Reviewerly/exHarmony huggingface dataset:
A Robust Self-Learning Framework for Cross-Lingual Text Classification

Based on massive amounts of data, recent pretrained contextual representation models have made significant strides in advancing a number of different English NLP tasks.However, for other languages, relevant training data may be lacking, while state-of-the-art deep learning methods are known to be data-hungry.In this paper, we present an elegantly simple robust self-learning framework to include unlabeled non-English samples in the fine-tuning process of pretrained multilingual representation models.We leverage a multilingual model's own predictions on unlabeled non-English data in order to obtain additional information that can be used during further finetuning.Compared with original multilingual models and other cross-lingual classification models, we observe significant gains in effectiveness on document and sentiment classification for a range of diverse languages.
Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment

Abstract The residual cancer burden index is an important quantitative measure used for assessing treatment response following neoadjuvant therapy for breast cancer. It has shown to be predictive of overall survival and is composed of two key metrics: qualitative assessment of lymph nodes and the percentage of invasive or in situ tumour cellularity (TC) in the tumour bed (TB). Currently, TC is assessed through eye-balling of routine histopathology slides estimating the proportion of tumour cells within the TB. With the advances in production of digitized slides and increasing availability of slide scanners in pathology laboratories, there is potential to measure TC using automated algorithms with greater precision and accuracy. We describe two methods for automated TC scoring: 1) a traditional approach to image analysis development whereby we mimic the pathologists’ workflow, and 2) a recent development in artificial intelligence in which features are learned automatically in deep neural networks using image data alone. We show strong agreements between automated and manual analysis of digital slides. Agreements between our trained deep neural networks and experts in this study (0.82) approach the inter-rater agreements between pathologists (0.89). We also reveal properties that are captured when we apply deep neural network to whole slide images, and discuss the potential of using such visualisations to improve upon TC assessment in the future.
An Improved Watermarking Technique for Copyright Protection Based on Tchebichef Moments

Watermarking technique is a method to protect ownership of digital multimedia.Most existing watermarking techniques achieve a good level of imperceptibility and robustness.The challenges to achieve higher invisibility and resistance with lower computational time motivate researchers to work on new watermarking schemes.Robustness against noise attacks and JPEG2000 compression needs to be improved to acquire a better resistance capability of the watermark.In this paper, we present a block-based Tchebichef watermarking technique for protecting copyrights.In this technique, the host image is first divided into non-overlapping blocks and Tchebichef moments are calculated for each block.The watermarks are embedded into the blocks with lower visual entropies.The watermark image is scrambled by Arnold transform before embedding into the Tchebichef moments of the selected image blocks.The proposed watermarking scheme was tested under noise additions, filtering, cropping and compressing attacks.Our scheme was verified and compared to the existing watermarking techniques under image geometric and processing attacks.Furthermore, the proposed scheme demonstrated a superior performance in robustness under noise attacks and JPEG2000.
Cyber security education is as essential as “the three R's”

Smartphones have diffused rapidly across South African society and constitute the most dominant information and communication technologies in everyday use. That being so, it is important to ensure that all South Africans know how to secure their smart devices. Doing so requires a high level of security awareness and knowledge. As yet, there is no formal curriculum addressing cyber security in South African schools. Indeed, it seems to be left to universities to teach cyber security principles, and they currently only do this when students take computing-related courses. The outcome of this approach is that only a very small percentage of South Africans, i.e. those who take computing courses at university, are made aware of cyber security risks and know how to take precautions. In this paper we found that, because this group is overwhelmingly male, this educational strategy disproportionately leaves young South African women vulnerable to cyber-attacks. We thus contend that cyber security ought to be taught as children learn the essential “3 Rs”—delivering requisite skills at University level does not adequately prepare young South Africans for a world where cyber security is an essential skill. Starting to provide awareness and knowledge at primary school, and embedding it across the curriculum would, in addition to ensuring that people have the skills when they need them, also remove the current gender imbalance in cyber security awareness.
Neural Module Networks for Reasoning over Text

Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module networks (NMNs) learn to parse such questions as executable programs composed of learnable modules, performing well on synthetic visual QA domains. However, we find that it is challenging to learn these models for non-synthetic questions on open-domain text, where a model needs to deal with the diversity of natural language and perform a broader range of reasoning. We extend NMNs by: (a) introducing modules that reason over a paragraph of text, performing symbolic reasoning (such as arithmetic, sorting, counting) over numbers and dates in a probabilistic and differentiable manner; and (b) proposing an unsupervised auxiliary loss to help extract arguments associated with the events in text. Additionally, we show that a limited amount of heuristically-obtained question program and intermediate module output supervision provides sufficient inductive bias for accurate learning. Our proposed model significantly outperforms state-of-the-art models on a subset of the DROP dataset that poses a variety of reasoning challenges that are covered by our modules.
Time pressure in software engineering: A systematic review

Large project overruns and overtime work have been reported in the software industry, resulting in additional expense for companies and personal issues for developers. Experiments and case studies have investigated the relationship between time pressure and software quality and productivity. The present work aims to provide an overview of studies related to time pressure in software engineering; specifically, existing definitions, possible causes, and metrics relevant to time pressure were collected, and a mapping of the studies to software processes and approaches was performed. Moreover, we synthesize results of existing quantitative studies on the effects of time pressure on software development, and offer practical takeaways for practitioners and researchers, based on empirical evidence. Our search strategy examined 5414 sources, found through repository searches and snowballing. Applying inclusion and exclusion criteria resulted in the selection of 102 papers, which made relevant contributions related to time pressure in software engineering. The majority of high quality studies report increased productivity and decreased quality under time pressure. The most frequent categories of studies focus on quality assurance, cost estimation, and process simulation. It appears that time pressure is usually caused by errors in cost estimation. The effect of time pressure is most often identified during software quality assurance. The majority of empirical studies report increased productivity under time pressure, while the most cost estimation and process simulation models assume that compressing the schedule increases the total needed hours. We also find evidence of the mediating effect of knowledge on the effects of time pressure, and that tight deadlines impact tasks with an algorithmic nature more severely. Future research should better contextualize quantitative studies to account for the existing conflicting results and to provide an understanding of situations when time pressure is either beneficial or harmful.
UAV Mission Planning with SAR Application

The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors' capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm-Vehicle Route Planning with Time Window (VRPTW)-using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.
SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects

Recent advances in image generation gave rise to powerful tools for semantic image editing. However, existing approaches can either operate on a single image or require an abundance of additional information. They are not capable of handling the complete set of editing operations, that is addition, manipulation or removal of semantic concepts. To address these limitations, we propose SESAME, a novel generator-discriminator pair for Semantic Editing of Scenes by Adding, Manipulating or Erasing objects. In our setup, the user provides the semantic labels of the areas to be edited and the generator synthesizes the corresponding pixels. In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former. We evaluate our model on a diverse set of datasets and report state-of-the-art performance on two tasks: (a) image manipulation and (b) image generation conditioned on semantic labels.
Named Entity Recognition Approaches and Their Comparison for Custom NER Model

Named entity recognition (NER) is a natural language processing tool for information extraction from unstructured text data such as e-mails, newspapers, blogs, etc. NER is the process of identifying nouns like people, place, organization, etc., that are mentioned in the string of the text, sentence, or paragraph. For building the NER system, many different libraries and natural language processing tools using Java, Python, and Cython languages are available. All these tools have pretrained NER models that can be imported, used and can be modified or customized according to requirements. This paper explains different NLP libraries including Python’s SpaCy, Apache OpenNLP, and TensorFlow. Some of these libraries provide a pre-build NER model that can be customized. The comparison of these libraries is done based on training accuracy, F-score, prediction time, model size, and ease of training. The training and testing data are the same for all the models. When considering the overall performance of all the models, Python’s Spacy gives a higher accuracy and the best result.
Multi-label Feature Selection via Global Relevance and Redundancy Optimization

Information theoretical based methods have attracted a great attention in recent years, and gained promising results to deal with multi-label data with high dimensionality. However, most of the existing methods are either directly transformed from heuristic single-label feature selection methods or inefficient in exploiting labeling information. Thus, they may not be able to get an optimal feature selection result shared by multiple labels. In this paper, we propose a general global optimization framework, in which feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, thus facilitating multi-label feature selection. Moreover, the proposed method has an excellent mechanism for utilizing inherent properties of multi-label learning. Specially, we provide a formulation to extend the proposed method with label-specific features. Empirical studies on twenty multi-label data sets reveal the effectiveness and efficiency of the proposed method. Our implementation of the proposed method is available online at: https://jiazhang-ml.pub/GRRO-master.zip.
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network

Verifying the correctness of a textual statement requires not only semantic reasoning about the meaning of words, but also symbolic reasoning about logical operations like count, superlative, aggregation, etc.In this work, we propose LogicalFactChecker, a neural network approach capable of leveraging logical operations for fact checking.It achieves the state-of-the-art performance on TABFACT, a large-scale, benchmark dataset built for verifying a textual statement with semi-structured tables.This is achieved by a graph module network built upon the Transformer-based architecture.With a textual statement and a table as the input, LogicalFactChecker automatically derives a program (a.k.a.logical form) of the statement in a semantic parsing manner.A heterogeneous graph is then constructed to capture not only the structures of the table and the program, but also the connections between inputs with different modalities.Such a graph reveals the related contexts of each word in the statement, the table and the program.The graph is used to obtain graphenhanced contextual representations of words in Transformer-based architecture.After that, a program-driven module network is further introduced to exploit the hierarchical structure of the program, where semantic compositionality is dynamically modeled along the program structure with a set of function-specific modules.Ablation experiments suggest that both the heterogeneous graph and the module network are important to obtain strong results.
A Pseudo-relevance feedback framework combining relevance matching and semantic matching for information retrieval

Pseudo-relevance feedback (PRF) is a well-known method for addressing the mismatch between query intention and query representation. Most current PRF methods consider relevance matching only from the perspective of terms used to sort feedback documents, thus possibly leading to a semantic gap between query representation and document representation. In this work, a PRF framework that combines relevance matching and semantic matching is proposed to improve the quality of feedback documents. Specifically, in the first round of retrieval, we propose a reranking mechanism in which the information of the exact terms and the semantic similarity between the query and document representations are calculated by bidirectional encoder representations from transformers (BERT); this mechanism reduces the text semantic gap by using the semantic information and improves the quality of feedback documents. Then, our proposed PRF framework is constructed to process the results of the first round of retrieval by using probability-based PRF methods and language-model-based PRF methods. Finally, we conduct extensive experiments on four Text Retrieval Conference (TREC) datasets. The results show that the proposed models outperform the robust baseline models in terms of the mean average precision (MAP) and precision P at position 10 ([email protected]), and the results also highlight that using the combined relevance matching and semantic matching method is more effective than using relevance matching or semantic matching alone in terms of improving the quality of feedback documents.
Symmetry Breaking and Error Correction in Open Quantum Systems

Symmetry-breaking transitions are a well-understood phenomenon of closed quantum systems in quantum optics, condensed matter, and high energy physics. However, symmetry breaking in open systems is less thoroughly understood, in part due to the richer steady-state and symmetry structure that such systems possess. For the prototypical open system---a Lindbladian---a unitary symmetry can be imposed in a ``weak'' or a ``strong'' way. We characterize the possible ${\mathbb{Z}}_{n}$ symmetry-breaking transitions for both cases. In the case of ${\mathbb{Z}}_{2}$, a weak-symmetry-broken phase guarantees at most a classical bit steady-state structure, while a strong-symmetry-broken phase admits a partially protected steady-state qubit. Viewing photonic cat qubits through the lens of strong-symmetry breaking, we show how to dynamically recover the logical information after any gap-preserving strong-symmetric error; such recovery becomes perfect exponentially quickly in the number of photons. Our study forges a connection between driven-dissipative phase transitions and error correction.
Posterior concentration for Bayesian regression trees and forests

Since their inception in the 1980s, regression trees have been one of the more widely used nonparametric prediction methods. Tree-structured methods yield a histogram reconstruction of the regression surface, where the bins correspond to terminal nodes of recursive partitioning. Trees are powerful, yet susceptible to overfitting. Strategies against overfitting have traditionally relied on pruning greedily grown trees. The Bayesian framework offers an alternative remedy against overfitting through priors. Roughly speaking, a good prior charges smaller trees where overfitting does not occur. While the consistency of random histograms, trees and their ensembles has been studied quite extensively, the theoretical understanding of the Bayesian counterparts has been missing. In this paper, we take a step toward understanding why/when do Bayesian trees and forests not overfit. To address this question, we study the speed at which the posterior concentrates around the true smooth regression function. We propose a spike-and-tree variant of the popular Bayesian CART prior and establish new theoretical results showing that regression trees (and forests) (a) are capable of recovering smooth regression surfaces (with smoothness not exceeding one), achieving optimal rates up to a log factor, (b) can adapt to the unknown level of smoothness and (c) can perform effective dimension reduction when $p>n$. These results provide a piece of missing theoretical evidence explaining why Bayesian trees (and additive variants thereof?) have worked so well in practice.
Multitask Mixture of Sequential Experts for User Activity Streams

It is often desirable to model multiple objectives in real-world web applications, such as user satisfaction and user engagement in recommender systems. Multi-task learning has become the standard approach for such applications recently.
DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer

A canonical wireless communication system consists of a transmitter and a receiver. The information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the effects of radio frequency (RF) impairments, channel fading, noise and interference, the signal arriving at the receiver will be distorted. The receiver needs to recover the original information from the distorted signal. In this paper, we propose a new receiver model, namely DeepReceiver, that uses a deep neural network to replace the traditional receiver's entire information recovery process. We design a one-dimensional convolution DenseNet (1D-Conv-DenseNet) structure, in which global pooling is used to improve the adaptability of the network to different input signal lengths. Multiple binary classifiers are used at the final classification layer to achieve multi-bit information stream recovery. We also exploit the DeepReceiver for unified blind reception of multiple modulation and coding schemes (MCSs) by including signal samples of corresponding MCSs in the training set. Simulation results show that the proposed DeepReceiver performs better than traditional step-by-step serial hard decision receiver in terms of bit error rate under the influence of various factors such as noise, RF impairments, multipath fading, cochannel interference, dynamic environment, and unified reception of multiple MCSs.
A privacy-preserving mechanism based on local differential privacy in edge computing

With the development of Internet of Things (IoT), the delay caused by network transmission has led to low data processing efficiency. At the same time, the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain, but edge computing could solve this problem. The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity. However, user data in edge computing is usually stored and processed in some honest-but-curious authorized entities, which leads to the leakage of users' privacy information. In order to solve these problems, this paper proposes a location data collection method that satisfies the local differential privacy to protect users' privacy. In this paper, a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located. A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid. In addition, the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments. Compared with the existing privacy-preserving methods, the proposed privacy-preserving mechanism can not only better meet users' privacy needs, but also have higher data availability.