Reviewerly/exHarmony huggingface dataset:
Unified Likelihood Ratio Estimation for High- to Zero-Frequency &lt;i&gt;N&lt;/i&gt;-Grams

Likelihood ratios (LRs), which are commonly used for probabilistic data processing, are often estimated based on the frequency counts of individual elements obtained from samples. In natural language processing, an element can be a continuous sequence of $N$ items, called an $N$-gram, in which each item is a word, letter, etc. In this paper, we attempt to estimate LRs based on $N$-gram frequency information. A naive estimation approach that uses only $N$-gram frequencies is sensitive to low-frequency (rare) $N$-grams and not applicable to zero-frequency (unobserved) $N$-grams; these are known as the low- and zero-frequency problems, respectively. To address these problems, we propose a method for decomposing $N$-grams into item units and then applying their frequencies along with the original $N$-gram frequencies. Our method can obtain the estimates of unobserved $N$-grams by using the unit frequencies. Although using only unit frequencies ignores dependencies between items, our method takes advantage of the fact that certain items often co-occur in practice and therefore maintains their dependencies by using the relevant $N$-gram frequencies. We also introduce a regularization to achieve robust estimation for rare $N$-grams. Our experimental results demonstrate that our method is effective at solving both problems and can effectively control dependencies.
Determining Surface Shape of Translucent Objects with the Combination of Laser-Beam-Based Structured Light and Polarization Technique

In this study, we focus on the 3D surface measurement and reconstruction of translucent objects. The proposed approach of surface-shape determination of translucent objects is based on the combination of the projected laser-beam-based sinusoidal structured light and the polarization technique. The theoretical analyses are rigorously completed in this work, including the formation, propagation, and physical features of the generated sinusoidal signal by the designed optical system, the reflection and transmission of the projected monochromatic fringe pattern on the surface of the translucent object, and the formation and the separation of the direct-reflection and the global components of the surface radiance of the observed object. The results of experimental investigation designed in accordance with our theoretical analyses have confirmed that accurate reconstructions can be obtained using the one-shot measurement based on the proposed approach of this study and Fourier transform profilometry, while the monochromaticity and the linearly-polarized characteristic of the projected sinusoidal signal can be utilized by using a polarizer and an optical filter simultaneously for removing the global component, i.e., the noised signal contributed by multiply-scattered photons and the background illuminance in the frame of our approach. Moreover, this study has also revealed that the developed method is capable of getting accurate measurements and reconstructions of translucent objects when the background illumination exists, which has been considered as a challenging issue for 3D surface measurement and reconstruction of translucent objects.
DeepCPCFG: Deep Learning and Context Free Grammars for End-to-End Information Extraction

We address the challenge of extracting structured information from business documents without detailed annotations. We propose Deep Conditional Probabilistic Context Free Grammars (DeepCPCFG) to parse two-dimensional complex documents and use Recursive Neural Networks to create an end-to-end system for finding the most probable parse that represents the structured information to be extracted. This system is trained end-to-end with scanned documents as input and only relational-records as labels. The relational-records are extracted from existing databases avoiding the cost of annotating documents by hand. We apply this approach to extract information from scanned invoices achieving state-of-the-art results despite using no hand-annotations.
Unknown-Box Approximation to Improve Optical Character Recognition Performance

Optical character recognition (OCR) is a widely used pattern recognition application in numerous domains. There are several feature-rich, general-purpose OCR solutions available for consumers, which can provide moderate to excellent accuracy levels. However, accuracy can diminish with difficult and uncommon document domains. Preprocessing of document images can be used to minimize the effect of domain shift. In this paper, a novel approach is presented for creating a customized preprocessor for a given OCR engine. Unlike the previous OCR agnostic preprocessing techniques, the proposed approach approximates the gradient of a particular OCR engine to train a preprocessor module. Experiments with two datasets and two OCR engines show that the presented preprocessor is able to improve the accuracy of the OCR up to 46% from the baseline by applying pixel-level manipulations to the document image. The implementation of the proposed method and the enhanced public datasets are available for download (https://github.com/paarandika/Gradient-Approx-to-improve-OCR).
Unsupervised Domain Adaptation with Semantic Consistency Across Heterogeneous Modalities for MRI Prostate Lesion Segmentation

Any novel medical imaging modality that differs from previous protocols e.g. in the number of imaging channels, introduces a new domain that is heterogeneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel space and introduce two new loss functions that promote semantic consistency. Firstly, we introduce a semantic cycle-consistency loss in the source domain to ensure that the translation preserves the semantics. Secondly, we introduce a pseudo-labelling loss, where we translate target data to source, label them by a source-domain network, and use the generated pseudo-labels to supervise the target-domain network. Our results show that this allows us to extract systematically better representations for the target domain. In particular, we address the challenge of enhancing performance on VERDICT-MRI, an advanced diffusion-weighted imaging technique, by exploiting labeled mp-MRI data. When compared to several unsupervised domain adaptation approaches, our approach yields substantial improvements, that consistently carry over to the semi-supervised and supervised learning settings.
Almost-Reed–Muller Codes Achieve Constant Rates for Random Errors

This paper considers “ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -almost Reed–Muller codes”, i.e., linear codes spanned by evaluations of all but a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> fraction of monomials of degree at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> . It is shown that for any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta &gt; 0$ </tex-math></inline-formula> and any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon &gt;0$ </tex-math></inline-formula> , there exists a family of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -almost Reed–Muller codes of constant rate that correct <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/2- \varepsilon $ </tex-math></inline-formula> fraction of random errors with high probability. For exact Reed–Muller codes, the analogous result is not known and represents a weaker version of the longstanding conjecture that Reed–Muller codes achieve capacity for random errors (Abbe-Shpilka-Wigderson STOC ’15). Our proof is based on the recent polarization result for Reed–Muller codes, combined with a combinatorial approach to establishing inequalities between the Reed–Muller code entropies.
Morse Code Detector and Decoder using Eye Blinks

Communication with the outside world or the caretakers is one of the major challenges for the people with disabilities. In this work, the authors propose a realtime algorithm to detect morse code from the eye blinks in a series from a live camera. The proposed algorithm detects eye landmarks and estimates the level of eye-opening using eye aspect ratio (EAR). The low-cost software decodes morse code precisely based on the duration of the eyes closed or opened which in turn is translated to English Language. So, this system is intended to provide an alternative form of communication for people with disabilities and to convey confidential messages.
Parallel Capsule Networks for Classification of White Blood Cells

Capsule Networks (CapsNets) is a machine learning architecture proposed to overcome some of the shortcomings of convolutional neural networks (CNNs). However, CapsNets have mainly outperformed CNNs in datasets where images are small and/or the objects to identify have minimal background noise. In this work, we present a new architecture, parallel CapsNets, which exploits the concept of branching the network to isolate certain capsules, allowing each branch to identify different entities. We applied our concept to the two current types of CapsNet architectures, studying the performance for networks with different layers of capsules. We tested our design in a public, highly unbalanced dataset of acute myeloid leukaemia images (15 classes). Our experiments showed that conventional CapsNets show similar performance than our baseline CNN (ResNeXt-50) but depict instability problems. In contrast, parallel CapsNets can outperform ResNeXt-50, is more stable, and shows better rotational invariance than both, conventional CapsNets and ResNeXt-50.
A Novel Framework for Facemask Detection Using R-Convolution Neural Network

Covid-19 virus has changed the total life style of human beings. This coivd-19 started in the country china and spread all over the world. World health organization (WHO) suggested the face masks to be covered which can reduce the spread of corona virus. All countries made it compulsory to wear masks to cut-off the covid spread. Hence detection of wearing masks or not has become an important area to work on for computer vision. Many woks are being done on face mask detection, but mostly basis on the classification of mask and no-mask. Here in this paper. a novel approach is proposed which not only classifies mask and no-mask people, but also identifies whether a mask is properly covered or not. Thus in this paper use of R-CNN (Convolution Neural Network) is proposed which results to achieve an accuracy up to 0.93.
Copernicus, his Latin style and comments to Commentariolus

A methodology of historical or higher criticism and of stylometry/stylochronometry known from Biblical and literary studies is applied to the examination of Nicolaus Copernicus’s writings. In particular, his early work Commentariolus is compared at the level of the Latin language with his later ones (Meditata, Letter against Werner and De revolutionibus) as well as the texts of some other authors. A number of striking stylistic dissimilarities between these works have been identified and interpreted in the light of stylometry/stylochronometry, historical criticism and the history of Copernican research. The conducted research allowed to draw some plausible conclusions about the Sitz im Leben (historical context), the dating of Commentariolus and related matters.
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers Using End-to-End Speaker-Attributed ASR

This paper presents Transcribe-to-Diarize, a new approach for neural speaker diarization that uses an end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR). The E2E SA-ASR is a joint model that was recently proposed for speaker counting, multi-talker speech recognition, and speaker identification from monaural audio that contains overlapping speech. Although the E2E SA-ASR model originally does not estimate any time-related information, we show that the start and end times of each word can be estimated with sufficient accuracy from the internal state of the E2E SA-ASR by adding a small number of learnable parameters. Similar to the target-speaker voice activity detection (TS-VAD)-based diarization method, the E2E SA-ASR model is applied to estimate speech activity of each speaker while it has the advantages of (i) handling unlimited number of speakers, (ii) leveraging linguistic information for speaker diarization, and (iii) simultaneously generating speaker-attributed transcriptions. Experimental results on the LibriCSS and AMI corpora show that the proposed method achieves significantly better diarization error rate than various existing speaker diarization methods when the number of speakers is unknown, and achieves a comparable performance to TS-VAD when the number of speakers is given in advance. The proposed method simultaneously generates speaker-attributed transcription with state-of-the-art accuracy.
Contextual Image Classification Through Fine-Tuned Graph Neural Networks

Nowadays, computer vision techniques have become popular in several domains (e.g., agriculture, industry, medicine, and others). Their success derives from the advances in computational resources and the large volume of complex data (i.e., images). These factors led to an increase in the use of convolutional neural networks. However, such deep learning architectures do not appropriately explore the relationships between the data (e.g., images) and their respective structure. To better gather and encodes these affinity connections into a deep neural network, we can use the so-called graph neural networks (GNNs). These graph-based networks also present drawbacks. The high number of relationships in a graph can be considered a bottleneck regarding the available resources and scalability. Hence, to mitigate this issue, we propose to use GNNs automatically tuned, defining their well-suited connections according to a given image context, which improves their efficiency and efficacy. We performed experiments considering different types of state-of-the-art deep features aggregated with the GNNs. The results demonstrate that our proposed method can achieve equal accuracy (statistically) to GNNs with complete and random connections. Moreover, we decreased the number of edges to a great extent (up to 96%), testifying to our method’s effectiveness.
Relevance-Sensitive Truth-Trees

Our goal is to articulate a clear rationale for relevance-sensitive propositional logic. The method: truth-trees. Familiar decomposition rules for truth-functional connectives, accompanied by novel ones for the for the arrow, together with a recursive rule, generate a set of ‘acceptable’ formulae that properly contains all theorems of the well-known system R and is closed under substitution, conjunction, and detachment. We conjecture that it satisfies the crucial letter-sharing condition.
Privacy-preserving Decentralized Federated Deep Learning

Deep learning has achieved the high-accuracy of state-of-the-art algorithms in long-standing AI tasks. Due to the obvious privacy issues of deep learning, Google proposes Federal Deep Learning (FDL), in which distributed participants only upload local gradients and and a centralized server updates parameters based on the collected gradients. But few users are willing to participate in federated learning due to the lack of contribution evaluation and reward mechanisms. So a decentralized federated deep learning, called DFDL, has been proposed by introducing blockchain to form an effective incentive mechanism for participants. However, DFDL still faces serious privacy issues as blockchain does not guarantee the privacy of training data and model. In this paper, in order to address the aforementioned issues, we propose a new Privacy-preserving DFDL scheme, called PDFDL. With PDFDL, parties can securely learn a global model with their local gradients in the assistance of blockchain, and the parties' sensitive data and the global model are well protected. Specifically, with a secure multi-party aggregation computing, all local gradients are encrypted by their owners before being sent to the smart contract, and can be directly aggregated without decryption. Detailed security analysis shows that PDFDL can resist various known security threats. Moreover, we give an implementation prototype by integrating deep learning module with a Blockchain development platform (Ethereum V1.6.4). We demonstrate the encryption performance and the training accuracy of our PDFDL on benchmark datasets.
VM Placement in Accelerator-Equipped Data Centers Using Variable-Length Modified Genetic Algorithm

With the increasing growth in computing demand and the complexity of applications, cloud computing has become very popular in recent years. To cope with the high demand for computing resources, data center providers have introduced various types of hardware accelerators such as GPUs and FPGAs in their environments. Accelerator virtualization is also introduced to overcome the underutilization of GPUs and FPGAs in such environments. However, resource provisioning can be quite challenging in large data centers with heterogeneous setups due to the massive solution space of the resulting VM placement problem. In scenarios where the VM resource requests exceed the amount of available resources on the hosts, finding an energy-efficient solution with maximum VM acceptance rate can get difficult for simple heuristics or even metaheuristic methods under tight decision time constraints. The aim of this paper is to tackle the inefficiency of genetic algorithm (GA) in producing good partial schedules in limited decision time. To this end, we introduce a GA-based VM placement method called VLMGA (variable-length modified genetic algorithm). Starting from a limited solution space, VLMGA iteratively tries to find a solution in each sub-space and enlarge the search space until no feasible solution could be found within the specified time frame. Using the proposed technique, the quality of the obtained solution can be greatly improved. Evaluated under real-world workload scenarios, the proposed method achieved 16% improvement on the energy-delay product compared to well-known VM placement methods.
Learning Shape Sensitive Descriptors for Classifying Functional Data

We propose a new method of learning descriptors for constructing classifiers of functional data. These descriptors are moments of a curve derivative, but their learning is based solely on samples of the curve itself. Furthermore, the derivative itself is not directly estimated. This is possible due to the trick of using simultaneously two different bases of a functional space.
How Knowledge Triggers Obligation

Obligations can be affected by knowledge. Several approaches exist to formalize knowledge-based obligations, but no formalism has been developed yet to capture the dynamic interaction between knowledge and obligations. We introduce the dynamic extension of an existing logic for knowledge-based obligations here. We motivate the logic by analyzing several scenarios and by showing how it can capture in an original manner several fundamental deontic notions such as absolute, prima facie and all-things-considered obligations. Finally, in the dynamic epistemic logic tradition, we provide reduction axioms for the dynamic operator of the new logic.
Binarization of Degraded Documents Using Linear Regression

In the age of digitization, it is essential to convert the printed copy of the documents to the digital form. These documents are available through large number of sources - old books, old legal documents, medical-based notes, and safety-related reports. These documents are deteriorated and lead to low contrast. Several approaches have been discussed and designed to overcome the challenges encountered in the binarization of these documents. In this paper, proposed a various challenges encountered in the binarization process and also the method of linear regression in encountered for the removal of various degradation. Common challenges such as Ink bleed, uneven illumination, blurred character and contrast issues have been discussed. The proposed implementation uses linear regression which gives the flexibility to demonstrate the information extraction from the degraded historical documents. In this paper we have used DIBCO data sets from years (2016, 2013, 2012, 2011, 2010 and 2009).
VOGUE: Answer Verbalization Through Multi-Task Learning

In recent years, there have been significant developments in Question Answering over Knowledge Graphs (KGQA). Despite all the notable advancements, current KGQA systems only focus on answer generation techniques and not on answer verbalization. However, in real-world scenarios (e.g., voice assistants such as Alexa, Siri, etc.), users prefer verbalized answers instead of a generated response. This paper addresses the task of answer verbalization for (complex) question answering over knowledge graphs. In this context, we propose a multi-task-based answer verbalization framework: VOGUE (Verbalization thrOuGh mUlti-task lEarning). The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm. Our framework can generate results based on using questions and queries as inputs concurrently. VOGUE comprises four modules that are trained simultaneously through multi-task learning. We evaluate our framework on existing datasets for answer verbalization, and it outperforms all current baselines on both BLEU and METEOR scores.
Delegating Supersingular Isogenies over $$\mathbb {F}_{p^2}$$ with Cryptographic Applications

Although isogeny-based cryptographic schemes enjoy the smallest key sizes amongst current post-quantum cryptographic candidates, they come at a high computational cost, making their deployment on the ever-growing number of resource-constrained devices difficult. Speeding up the expensive post-quantum cryptographic operations by delegating these computations from a weaker client to untrusted powerful external servers is a promising approach. Following this, we present in this work mechanisms allowing computationally restricted devices to securely and verifiably delegate isogeny computations to potentially untrusted third parties. In particular, we propose two algorithms that can be integrated into existing isogeny-based protocols and which lead to a much lower cost for the delegator than the full, local computation. For example, compared to the local computation cost, we reduce the public-key computation step of SIDH/SIKE by a factor 5 and zero-knowledge proofs of identity by a factor 16 for the prover, while it becomes almost free for the verifier, respectively, at the NIST security level 1.