Reviewerly/exHarmony huggingface dataset:
The Tabu_Genetic Algorithm: A Novel Method for Hyper-Parameter Optimization of Learning Algorithms

Machine learning algorithms have been widely used to deal with a variety of practical problems such as computer vision and speech processing. But the performance of machine learning algorithms is primarily affected by their hyper-parameters, as without good hyper-parameter values the performance of these algorithms will be very poor. Unfortunately, for complex machine learning models like deep neural networks, it is very difficult to determine their hyper-parameters. Therefore, it is of great significance to develop an efficient algorithm for hyper-parameter automatic optimization. In this paper, a novel hyper-parameter optimization methodology is presented to combine the advantages of a Genetic Algorithm and Tabu Search to achieve the efficient search for hyper-parameters of learning algorithms. This method is defined as the Tabu_Genetic Algorithm. In order to verify the performance of the proposed algorithm, two sets of contrast experiments are conducted. The Tabu_Genetic Algorithm and other four methods are simultaneously used to search for good values of hyper-parameters of deep convolutional neural networks. Experimental results show that, compared to Random Search and Bayesian optimization methods, the proposed Tabu_Genetic Algorithm finds a better model in less time. Whether in a low-dimensional or high-dimensional space, the Tabu_Genetic Algorithm has better search capabilities as an effective method for finding the hyper-parameters of learning algorithms. The presented method in this paper provides a new solution for solving the hyper-parameters optimization problem of complex machine learning models, which will provide machine learning algorithms with better performance when solving practical problems.
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning.

Handling previously unseen tasks after given only a few training examples continues to be a tough challenge in machine learning. We propose TapNets, neural networks augmented with task-adaptive projection for improved few-shot learning. Here, employing a meta-learning strategy with episode-based training, a network and a set of per-class reference vectors are learned across widely varying tasks. At the same time, for every episode, features in the embedding space are linearly projected into a new space as a form of quick task-specific conditioning. The training loss is obtained based on a distance metric between the query and the reference vectors in the projection space. Excellent generalization results in this way. When tested on the Omniglot, miniImageNet and tieredImageNet datasets, we obtain state of the art classification accuracies under various few-shot scenarios.
Learning Sparse Networks Using Targeted Dropout

Neural networks are easier to optimise when they have many more weights than are required for modelling the mapping from inputs to outputs. This suggests a two-stage learning procedure that first learns a large net and then prunes away connections or hidden units. But standard training does not necessarily encourage nets to be amenable to pruning. We introduce targeted dropout, a method for training a neural network so that it is robust to subsequent pruning. Before computing the gradients for each weight update, targeted dropout stochastically selects a set of units or weights to be dropped using a simple self-reinforcing sparsity criterion and then computes the gradients for the remaining weights. The resulting network is robust to post hoc pruning of weights or units that frequently occur in the dropped sets. The method improves upon more complicated sparsifying regularisers while being simple to implement and easy to tune.
Gender Differences in Perceptions of Conventional Video, Virtual Reality and Augmented Reality

&lt;p class="Abstract"&gt;&lt;strong&gt;Abstract--&lt;/strong&gt;The user’s emotional involvement plays an important role in adopting new technologies. The level of engagement with and adoption of new digital applications depends on various personal, contextual, and emotional factors. In our study, we assessed the personal factors, such as gender differences, of perceiving and adopting technologies such as virtual reality (VR), augmented reality (AR), and conventional video. Furthermore, we assessed what kind of emotions are involved in and invoked by the context of new technologies. This is a quantitative study in which students were asked to perform experiments on VR, AR, and conventional videos. After the experiments, participants were asked to fill out a predefined survey about their emotional reactions to the experiments. The results show, unlike the prior research, that female participants were more enthusiastic about the usage of new technologies than males. The user experience of VR, AR and conventional videos triggered more positive emotions among females than males. For practitioners, the results suggest that the audio-visual technologies could engage more females than males. For academics, this study provides further research on how to trigger users to adopt new audio-visual technologies&lt;em&gt;. &lt;/em&gt;&lt;/p&gt;
Leveraging BERT for Extractive Text Summarization on Lectures

In the last two decades, automatic extractive text summarization on lectures has demonstrated to be a useful tool for collecting key phrases and sentences that best represent the content. However, many current approaches utilize dated approaches, producing sub-par outputs or requiring several hours of manual tuning to produce meaningful results. Recently, new machine learning architectures have provided mechanisms for extractive summarization through the clustering of output embeddings from deep learning models. This paper reports on the project called Lecture Summarization Service, a python based RESTful service that utilizes the BERT model for text embeddings and KMeans clustering to identify sentences closes to the centroid for summary selection. The purpose of the service was to provide students a utility that could summarize lecture content, based on their desired number of sentences. On top of the summary work, the service also includes lecture and summary management, storing content on the cloud which can be used for collaboration. While the results of utilizing BERT for extractive summarization were promising, there were still areas where the model struggled, providing feature research opportunities for further improvement.
Transforming Complex Sentences into a Semantic Hierarchy

We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE).Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a twolayered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations.In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications.Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification.Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall.To enable reproducible research, all code is provided online.
Synthetic Knowing: The Politics of the Internet of Things

All knowing is material. The challenge for Information Systems (IS) research is to specify how knowing is material by drawing on theoretical characterizations of the digital. Synthetic knowing is knowing informed by theorizing digital materiality. We focus on two defining qualities: liquefaction (unhinging digital representations from physical objects, qualities, or processes) and open-endedness (extendable and generative). The Internet of Things (IoT) is crucial because sensors are vehicles of liquefaction. Their expanding scope for real-time seeing, hearing, tasting, smelling, and touching increasingly mimics phenomenologically perceived reality. Empirically, we present a longitudinal case study of IoT-rendered marine environmental monitoring by an oil and gas company operating in the politically contested Arctic. We characterize synthetic knowing into four concepts, the former three tied to liquefaction and the latter to open-endedness: (i) the objects of knowing are algorithmic phenomena; (ii) the sensors increasingly conjure up phenomenological reality; (iii) knowing is scoped (configurable); and (iv) open knowing/data is politically charged.
Quality of Uncertainty Quantification for Bayesian Neural Network Inference

Bayesian Neural Networks (BNNs) place priors over the parameters in a neural network. Inference in BNNs, however, is difficult; all inference methods for BNNs are approximate. In this work, we empirically compare the quality of predictive uncertainty estimates for 10 common inference methods on both regression and classification tasks. Our experiments demonstrate that commonly used metrics (e.g. test log-likelihood) can be misleading. Our experiments also indicate that inference innovations designed to capture structure in the posterior do not necessarily produce high quality posterior approximations.
An improved differential-based harmony search algorithm with linear dynamic domain

As a relatively new optimization algorithm, Harmony Search (HS) has been widely used to solve global optimization tasks in various fields due to its simplicity of operation and good performance. However, the basic HS has low fine-tuning ability, easy trapping into local optimum and premature convergence. To overcome the drawbacks and further enhance the precision of calculation results, an improved differential-based harmony search algorithm with linear dynamic domain (ID-HS-LDD) is proposed. In the ID-HS-LDD, two main innovative strategies are adopted: Firstly, inspired by one mutation in the Differential Evolution (DE) algorithm, an improved differential-based method is used as a new pitch adjuster. Secondly, for the search domain of optimal values, introducing a linear dynamic change model is considered. In addition, a parameter is also introduced to modify the new vectors generation formula for updating the harmony memory (HM) in the process of computation. A series of comparative experiments is carried out to verify the performance of the ID-HS-LDD using twenty-four typical benchmark functions. The experimental results show that, for most cases, the ID-HS-LDD has superior performance compared with other HS variants and advanced nature-inspired optimizations. Therefore, the proposed ID-HS-LDD is successfully implemented as a novel optimization method.
Blockchain-Based Secure Device Management Framework for an Internet of Things Network in a Smart City

The broadly configured smart city network requires a variety of security considerations for a heterogeneous device environment. Because a network of heterogeneous devices facilitates an attacker’s intrusion through a specific device or node, a device management framework is required to manage each node comprehensively. This paper proposes a blockchain-based device management framework for efficient device management, scalable firmware update and resiliences on attacks against smart city network. This framework offers four device management and firmware update mechanisms based on the performance and requirements of each device: bidirectional mechanism of general end node and a unidirectional mechanism of the lightweight end node. This difference optimizes the resource of network and devices in terms of management and security. All management history of each device is stored in the blockchain and transmitting firmware between vendor and management node is conducted through a smart contract of blockchain for security and resilience on the attack. Through the framework proposed in this paper, the confidentiality and availability of device management on smart city network as well as integrity, auditability, adaptability and authentication for each node are ensured and the effectiveness of the proposed framework is presented through the security analysis.
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems

Deep neural networks (DNNs) are found to be vulnerable against adversarial examples, which are carefully crafted inputs with a small magnitude of perturbation aiming to induce arbitrarily incorrect predictions. Recent studies show that adversarial examples can pose a threat to real-world security-critical applications: a adversarial Stop Sign can be synthesized such that the autonomous driving cars will misrecognize it as others (e.g., a speed limit sign). However, these image-space adversarial examples cannot easily alter 3D scans of widely equipped LiDAR or radar on autonomous vehicles. In this paper, we reveal the potential vulnerabilities of LiDAR-based autonomous driving detection systems, by proposing an optimization based approach LiDAR-Adv to generate adversarial objects that can evade the LiDAR-based detection system under various conditions. We first show the vulnerabilities using a blackbox evolution-based algorithm, and then explore how much a strong adversary can do, using our gradient-based approach LiDAR-Adv. We test the generated adversarial objects on the Baidu Apollo autonomous driving platform and show that such physical systems are indeed vulnerable to the proposed attacks. We also 3D-print our adversarial objects and perform physical experiments to illustrate that such vulnerability exists in the real world. Please find more visualizations and results on the anonymous website: this https URL.
Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition

Communicating through hand gestures with each other is simply called the language of signs. It is an acceptable language for communication among deaf and dumb people in this society. The society of the deaf and dumb admits a lot of obstacles in day to day life in communicating with their acquaintances. The most recent study done by the World Health Organization reports that very large section (around 360 million folks) present in the world have hearing loss, i.e. 5.3% of the earth's total population. This gives us a need for the invention of an automated system which converts hand gestures into meaningful words and sentences. The Convolutional Neural Network (CNN) is used on 24 hand signals of American Sign Language in order to enhance the ease of communication. OpenCV was used in order to follow up on further execution techniques like image preprocessing. The results demonstrated that CNN has an accuracy of 99.7% utilizing the database found on kaggle.com.
Unifying Search-based and Compilation-based Approaches to Multi-agent Path Finding through Satisfiability Modulo Theories

We unify search-based and compilation-based approaches to multi-agent path finding (MAPF) through satisfiability modulo theories (SMT). The task in MAPF is to navigate agents in an undirected graph to given goal vertices so that they do not collide. We rephrase Conflict-Based Search (CBS), one of the state-of-the-art algorithms for optimal MAPF solving, in the terms of SMT. This idea combines SAT-based solving known from MDD-SAT, a SAT-based optimal MAPF solver, at the low-level with conflict elimination of CBS at the high-level. Where the standard CBS branches the search after a conflict, we refine the propositional model with a disjunctive constraint. Our novel algorithm called SMT-CBS hence does not branch at the high-level but incrementally extends the propositional model. We experimentally compare SMT-CBS with CBS, ICBS, and MDD-SAT.
Optimal Feature Selection for Learning-Based Algorithms for Sentiment Classification

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment classification performance of the learning-based methods. Therefore, we investigate the relationship between the number of features selected and the sentiment classification performance of the learning-based methods. A new method for the selection of a suitable number of features is proposed in which the Chi Square feature selection algorithm is employed and the features are selected using a preset score threshold. It is discovered that there is a relationship between the logarithm of the number of features selected and the sentiment classification performance of the learning-based method, and it is also found that this relationship is independent of the learning-based method involved. The new findings in this research indicate that it is always possible for researchers to select the appropriate number of features for learning-based methods to obtain the best sentiment classification performance. This can guide researchers to select the proper features for optimizing the performance of learning-based algorithms. (A preliminary version of this paper received a Best Paper Award at the International Conference on Extreme Learning Machines 2018.)
Abductive Commonsense Reasoning

Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation. While abduction has long been considered to be at the core of how people interpret and read between the lines in natural language (Hobbs et al., 1988), there has been relatively little research in support of abductive natural language inference and generation. We present the first study that investigates the viability of language-based abductive reasoning. We introduce a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations. Based on this dataset, we conceptualize two new tasks -- (i) Abductive NLI: a multiple-choice question answering task for choosing the more likely explanation, and (ii) Abductive NLG: a conditional generation task for explaining given observations in natural language. On Abductive NLI, the best model achieves 68.9% accuracy, well below human performance of 91.4%. On Abductive NLG, the current best language generators struggle even more, as they lack reasoning capabilities that are trivial for humans. Our analysis leads to new insights into the types of reasoning that deep pre-trained language models fail to perform--despite their strong performance on the related but more narrowly defined task of entailment NLI--pointing to interesting avenues for future research.
On the Utility of Learning about Humans for Human-AI Coordination

While we would like agents that can coordinate with humans, current algorithms such as self-play and population-based training create agents that can coordinate with themselves. Agents that assume their partner to be optimal or similar to them can converge to coordination protocols that fail to understand and be understood by humans. To demonstrate this, we introduce a simple environment that requires challenging coordination, based on the popular game Overcooked, and learn a simple model that mimics human play. We evaluate the performance of agents trained via self-play and population-based training. These agents perform very well when paired with themselves, but when paired with our human model, they are significantly worse than agents designed to play with the human model. An experiment with a planning algorithm yields the same conclusion, though only when the human-aware planner is given the exact human model that it is playing with. A user study with real humans shows this pattern as well, though less strongly. Qualitatively, we find that the gains come from having the agent adapt to the human's gameplay. Given this result, we suggest several approaches for designing agents that learn about humans in order to better coordinate with them. Code is available at https://github.com/HumanCompatibleAI/overcooked_ai.
Explanations for Attributing Deep Neural Network Predictions

Given the recent success of deep neural networks and their applications to more high impact and high risk applications, like autonomous driving and healthcare decision-making, there is a great need for faithful and interpretable explanations of “why” an algorithm is making a certain prediction. In this chapter, we introduce 1. Meta-Predictors as Explanations, a principled framework for learning explanations for any black box algorithm, and 2. Meaningful Perturbations, an instantiation of our paradigm applied to the problem of attribution, which is concerned with attributing what features of an input (i.e., regions of an input image) are responsible for a model’s output (i.e., a CNN classifier’s object class prediction). We first introduced these contributions in [8]. We also briefly survey existing visual attribution methods and highlight how they faith to be both faithful and interpretable.
Classification of time-domain waveforms using a speckle-based optical reservoir computer

Reservoir computing is a recurrent machine learning framework that expands the dimensionality of a problem by mapping an input signal into a higher-dimension reservoir space that can capture and predict features of complex, non-linear temporal dynamics. Here, we report on a bulk electro-optical demonstration of a reservoir computer using speckles generated by propagating a laser beam modulated with a spatial light modulator through a multimode waveguide. We demonstrate that the hardware can successfully perform a multivariate audio classification task performed using the Japanese vowel speakers public data set. We perform full wave optical calculations of this architecture implemented in a chip-scale platform using an SiO 2 waveguide and demonstrate that it performs as well as a fully numerical implementation of reservoir computing. As all the optical components used in the experiment can be fabricated using a commercial photonic integrated circuit foundry, our result demonstrates a framework for building a scalable, chip-scale, reservoir computer capable of performing optical signal processing.