precise.
Hierarchical gated recurrent neural network with adversarial and virtual adversarial training on text classification

Document classification aims to assign one or more classes to a document for ease of management by understanding the content of a document. Hierarchical attention network (HAN) has been showed effective to classify documents that are ambiguous. HAN parses information-intense documents into slices (i.e., words and sentences) such that each slice can be learned separately and in parallel before assigning the classes. However, introducing hierarchical attention approach leads to the redundancy of training parameters which is prone to overfitting. To mitigate the concern of overfitting, we propose a variant of hierarchical attention network using adversarial and virtual adversarial perturbations in 1) word representation, 2) sentence representation and 3) both word and sentence representations. The proposed variant is tested on eight publicly available datasets. The results show that the proposed variant outperforms the hierarchical attention network with and without using random perturbation. More importantly, the proposed variant achieves state-of-the-art performance on multiple benchmark datasets. Visualizations and analysis are provided to show that perturbation can effectively alleviate the overfitting issue and improve the performance of hierarchical attention network.
Enhancing wave function collapse with design-level constraints

Wave Function Collapse (WFC) is a non-backtracking, greedy search algorithm that is commonly known for its ability to take an example image and generate similar images. Since its inception, technical artists have explored the algorithm's extensibility and usability through various implementations spanning from 3D world generation to poetry creation. However, there has been no integration of design constraints into the generative process. In this paper, we explore WFC as a constraint satisfaction solver to integrate design principles and practices by modifying components within the algorithm. First, we extend the local constraint reasoning by incorporating non-local constraints as well as upper and lower bounds. Next, we further manipulate the generative space by introducing weight recalculation and dependencies. Lastly, we evaluate our design-focused variant of WFC against the original implementation to examine the associated costs in computational time and memory usage. In summary, this paper describes a technical implementation of integrating design constraints into WFC and analyzes the computational trade-offs.
Learning to compose over tree structures via POS tags for sentence representation

Recursive Neural Network (RecNN), a type of model which composes words or phrases recursively over syntactic tree structures, has been proven to have superior ability to obtain sentence representation for a variety of NLP tasks. However, RecNN is born with a thorny problem that a shared compositional function for each node of trees can't capture the complex semantic compositionality so that the expressive power of model is limited. In this paper, in order to address this problem, we propose Tag-Guided HyperRecNN/TreeLSTM (TG-HRecNN/TreeLSTM), which introduces hypernetwork into RecNNs to take as inputs Part-of-Speech (POS) tags of word/phrase and generate the semantic composition parameters dynamically. Experimental results on five datasets for two typical NLP tasks show proposed models both obtain significant improvement compared with RecNN and TreeLSTM consistently. Our TG-HTreeLSTM outperforms all existing RecNN-based models and achieves or is competitive with state-of-the-art on four sentence classification benchmarks. The effectiveness of our models is also demonstrated by qualitative analysis.
An efficient predictive analytics system for high dimensional big data

The excessive growth of high dimensional big data has resulted in a greater challenge for data scientists to efficiently obtain valuable knowledge from these data. Traditional data mining techniques are not fit to process big data. Predictive analytics has grown in prominence alongside the emergence of big data. In this paper, an efficient predictive analytics system for high dimensional big data is proposed by enhancing scalable random forest (SRF) algorithm on the Apache Spark platform. SRF is enhanced by optimizing the hyperparameters and prediction performance is improved by reducing the dimensions. The effectiveness of the proposed system is examined on five real-world datasets. Experimental results demonstrated that the proposed system achieves the highly competitive performance compared with RF algorithm implemented by Spark MLlib.
Analyzing the Footprint of Classifiers in Adversarial Denial of Service Contexts

Adversarial machine learning is an area of study that examines both the generation and detection of adversarial examples, which are inputs specially crafted to deceive classifiers, and has been extensively researched specifically in the area of image recognition, where humanly imperceptible modifications are performed on images that cause a classifier to perform incorrect predictions. The main objective of this paper is to study the behavior of multiple state of the art machine learning algorithms in an adversarial context. To perform this study, six different classification algorithms were used on two datasets, NSL-KDD and CICIDS2017, and four adversarial attack techniques were implemented with multiple perturbation magnitudes. Furthermore, the effectiveness of training the models with adversaries to improve recognition is also tested. The results show that adversarial attacks successfully deteriorate the performance of all the classifiers between 13% and 40%, with the Denoising Autoencoder being the technique with highest resilience to attacks.
A survey on cybersecurity awareness concerns, practices and conceptual measures

Cybersecurity, a word that attained considerable attention and is regarded as most widely used term across all the domains that use internet. Recent technological advances have mandated the necessity of exploring various aspects of cybersecurity. Rapid cyberisation with the introduction of smart devices has enforced both government and private organizations to create an awareness on cyber threats and cybersecurity. Developed countries like New Zealand has been a frontrunner in introducing new technology and in some cases has legally mandated to implement cybersecurity procedures in various sectors including educational institutions. Though, the necessity of such implementations are never questioned, however, there has been a continues debate on implementation framework particularly for school, bursary and undergraduate syllabus.This paper is divided into three parts. Firstly, the importance of cybersecurity awareness is established by presenting various statistics, followed by the current implementations for cybersecurity awareness in terms of courses, seminars etc. The results of a comprehensive survey among various age groups is presented which gives a generic opinion on different implementations. Finally, we propose a framework that leads to the process of implementing cybersecurity awareness.The survey provides a comprehensive understand of cyber security awareness. This work will contributes on understanding the cyber security awareness state of internet users, what are important aspects and statistics to be collected as well as on the important concepts of internet security that are to be considered while designing a survey. Further, the conclusions obtained from the survey result will guide the new works on cyber security awareness programmes.
Joint Cognition of Both Human and Machine for Predicting Criminal Punishment in Judicial System

Thousands of research have been taking place to develop advanced Artificial Intelligence System which can’t only perform faster but also predict better than human. But a human has some qualities which can never be gained by a machine like creativity, empathy, sensing, and critical thinking. By aggregating the best sides of both, a novel paradigm for the judicial system can be anticipated. For this purpose, we prepare a dataset both from an online survey (n=103) and interviews conducted in Bangladesh on cases related to ‘Women and Children Repression Prevention Act, 2000’. We apply several machine learning algorithms to make a machine that can predict punishment like a judge and calculate both the test accuracy and the predictive power of the models to observe which algorithm performs better and stable than the others. Even human can guide machine for judging a delinquent.
Analysis of user emotions and opinion using Multinomial Naive Bayes Classifier

The user emotion analysis has been a great deal, Here primarily, the twitter dataset is used for analysis. [6] In the current trend, twitter application usage is getting drastically increased where people share their thoughts with each other throughout the world. Generally people use social media as a platform to express their opinion to others, In the meantime Facebook and Twitter serve as the major source for them. Hence they make use of this when needed. The volume of the tweets remain quite high since it is a collection of so many tweets throughout the world, and also it consists of both positive and negative tweets, For the purpose of analysis it is necessary to find the positive and negative tweets and categorize them accordingly. In this project the input dataset consists of a compilation of a number of tweets which expresses the thoughts of various people regarding the election results and their own thoughts about different election candidates who compete in the election, correspondingly each person may have varying opinion on each candidate which will be positive or negative. The analysis of these tweets will help in recording a small survey over those different candidates. The dataset consists of more than 2000 records. The analysis is being done with the help of python programming by importing various packages and implementing a number of functions required for analysis. The results are obtained in two different categories namely positive and negative. Multinomial Naive Bayes classifier has been used for the purpose of analysis. The work is further extended and tested for the imdb movie review dataset, mobile review dataset and the results are categorized as positive and negative.