Reviewerly/exHarmony huggingface dataset:
Multimodal Fusion Based on LSTM and a Couple Conditional Hidden Markov Model for Chinese Sign Language Recognition

A novel multimodal fusion approach is proposed for Chinese sign language (CSL) recognition. This framework, the LSTM2+CHMM model, uses dual long short-term memory (LSTM) and a couple hidden Markov model (CHMM) to fuse hand and skeleton sequence information. Novel contributions, first, include a unique hand segmentation algorithm using power rate transforms and the RGB-D image fusion. This approach effectively overcomes common limitations, such as complex backgrounds, inconsistent lighting, and variable skin tones. Then, as a result, the proposed skeleton-hand fusion framework can be used for the vision-based sign language recognition (SLR) of non-specific people in non-specific environments. Finally, this LSTM2+CHMM model combines the probability theory with a neural network to provide a unified methodology for multiple-sequence fusion. The proposed SLR framework was tested using the two CSL datasets, and the experimental results showed it to be effective.
Decoherence and visibility enhancement in multipath interference

We theoretically analyze the observations reported in a four-path quantum interference experiment via multiple beam Ramsey interference [Phys. Rev. Lett. 86, 559 (2001)]. In this experiment, a selective scattering of photons from just one interfering path causes decoherence. However, contrary to expectations, there is an increase in the contrast of the interference pattern, demonstrating that path selective decoherence can not only lead to a decrease, but under certain conditions, to an increase of the fringe contrast. Here we explain this seemingly counter-intuitive effect based on a model for a multi-path interference, with four to six slits, in the presence of decoherence. The effect of the environment is modeled via a coupling to a bath of harmonic oscillators. When decoherence is introduced in one of the multiple paths, an enhancement in fringe contrast is seen under certain conditions. A similar effect is shown to appear if instead of path-selective decoherence, a selective path detector is introduced. Our analysis points to the fact that while traditional fringe visibility captures the wave nature in the two-path case, it can fail in multi-path situations. We explain the enhancement of fringe visibility and also show that quantum coherence based on the l1 norm of coherence, in contrast to traditional visibility, remains a good quantifier of wave nature, even in such situations. The enhancement of fringe contrast in the presence of environmental decoherence underscores the limitations of traditional visibility as a good measure for wave nature in quantifying complementarity and also makes it an unlikely candidate for quantifying decoherence. Our analysis could lead to better insight in ways to quantify decoherence in multi-path interference, and in studies that seek to exploit quantum superpositions and quantum coherence for quantum information applications.
Boosting Cooperative Coevolution for Large Scale Optimization With a Fine-Grained Computation Resource Allocation Strategy

Cooperative coevolution (CC) has shown great potential for solving large-scale optimization problems (LSOPs). However, traditional CC algorithms often waste part of the computation resource (CR) as they equally allocate CR among all subproblems. The recently developed contribution-based CC algorithms improve the traditional ones to a certain extent by adaptively allocating CR according to some heuristic rules. Different from existing works, this paper explicitly constructs a mathematical model for the CR allocation (CRA) problem in CC and proposes a novel fine-grained CRA (FCRA) strategy by fully considering both the theoretically optimal solution of the CRA model and the evolution characteristics of CC. FCRA takes a single iteration as a basic CRA unit and always selects the subproblem which is most likely to make the largest contribution to the total fitness improvement to undergo a new iteration, where the contribution of a subproblem at a new iteration is estimated according to its current contribution, current evolution status, as well as the estimation for its current contribution. We verified the efficiency of FCRA by combining it with the success-history-based adaptive differential evolution which is an excellent DE variant but has never been employed in the CC framework. Experimental results on two benchmark suites for LSOPs demonstrate that FCRA significantly outperforms existing CRA strategies and the resulting CC algorithm is highly competitive in solving LSOPs.
Race, Ethnicity and National Origin-Based Discrimination in Social Media and Hate Crimes across 100 U.S. Cities

We study malicious online content via a specific type of hate speech: race, ethnicity and national-origin based discrimination in social media, alongside hate crimes motivated by those characteristics, in 100 cities across the United States. We develop a spatially-diverse training dataset and classification pipeline to delineate targeted and self-narration of discrimination on social media, accounting for language across geographies. Controlling for census parameters, we find that the proportion of discrimination that is targeted is associated with the number of hate crimes. Finally, we explore the linguistic features of discrimination Tweets in relation to hate crimes by city, features used by users who Tweet different amounts of discrimination, and features of discrimination compared to non-discrimination Tweets. Findings from this spatial study can inform future studies of how discrimination in physical and virtual worlds vary by place, or how physical and virtual world discrimination may synergize.
Deep ReLU network approximation of functions on a manifold

Whereas recovery of the manifold from data is a well-studied topic, approximation rates for functions defined on manifolds are less known. In this work, we study a regression problem with inputs on a $d^*$-dimensional manifold that is embedded into a space with potentially much larger ambient dimension. It is shown that sparsely connected deep ReLU networks can approximate a Hölder function with smoothness index $β$ up to error $ε$ using of the order of $ε^{-d^*/β}\log(1/ε)$ many non-zero network parameters. As an application, we derive statistical convergence rates for the estimator minimizing the empirical risk over all possible choices of bounded network parameters.
A Detailed Survey on Topic Modeling for Document and Short Text Data

Text mining is one of the most significant field in the digital era due to the rapid growth of textual information.Topic models are gaining popularity in the last few years.A topic comprises of a group of words that are often take place together.Topic models are better performing techniques to extract semantic knowledge presented in the data.The various methods used for topic models are, LSA (Latent Semantic Analysis), PLSA (Probabilistic Latent Semantic Analysis), LDA (Latent Dirichlet Allocation).These methods gained popularity in extracting hidden themes from the document (corpus).Various topic modeling algorithms are developed to inquiry, summarize and extract hidden semantic structures of large corpus.In this paper, we present a detailed survey covering the various topic modeling techniques proposed in last decade.Additionally, we focus on different strategies of extracting the topics in social media text, where the goal is to find and aggregate the topic within short texts.Further, we summarize the various applications and quantitative evaluation of the various methods, with statistical and mathematical knowledge to predict the convergence of results.
Automatically Learning Data Augmentation Policies for Dialogue Tasks

Automatic data augmentation (AutoAugment) (Cubuk et al., 2019) searches for optimal perturbation policies via a controller trained using performance rewards of a sampled policy on the target task, hence reducing data-level model bias.While being a powerful algorithm, their work has focused on computer vision tasks, where it is comparatively easy to apply imperceptible perturbations without changing an image's semantic meaning.In our work, we adapt AutoAugment to automatically discover effective perturbation policies for natural language processing (NLP) tasks such as dialogue generation.We start with a pool of atomic operations that apply subtle semantic-preserving perturbations to the source inputs of a dialogue task (e.g., different POS-tag types of stopword dropout, grammatical errors, and paraphrasing).Next, we allow the controller to learn more complex augmentation policies by searching over the space of the various combinations of these atomic operations.Moreover, we also explore conditioning the controller on the source inputs of the target task, since certain strategies may not apply to inputs that do not contain that strategy's required linguistic features.Empirically, we demonstrate that both our input-agnostic and input-aware controllers discover useful data augmentation policies, and achieve significant improvements over the previous state-of-the-art, including trained on manually-designed policies.
Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses

Sentence position is a strong feature for news summarization, since the lead often (but not always) summarizes the key points of the article.In this paper, we show that recent neural systems excessively exploit this trend, which although powerful for many inputs, is also detrimental when summarizing documents where important content should be extracted from later parts of the article.We propose two techniques to make systems sensitive to the importance of content in different parts of the article.The first technique employs 'unbiased' data; i.e., randomly shuffled sentences of the source document, to pretrain the model.The second technique uses an auxiliary ROUGEbased loss that encourages the model to distribute importance scores throughout a document by mimicking sentence-level ROUGE scores on the training data.We show that these techniques significantly improve the performance of a competitive reinforcement learning based extractive system, with the auxiliary loss being more powerful than pretraining.
Continuous Finger Gesture Recognition Based on Flex Sensors

The goal of this work is to present a novel continuous finger gesture recognition system based on flex sensors. The system is able to carry out accurate recognition of a sequence of gestures. Wireless smart gloves equipped with flex sensors were implemented for the collection of the training and testing sets. Given the sensory data acquired from the smart gloves, the gated recurrent unit (GRU) algorithm was then adopted for gesture spotting. During the training process for the GRU, the movements associated with different fingers and the transitions between two successive gestures were taken into consideration. On the basis of the gesture spotting results, the maximum a posteriori (MAP) estimation was carried out for the final gesture classification. Because of the effectiveness of the proposed spotting scheme, accurate gesture recognition was achieved even for complicated transitions between successive gestures. From the experimental results, it can be observed that the proposed system is an effective alternative for robust recognition of a sequence of finger gestures.
Conceptor Debiasing of Word Representations Evaluated on WEAT

Bias in word embeddings such as Word2Vec has been widely investigated, and many efforts made to remove such bias.We show how to use conceptors debiasing to post-process both traditional and contextualized word embeddings.Our conceptor debiasing can simultaneously remove racial and gender biases and, unlike standard debiasing methods, can make effect use of heterogeneous lists of biased words.We show that conceptor debiasing diminishes racial and gender bias of word representations as measured using the Word Embedding Association Test (WEAT) of Caliskan et al. (2017).
3D-GIoU: 3D Generalized Intersection over Union for Object Detection in Point Cloud

Three-dimensional (3D) object detection is an important research in 3D computer vision with significant applications in many fields, such as automatic driving, robotics, and human-computer interaction. However, the low precision is an urgent problem in the field of 3D object detection. To solve it, we present a framework for 3D object detection in point cloud. To be specific, a designed Backbone Network is used to make fusion of low-level features and high-level features, which makes full use of various information advantages. Moreover, the two-dimensional (2D) Generalized Intersection over Union is extended to 3D use as part of the loss function in our framework. Empirical experiments of Car, Cyclist, and Pedestrian detection have been conducted respectively on the KITTI benchmark. Experimental results with average precision (AP) have shown the effectiveness of the proposed network.
Loss Decomposition and Centroid Estimation for Positive and Unlabeled Learning

This paper studies Positive and Unlabeled learning (PU learning), of which the target is to build a binary classifier where only positive data and unlabeled data are available for classifier training. To deal with the absence of negative training data, we first regard all unlabeled data as negative examples with false negative labels, and then convert PU learning into the risk minimization problem in the presence of such one-side label noise. Specifically, we propose a novel PU learning algorithm dubbed “Loss Decomposition and Centroid Estimation” (LDCE). By decomposing the loss function of corrupted negative examples into two parts, we show that only the second part is affected by the noisy labels. Thereby, we may estimate the centroid of corrupted negative set via an unbiased way to reduce the adverse impact of such label noise. Furthermore, we propose the “Kernelized LDCE” (KLDCE) by introducing the kernel trick, and show that KLDCE can be easily solved by combining Alternative Convex Search (ACS) and Sequential Minimal Optimization (SMO). Theoretically, we derive the generalization error bound which suggests that the generalization risk of our model converges to the empirical risk with the order of O(1= k + 1= n k+ 1=%/n) (n and k are the amounts of training data and positive data correspondingly). Experimentally, we conduct intensive experiments on synthetic dataset, UCI benchmark datasets and real-world datasets, and the results demonstrate that our approaches (LDCE and KLDCE) achieve the top-level performance when compared with both classic and state-of-the-art PU learning methods.
Phishing in healthcare organisations: threats, mitigation and approaches

Healthcare data have significant value as a potential target for hackers. Phishing is a method of exploitation for malicious reasons using targeted communications (email/messaging). This study reports on an internal evaluation targeting hospital staff and summarises peer-reviewed literature regarding phishing and healthcare.An assessment was performed as part of cybersecurity activity during a designated test period using multiple credential harvesting approaches through staff email. We also searched the medical-related literature to identify relevant phishing-related publications.During the 1-month testing period, the organisation received 858 200 emails: 139 400 (16%) marketing, 18 871 (2%) identified as potential threats. Of 143 million internet transactions, around 5 million (3%) were suspected threats. 468 employee email addresses were identified from public data and targeted through phishing using a range of payloads including attachments and malicious links; however, no credentials were recovered or malicious files downloaded. Several hospital employees were, however, identified on social media profiles, including some tricked into accepting false friend requests.Healthcare organisations are increasingly moving to digital systems, but healthcare professionals have limited awareness of threats. Increasing emphasis on 'cyberhygiene' and information governance through mandatory training increases understanding of these risks. While no credentials were harvested in this study, since up to 5% of emails/internet traffic are suspicious, the need for robust firewalls, cybersecurity infrastructure, IT policies and, most importantly of all, staff training, is emphasised.Hospitals receive a significant volume of potentially malicious emails. While many staff appear to be aware of phishing and respond appropriately, ongoing education is required across the spectrum of cybersecurity, with specific emphasis around 'leakage' of information on social media.
Development of the methodological basis of the simulation modelling of the multi-energy systems

The most modern technical systems are an integration of various energy converters and sources of various physical nature. Mechanical, thermal, electromagnetic, as well as computer (information) monitoring and control system. Multi-energy systems (MES) also belong to the class of such technical devices. The main task in creating mathematical models is a methodological approach that allows joint modelling of 6 energy objects of various nature on a single methodological basis. The article considers the main approaches to the mathematical description of MES, as well as the assumptions and stages of formation for modelling this class of technical systems. The main advantages and disadvantages of the proposed methods for creating mathematical models are considered taking into account the possibility of using optimization methods and using the above models as digital twins for control systems. In addition, the article will present a methodological approach to the formation of mathematical models of an integrated energy system based on the concept of an energy hub using MATLAB.
Malaria Disease Detection Using CNN Technique with SGD, RMSprop and ADAM Optimizers

Malaria is life-threatening disease spread when an infected female Anopheles mosquito bites a person. Malaria is one of the predominant diseases in the world. There exists many drugs which make malaria a curable disease but due to inadequate technologies and equipments, we are unable to detect and cure it. The method of diagnosing malaria involves counting of parasite and red blood cells drugs physically which is a labor-intensive and error-prone process, especially if patients have to be tested several times a day. This issue can be solved by training machines to do the work of pathologists. We can the train the machine using many deep learning algorithms. Our model uses CNN based classification to classify the blood films to infected and normal blood films. The experimental result show our model works well on microscopic image and achieves an accuracy of 96.62% and the model has a lower model complexity are requires less computation time. Thus outperforming the state of art used previously.
Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research

We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research. Kaolin provides efficient implementations of differentiable 3D modules for use in deep learning systems. With functionality to load and preprocess several popular 3D datasets, and native functions to manipulate meshes, pointclouds, signed distance functions, and voxel grids, Kaolin mitigates the need to write wasteful boilerplate code. Kaolin packages together several differentiable graphics modules including rendering, lighting, shading, and view warping. Kaolin also supports an array of loss functions and evaluation metrics for seamless evaluation and provides visualization functionality to render the 3D results. Importantly, we curate a comprehensive model zoo comprising many state-of-the-art 3D deep learning architectures, to serve as a starting point for future research endeavours. Kaolin is available as open-source software at https://github.com/NVIDIAGameWorks/kaolin/.
Transfer Learning in Biomedical Named Entity Recognition: An Evaluation of BERT in the PharmaCoNER task

To date, a large amount of biomedical content has been published in non-English texts, especially for clinical documents.Therefore, it is of considerable significance to conduct Natural Language Processing (NLP) research in non-English literature.PharmaCoNER is the first Named Entity Recognition (NER) task to recognize chemical and protein entities from Spanish biomedical texts.Since there have been abundant resources in the NLP field, how to exploit these existing resources to a new task to obtain competitive performance is a meaningful study.Inspired by the success of transfer learning with language models, we introduce the BERT benchmark to facilitate the research of PharmaCoNER task.In this paper, we evaluate two baselines based on Multilingual BERT and BioBERT on the Pharma-CoNER corpus.Experimental results show that transferring the knowledge learned from source large-scale datasets to the target domain offers an effective solution for the Phar-maCoNER task.
Training individually fair ML models with Sensitive Subspace Robustness

We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the performance of a resume screening system should be invariant under changes to the gender and/or ethnicity of the applicant. We formalize this notion of algorithmic fairness as a variant of individual fairness and develop a distributionally robust optimization approach to enforce it during training. We also demonstrate the effectiveness of the approach on two ML tasks that are susceptible to gender and racial biases.
Perfect secrecy cryptography via mixing of chaotic waves in irreversible time-varying silicon chips

Abstract Protecting confidential data is a major worldwide challenge. Classical cryptography is fast and scalable, but is broken by quantum algorithms. Quantum cryptography is unclonable, but requires quantum installations that are more expensive, slower, and less scalable than classical optical networks. Here we show a perfect secrecy cryptography in classical optical channels. The system exploits correlated chaotic wavepackets, which are mixed in inexpensive and CMOS compatible silicon chips. The chips can generate 0.1 Tbit of different keys for every mm of length of the input channel, and require the transmission of an amount of data that can be as small as 1/1000 of the message’s length. We discuss the security of this protocol for an attacker with unlimited technological power, and who can access the system copying any of its part, including the chips. The second law of thermodynamics and the exponential sensitivity of chaos unconditionally protect this scheme against any possible attack.