Reviewerly/exHarmony huggingface dataset:
Plug-and-Play Conversational Models

There has been considerable progress made towards conversational models that generate coherent and fluent responses; however, this often involves training large language models on large dialogue datasets, such as Reddit.These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model.In this paper, we first propose and evaluate plug-and-play methods for controllable response generation, which does not require dialogue specific datasets and does not rely on fine-tuning a large model.While effective, the decoding procedure induces considerable computational overhead, rendering the conversational model unsuitable for interactive usage.To overcome this, we introduce an approach that does not require further computation at decoding time, while also does not require any fine-tuning of a large language model.We demonstrate, through extensive automatic and human evaluation, a high degree of control over the generated conversational responses with regard to multiple desired attributes, while being fluent.
Compilation by stochastic Hamiltonian sparsification

Simulation of quantum chemistry is expected to be a principal application of quantum computing. In quantum simulation, a complicated Hamiltonian describing the dynamics of a quantum system is decomposed into its constituent terms, where the effect of each term during time-evolution is individually computed. For many physical systems, the Hamiltonian has a large number of terms, constraining the scalability of established simulation methods. To address this limitation we introduce a new scheme that approximates the actual Hamiltonian with a sparser Hamiltonian containing fewer terms. By stochastically sparsifying weaker Hamiltonian terms, we benefit from a quadratic suppression of errors relative to deterministic approaches. Relying on optimality conditions from convex optimisation theory, we derive an appropriate probability distribution for the weaker Hamiltonian terms, and compare its error bounds with other probability ansatzes for some electronic structure Hamiltonians. Tuning the sparsity of our approximate Hamiltonians allows our scheme to interpolate between two recent random compilers: qDRIFT and randomized first order Trotter. Our scheme is thus an algorithm that combines the strengths of randomised Trotterisation with the efficiency of qDRIFT, and for intermediate gate budgets, outperforms both of these prior methods.
Improving robustness against common corruptions by covariate shift adaptation

Today's state-of-the-art machine vision models are vulnerable to image corruptions like blurring or compression artefacts, limiting their performance in many real-world applications. We here argue that popular benchmarks to measure model robustness against common corruptions (like ImageNet-C) underestimate model robustness in many (but not all) application scenarios. The key insight is that in many scenarios, multiple unlabeled examples of the corruptions are available and can be used for unsupervised online adaptation. Replacing the activation statistics estimated by batch normalization on the training set with the statistics of the corrupted images consistently improves the robustness across 25 different popular computer vision models. Using the corrected statistics, ResNet-50 reaches 62.2% mCE on ImageNet-C compared to 76.7% without adaptation. With the more robust DeepAugment+AugMix model, we improve the state of the art achieved by a ResNet50 model up to date from 53.6% mCE to 45.4% mCE. Even adapting to a single sample improves robustness for the ResNet-50 and AugMix models, and 32 samples are sufficient to improve the current state of the art for a ResNet-50 architecture. We argue that results with adapted statistics should be included whenever reporting scores in corruption benchmarks and other out-of-distribution generalization settings.
Graphene: Strong yet Lightweight Row Hammer Protection

Row Hammer is a serious security threat to modern computing systems using DRAM as main memory. It causes charge loss in DRAM cells adjacent to a frequently activated aggressor row and eventually leads to data bit flips in those cells. Even with countermeasures from hardware vendors for years, many latest DDR4 DRAM-based systems are still vulnerable to Row Hammer. Furthermore, technology scaling continues to reduce the Row Hammer threshold, hence posing even greater challenges than before. Although many architectural solutions for Row Hammer have been proposed in both industry and academia, they still incur substantial overhead in terms of chip area, energy, and performance, fail to provide a sufficient level of protection, or both. Thus, we propose Graphene, a low-cost Row Hammer prevention technique based on a space-efficient algorithm that identifies frequent elements from an incoming data stream. Graphene is provably secure without false negatives and with tightly bounded false positives. Furthermore, Graphene has an order of magnitude smaller area overhead compared to a state-of-the-art counter-based scheme. This makes Graphene a scalable solution to Row Hammer attacks for the memory systems of today and the future. Our evaluation shows that Graphene features nearly zero performance and energy overhead when running realistic workloads. Even for the most adversarial memory access patterns, Graphene increases refresh energy only by 0.34%.
LibKGE - A knowledge graph embedding library for reproducible research

LIBKGE 1 is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction.The key goals of LIBKGE are to enable reproducible research, to provide a framework for comprehensive experimental studies, and to facilitate analyzing the contributions of individual components of training methods, model architectures, and evaluation methods.LIBKGE is highly configurable and every experiment can be fully reproduced with a single configuration file.Individual components are decoupled to the extent possible so that they can be mixed and matched with each other.Implementations in LIBKGE aim to be as efficient as possible without leaving the scope of Python/Numpy/PyTorch. A comprehensive logging mechanism and tooling facilitates indepth analysis.LIBKGE provides implementations of common knowledge graph embedding models and training methods, and new ones can be easily added.A comparative study (Ruffinelli et al., 2020) showed that LIBKGE reaches competitive to state-of-the-art performance for many models with a modest amount of automatic hyperparameter tuning.
Deep Unsupervised Image Hashing by Maximizing Bit Entropy

Unsupervised hashing is important for indexing huge image or video collections without having expensive annotations available. Hashing aims to learn short binary codes for compact storage and efficient semantic retrieval. We propose an unsupervised deep hashing layer called Bi-Half Net that maximizes entropy of the binary codes. Entropy is maximal when both possible values of the bit are uniformly (half-half) distributed. To maximize bit entropy, we do not add a term to the loss function as this is difficult to optimize and tune. Instead, we design a new parameter-free network layer to explicitly force continuous image features to approximate the optimal half-half bit distribution. This layer is shown to minimize a penalized term of the Wasserstein distance between the learned continuous image features and the optimal half-half bit distribution. Experimental results on the image datasets FLICKR25K, NUS-WIDE, CIFAR-10, MS COCO, MNIST and the video datasets UCF-101 and HMDB-51 show that our approach leads to compact codes and compares favorably to the current state-of-the-art.
Evaluation of the performance of traditional machine learning algorithms, convolutional neural network and AutoML Vision in ultrasound breast lesions classification: a comparative study

Background: In recent years, there was an increasing popularity in applying artificial intelligence in the medical field from computer-aided diagnosis (CAD) to patient prognosis prediction.Given the fact that not all healthcare professionals have the required expertise to develop a CAD system, the aim of this study was to investigate the feasibility of using AutoML Vision, a highly automatic machine learning model, for future clinical applications by comparing AutoML Vision with some commonly used CAD algorithms in the differentiation of benign and malignant breast lesions on ultrasound.Methods: A total of 895 breast ultrasound images were obtained from the two online open-access ultrasound breast images datasets.Traditional machine learning models (comprising of seven commonly used CAD algorithms) with three content-based radiomic features (Hu Moments, Color Histogram, Haralick Texture) extracted, and a convolutional neural network (CNN) model were built using python language.AutoML Vision was trained in Google Cloud Platform.Sensitivity, specificity, F1 score and average precision (AUCPR) were used to evaluate the diagnostic performance of the models.Cochran's Q test was used to evaluate the statistical significance between all studied models and McNemar test was used as the post-hoc test to perform pairwise comparisons.The proposed AutoML model was also compared with the current related studies that involve similar medical imaging modalities in characterizing benign or malignant breast lesions.Results: There was significant difference in the diagnostic performance among all studied traditional machine learning classifiers (P<0.05).Random Forest achieved the best performance in the differentiation of benign and malignant breast lesions (accuracy: 90%; sensitivity: 71%; specificity: 100%; F1 score: 0.83; AUCPR: 0.90) which was statistically comparable to the performance of CNN (accuracy: 91%; sensitivity: 82%; specificity: 96%; F1 score: 0
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network

Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages.Considering that a cause usually appears around its corresponding emotion, we construct a pair graph and a Pair Graph Convolutional Network (PairGCN) to model dependency relations among local neighborhood candidate pairs.Moreover, in our proposed graph, there are three types of dependency relations and each type of dependency relations has its own way to propagate contextual information.Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.
Generating Natural Language Attacks in a Hard Label Black Box Setting

We study an important and challenging task of attacking natural language processing models in a hard label black box setting. We propose a decision-based attack strategy that crafts high quality adversarial examples on text classification and entailment tasks. Our proposed attack strategy leverages population-based optimization algorithm to craft plausible and semantically similar adversarial examples by observing only the top label predicted by the target model. At each iteration, the optimization procedure allow word replacements that maximizes the overall semantic similarity between the original and the adversarial text. Further, our approach does not rely on using substitute models or any kind of training data. We demonstrate the efficacy of our proposed approach through extensive experimentation and ablation studies on five state-of-the-art target models across seven benchmark datasets. In comparison to attacks proposed in prior literature, we are able to achieve a higher success rate with lower word perturbation percentage that too in a highly restricted setting.
A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection

In this paper our group proposes and designs a lightweight convolutional neural network (CNN) for detecting facial emotions in real-time and in bulk to achieve a better classification effect. We verify whether our model is effective by creating a real-time vision system. This system employs multi-task cascaded convolutional networks (MTCNN) to complete face detection and transmit the obtained face coordinates to the facial emotions classification model we designed firstly. Then it accomplishes the task of emotion classification. Multi-task cascaded convolutional networks have a cascade detection feature, one of which can be used alone, thereby reducing the occupation of memory resources. Our expression classification model employs Global Average Pooling to replace the fully connected layer in the traditional deep convolution neural network model. Each channel of the feature map is associated with the corresponding category, eliminating the black box characteristics of the fully connected layer to a certain extent. At the same time, our model marries the residual modules and depth-wise separable convolutions, reducing large quantities of parameters and making the model more portable. Finally, our model is tested on the FER-2013 dataset. It only takes 3.1% of the 16GB memory, that is, only 0.496GB memory is needed to complete the task of classifying facial expressions. Not only can our model be stored in an 872.9 kilobytes file, but also its accuracy has reached 67% on the FER-2013 dataset. And it has good detection and recognition effects on those figures which are out of the dataset.
Gamification and flipped learning and their influence on aspects related to the teaching-learning process

Nowadays, education is going through a moment of methodological transformation. Two of the active methodologies with excellent projection within the educational field are gamification and flipped learning. This study analyzed the methodological contrast between gamification and flipped learning in the subject of Physical Education in three different educational stages to determine the most influential methodology in the training process. Research design was a quasi-experimental research with a sample of 356 students in Spain. Data collection took place with a questionnaire. The results show that gamification as a teaching and learning methodology is better valued at an early stage, while the more aged participants better value the flipped learning methodology. Both methodologies have shown great potential in the development of educational processes in Physical Education. Gamification indeed seems to have positive effects on students from the lower stages. On the other hand, flipped learning produces positive effects on the group stages, pre-university students.
Chalcogenide phase-change devices for neuromorphic photonic computing

The integration of artificial intelligence systems into daily applications like speech recognition and autonomous driving rapidly increases the amount of data generated and processed. However, satisfying the hardware requirements with the conventional von Neumann architecture remains challenging due to the von Neumann bottleneck. Therefore, new architectures inspired by the working principles of the human brain are developed, and they are called neuromorphic computing. The key principles of neuromorphic computing are in-memory computing to reduce data shuffling and parallelization to decrease computation time. One promising framework for neuromorphic computing is phase-change photonics. By switching to the optical domain, parallelization is inherently possible by wavelength division multiplexing, and high modulation speeds can be deployed. Non-volatile phase-change materials are used to perform multiplications and non-linear operations in an energetically efficient manner. Here, we present two prototypes of neuromorphic photonic computation units based on chalcogenide phase-change materials. First is a neuromorphic hardware accelerator designed to carry out matrix vector multiplication in convolutional neural networks. Due to the neuromorphic architecture, this prototype can already operate at tera-multiply-accumulate per second speeds. Second is an all-optical spiking neuron, which can serve as a building block for large-scale artificial neural networks. Here, the whole computation is carried out in the optical domain, and the device only needs an electrical interface for data input and readout.
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods

The main objective of eXplainable Artificial Intelligence (XAI) is to provide effective explanations for black-box classifiers. The existing literature lists many desirable properties for explanations to be useful, but there is a scarce consensus on how to quantitatively evaluate explanations in practice. Moreover, explanations are typically used only to inspect black-box models, and the proactive use of explanations as a decision support is generally overlooked. Among the many approaches to XAI, a widely adopted paradigm is Local Linear Explanations-with LIME and SHAP emerging as state-of-the-art methods. We show that these methods are plagued by many defects including unstable explanations, divergence of actual implementations from the promised theoretical properties, and explanations for the wrong label. This highlights the need to have standard and unbiased evaluation procedures for Local Linear Explanations in the XAI field. In this paper we address the problem of identifying a clear and unambiguous set of metrics for the evaluation of Local Linear Explanations. This set includes both existing and novel metrics defined specifically for this class of explanations. All metrics have been included in an open Python framework, named LEAF. The purpose of LEAF is to provide a reference for end users to evaluate explanations in a standardised and unbiased way, and to guide researchers towards developing improved explainable techniques.
Blockchain-Enabled Asynchronous Federated Learning in Edge Computing

The fast proliferation of edge computing devices brings an increasing growth of data, which directly promotes machine learning (ML) technology development. However, privacy issues during data collection for ML tasks raise extensive concerns. To solve this issue, synchronous federated learning (FL) is proposed, which enables the central servers and end devices to maintain the same ML models by only exchanging model parameters. However, the diversity of computing power and data sizes leads to a significant difference in local training data consumption, and thereby causes the inefficiency of FL. Besides, the centralized processing of FL is vulnerable to single-point failure and poisoning attacks. Motivated by this, we propose an innovative method, federated learning with asynchronous convergence (FedAC) considering a staleness coefficient, while using a blockchain network instead of the classic central server to aggregate the global model. It avoids real-world issues such as interruption by abnormal local device training failure, dedicated attacks, etc. By comparing with the baseline models, we implement the proposed method on a real-world dataset, MNIST, and achieve accuracy rates of 98.96% and 95.84% in both horizontal and vertical FL modes, respectively. Extensive evaluation results show that FedAC outperforms most existing models.
Transform Quantization for CNN Compression

In this paper, we compress convolutional neural network (CNN) weights post-training via transform quantization. Previous CNN quantization techniques tend to ignore the joint statistics of weights and activations, producing sub-optimal CNN performance at a given quantization bit-rate, or consider their joint statistics during training only and do not facilitate efficient compression of already trained CNN models. We optimally transform (decorrelate) and quantize the weights post-training using a rate-distortion framework to improve compression at any given quantization bit-rate. Transform quantization unifies quantization and dimensionality reduction (decorrelation) techniques in a single framework to facilitate low bit-rate compression of CNNs and efficient inference in the transform domain. We first introduce a theory of rate and distortion for CNN quantization and pose optimum quantization as a rate-distortion optimization problem. We then show that this problem can be solved using optimal bit-depth allocation following decorrelation by the optimal End-to-end Learned Transform (ELT) we derive in this paper. Experiments demonstrate that transform quantization advances the state of the art in CNN compression in both retrained and non-retrained quantization scenarios. In particular, we find that transform quantization with retraining is able to compress CNN models such as AlexNet, ResNet and DenseNet to very low bit-rates (1-2 bits).
Current Status and Performance Analysis of Table Recognition in Document Images With Deep Neural Networks

The first phase of table recognition is to detect the tabular area in a document.Subsequently, the tabular structures are recognized in the second phase in order to extract information from the respective cells.Table detection and structural recognition are pivotal problems in the domain of table understanding.However, table analysis is a perplexing task due to the colossal amount of diversity and asymmetry in tables.Therefore, it is an active area of research in document image analysis.Recent advances in the computing capabilities of graphical processing units have enabled the deep neural networks to outperform traditional state-of-the-art machine learning methods.Table understanding has substantially benefited from the recent breakthroughs in deep neural networks.However, there has not been a consolidated description of the deep learning methods for table detection and table structure recognition.This review paper provides a thorough analysis of the modern methodologies that utilize deep neural networks.Moreover, it presents a comprehensive understanding of the current state-of-the-art and related challenges of table understanding in document images.The leading datasets and their intricacies have been elaborated along with the quantitative results.Furthermore, a brief overview is given regarding the promising directions that can further improve table analysis in document images.
Few-Shot Question Answering by Pretraining Span Selection

In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers.We explore the more realistic few-shot setting, where only a few hundred training examples are available, and observe that standard models perform poorly, highlighting the discrepancy between current pretraining objectives and question answering.We propose a new pretraining scheme tailored for question answering: recurring span selection.Given a passage with multiple sets of recurring spans, we mask in each set all recurring spans but one, and ask the model to select the correct span in the passage for each masked span.Masked spans are replaced with a special token, viewed as a question representation, that is later used during fine-tuning to select the answer span.The resulting model obtains surprisingly good results on multiple benchmarks (e.g., 72.7 F1 on SQuAD with only 128 training examples), while maintaining competitive performance in the high-resource setting.