Reviewerly/exHarmony huggingface dataset:
Fundamental Limits of Bosonic Broadcast Channels

We develop the capacity region for the bosonic broadcast channel in the presence of thermal noise and photon loss due to the environment. The bosonic channel is a quantum-mechanical description of many practical communication links such as optical, microwave, and radiofrequency. We employ our results to find the capacity region for quantum-secure covert broadcast over such channels and show that time-division is optimal as in the classical covert broadcast scenario. We rely on a strong minimum entropy output conjecture, a direct result of the entropy photon-number inequality (EPnI) conjecture.
Introduction to Text Classification: Impact of Stemming and Comparing TF-IDF and Count Vectorization as Feature Extraction Technique

Natural language processing is a widely used application in research and industry. Amongst other, use cases are sentiment analysis, speech recognition, classification, query answering and machine translation. In this research we investigate widely applied preprocessing methods, to improve the results of different Algorithms trained on a Fake News data set. As feature extraction methods we compared TF-IDF and Count-Vectorization. TF-IDF yielded slightly better results in terms of accuracy. We found that, as opposed to current research, stemming leads to a minor increase of false positive and false negative classifications, hence to a decrease in accuracy. Among the compared models, logistic regression and support vector machine yielded the best results.
Sanskrit Stemmer Design: A Literature Perspective

Nair, Jayashree Nair, Sooraj S. Abhishek, U.Sanskrit, an ancient Indian language, lays the grammatical foundation for most of the languages present in India. These Indian languages, unlike English, are morphologically rich as their words have many inflections or variations. Consequently, computationally processing these languages is more challenging. This is where linguistics tools like a stemmer comes into play. A stemmer strips off the inflected word to its base form. It removes the affixes present in a word and reduces the word to its root or stem form. The stemming process is a language dependent task, as each language has its own linguistics and morphological rules in word formation. However, there are few existing works which have attempted in building language independent stemmers too. This paper explore most of the important stemmers built so for. It also explores the working and methodologies applied in these stemmers, along with their features and uniqueness. This paper also explicates the jargon related to the stemming process. Finally, a proposed design for a Sanskrit verb stemmer, which is based on the existing literature strategies, is also presented. This design sketches the important modules that can be included in rule-based stemmers for a morphologically rich languages, like Sanskrit.
LaFlector: a Privacy-preserving LiDAR-based Approach for Accurate Indoor Tracking

Light Detection and Ranging (LiDAR) is used in various applications, from mapping the environment’s topography to self-driving vehicles. Among such applications, the use of LiDAR for indoor tracking and quantifying visitors’ interest (and ensuring safe distancing) is still not widely explored. Technologies based on wireless signals and video captures are typically used for indoor tracking, but they deficits concerning the lack of accuracy of captured signals or the lack of user privacy in the case of traditional surveillance cameras. Despite introducing tracking challenges inherent in the detection based on light reflection, LiDAR-based approaches represent a relatively low-cost solution for accurate indoor tracking. Thus, this paper presents LaFlector, a LiDAR-based indoor tracking system introducing tracking heuristics capable of detecting, classifying, and tracking several objects simultaneously, which are recorded and dynamically displayed in a 2D coordinate system. LaFlector was evaluated based on a low-cost 2D LiDAR hardware (Slamtec Mapper M1M1) and capable of detecting moving objects with high precision, showing in practice that a LiDAR-based can be used to track visitors’ interest and count the number of moving objects.
Safety Warning! Decentralised and Automated Incentives for Disqualified Drivers Auditing in Ride-Hailing Services

Since 2011, the private ride-hailing companies Didi (2019), Uber (2019) and Lyft (2021) have expanded into more and more cities. These ride-hailing services (RHS) bring convenience to our life; however, at the same time they, also raise security concerns for users. For example, several recent news items show that a considerable number of registered drivers whose licenses have been revoked are still taking RHS orders on the respective platforms; this phenomenon directly leads to insecurity on part of its users and the bad reputation of the ride-hailing service provider (SP). The traditional solution to solve this problem is to periodically check the validity of the drivers’ licenses; however, it is a considerably time-consuming and costly process since the SPs have to manually interact with the governing authorities. Therefore, in this paper, we have presented an auditable self-sovereign identity system (named AudiSSI), which provides an efficient approach for the SPs to manage their registered drivers’ qualifications in a decentralized and automatic manner. Further, using smart contract technology, we propose a safety guarantee insurance in the form of an auditing contract to enable the RHS rider to check their driver's qualifications before the trip starts and get incentives once they detect a disqualified driver. We designed an incentive mechanism and have provided a game theoretical analysis. Finally, we implemented a prototype of AudiSSI and deployed it on Hyperledger Indy and Fabric to show that self-sovereign identity system for RHS driver with qualification auditing is efficient and technically feasible.
Transfer Learning for Encrypted Malicious Traffic Detection Based on Efficientnet

With the development of data encryption technology, encrypted traffic has shown an explosive growth trend. More and more malicious network services rely on encryption to evade detection, which brings huge challenges to traditional rule-based traffic classification methods. In recent years, artificial intelligence technology provides feasible ideas to solve this problem. However, conventional machine learning depends on expert experience for manual feature extraction, and the training of deep learning models requires large number of high-quality labeled data, which makes the research of encrypted traffic detection hard to conduct. To address these issues, we propose a transfer learning method based on Efficientnet to detect the encrypted malicious traffic. Efficientnet-B0, a pre-trained model based on the Imagenet dataset, is transferred to an encrypted traffic dataset, extracting features automatically from the original traffic data without expert experience. The experimental results show that our method can achieve 100% detection accuracy and recall rate. Furthermore, our method can also achieve high detection performance with a small amount of training samples.
Generalisation Power Analysis for finding a stable set of features using evolutionary algorithms for feature selection

Evolutionary Computation (EC) algorithms are powerful techniques for feature selection tasks however, they reach different solutions in each run, and this is known as the stability issue. Existing solutions to finding a stable subset of features when using an EC algorithm include aggregation and frequency-based methods. These methods may return feature subsets that achieve weak or inconsistent classification performance when utilised to build classifiers, and this limitation is known as 'lack of generalisation power'. To address this limitation, this paper proposes a novel algorithm called Generalisation Power Analysis (GPA) that measures the performance of feature subsets in terms of generalisation power and hence evaluates their ability to achieve optimal or near-optimal accuracy over multiple classifiers. GPA has been designed to work with the stochastic nature of EC algorithms. Experiments with eleven benchmark datasets revealed that the proposed GPA approach consistently outperformed alternative methods in finding subsets that achieved high generalisation power. Although GPA requires relatively higher computation time compared to alternative approaches as it embeds multiple classifiers, the advantages of using GPA during feature selection outweigh this limitation since the outcome will be a robust prediction model that has been developed using a subset of features that are not biased towards a specific classifier.
Computing Sum of Sources over a Classical-Quantum MAC

We consider the problem of communicating a general bivariate function of two classical sources observed at the encoders of a classical-quantum multiple access channel. Building on the techniques developed for the case of a classical channel, we propose and analyze a coding scheme based on coset codes. The proposed technique enables the decoder recover the desired function without recovering the sources themselves. We derive a new set of sufficient conditions that are weaker than the current known for identified examples. This work is based on a new ensemble of coset codes that are proven to achieve the capacity of a classical-quantum point-to-point channel.
Dead pixel test using effective receptive field

Deep neural networks have been used in various fields, but their internal behavior in how they understand images is not well known. In this study, we discuss two counterintuitive properties of convolutional neural networks (CNNs). First, we evaluated the size of the receptive field of CNNs with their classification accuracy. Previous studies have attempted to increase the size of the receptive field for performance gain. However, we observed that some CNNs with a smaller receptive field can achieve higher classification accuracy. In this regard, we claim that a larger receptive field does not guarantee improved classification accuracy. Second, using the effective receptive field, we examined the contribution of each pixel to the output of CNN. Intuitively, each pixel is expected to equally contribute to the final output, but we found that there exist pixels in a partially dead state with little contribution to the output. We reveal that the reason for dead pixels lies in even stride operations with odd-sized kernels in CNN and propose a kernel padding method to remove the dead pixels. We demonstrated the vulnerability of CNNs with dead pixels when we detect a noise or small box that is on dead pixels. Our findings on dead pixels should be understood and considered in practical applications of CNN.
Radar Object Detection Using Data Merging, Enhancement and Fusion

Compared to visible images, radar images are generally considered to be an active and robust solution, even in adverse driving situations, for object detection. However, the accuracy of radar object detection (ROD) is always poor. Owing to taking full advantage of data merging, enhancement and fusion, this paper proposes an effective ROD system with only radar images as the input. First, an aggregation module is designed to merge the data from all chirps in the same frame. Then, various gaussian noises with different parameters are employed to increase data diversity and reduce over-fitting based on the analysis of training data. Moreover, due to the process of inference with default parameters is not accurate enough, some hyperparameters are changed to increase the accuracy performance. Finally, a combination strategy is adopted to benefit from multi-model fusion. ROD2021 Challenge is supported by ACM ICMR 2021, and our team (ustc-nelslip) ranked 2nd in the test stage of this challenge. Diverse evaluations also verify the superiority of the proposed system.
Convex geometry of Markovian Lindblad dynamics and witnessing non-Markovianity

We develop a theory of linear witnesses for detecting non-Markovianity, based on the geometric structure of the set of Choi states for all Markovian evolutions having Lindblad-type generators. We show that the set of all such Markovian Choi states form a convex and compact set under the small time interval approximation. Invoking geometric Hahn–Banach theorem, we construct linear witnesses to separate a given non-Markovian Choi state from the set of Markovian Choi states. We present examples of such witnesses for dephasing channel and Pauli channel in case of qubits. Furthermore, we have devised another method of detection NM of various qubit channels, by using projective measurements in the Bell basis. This can be done without the knowledge of what specific kind of channel we are dealing with. This gives us a huge operational advantage for NM detection. We further investigate the geometric structure of the Markovian Choi states to find that they do not form a polytope. This presents a platform to consider nonlinear improvement of non-Markovianity witnesses.
Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired

As the scene information, including objectness and scene type, are important for people with visual impairment, in this work we present a multi-task efficient perception system for the scene parsing and recognition tasks. Building on the compact ResNet backbone, our designed network architecture has two paths with shared parameters. In the structure, the semantic segmentation path integrates fast attention, with the aim of harvesting long-range contextual information in an efficient manner. Simultaneously, the scene recognition path attains the scene type inference by passing the semantic features into semantic-driven attention networks and combining the semantic extracted representations with the RGB extracted representations through a gated attention module. In the experiments, we have verified the systems' accuracy and efficiency on both public datasets and real-world scenes. This system runs on a wearable belt with an Intel RealSense LiDAR camera and an Nvidia Jetson AGX Xavier processor, which can accompany visually impaired people and provide assistive scene information in their navigation tasks.
How do Metric Score Distributions affect the Type I Error Rate of Statistical Significance Tests in Information Retrieval?

Statistical significance tests are the main tool that IR practitioners use to determine the reliability of their experimental evaluation results. The question of which test behaves best with IR evaluation data has been around for decades, and has seen all kinds of results and recommendations. Definitive answer to this question has recently been attempted via stochastic simulation of IR evaluation data, allowing researchers to compute actual Type I error rates because they can control the null hypothesis. One such research line simulates metric scores for a fixed set of systems on random topics, and concluded that the t-test behaves the best. Another such line simulates retrieval runs by random systems on a fixed set of topics, and concluded that the Wilcoxon test behaves the best. Interestingly, two recent surveys of the IR literature have shown that the community has a clear preference precisely for these two tests, so further investigation is critical to understand why the above simulation studies reach opposite conclusions. It has been recently postulated that a reason for the disagreement is the distributions of metric scores used by one of these simulation methods. In this paper we investigate this issue and extend the argument to another key aspect of the simulation, namely the dependence between systems. Following a principled approach, we analyze the robustness of statistical tests to different factors, thus identifying under what conditions they behave well or not with respect to the Type I error rate. Our results suggest that differences between the Wilcoxon and t-test may be explained by the skewness of score differences.
Secure Communication in Smart Meters using Elliptic Curve Cryptography and Digital Signature Algorithm

With the advancement in the growth of Internet-of-Things (IoT), its number of applications has also increased such as in healthcare, smart cities, vehicles, industries, household appliances, and Smart Grids (SG). One of the major applications of IoT is the SG and smart meter which consists of a large number of internet-connected sensors and can communicate bi-directionally in real-time. The SG network involves smart meters, data collectors, generators, and sensors connected with the internet. SG networks involve the generation, distribution, transmission, and consumption of electrical power supplies. It consists of Household Area Network (HAN), and Neighborhood Area Network (NAN) for communication. Smart meters can communicate bidirectionally with consumers and provide real-time information to utility offices. But this communication channel is a wide-open network for data transmission. Therefore, it makes the SG network and smart meter vulnerable to outside hacker and various Cyber-Physical System (CPS) attacks such as False Data Injection (FDI), inserting malicious data, erroneous data, manipulating the sensor reading values. Here cryptography techniques can play a major role along with the private blockchain model for secure data transmission in smart meters. Hence, to overcome these existing issues and challenges in smart meter communication we have proposed a blockchain-based system model for secure communication along with a novel Advanced Elliptic Curve Cryptography Digital Signature (AECCDS) algorithm in Fog Computing (FC) environment. Here FC nodes will work as miners at the edge of smart meters for secure and real-time communication. The algorithm is implemented using iFogSim, Geth version 1.9.25, Ganache, Truffle for compiling smart contracts, Anaconda (Python editor), and ATOM as language editor for the smart contracts.
Graph Representation Learning in Document Wikification

Wikification (entity annotation) is a challenging task in Natural Language Processing (NLP). It is a method to automatically enrich a text with links to Wikipedia as a knowledge base. Wikification starts from detecting ambiguous mentions in the document, and later tries to disambiguate those mentions. In the core of the Wikification task, there is one other important NLP task: word representation. This paper proposes a new word representation for senses of a mention with Graph convolutional networks architecture. Senses are the possible meanings of one mention, based on the knowledge base. In our representation modeling, we used the context document and the first paragraph of each Wikipedia page to enhance our contextual representation. Using the nearest neighbor algorithm for disambiguating the mentions via our sense representations, we show the efficiency of our representations. The results of comparing our method with recent state-of-the-art methods show the efficiency of our solution.
Mutual Variational Inference: An Indirect Variational Inference Approach for Unsupervised Domain Adaptation

In this article, the unsupervised domain adaptation problem, where an approximate inference model is to be learned from a labeled dataset and expected to generalize well on an unlabeled dataset, is considered. Unlike the existing work, we explicitly unveil the importance of the latent variables produced by the feature extractor, that is, encoder, where contains the most representative information about their input samples, for the knowledge transfer. We argue that an estimator of the representation of the two datasets can be used as an agent for knowledge transfer. To be specific, a novel variational inference approach is proposed to approximate a latent distribution from the unlabeled dataset that can be used to accurately predict its input samples. It is demonstrated that the discriminative knowledge of the latent distribution that is learned from the labeled dataset can be progressively transferred to that is learned from the unlabeled dataset by simultaneously optimizing the estimator via the variational inference and our proposed regularization for shifting the mean of the estimator. The experiments on several benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art methods for both object classification and digit classification.
Data Augmentation Based on CycleGAN for Improving Woodblock-Printing Mongolian Words Recognition

In order to improve the performance of woodblock printing Mongolian words recognition, a method based on cycle-consistent generative adversarial network (CycleGAN) has been proposed for data augmentation. A well-trained CycleGAN model can learn image-to-image translation without paired examples. To be specific, the style of machine printing word images can be transformed into the corresponding word images with the style of woodblock printing by utilizing a CycleGAN, and vice versa. In this way, new instances of woodblock printing Mongolian word images are able to be generated by using the two generative models of CycleGAN. Thus, the aim of data augmentation could be attained. Given a dataset of woodblock printing Mongolian word images, experimental results demonstrate that the performance of woodblock printing Mongolian words recognition can be improved through such the data augmentation.
AraCap: A hybrid deep learning architecture for Arabic Image Captioning

Automatic captioning of images no only enrich multimedia content with descriptive features, but also helps in detecting patterns, trends, and events of interest. Particularly, Arabic Image Caption Generation is a very challenging topic in the machine learning field. This paper presents, AraCap, a hybrid object-based, attention-enriched image captioning architecture, with a focus on Arabic language. Three models are demonstrated, all of them are implemented and trained on COCO and Flickr30k datasets, and then tested by building an Arabic version of a subset of COCO dataset. The first model is an object-based captioner that can handle one or multiple detected objects. The second is a combined pipeline that uses both object detector and attention-based captioning; while the third one is based on a pure soft attention mechanism. The models are evaluated using multi-lingual semantic sentence similarity techniques to assess the generated captions accuracy against the actual ground truth captions. Results show that similarity scores for Arabic generated captions from all three proposed models outperformed the basic captioning technique.
SELF-REFERENCE UPFRONT: A STUDY OF SELF-REFERENTIAL GÖDEL NUMBERINGS

Abstract In this paper we examine various requirements on the formalisation choices under which self-reference can be adequately formalised in arithmetic. In particular, we study self-referential numberings, which immediately provide a strong notion of self-reference even for expressively weak languages. The results of this paper suggest that the question whether truly self-referential reasoning can be formalised in arithmetic is more sensitive to the underlying coding apparatus than usually believed. As a case study, we show how this sensitivity affects the formal study of certain principles of self-referential truth.