Reviewerly/exHarmony huggingface dataset:
CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter Performance

We propose a novel direction to improve the denoising quality of filtering-based denoising algorithms in real time by predicting the best filter parameter value using a Convolutional Neural Network (CNN). We take the use case of BM3D, the state-of-the-art filtering-based denoising algorithm, to demonstrate and validate our approach. We propose and train a simple, shallow CNN to predict in real time, the optimum filter parameter value, given the input noisy image. Each training example consists of a noisy input image (training data) and the filter parameter value that produces the best output (training label). Both qualitative and quantitative results using the widely used PSNR and SSIM metrics on the popular BSD68 dataset show that the CNN-guided BM3D outperforms the original, unguided BM3D across different noise levels. Thus, our proposed method is a CNN-based improvement on the original BM3D which uses a fixed, default parameter value for all images.
IITP-AI-NLP-ML@ CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020

The publication rate of scientific literature increases rapidly, which poses a challenge for researchers to keep themselves updated with new state-of-the-art.Scientific document summarization solves this problem by summarizing the essential fact and findings of the document.In the current paper, we present the participation of IITP-AI-NLP-ML team in three shared tasks, namely, CL-SciSumm 2020, LaySumm 2020, LongSumm 2020, which aims to generate medium, lay, and long summaries of the scientific articles, respectively.To solve CL-SciSumm 2020 and LongSumm 2020 tasks, three well-known clustering techniques are used, and then various sentence scoring functions, including textual entailment, are used to extract the sentences from each cluster for a summary generation.For LaySumm 2020, an encoder-decoder based deep learning model has been utilized.Performances of our developed systems are evaluated in terms of ROUGE measures on the associated datasets with the shared task.
Comparison of a Chaotic Cryptosystem with Other Cryptography Systems

The keyspace of a cryptography system must be long enough in order to protect it from brute force attacks. The One-Time Pad (OTP) encryption is unconditionally secure because of its truly random keystream that is used only once. This paper proposes a new chaotic symmetric cryptosystem approach, comparable to OTP. The proposed system utilizes two Lorenz generators, a main and an auxiliary, where the aim of the second one is to make one of the main Lorenz generator’s parameters to vary continually with time in a chaotic manner. This technique was built on digitizing two Lorenz chaotic models to increase the security level. The scrambling scheme was developed and the Lorenz stream cipher binary stream successfully passed the NIST randomness test. The cryptosystem showed a high degree of security, as it had a keyspace of 2576, and it was compared with existing symmetric key cryptography systems, such as DES, 3DES, AES, Blowfish, and OTP.
User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees

In this article, we propose a data-driven approach to group users in a Non-Orthogonal Multiple Access (NOMA) MIMO setting. Specifically, we formulate user clustering as a multi-label classification problem and solve it by coupling a Classifier Chain (CC) with a Gradient Boosting Decision Tree (GBDT), namely, the LightGBM algorithm. The performance of the proposed CC-LightGBM scheme is assessed via numerical simulations. For benchmarking, we consider two classical adaptation learning schemes: Multi-Label k-Nearest Neighbours (ML-KNN) and Multi-Label Twin Support Vector Machines (ML-TSVM); as well as other naive approaches. Besides, we also compare the computational complexity of the proposed scheme with those of the aforementioned benchmarks.
Designing adiabatic time evolution from high-frequency bichromatic sources

We investigate the quantum dynamics of a two-level system driven by a bichromatic field, using a nonperturbative analysis. We make special emphasis in the case of two large frequencies, where the Magnus expansion can fail, and in the case of a large and a small frequency, where resonances can dominate. In the first case, we show that two large frequencies can be combined to produce an effective adiabatic evolution. In the second case, we show that high-frequency terms (which naturally arise as corrections to the adiabatic evolution obtained in the first case) can be used to produce a highly tunable adiabatic evolution over the whole Bloch sphere, controlled by multiphoton resonances.Received 25 February 2020Accepted 24 August 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.033412Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAtom opticsGeometric & topological phasesOptical pumpingQuantum description of light-matter interactionQuantum opticsSpin dynamicsTechniquesTwo-level modelsCondensed Matter & Materials PhysicsAtomic, Molecular & OpticalQuantum Information
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation

In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process to produce more robust representations. By relying on the accurate reference mask in the starting frame, we show that the error propagation problem can be mitigated. Next, we introduce a simple gradient correction module, which extends the offline pipeline to an online method while maintaining the efficiency of the former. Finally we develop cycle effective receptive field (cycle-ERF) based on gradient correction to provide a new perspective into analyzing object-specific regions of interests. We conduct comprehensive experiments on challenging benchmarks of DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is beneficial to segmentation quality.
DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks

Deep neural networks have achieved state-of-the art performance on various computer vision tasks. However, their deployment on resource-constrained devices has been hindered due to their high computational and storage complexity. While various complexity reduction techniques, such as lightweight network architecture design and parameter quantization, have been successful in reducing the cost of implementing these networks, these methods have often been considered orthogonal. In reality, existing quantization techniques fail to replicate their success on lightweight architectures such as MobileNet. To this end, we present a novel fully differentiable non-uniform quantizer that can be seamlessly mapped onto efficient ternary-based dot product engines. We conduct comprehensive experiments on CIFAR-10, ImageNet, and Visual Wake Words datasets. The proposed quantizer (DBQ) successfully tackles the daunting task of aggressively quantizing lightweight networks such as MobileNetV1, MobileNetV2, and ShuffleNetV2. DBQ achieves state-of-the art results with minimal training overhead and provides the best (pareto-optimal) accuracy-complexity trade-off.
SAT‐based and CP‐based declarative approaches for Top‐Rank‐ <i>K</i> closed frequent itemset mining

Top-Rank-K Frequent Itemset (or Pattern) Mining (FPM) is an important data mining task, where user decides on the number of top frequency ranks of patterns (itemsets) they want to mine from a transactional dataset. This problem does not require the minimum support threshold parameter that is typically used in FPM problems. Rather, the algorithms solving the Top-Rank-K FPM problem are fed with K, the number of frequency ranks of itemsets required, to compute the threshold internally. This paper presents two declarative approaches to tackle the Top-Rank-K Closed FPM problem. The first approach is Boolean Satisfiability-based (SAT-based) where we propose an effective encoding for the problem along with an efficient algorithm employing this encoding. The second approach is CP-based, that is, utilizes Constraint Programming technique, where a simple CP model is exploited in an innovative manner to mine the Top-Rank-K Closed FPM itemsets from transactional datasets. Both approaches are evaluated experimentally against other declarative and imperative algorithms. The proposed SAT-based approach significantly outperforms IM, another SAT-based approach, and outperforms the proposed CP-approach for sparse and moderate datasets, whereas the latter excels on dense datasets. An extensive study has been conducted to assess the proposed approaches in terms of their feasibility, performance factors, and practicality of use.
Digital Avatars: Promoting Independent Living for Older Adults

Population ageing, together with the desire to maintain an autonomous lifestyle, poses today’s societies with a challenge that technological advances can help considerably to cope with. The widespread use of smartphones and their increasing computing power and storage capacity make them the ideal tool to achieve this goal. In this paper, we present Digital Avatars, a software framework adapted to the needs of older adults who wish to preserve their lifestyle, but who require assistance through technology. Building on previous work on the People as a Service model, Digital Avatars takes advantage of a smartphone’s capabilities and services to collect information about the people who own them. To do this, it applies Complex Event Processing techniques extended with uncertainty to infer the habits, preferences, and needs of the device owner to build with them an enhanced virtual profile of the user. These virtual profiles are the mechanism for monitoring the quality of life of older adults: analyzing their patterns of activity, reminding them of medication schedules, or detecting risky situations that generate alerts to relatives, caregivers, or the community health system.
Improving Performance and Adaptivity of Anchor-Based Detector Using Differentiable Anchoring With Efficient Target Generation

Most anchor-based object detection methods have adopted predefined anchor boxes as regression references. However, the proper setting of anchor boxes may vary significantly across different datasets, improperly designed anchors severely limit the performances and adaptabilities of detectors. Recently, some works have tackled this problem by learning anchor shapes from datasets. However, all of these works explicitly or implicitly rely on predefined anchors, limiting universalities of detectors. In this paper, we propose a simple learning anchoring scheme with an effective target generation method to cast off predefined anchor dependencies. The proposed anchoring scheme, named as differentiable anchoring, simplifies learning anchor shape process by adding only one branch in parallel with the existing classification and bounding box regression branches. The proposed target generation method, including the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{p}$ </tex-math></inline-formula> norm ball approximation and the optimization difficulty-based pyramid level assignment approach, generates positive samples for the new branch. Compared with existing learning anchoring-based approaches, the proposed method doesn’t require any predefined anchors, while tremendously improving performances and adaptiveness of detectors. The proposed method can be seamlessly integrated to Faster RCNN, RetinaNet, and SSD, improving the detection mAP by 2.8%, 2.1% and 2.3% respectively on MS COCO 2017 test-dev set. Moreover, the differentiable anchoring-based detectors can be directly applied to specific scenarios without any modification of the hyperparameters or using a specialized optimization. Specifically, the differentiable anchoring-based RetinaNet achieves very competitive performances on tiny face detection and text detection tasks, which are not well handled by the conventional and guided anchoring based RetinaNets for the MS COCO dataset.
Cryptocurrency Regulation: Countering Money Laundering and Terrorist Financing

Bitcoin was created in 2008 to serve as an alternative payment mechanism for both the under-banked and un-banked, or those in regions where the formal financial system suffers from broad corruption and efficient regulation. However, criminals and terrorists quickly exploited Bitcoin’s unique properties, namely its peer-to-peer nature and pseudo-anonymity, to facilitate extensive terrorist financing and money laundering schemes. Government reactions to safeguard national security interests have been extremely varied, ranging from outright bans to passive tolerance. This inconsistency stems from how to effectively classify Bitcoin. On one side are those who argue Bitcoin is a currency, and on the other are those who claim it is a type of asset. In the US alone, these discrepancies have led to a bureaucratic turf war between different regulatory bodies, namely the Financial Crimes Enforcement Network, the Commodity Futures Trading Association, the Securities and Exchange Commission, and the Internal Revenue Service. This study seeks to move beyond the existing legal frameworks, arguing that Bitcoin should be classified as a technology and regulation should rest with private sector technology companies.
Multidimensional Uncertainty-Aware Evidential Neural Networks

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.
Driver Drowsiness Detection Based on Humantenna Effect for Automotive Safety Systems

Car accidents due to driver drowsiness are a major concern for the automotive industry, hence, great effort and budget are invested towards advanced automotive safety. A wide range of sensors are currently being considered in automotive safety. The majority of these sensors are intended for biological measurements such as respiration activity, heartbeat, blood’s alcohol concentration, etc. System complexity and implementation cost are some of the challenging design requirements, which affect the widespread usage of such sensors. In this paper, a simple and inexpensive passive touch sensor based on the humantenna effect is proposed and tested to detect driver drowsiness.
On the Concurrent Composition of Quantum Zero-Knowledge

We study the notion of zero-knowledge secure against quantum polynomial-time verifiers (referred to as quantum zero-knowledge) in the concurrent composition setting. Despite being extensively studied in the classical setting, concurrent composition in the quantum setting has hardly been studied. We initiate a formal study of concurrent quantum zero-knowledge. Our results are as follows:
Extreme Model Compression for On-device Natural Language Understanding

In this paper, we propose and experiment with techniques for extreme compression of neural natural language understanding (NLU) models, making them suitable for execution on resource-constrained devices. We propose a task-aware, end-to-end compression approach that performs word-embedding compression jointly with NLU task learning. We show our results on a large-scale, commercial NLU system trained on a varied set of intents with huge vocabulary sizes. Our approach outperforms a range of baselines and achieves a compression rate of 97.4% with less than 3.7% degradation in predictive performance. Our analysis indicates that the signal from the downstream task is important for effective compression with minimal degradation in performance.
newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations for Propaganda Classification

This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification.We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text.For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques.We were ranked 5 th in the propaganda technique classification subtask.
Hypersequent calculi for non-normal modal and deontic logics: countermodels and optimal complexity

Abstract We present some hypersequent calculi for all systems of the classical cube and their extensions with axioms ${T}$, ${P}$ and ${D}$ and for every $n \geq 1$, rule ${RD}_n^+$. The calculi are internal as they only employ the language of the logic, plus additional structural connectives. We show that the calculi are complete with respect to the corresponding axiomatization by a syntactic proof of cut elimination. Then, we define a terminating proof search strategy in the hypersequent calculi and show that it is optimal for coNP-complete logics. Moreover, we show that from every failed proof of a formula or hypersequent it is possible to directly extract a countermodel of it in the bi-neighbourhood semantics of polynomial size for coNP logics, and for regular logics also in the relational semantics. We finish the paper by giving a translation between hypersequent rule applications and derivations in a labelled system for the classical cube.
Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads

Transformer-based pre-trained language models (PLMs) have dramatically improved the state of the art in NLP across many tasks. This has led to substantial interest in analyzing the syntactic knowledge PLMs learn. Previous approaches to this question have been limited, mostly using test suites or probes. Here, we propose a novel fully unsupervised parsing approach that extracts constituency trees from PLM attention heads. We rank transformer attention heads based on their inherent properties, and create an ensemble of high-ranking heads to produce the final tree. Our method is adaptable to low-resource languages, as it does not rely on development sets, which can be expensive to annotate. Our experiments show that the proposed method often outperform existing approaches if there is no development set present. Our unsupervised parser can also be used as a tool to analyze the grammars PLMs learn implicitly. For this, we use the parse trees induced by our method to train a neural PCFG and compare it to a grammar derived from a human-annotated treebank.
WMT20 Document-Level Markable Error Exploration.

Even though sentence-centric metrics are used widely in machine translation evaluation, document-level performance is at least equally important for professional usage. In this paper, we bring attention to detailed document-level evaluation focused on markables (expressions bearing most of the document meaning) and the negative impact of various markable error phenomena on the translation. For an annotation experiment of two phases, we chose Czech and English documents translated by systems submitted to WMT20 News Translation Task. These documents are from the News, Audit and Lease domains. We show that the quality and also the kind of errors varies significantly among the domains. This systematic variance is in contrast to the automatic evaluation results. We inspect which specific markables are problematic for MT systems and conclude with an analysis of the effect of markable error types on the MT performance measured by humans and automatic evaluation tools.
E-Learn: An Online Platform for Teaching and Learning

On the 18 th of March, 2020, a Movement Control Order (MCO) was announced by the Malaysian government, and with the announcement, numerous regulations have been imposed to the public under the Prevention and Control of Infectious Diseases Act 1988, to prevent and curb the spread of COVID-19.The whole world was shocked and the experts were rattled.COVID-19 has brought the world to a standstill.The pandemic has affected all walks of life and sectors, including the education sector.Nationwide lockdown has prompted drastic changes, pushing the stakeholders to completely overhaul existing educational practices and strategies, with limited time and resources.Although the past decades have seen radical change in underlying beliefs and theories in the context of teaching and learning, the unforeseen, inadvertent and pushed transition from the orthodoxy of face to face, classroom-centric to 100% online has become a gamechanger, instigating significant and widespread presence of technological-enhanced or digital-supported instructions.Amid the upheaval, and despite institutional and individual constraints, academics and universities were resilient, migrating and converting lessons online, leveraging on numerous digital/ online learning platforms (OLPs), at a breakneck pace.However, the questions lie in how quickly can the students adapt, and how effective are these OLPs in scaffolding the 'new norm' of interactions?In fact, some researchers have argued that there are disparities between potentials and solutions fashioned by technology in education.In view of these major adjustments to the teaching and learning environments, this study is aimed to examine the effectiveness of eLearn, a web-based online learning platform, specifically its perceived usefulness, perceived satisfaction and perceived facilitation of student-teacher and