Reviewerly/exHarmony huggingface dataset:
Top-KAST: Top-K Always Sparse Training

Sparse neural networks are becoming increasingly important as the field seeks to improve the performance of existing models by scaling them up, while simultaneously trying to reduce power consumption and computational footprint. Unfortunately, most existing methods for inducing performant sparse models still entail the instantiation of dense parameters, or dense gradients in the backward-pass, during training. For very large models this requirement can be prohibitive. In this work we propose Top-KAST, a method that preserves constant sparsity throughout training (in both the forward and backward-passes). We demonstrate the efficacy of our approach by showing that it performs comparably to or better than previous works when training models on the established ImageNet benchmark, whilst fully maintaining sparsity. In addition to our ImageNet results, we also demonstrate our approach in the domain of language modeling where the current best performing architectures tend to have tens of billions of parameters and scaling up does not yet seem to have saturated performance. Sparse versions of these architectures can be run with significantly fewer resources, making them more widely accessible and applicable. Furthermore, in addition to being effective, our approach is straightforward and can easily be implemented in a wide range of existing machine learning frameworks with only a few additional lines of code. We therefore hope that our contribution will help enable the broader community to explore the potential held by massive models, without incurring massive computational cost.
Self-Supervised MultiModal Versatile Networks

Videos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: vision, audio and language. To this end, we introduce the notion of a multimodal versatile network -- a network that can ingest multiple modalities and whose representations enable downstream tasks in multiple modalities. In particular, we explore how best to combine the modalities, such that fine-grained representations of audio and vision can be maintained, whilst also integrating text into a common embedding. Driven by versatility, we also introduce a novel process of deflation, so that the networks can be effortlessly applied to the visual data in the form of video or a static image. We demonstrate how such networks trained on large collections of unlabelled video data can be applied on video, video-text, image and audio tasks. Equipped with these representations, we obtain state-of-the-art performance on multiple challenging benchmarks including UCF101, HMDB51 and ESC-50 when compared to previous self-supervised work.
Entangling three qubits without ever touching

Abstract All identical particles are inherently correlated from the outset, regardless of how far apart their creation took place. In this paper, this fact is used for extraction of entanglement from independent particles unaffected by any interactions. Specifically, we are concerned with operational schemes for generation of all tripartite entangled states, essentially the GHZ state and the W state, which prevent the particles from touching one another over the entire evolution. The protocols discussed in the paper require only three particles in linear optical setups with equal efficiency for boson, fermion or anyon statistics. Within this framework indistinguishability of particles presents itself as a useful resource of entanglement accessible for practical applications.
Multi-Encoder Towards Effective Anomaly Detection in Videos

Given normal training samples, anomaly detection in videos can be regarded as a challenging problem of identifying unexpected events. The state-of-the-art approaches generally resort to the autoencoder model by using a single encoder to capture the motion and content patterns jointly. Nevertheless, due to the lack of accurate labels of normal and abnormal samples, how to detect anomalies is decided by the subjective understanding of models. It infers that different models will prefer to mine different patterns according to the characteristics of models. We call this problem as a pattern bias problem. To alleviate this problem, a novel Multi-Encoder Single-Decoder network, termed as MESDnet, is proposed in the spirit of encoding motion and content cues individually with multiple encoders. MESDnet is of end-to-end learning ability and real-time running speed. Particularly, the differences between adjacent frames and the raw frames are used as the motion and content sources, respectively. Then, a decoder takes charge of detecting anomalies in the way of observing reconstructing error towards the video frames by using the multi-stream encoded motion and content features simultaneously. The experiments on the CUHK Avenue dataset, the UCSD Pedestrian dataset, and the ShanghaiTech Campus dataset verify the effectiveness of MESDnet.
On the Expressiveness of Approximate Inference in Bayesian Neural Networks

While Bayesian neural networks (BNNs) hold the promise of being flexible, well-calibrated statistical models, inference often requires approximations whose consequences are poorly understood. We study the quality of common variational methods in approximating the Bayesian predictive distribution. For single-hidden layer ReLU BNNs, we prove a fundamental limitation in function-space of two of the most commonly used distributions defined in weight-space: mean-field Gaussian and Monte Carlo dropout. We find there are simple cases where neither method can have substantially increased uncertainty in between well-separated regions of low uncertainty. We provide strong empirical evidence that exact inference does not have this pathology, hence it is due to the approximation and not the model. In contrast, for deep networks, we prove a universality result showing that there exist approximate posteriors in the above classes which provide flexible uncertainty estimates. However, we find empirically that pathologies of a similar form as in the single-hidden layer case can persist when performing variational inference in deeper networks. Our results motivate careful consideration of the implications of approximate inference methods in BNNs.
Implicit Regularization in Deep Learning May Not Be Explainable by Norms

Mathematically characterizing the implicit regularization induced by gradient-based optimization is a longstanding pursuit in the theory of deep learning. A widespread hope is that a characterization based on minimization of norms may apply, and a standard test-bed for studying this prospect is matrix factorization (matrix completion via linear neural networks). It is an open question whether norms can explain the implicit regularization in matrix factorization. The current paper resolves this open question in the negative, by proving that there exist natural matrix factorization problems on which the implicit regularization drives all norms (and quasi-norms) towards infinity. Our results suggest that, rather than perceiving the implicit regularization via norms, a potentially more useful interpretation is minimization of rank. We demonstrate empirically that this interpretation extends to a certain class of non-linear neural networks, and hypothesize that it may be key to explaining generalization in deep learning.
Outlier-SMOTE: A refined oversampling technique for improved detection of COVID-19

Almost every dataset these days continually faces the predicament of class imbalance. It is difficult to train classifiers on these types of data as they become biased towards a set of classes, hence leading to reduction in classifier performance. This setback is often tackled by the use of various over-sampling or under-sampling algorithms. But, the method which stood out of all the numerous algorithms was the Synthetic Minority Oversampling Technique (SMOTE). SMOTE generates synthetic samples of the minority class by oversampling each data-point by considering linear combinations of existing minority class neighbors. Each minority data sample generates an equal number of synthetic data. As the world is suffering from the plight of COVID-19 pandemic, the authors applied the idea to help boost the classifying performance whilst detecting this deadly virus. This paper presents a modified version of SMOTE known as Outlier-SMOTE wherein each data-point is oversampled with respect to its distance from other data-points. The data-point which is farther than the other data-points is given greater importance and is oversampled more than its counterparts. Outlier-SMOTE reduces the chances of overlapping of minority data samples which often occurs in the traditional SMOTE algorithm. This method is tested on five benchmark datasets and is eventually tested on a COVID-19 dataset. F-measure, Recall and Precision are used as principle metrics to evaluate the performance of the classifier as is the case for any class imbalanced data set. The proposed algorithm performs considerably better than the traditional SMOTE algorithm for the considered datasets.
Non-autoregressive Machine Translation with Disentangled Context Transformer

State-of-the-art neural machine translation models generate a translation from left to right and every step is conditioned on the previously generated tokens. The sequential nature of this generation process causes fundamental latency in inference since we cannot generate multiple tokens in each sentence in parallel. We propose an attention-masking based model, called Disentangled Context (DisCo) transformer, that simultaneously generates all tokens given different contexts. The DisCo transformer is trained to predict every output token given an arbitrary subset of the other reference tokens. We also develop the parallel easy-first inference algorithm, which iteratively refines every token in parallel and reduces the number of required iterations. Our extensive experiments on 7 translation directions with varying data sizes demonstrate that our model achieves competitive, if not better, performance compared to the state of the art in non-autoregressive machine translation while significantly reducing decoding time on average. Our code is available at this https URL.
Representing narratives in digital libraries: The narrative ontology

Digital Libraries (DLs), especially in the Cultural Heritage domain, are rich in narratives. Every digital object in a DL tells some kind of story, regardless of the medium, the genre, or the type of the object. However, DLs do not offer services about narratives, for example it is not possible to discover a narrative, to create one, or to compare two narratives. Certainly, DLs offer discovery functionalities over their contents, but these services merely address the objects that carry the narratives (e.g. books, images, audiovisual objects), without regard for the narratives themselves. The present work aims at introducing narratives as first-class citizens in DLs, by providing a formal expression of what a narrative is. In particular, this paper presents a conceptualisation of the domain of narratives, and its specification through the Narrative Ontology (NOnt for short), expressed in first-order logic. NOnt has been implemented as an extension of three standard vocabularies, i.e. the CIDOC CRM, FRBRoo, and OWL Time, and using the SWRL rule language to express the axioms. On the basis of NOnt, we have developed the Narrative Building and Visualising (NBVT) tool, and applied it in four case studies to validate the ontology. NOnt is also being validated in the context of the Mingei European project, in which it is applied to the representation of knowledge about Craft Heritage.
L<sup>2</sup>C – Learning to Learn to Compress

In this paper we present an end-to-end meta-learned system for image compression. Traditional machine learning based approaches to image compression train one or more neural network for generalization performance. However, at inference time, the encoder or the latent tensor output by the encoder can be optimized for each test image. This optimization can be regarded as a form of adaptation or benevolent overfitting to the input content. In order to reduce the gap between training and inference conditions, we propose a new training paradigm for learned image compression, which is based on meta-learning. In a first phase, the neural networks are trained normally. In a second phase, the Model-Agnostic Meta-learning approach is adapted to the specific case of image compression, where the inner-loop performs latent tensor overfitting, and the outer loop updates both encoder and decoder neural networks based on the overfitting performance. Furthermore, after meta-learning, we propose to overfit and cluster the bias terms of the decoder on training image patches, so that at inference time the optimal content-specific bias terms can be selected at encoder-side. Finally, we propose a new probability model for lossless compression, which combines concepts from both multi-scale and super-resolution probability model approaches. We show the benefits of all our proposed ideas via carefully designed experiments.
Blockchain Queuing Model with Non-preemptive Limited-priority

Blockchain technology has recently obtained widespread attention. And it is being regarded as potentially even more disruptive than the Internet, whose usage includes large areas of applications ranging from crypto currency, financial services, reputation system, Internet of Things, sharing economy to public and social services. The existing works of blockchain primarily are focused on key components and potential applications. However, in the existing blockchain systems, the waiting time of transactions is too long. Furthermore, it may produce serious consequences because many important transactions are not handled timely. To solve the problem, in the paper, the blockchain Queuing model with non-preemptive limited-priority is established, which considers the different transactions having different priority when being mined. There are two classes of transactions in the model, one is high-priority transaction with pay or with prior interest, the other is low-priority transaction without pay or without prior interest. And high-priority transactions can be mined preferentially when mining process is not occupied. If low-priority transaction is being mined, the arriving high-priority transaction will wait for the mining accomplishment. Through the analysis of the model, we compute average waiting time, average staying time and average length of queue. From simulation of the model, we find that transactions with pay or with prior interest and increase service rate of mining are effective for reducing waiting time. Besides, the two factors mutually reinforce to shorten waiting time. Finally, we conclude this paper and point out the direction of future research.
Will fouls in politics be punished? An experiment on the effect of incivility on the support for the sender and for the political system

There is a widespread concern in Western democracies that political communication is increasingly becoming “nasty, negative and uncivil” ( Shea and Fiorina, 2013 ). Whereas opinions differ on the question whether a certain level of negativity is “good” or “bad” for democracy, judgments about incivility are quite clear-cut: incivility – i.e. the violation of the norms of politeness, e.g. by using rude language, exaggerations, or interrupting the political opponent – is usually seen as a threat. Although empirical analyses of the consequences of uncivil political discourse are rare, there is evidence that incivility has a negative impact on candidate evaluation and political trust. In contrast, other studies do not confirm these results. Unfortunately, most of the research done on incivility refers to the United States. The aim of the chapter is to investigate the impact of uncivil messages in Germany. Based on an experimental study with N=1107 subjects we demonstrate that the uncivil behavior of a well-known politician (Sigmar Gabriel, chairman of the Social Democrats and vice chancellor) on TV has – in general – a negative impact on his evaluation. In addition, his uncivil statements also influence the evaluation of politicians as a group. “Do you know what the most important difference between politics and sports is? In politics, fouls will not be punished.” 1 Markus Söder, former Bavarian secretary of finance, state development, and homeland and current Prime Minister of Bavaria
YOLO v4 Based Human Detection System Using Aerial Thermal Imaging for UAV Based Surveillance Applications

This work is related to building a Human Detection system based on You Only Look Once (YOLO) v4. It is one of the most recent Deep Learning approaches primitively built using single shot detection proposal. Unlike the double stage region-based object detection schemes this technique do not follow semantic segmentation, it does not undergo loss of the object information such as disappearance of the gradients and it does not require pre-defined anchors. This technique comprises strong feature extractors and reinforce multi scale object detection and it is very quick in the multi-threaded GPU environments. Since our fundamental research is concentrated on object classification related to Unmanned Aerial Vehicle (UAV) applications, as a first step we choose to detect the humans from thermal dataset. Therefore, we used thermal images and videos possessed from thermal cameras of UAV 1m to 50m above ground level as our dataset in building the model and testing. The YOLO v4 uses ground truth bounding boxes to extract the features like Weighted Residual Connections (WRC), Cross Stage Partial Connections (CSP), Cross mini Batch Normalization (CmBN), Self-Adversarial Training (SAT), Mish Activation (MA), Mosaic Data Augmentation (MDA) and Drop Block Regularization (DBR). Finally, the performance analysis of these model in terms of mean Average Precision (mAP) indicates that the modelling using YOLO v4 performs in a promising way and it can be used in automatic human detection systems.
A System for Interactive Multimedia Retrieval Evaluations

The evaluation of the performance of interactive multimedia retrieval systems is a methodologically non-trivial endeavour and requires specialized infrastructure. Current evaluation campaigns have so far relied on a local setting, where all retrieval systems needed to be evaluated at the same physical location at the same time. This constraint does not only complicate the organization and coordination but also limits the number of systems which can reasonably be evaluated within a set time frame. Travel restrictions might further limit the possibility for such evaluations. To address these problems, evaluations need to be conducted in a (geographically) distributed setting, which was so far not possible due to the lack of supporting infrastructure. In this paper, we present the Distributed Retrieval Evaluation Server (DRES), an open-source evaluation system to facilitate evaluation campaigns for interactive multimedia retrieval systems in both traditional on-site as well as fully distributed settings which has already proven effective in a competitive evaluation.
User Perception of Text-Based Chatbot Personality

This work explores the effect of chatbot personality on user experience and investigates how users perceive agent personality when conveyed through text. Building on previous work in the field of human-computer interaction on designing chatbot personality, we investigate whether users in a low-stakes conversation have a preference for a specific personality type when the agent does not use voice, is not visually represented, and does not provide identity cues such as gender. We developed two chatbots that interact with users in a multi-turn conversation and designed them to have distinct personalities along two axes of the Five Factor Model (extraversion and agreeableness). We conducted a user study to evaluate user engagement, user perception of the agents, and the effect of user personality on user experience.
Disentangling contextual diversity: Communicative need as a lexical organizer.

Contextual diversity (CD; Adelman, Brown, & Quesada, 2006) modifies word frequency by ignoring word repetition in context. It has been repeatedly found that a CD count provides a better fit to lexical organization data than does word frequency (e.g., Adelman & Brown, 2008; Brysbaert & New, 2009). The importance of CD has been interpreted with the principle of likely need, adapted from the rational analysis of memory (Anderson & Schooler, 1991), which states that words that have been used in many past contexts are more likely to be needed in a future context. Central to the cognitive mechanisms of computing likely need is a definition of linguistic context itself. Typically, linguistic context is defined by relatively small units of language, such as a document within a corpus. However, recent research has demonstrated that larger definitions of context, some spanning tens or hundreds of thousands of words, provide a better accounting of lexical organization data (Johns, Dye, & Jones, 2020). This article attempts to redefine the notion of linguistic context by using socially based contextual measures, derived from the online communication patterns of hundreds of thousands of individuals from the discussion forum Reddit, consisting of over 55 billion words. Multiple count-based and semantic diversity models of contextual diversity were derived from this data. The results demonstrate that the communication patterns of individuals across discourses provides the best accounting of lexical organization data, indicating that classic notions of using local linguistic context to update a word's strength in the lexicon need to be reevaluated. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
A blockchain-enabled relay selection for QoS-OLSR in urban VANET: A Stackelberg game model

In this paper, a blockchain-enabled Stackelberg game model is proposed for the Quality-of-Service Optimized Link State Routing (QoS-OLSR) protocol in urban VANET. While QoS-OLSR protocols introduce game models, Stackelberg in specific, to select stable relays, the information exchanged by nodes and the selection process cannot be trusted. The introduction of Blockchain to VANET arose as a means for adding trust. However, existing works integrating blockchain do not fully exploit its capabilities in hosting verified reputations, selecting relays, or sharing monetary incentives to motivate cooperation of relays. Therefore, the proposed Stackelberg game model integrates off-chain QoS-OLSR protocol and designed on-chain smart contracts, which consists of two managers: Node Reputation Manager (NRM) and Relay Selection Game Manager (RSGM) to select relays. The proposed game model has three stages: 1) the role identification, 2) the off-chain follower incentive selection, and 3) the on-chain leader profit maximization. The first stage is for nodes identify their role in the game model; leader or follower. Then, the second stage allows followers (nodes) to calculate incentives for leaders (relays) based on off-chain exchanged QoS-OLSR messages including QoS and reputation. NRM allows verifying the reputations within the messages of nodes. The calculated incentives are deposited to RSGM as followers' strategies. The third stage, executed on-chain in RSGM, generates the set of accepted followers for a leader reliably and forwards the deposited incentives to leaders. Simulations were conducted to demonstrate that the proposed game model outperforms in terms of leaders' utility and followers' payments while keeping the percentage of relays constant compared to the benchmark model. Furthermore, cost analysis confirms the feasibility and cost-efficiency of such a model.