Reviewerly/exHarmony huggingface dataset:
MazeExplorer: A Customisable 3D Benchmark for Assessing Generalisation in Reinforcement Learning

This paper presents a customisable 3D benchmark for assessing generalisability of reinforcement learning agents based on the 3D first-person game Doom and open source environment VizDoom. As a sample use-case we show that different domain randomisation techniques during training in a key-collection navigation task can help to improve agent performance on unseen evaluation maps.
Mimicking Short-Term Memory in Shape-Reconstruction Task Using an EEG-Induced Type-2 Fuzzy Deep Brain Learning Network

The paper attempts to model short-term memory (STM) for shape-reconstruction tasks by employing a 4-stage deep brain leaning network (DBLN), where the first two stages are built with Hebbian learning and the last two stages with Type-2 Fuzzy logic. The model is trained stage-wise independently with visual stimulus of the object-geometry as the input of the first stage, EEG acquired from different cortical regions as input and output of respective intermediate stages, and recalled object-geometry as the output of the last stage. Two error feedback loops are employed to train the proposed DBLN. The inner loop adapts the weights of the STM based on a measure of error in model-predicted response with respect to the object-shape recalled by the subject. The outer loop adapts the weights of the iconic (visual) memory based on a measure of error of the model predicted response with respect to the desired object-shape. In the test phase, the DBLN model reproduces the recalled object shape from the given input object geometry. The motivation of the paper is to test the consistency in STM encoding (in terms of similarity in network weights) for repeated visual stimulation with the same geometric object. Experiments undertaken on healthy subjects, yield high similarity in network weights, whereas patients with pre-frontal lobe Amnesia yield significant discrepancy in the trained weights for any two trials with the same training object. This justifies the importance of the proposed DBLN model in automated diagnosis of patients with learning difficulty. The novelty of the paper lies in the overall design of the DBLN model with special emphasis to the last two stages of the network, built with vertical slice based type-2 fuzzy logic, to handle uncertainty in function approximation (with noisy EEG data). The proposed technique outperforms the state-of-the-art functional mapping algorithms with respect to the (pre-defined outer loop) error metric, computational complexity and runtime.
Particle Subswarms Collaborative Clustering

Collaborative clustering aims to find a common data structure between several distributed data sets governed by different privacy constraints and technical limitations that prohibit a central collection of data for processing. Therefore, it is required to process the data sets separately using collaboration, which allows clustering algorithms to work locally on an individual data set while exchanging information about the finding with algorithms in other data locations. Thus, the different data locations share information to improve individual clustering result amidst technical and privacy limitations but without breaching privacy. In this article, we present a framework of collaborative clustering that does not require interaction coefficients to regulate the effect of collaboration. We further adapt the framework to cluster distributed data using crisp and fuzzy clustering algorithms. We use particle swarm optimization techniques to inference the framework and, therefore, call it particle subswarms. Moreover, the collaboration increases the number of particles in the swarm without increasing the number of clusters in the data set. This article, therefore, provides the theoretical foundations of particle subswarms and some experimental results on several data sets.
Synthesizing Differentially Private Datasets using Random Mixing

The goal of differentially private data publishing is to release a modified dataset so that its privacy can be ensured while allowing for efficient learning. We propose a new data publishing algorithm in which a released dataset is formed by mixing ℓ randomly chosen data points and then perturbing them with an additive noise. Our privacy analysis shows that as ℓ increases, noise with smaller variance is sufficient to achieve a target privacy level. In order to quantify the usefulness of our algorithm, we adopt the accuracy of a predictive model trained with our synthetic dataset, which we call the utility of the dataset. By characterizing the utility of our dataset as a function of ℓ, we show that one can learn both linear and nonlinear predictive models so that they yield reasonably good prediction accuracies. Particularly, we show that there exists a sweet spot on ℓ that maximizes the prediction accuracy given a required privacy level, or vice versa. We also demonstrate that given a target privacy level, our datasets can achieve higher utility than other datasets generated with the existing data publishing algorithms.
Interpolation-based Decoding of Nonlinear Maximum Rank Distance Codes

In this paper, we formulate a generic construction of MRD codes that covers almost all the newly found MRD codes. Among those MRD codes, we particularly investigate the encoding and decoding of a family of nonlinear MRD codes recently by Otal and Özbudak.
Equiangular lines and the Lemmens–Seidel conjecture

In this paper, claims by Lemmens and Seidel in 1973 about equiangular sets of lines with angle 1∕5 are proved by carefully analyzing pillar decomposition, with the aid of the uniqueness of two-graphs on 276 vertices. The Neumann Theorem is generalized in the sense that if there are more than 2r−2 equiangular lines in Rr, then the angle is quite restricted. Together with techniques on finding saturated equiangular sets, we determine the maximum size of equiangular sets “exactly” in an r-dimensional Euclidean space for r=8, 9, and 10.
First Steps in Dance Data Science

We report results of a design-research effort to develop a culturally-relevant educational experience that can engage high school dancers in statistics and data science. In partnership with a local high school and members of its step team, we explore quantitative analysis of both visual and acoustic data captured from student dance. We describe prototype visualizations and interactive applications for evaluating pose precision, tempo, and timbre. With educational goals in mind, we have constrained our design to using only interpretable features and simple, accessible algorithms.
Gated Mixture Variational Autoencoders for Value Added Tax audit case selection

In this work, we address the problem of targeted Value Added Tax (VAT) audit case selection by means of machine learning. This is a challenging problem that has remained rather elusive for EU-based Tax Departments, due to the inadequate quantity of tax audits that can be used for conventional supervised model training. To this end, we devise a novel Gated Mixture Variational Autoencoder deep network, that can be effectively trained with data from a limited number of audited taxpayers, combined with a large corpus of filed VAT returns. This gives rise to a semi-supervised learning framework that leverages the latest advances in deep learning and robust regularization using variational inference. We developed our approach in collaboration with the Cyprus Tax Department and experimentally deployed it to facilitate its audit selection process; to this end, we used actual VAT data from Cyprus-based taxpayers. This way, we obtained strong empirical evidence that our approach can greatly facilitate the VAT audit case selection process. Specifically, we obtained up to 76% out-of-sample accuracy in detecting whether a significant tax yield will be generated from a specific prospective VAT audit.
Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy of Nano-sized Objects) represents a viable technology for mobile real-time detection of viruses and virus-like particles. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. For analysis of the images provided by the sensor, state-of-the-art methods based on convolutional neural networks (CNNs) can achieve high accuracy. However, such computationally intensive methods may not be suitable on most mobile systems. In this work, we propose nanoparticle classification approaches based on frequency domain analysis, which are less resource-intensive. We observe that on average the classification takes 29 μ s per image for the Fourier features and 17 μ s for the Haar wavelet features. Although the CNN-based method scores 1-2.5 percentage points higher in classification accuracy, it takes 3370 μ s per image on the same platform. With these results, we identify and explore the trade-off between resource efficiency and classification performance for nanoparticle classification of images provided by the PAMONO sensor.
A Fighting Game AI Using Highlight Cues for Generation of Entertaining Gameplay

In this paper, we propose a fighting game AI that selects its actions from the perspective of highlight generation using Monte-Carlo tree search (MCTS) with three highlight cues in the evaluation function. The proposed AI is targeted for being used to generate gameplay in live streaming platforms such as Twitch and YouTube where a large number of spectators watch gameplay to entertain themselves. Our results in a user study conducted using FightingICE, a fighting game platform used in an international game AI competition since 2013, show that gameplay generated by the proposed AI is more entertaining than that by a typical MCTS AI. Detailed analyses of gameplay from all the methods assessed in the user study are also given in the paper.
Decision Procedure for the Existence of Two-Channel Prefix-Free Codes

The Kraft inequality gives a necessary and sufficient condition for the existence of a single channel prefix-free code. However, the multichannel Kraft inequality does not imply the existence of a multichannel prefix-free code in general. It is natural to ask whatever there exists an efficient decision procedure for the existence of multichannel prefix-free codes. In this paper, we tackle the two-channel case of the above problem by relating it to a constrained rectangle packing problem. Although a general rectangle packing problem is NP-complete, the extra imposed constraints allow us to propose an algorithm which can solve the problem efficiently.
User-Centric Interference-Aware Load Balancing for Cloud-Deployed Applications

VMs deployed in cloud environments are prone to performance interference due to dynamic and unpredictable contention for shared physical resources among colocated tenants. Current provider-centric solutions, such as careful co-scheduling of VMs and/or VM migration, require a priori profiling of customer VMs, which is infeasible in public clouds. Further, such solutions are not always aware of the user's SLO requirements or application bottlenecks. This paper presents DIAL, an interference-aware load balancing framework that can directly be employed by cloud users without requiring any assistance from the provider. The key idea behind DIAL is to infer the demand for contended resources on the physical hosts, which is otherwise hidden from users. Estimates of the colocated load are then used to dynamically shift load away from compromised VMs without violating the application's tail latency SLOs. We implement DIAL for web and online analytical processing applications, and show, via experimental results on OpenStack and AWS clouds, that DIAL can reduce tail latencies by as much as 70 percent compared to existing solutions.
A Detailed Study on Classification Algorithms in Big Data

After an era of managing data collection difficulties, these days the issue has turned into the problem of how to process these vast amounts of information. Scientists, as well as researchers, think that today, probably the most essential topic in computing science is Big Data. Big Data is used to clarify the huge volume of data that could exist in any structure. This makes it difficult for standard controlling approaches for mining the best possible data through such large data sets. Classification in Big Data is a procedure of summing up data sets dependent on various examples. There are distinctive classification frameworks which help us to classify data collections. A few methods that discussed in the chapter are Multi-Layer Perception Linear Regression, C4.5, CART, J48, SVM, ID3, Random Forest, and KNN. The target of this chapter is to provide a comprehensive evaluation of classification methods that are in effect commonly utilized.
Co-attention Networks for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis has identified its significance in sentiment polarity classification of consumer review. For the purpose of specific target sentiment analysis, we put forward a co-attentive deep learning method in the manner of human processing. To start with, the GRUs are taken to extract the hidden states of the different word embeddings. Further, via the interactive learning of the co-attention network, the representations of the target and the context can be obtained. In addition, the attention weights are determined based on the self-attention mechanism to update the final representations. The experimental results evaluated on the SemEval 2014 and Twitter establish a strong evidence of the high accuracy.
Byzantine-Tolerant Distributed Coordinate Descent

We study distributed coordinate descent (CD) in the master-worker architecture under adversarial attacks, where the data is partitioned (across the parameter space) and distributed among m worker nodes (t of which can be maliciously corrupt), which update some coordinates of their part of the parameter vector, in parallel and iteratively, using CD updates, with the help of the master. We propose a method based on data encoding and real error correction to combat the adversary. Our method can tolerate up to ⌈m-1/2⌉ corrupt nodes, which is information-theoretically optimal. Our design gives a trade-off between the resiliency t, the required redundancy, and the computation at master and worker nodes. For example, with constant overhead in the storage and computational complexity over that required by the plain distributed CD, we can tolerate up to m/3 corrupt nodes. We design a sparse encoding scheme, which yields low encoding complexity.
Automatic Turn-Level Language Identification for Code-Switched Spanish–English Dialog

We examine the efficacy of text and speech-based features for language identification in code-switched human-human dialog interactions at the turn level. Ramanarayanan, Vikram We extract a variety of character- and word-based text features and pass them into multiple learners, including conditional random fields, logistic regressors and deep neural networks. Pugh, Robert We observe that our best-performing text system significantly outperforms a majority vote baseline. Qian, Yao We further leverage the popular i-Vector approach in extracting features from the speech signal and show that this outperforms a traditional spectral feature-based front-end as well as the majority vote baseline. Suendermann-Oeft, David
Image Encryption Using Modified Elliptic Curve Cryptography and Hill Cipher

Currently, the digital communication generates millions of digital data using the digital images. These confidential images must be protected from the intruders over transmission in network channels. To protect the data, in this paper, a combination of Modified Elliptic Curve Cryptography (MECC) and Hill Cipher (HC) (MECCHC) method is proposed. Elliptic curve cryptography (ECC) is an asymmetric key encryption and enhanced further using symmetric encryption of Hill Cipher, allowing simple and fast computations over complex encryption methods of ECC. Adding a layer of Hill Cipher over ECC makes it even more difficult for the intruder attack. Hill Cipher encryption involves multiplication of 4 × 4 key matrix with 4 × 4 chunks of image pixels, where the self-invertible key matrix is derived from the elliptic curve parameters which make decryption process easier and faster without computing the matrix inverse. The MECCHC method is used to encrypt grayscale and color images. The process efficiency of the MECCHC method is evaluated using entropy, peak signal to noise ratio, number of pixels change rate, and unified average changing intensity measures.
Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks

In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level. We propose a systematic pipeline for interpreting individual hidden state dynamics within the network using response characterization methods. The ranked contribution of individual cells to the network's output is computed by analyzing a set of interpretable metrics of their decoupled step and sinusoidal responses. As a result, our method is able to uniquely identify neurons with insightful dynamics, quantify relationships between dynamical properties and test accuracy through ablation analysis, and interpret the impact of network capacity on a network's dynamical distribution. Finally, we demonstrate the generalizability and scalability of our method by evaluating a series of different benchmark sequential datasets.
Local Critic Training of Deep Neural Networks

This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward and backward propagation processes. We propose a cascaded learning strategy for these local networks. In addition, the approach is also useful from multi-model perspectives, including structural optimization of neural networks, computationally efficient progressive inference, and ensemble classification for performance improvement. Experimental results show the effectiveness of the proposed approach and suggest guidelines for determining appropriate algorithm parameters.
Resource Shared Galois Field Computation for Energy Efficient AES/CRC in IoT Applications

End-to-end encryption and reliability of the transmitted data are essential requirements in the present era of internet enabled smart devices. Adhering to current industry standards, the Advanced Encryption Standard (AES) and Cyclic Redundancy Check (CRC) are the two most utilized methods for ensuring security and reliability. To integrate AES and CRC functionality in ultra-low-power embedded System on Chips (SoCs), dedicated computation engines/co-processors are often used, consuming valuable silicon area and additional battery power. This paper presents the design of an energy-efficient multipurpose encryption engine capable of processing both AES and CRC algorithms using a shared Galois Field Computation Unit (GFCU). By decomposing the necessary Galois Field operations of AES and CRC to their fundamental binary steps, it was possible to identify shared operations in these two algorithms. This approach allowed the development of a resource shared system architecture capable of computing AES-128 and CRC-32 using a single computation unit. The GFCU based design was implemented in an area of 151μm × 151μm in 90nm technology node. The energy consumption of the design operating at 0.8 V supply voltage for a 25.6 Mbps throughput was less than 280pJ and 140pJ for AES-128 encryption and CRC-32, respectively.
Software and Application Patterns for Explanation Methods

Deep neural networks successfully pervaded many applications domains and are increasingly used in critical decision processes. Understanding their workings is desirable or even required to further foster their potential as well as to access sensitive domains like medical applications or autonomous driving. One key to this broader usage of explaining frameworks is the accessibility and understanding of respective software. In this work we introduce software and application patterns for explanation techniques that aim to explain individual predictions of neural networks. We discuss how to code well-known algorithms efficiently within deep learning software frameworks and describe how to embed algorithms in downstream implementations. Building on this we show how explanation methods can be used in applications to understand predictions for miss-classified samples, to compare algorithms or networks, and to examine the focus of networks. Furthermore, we review available open-source packages and discuss challenges posed by complex and evolving neural network structures to explanation algorithm development and implementations.
The Next 7000 Programming Languages

Landin’s seminal paper “The next 700 programming languages” considered programming languages prior to 1966 and speculated on the next 700. Half-a-century on, we cast programming languages in a Darwinian ‘tree of life’ and explore languages, their features (genes) and language evolution from the viewpoint of ‘survival of the fittest’. We investigate this thesis by exploring how various languages fared in the past, and then consider the divergence between the languages empirically used in 2017 and the language features one might have expected if the languages of the 1960s had evolved optimally to fill programming niches. This leads us to characterise three divergences, or ‘elephants in the room’, where actual current language use, or feature provision, differs from that which evolution might suggest. We conclude by speculating on future language evolution.
An image denoising approach based on adaptive nonlocal total variation

In the nonlocal total variation (NLTV) model the constant regularization parameter λ cannot adaptively control the balance between the regularization term and the fidelity term, which may results in over-smoothing and the more losing image details in non-flat areas when λ is small, or insufficient noise removal in flat areas when λ is large. It is better that λ has different values according to the characteristics of image areas. In this paper, we introduce an adaptive regularization parameter λ(x) which can recognize flat areas and non-flat areas of an image and propose an improved NLTV model by replacing regularization parameter λ in NLTV model with the function λ(x). In addition, we calculate the similarity weight function of our model from the pre-filtered image to reduce the influence of noise on it. Experimental results demonstrate our approach outperforms some existing methods in terms of objective criteria and subjective visual perception.
Learning Semantic Coherence for Machine Generated Spam Text Detection

Using machine to generate text has attracted considerable attention recently. However, low quality text generated by machine will seriously impact the user experience due to the poor readability. Traditional methods for detecting machine generated text heavily depend on hand-crafted features. While most deep learning methods for general text classification tend to model the semantic representation of topics, and thus overlook the semantic coherence that is also useful for detecting machine generated text. In this paper, we propose an end-to-end neural architecture that learns semantic coherence of text sequences. We conduct experiments on both Chinese and English datasets with more than two million articles containing manually written and machine generated ones. Results show that our method is effective and achieves the state-of-the-art performance.