Reviewerly/exHarmony huggingface dataset:
An Open-Source Tesseract Based Optical Character Recognizer for Bengali Language

Optical Character Recognition (OCR) is the process of extracting text from an image. The main purpose of an OCR is to make editable documents from existing paper documents or image files. OCR primarily works in two phases; they are character and word detection. In case of more sophisticated approach, an OCR also works on sentence detection to preserve documents’ structures. In this paper, we would discuss the process of developing an OCR for Bengali language. Lots of efforts have been put on developing an OCR for Bengali. Though some OCRs have been developed, none of them is completely error free. For our thesis, we trained Tesseract OCR Engine to develop an OCR for Bengali language. Tesseract is currently the most accurate OCR engine. This engine was developed at HP labs and currently sponsored by Google. In Tesseract there are two option to training first one is Legacy Training and second is LSTM Training. We do both of them.
A working model for textual Membership Query Synthesis

Membership Query Synthesis (MQS) is an active learning paradigm in which one labels generated artificial examples instead of genuine ones to extend a dataset.Despite prodigious advances in the power of generative models, an essential component of MQS, the field stays severely under-studied, especially in the textual domain.We found only one other paper, which selects examples in a latent space close to the decision boundary and shows good results on a curated dataset of short sentences.We show that this performs poorly when used on a real dataset.We propose and report better results than random selection of unlabelled genuine data with random generation of artificial data from a variational auto-encoder coupled with a simple set of filtering mechanisms.This provides an improvement of 31.1% over the previous MQS state-of-the-art on the SST-2 dataset, and of 2.7% over random active learning.To the best of our knowledge, this is the first time MQS is reported to work on a textual task with no constraint on the size of the input sentences.
Towards Weak Information Theory: Weak-Joint Typicality Decoding Using Support Vector Machines May Lead to Improved Error Exponents

In this paper, the authors report a way to use concepts from statistical learning to gain an advantage in terms of error exponents while communicating over a discrete memoryless channel. The study utilizes the simulation capability of the scientific computing package MATLAB to show that the proposed decoding method performs better than the traditional method of joint typicality decoding. The advantage is secured by modifying the traditional specification of what constitutes a decoding error. This is justified by the paradigm, also used in the program of `utilizing' noisy feedback, that one ought not to declare a condition as an error if some further processing can extract useful information from it.
Topologically Protected Entanglement Emitters

We report topologically-protected entanglement emitters, that emit topological Einstein-Podolsky-Rosen state and multiphoton entangled state from a plug-and-play silicon-photonic chip in ambient conditions. The device emulating a photonic anomalous Floquet insulator allows the generation of four-photon topological entangled states at nontrivial edge modes.
Fast Online and Relational Tracking

To overcome challenges in multiple object tracking task, recent algorithms use interaction cues alongside motion and appearance features. These algorithms use graph neural networks or transformers to extract interaction features that lead to high computation costs. In this paper, a novel interaction cue based on geometric features is presented aiming to detect occlusion and re-identify lost targets with low computational cost. Moreover, in most algorithms, camera motion is considered negligible, which is a strong assumption that is not always true and leads to ID Switch or mismatching of targets. In this paper, a method for measuring camera motion and removing its effect is presented that efficiently reduces the camera motion effect on tracking. The proposed algorithm is evaluated on MOT17 and MOT20 datasets and it achieves the state-of-the-art performance of MOT17 and comparable results on MOT20. The code is also publicly available.
Rethinking of AlphaStar

We present a different view for AlphaStar (AS), the program achieving Grand-Master level in the game StarCraft II. It is considered big progress for AI research. However, in this paper, we present problems with the AS, some of which are the defects of it, and some of which are important details that are neglected in its article. These problems arise two questions. One is that what can we get from the built of AS? The other is that does the battle between it with humans fair? After the discussion, we present the future research directions for these problems. Our study is based on a reproduction code of the AS, and the codes are available online.
Lethal Dose Conjecture on Data Poisoning

Data poisoning considers an adversary that distorts the training set of machine learning algorithms for malicious purposes. In this work, we bring to light one conjecture regarding the fundamentals of data poisoning, which we call the Lethal Dose Conjecture. The conjecture states: If $n$ clean training samples are needed for accurate predictions, then in a size-$N$ training set, only $\Theta(N/n)$ poisoned samples can be tolerated while ensuring accuracy. Theoretically, we verify this conjecture in multiple cases. We also offer a more general perspective of this conjecture through distribution discrimination. Deep Partition Aggregation (DPA) and its extension, Finite Aggregation (FA) are recent approaches for provable defenses against data poisoning, where they predict through the majority vote of many base models trained from different subsets of training set using a given learner. The conjecture implies that both DPA and FA are (asymptotically) optimal -- if we have the most data-efficient learner, they can turn it into one of the most robust defenses against data poisoning. This outlines a practical approach to developing stronger defenses against poisoning via finding data-efficient learners. Empirically, as a proof of concept, we show that by simply using different data augmentations for base learners, we can respectively double and triple the certified robustness of DPA on CIFAR-10 and GTSRB without sacrificing accuracy.
The Impact of Performance Appraisals on Employee Productivity: The Case of the Lebanese Retail Sector

Employee motivation is an ongoing fight for organizations. Arousal, direction, and persistence of planned activity with a purpose are all characteristics of motivation. A company's workers are critical to its future development and success. In many ways, they represent a company's image. Employees are the most critical resource a business may utilize to complete its tasks since they are the most valuable, expensive, and inconsistent. The research focused on quantitative methodology for collecting data and for evaluating the impact of performance appraisals on the performance of employees in the workplace. Data from primary and secondary sources will be gathered and merged to provide a complete picture of the relationship between 360-degree performance evaluations and employee motivation. The survey had been distributed using google forms over a sample of 100 respondents in Lebanese Companies, and the data had been treated using SPSS statistical tool and the results had been displayed in the form of descriptive and inferential statistics. The results showed a direct relationship between management by objectives, 360 degree appraisal, performance appraisal, and organizational performance.
Preventing medical service provider from launching known emergency attack in real-time healthcare

Abstract In cloud-assisted body area network (BAN), medical user (MU) is equipped with sensor nodes to measure different physiological health information (PHI). Whenever the PHI reading is found to be abnormal, BAN notifies this medical emergency event to cloud. Cloud broadcasts this medical emergency event to all medical service providers (MSPs) attached to cloud while preserving privacy of MU. There can be multiple MSPs competing for admitting the MU in need of medical treatment. For this, MSP uploads its infrastructural parameters to cloud. However, some MSPs can upload false data to cloud for attracting MU. This is possible when malicious MSP is aware of some medical emergency event and ready to exploit the system to get more business. We classify this attack as Known Emergency Attack. This attack can severely deteriorate the appropriate MSP selection process during emergency. However, as per our knowledge, there is no prior work addressing this problem. We propose a blockchain based framework, whcih can prevent an MSP from uploading false data and thereby protect from known emergency attack. In response to MU emergency, MSPs send its encrypted attributes to miners in the blockchain network. Miners compute encrypted difference between registered MSP attribute and currently received MSP attribute. If the difference is within a threshold, only then this MSP is considered as a candidate for MSP selection. Security and privacy of proposed framework is verified using Random Oracle Model and AVISPA tool. Computational requirement of proposed framework has also been evaluated.
Perturbed Iterate SGD for Lipschitz Continuous Loss Functions

This paper presents an extension of stochastic gradient descent for the minimization of Lipschitz continuous loss functions. Our motivation is for use in non-smooth non-convex stochastic optimization problems, which are frequently encountered in applications such as machine learning. Using the Clarke $ε$-subdifferential, we prove the non-asymptotic convergence to an approximate stationary point in expectation for the proposed method. From this result, a method with non-asymptotic convergence with high probability, as well as a method with asymptotic convergence to a Clarke stationary point almost surely are developed. Our results hold under the assumption that the stochastic loss function is a Carathéodory function which is almost everywhere Lipschitz continuous in the decision variables. To the best of our knowledge this is the first non-asymptotic convergence analysis under these minimal assumptions.
Span-based Joint Entity and Relation Extraction with Light Attention Encoder

Abstract The construction and updating of the knowledge graph need to obtain a lot of knowledge in time. Text data generated continuously on the Internet is an important source of this knowledge. Especially for the knowledge graph related to public opinion monitoring, it is necessary to quickly extract entities and relations from the text. Although the entity and relation extraction (ERE) method based on the pre-training model can obtain good performance, the training efficiency is low due to the complexity of the model, and it needs to perform better on large datasets. In order to reduce the model complexity and better apply related models to small datasets and apply the ERE task on the edge devices, this work proposes a novel span-based joint ERE model with a light attention encoder. The specific operation is to discard the Value transform matrix originally possessed by the self-attention mechanism, modify it to an identity map, and change the calculation method of dot product similarity to the cosine similarity calculation method. Through experiments on three public datasets, it is verified that the proposed approach can greatly reduce the training cost on the basis of ensuring the good performance of the model.
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer

Expert-layman text style transfer technologies have the potential to improve communication between members of scientific communities and the general public. High-quality information produced by experts is often filled with difficult jargon laypeople struggle to understand. This is a particularly notable issue in the medical domain, where layman are often confused by medical text online. At present, two bottlenecks interfere with the goal of building high-quality medical expert-layman style transfer systems: a dearth of pretrained medical-domain language models spanning both expert and layman terminologies and a lack of parallel corpora for training the transfer task itself. To mitigate the first issue, we propose a novel language model (LM) pretraining task, Knowledge Base Assimilation, to synthesize pretraining data from the edges of a graph of expert- and layman-style medical terminology terms into an LM during self-supervised learning. To mitigate the second issue, we build a large-scale parallel corpus in the medical expert-layman domain using a margin-based criterion. Our experiments show that transformer-based models pretrained on knowledge base assimilation and other well-established pretraining tasks fine-tuning on our new parallel corpus leads to considerable improvement against expert-layman transfer benchmarks, gaining an average relative improvement of our human evaluation, the Overall Success Rate (OSR), by 106%. We release our code and parallel corpus for future research.
Toward practical and high-fidelity user digitization in extended reality environments

editorial Free Access Share on Toward practical and high-fidelity user digitization in extended reality environments Author: Karan Ahuja View Profile Authors Info & Claims XRDS: Crossroads, The ACM Magazine for StudentsVolume 29Issue 1Fall 2022 pp 5–7https://doi.org/10.1145/3558185Published:06 October 2022Publication History 0citation81DownloadsMetricsTotal Citations0Total Downloads81Last 12 Months81Last 6 weeks61 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Scalable Variational Quantum Circuits for Autoencoder-based Drug Discovery

The de novo design of drug molecules is recognized as a time-consuming and costly process, and computational approaches have been applied in each stage of the drug discovery pipeline. Variational autoencoder is one of the computer-aided design methods which explores the chemical space based on existing molecular dataset. Quantum machine learning has emerged as an atypical learning method that may speed up some classical learning tasks because of its strong expressive power. However, near-term quantum computers suffer from limited number of qubits which hinders the representation learning in high dimensional spaces. We present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction. The architectural strategies in hybrid quantum classical networks such as, adjustable quantum layer depth, heterogeneous learning rates, and patched quantum circuits are proposed to learn high dimensional dataset such as, ligand-targeted drugs. Extensive experimental results are reported for different dimensions including 8x8 and 32x32 after choosing suitable architectural strategies. The performance of quantum generative autoencoder is compared with the corresponding classical counterpart throughout all experiments. The results show that quantum computing advantages can be achieved for normalized low-dimension molecules, and that high-dimension molecules generated from quantum generative autoencoders have better drug properties within the same learning period.
INFLUENCE OF THE ANCIENT SUBDUCTION PROCESS ON THE ORICINE OF LATE COLLISION VOLCANISM IN THE LESSER CAUCASUS

The article considers the petrogeochemical features of the Late Cenozoic collisional volcanism of the Lesser Caucasus. Based on new geochemical data, it is shown that Nb, Ta, Hf, and Zr minima are observed for basic and intermediate rocks. The salic rocks are strongly enriched in Rb, Ba, Th, La and depleted in Ti, Yb, Y relative to the primitive mantle. The enrichment in incompatible elements suggests that the source of the melt from which the magma was derived was a metasomatized lithospheric mantle enriched in potassium and incompatible elements. Late Miocene-Quaternary volcanics were formed at different degrees of melting of subduction-enriched subcontinental lithospheric mantle. The role of the suprasubduction material decreases during the evolution of volcanism over time due to an increase in the share of the asthenospheric component in the interval from the Upper Miocene to the Quaternary.
P-T-T TRENDS OF METAMORPHIC TRANSFORMATION OF ECLOGITES IN THE GRIDINO COMPLEX OF THE BELOMORIAN PROVINCE (STOLBIKHA ISLAND AND SAMYLINO AREA)

Eclogites in the Gridino complex of the Belomorian province have complex history of metamorphic transformations. Their polymetamorphism is revealed from relics of early and late high-pressure mineral assemblages (omphacite+garnet), reaction microstructures, changes of the garnet chemical zoning, results of thermobarometric calculations, and isotope-geochronological studies. Geochronological data indicate two stages of eclogitic metamorphism in the Gridino complex at the Archean and Paleoproterozoic. As a result of the study, we obtained two P-T-t trends for the regressive stage of rock evolution with a successive transition from eclogitic facies to granulite (amphibolite). The peak of the first metamorphic event responds to metamorphic zircons with an age of 2,7 Ga. The maximal values of the P-T conditions of the second metamorphic event are reliably correlated with 1,9 Ga metamorphic zircons.
Rethinking Importance Weighting for Transfer Learning

A key assumption in supervised learning is that training and test data follow the same probability distribution. However, this fundamental assumption is not always satisfied in practice, e.g., due to changing environments, sample selection bias, privacy concerns, or high labeling costs. Transfer learning (TL) relaxes this assumption and allows us to learn under distribution shift. Classical TL methods typically rely on importance-weighting -- a predictor is trained based on the training losses weighted according to the importance (i.e., the test-over-training density ratio). However, as real-world machine learning tasks are becoming increasingly complex, high-dimensional, and dynamical, novel approaches are explored to cope with such challenges recently. In this article, after introducing the foundation of TL based on importance-weighting, we review recent advances based on joint and dynamic importance-predictor estimation. Furthermore, we introduce a method of causal mechanism transfer that incorporates causal structure in TL. Finally, we discuss future perspectives of TL research.
MAGMATISM OF THE DON TERRANE VOLGA-DON OROGEN

The Paleoproterozoic igneous complexes of the western part of the Volga-Don orogen are considered. The Don terrane developed as a volcanic arc on the continental crust, while the Losevо terrane developed on the oceanic one. This is evidenced by granitoids with different geochemical and isotope-geochemical characteristics, they were formed by postcollisoin extension of the orogen crust.
Multiisotope (δ33S, δ34S, δ36S) composition of sulfur in sulfides of ancient ores: significance for determining sulfur sources, biogeochemical processes and deposit genesis

New data on the isotopic (δ 33 S, δ 34 S, δ 36 S) composition of ancient sulfide ores of the Karelian and Siberian cratons have been obtained. An in situ analysis of the isotope composition in sulfides made it possible to draw conclusions about the sources of sulfur during the formation of Archean deposits, the biological influence on the formation of sulfide ores, and the features of the environmental impact (atmosphere, hydrosphere) on sulfide formation.
POPS: An Efficient Framework for GPU-based Feature Extraction of Massive Gridded Planetary LiDAR Data [Scalable Data Science]

One of the long-standing question in geoscience is whether there is a topographical signature of life.Recent development of space-borne LiDAR has led to massive data depicting planetary topographies, opening up unprecedented opportunities to make progress in answering this question.This is what we set out to do in an ongoing project named .A key step of PARKER is to find topographical features that are potentially relevant to intrinsic differences between Earth and alien worlds.Due to the huge data volume, sequential feature extraction cannot meet our needs in PARKER.Hence, in this work we propose a GPU-accelerated framework for fast feature extraction of planetary LiDAR, which as far as we know is the first GPU-based solution for this task.Faced with multi-scale features and limited GPU memory, we present a novel pseudoone-pass sweep (POPS) approach, leveraging memory-aware data grouping and incremental data transfer to address these challenges.We also develop a GPU-based solution to aggregate features extracted by POPS.Experiments on real and simulated data show that our algorithms are 2-3 orders of magnitude faster than their sequential counterparts and 1-2 orders of magnitude faster than MPI-based multi-core parallelism, enabling near real-time analytics of datasets with almost a billion points.Based on POPS, we have been able to efficiently evaluate the relevance of topographical features to intrinsic inter-planetary differences.So far, we have assessed the abilities of two feature extractions methods, PCA and STAT, to capture differences between Earth and Mars.Results show that PCA features on scales of 300-500m can best capture such differences.Thanks to the generic nature of POPS, we will be able to expand our studies to new feature extraction methods and other alien worlds than Mars in the next phase of PARKER.
Divisible Codes

A linear code over $\mathbb{F}_q$ with the Hamming metric is called $\Delta$-divisible if the weights of all codewords are divisible by $\Delta$. They have been introduced by Harold Ward a few decades ago. Applications include subspace codes, partial spreads, vector space partitions, and distance optimal codes. The determination of the possible lengths of projective divisible codes is an interesting and comprehensive challenge.
Intelligent Online Selling Point Extraction for E-Commerce Recommendation

In the past decade, automatic product description generation for e-commerce have witnessed significant advancement. As the services provided by e-commerce platforms become diverse, it is necessary to dynamically adapt the patterns of descriptions generated. The selling point of products is an important type of product description for which the length should be as short as possible while still conveying key information. In addition, this kind of product description should be eye-catching to the readers. Currently, product selling points are normally written by human experts. Thus, the creation and maintenance of these contents incur high costs. These costs can be significantly reduced if product selling points can be automatically generated by machines. In this paper, we report our experience developing and deploying the Intelligent Online Selling Point Extraction (IOSPE) system to serve the recommendation system in the JD.com e-commerce platform. Since July 2020, IOSPE has become a core service for 62 key categories of products (covering more than 4 million products). So far, it has generated more than 0.1 billion selling points, thereby significantly scaling up the selling point creation operation and saving human labour. These IOSPE generated selling points have increased the click-through rate (CTR) by 1.89\% and the average duration the customers spent on the products by more than 2.03\% compared to the previous practice, which are significant improvements for such a large-scale e-commerce platform.