Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Telugu–English Code-Mixed Data

With the increase of communication over social media, in a multilingual country like India, people tend to use more than one language in day-to-day communication and also on social media platforms in order to showcase their linguistic proficiency. For instance, combining Telugu and English or Tamil and English in the same sentence is commonly observed. This is called code-mixing. Code-mixed text demands a different level of processing, and in this paper, we have attempted to extract sentiment from the Telugu–English code-mixed sentences. We classify the polarity of the code-mixed sentences into positive and negative sentiments classes using lexicon-based approach and also using machine learning approaches, namely naïve Bayes and support vector machine classifiers. We achieved an accuracy of 82% and 85%, respectively.
Personalized Image Semantic Segmentation

Semantic segmentation models trained on public datasets have achieved great success in recent years. However, these models didn’t consider the personalization issue of segmentation though it is important in practice. In this paper, we address the problem of personalized image segmentation. The objective is to generate more accurate segmentation results on unlabeled personalized images by investigating the data’s personalized traits. To open up future research in this area, we collect a large dataset containing various users’ personalized images called PSS (Personalized Semantic Segmentation). We also survey some recent researches related to this problem and report their performance on our dataset. Furthermore, by observing the correlation among a user’s personalized images, we propose a baseline method that incorporates the inter-image context when segmenting certain images. Extensive experiments show that our method outperforms the existing methods on the proposed dataset. The code and the PSS dataset are available at https://mmcheng.net/pss/.
Breaking A Playfair Cipher Using Single and Multipoints Crossover Based on Heuristic Algorithms

The process of data protection in our time is one of the most important challenges to maintain data integrity. The massive problem of breaking ciphertext is to find the key. In this work, cryptanalyze Playfair cipher is presented using a heuristic, and optimization methods search such as Memetic algorithm (MA) and Genetic algorithms (GA) to optimize fitness function. Di-gram letters are used as an essential factor in cryptanalyzing cipher. Mathematical methods are used to identify plain text. Furthermore, Single and multi-points crossover are used as parameters in the heuristic algorithm, and two points mutation as well. The results revealed that the multi-points are better than single point, which take a chance for exchanging the key to close the key encryption. In addition to, MA is better than GA, which the number of maximum correct letters in MA was 24 out of 25 letters after 100 generations, while in GA was 20 out of 24 letters after 200 generations. MATLAB application is used for programming and tested successfully
Analyzing the Impact of COVID-19 on Economy from the Perspective of User’s Reviews

One of the most important incidents in the world in 2020 is the outbreak of the Coronavirus. Users on social networks publish a large number of comments about this event. These comments contain important hidden information of public’ opinion regarding this pandemic. In this research, a large number of Coronavirus- related tweets are considered and analyzed using natural language processing and information retrieval science. Initially, the location of the tweets is determined using a dictionary prepared through the Geo-Names geographic database, which contains detailed and complete information of places such as city names, streets, and postal codes. Then, using a large dictionary prepared from the terms of economics, related tweets are extracted and sentiments corresponded to tweets are analyzed with the help of the RoBERTa language-based model, which has high accuracy and good performance. Finally, the frequency chart of tweets related to economy and their sentiment scores (positive and negative tweets) is plotted over time for the entire world and the top 10 economies. From the analysis of the charts, we learn that the reason for publishing economic tweets is not only the increase in the number of people infected with the Coronavirus but also imposed restrictions and lockdowns in countries. The consequences of these restrictions include the loss of millions of jobs and the economic downturn.
All equation, no human

This is a forum for perspectives on designing for marginalized communities worldwide. Articles will discuss design methods, theoretical/conceptual contributions, and participatory interventions with underserved communities. --- Nithya Sambasivan, Editor
Face Masks Usage Monitoring for Public Health Security using Computer Vision on Hardware

Wearing face masks is one of the direct measures that can help tackling the spread of the new coronavirus. In this paper we presented an architecture for face mask wearing detection using pre-trained deep learning models for computer vision and implementation of them on embedded hardware platforms. Three object detection models were fine-tuned and optimized to run on 4 different hardware platforms. The fine tuning and optimization of the models resulted in significant reduction of the inference time, thus making the use of this technology in IoT based security systems for real-time automatic monitoring of face masks wearing realisable.
Statistically Optimal Estimation of Signals in Modulation Spaces Using Gabor Frames

Time-frequency analysis deals with signals for which the underlying spectral characteristics change over time. The essential tool is the short-time Fourier transform, which localizes the Fourier transform in time by means of a window function. In a white noise model, we derive rate-optimal and adaptive estimators of signals in modulation spaces, which measure smoothness in terms of decay properties of the short-time Fourier transform. The estimators are based on series expansions by means of Gabor frames and on thresholding the coefficients. The minimax rates have interesting new features, and the derivation of the lower bounds requires the use of test functions which approximately localize both in time and in frequency. Simulations and applications to audio recordings illustrate the practical relevance of our methods. We also discuss the best <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> -term approximation and the approximation of variational problems in modulation spaces by Gabor frame expansions.
Improving a Graph-to-Tree Model for Solving Math Word Problems

In the area of Math Word Problem (MWP), various methods based on deep learning technology have been actively researched. Graph-to-Tree (Graph2Tree) is one of those methods which uses a graph-based encoder and a tree-based decoder to understand the word problem and to generate a valid equation. This method is proven to be well-performed by achieving state-of-the-art on several benchmarks. However, on the benchmark of SVAMP, recent methods including Sequence-to-Sequence (Seq2Seq), Goal-driven Tree-Structured MWP Solver (GTS), and Graph2Tree performs poorly, unable to cope with several variation types that requires natural language comprehension capability. In this paper, we propose an improved version of Graph2Tree which considers the characteristics of natural language to understand the word problems. On top of the original Graph2Tree model, we additionally build Dependency Graph and enhance the Quantity Cell Graph to Softly Expanded Quantity Cell Graph. This helps a graph-based encoder to capture the relationship among words. Also, we introduce question embedding for the tree-based decoder to generate equation based on the question given as input. We conduct experiments to evaluate our model against the original Graph2Tree model on three available datasets: MAWPS, ASDiv-A, and SVAMP. We also present case studies to qualitatively examine the effectiveness of the methods and showed that our methods have improved the original Graph2Tree model.
Phishing Detection Using Machine Learning Algorithms

Phishing attacks are one of the most serious hazards to individuals and businesses in today's digital world. Many attacks are performed each month with the goal of convincing consumers that they are visiting a reputable website or online application in order to obtain account information. A strategy is provided for detecting these types of assaults by modifying existing Document Object Model (DOM) comparison tools such as Proportional Distance, false positive and false negative, and the favicon image recognition algorithm. There may be a different impact, misclassifying a phishing website than misclassifying a legitimate website if the proportional distance approach is used. To solve this, the proportional distance method is used.
Multimedia Traffic Classification for Imbalanced Environment

With ever-increasing volume and variety of multimedia traffic on the Internet, machine learning-empowered techniques nowadays tend to become indispensable for future intelligent network management. To realize automatic traffic management with Quality of Service (QoS) guarantees, there is a pressing need for accurate traffic classification. However, the inherent characteristics of networks cause imbalanced class distribution in traffic classification, which could degrade the performance of classification, especially on the minority classes. To address the issue of class imbalance in both stationary and nonstationary environments, this paper proposes a novel scheme called CHS (chain hierarchical structure) which is able to characterize class distribution from a new perspective. By building an error model, we can compute the error propagation generated by CHS and analyze the factors that affect it. More importantly, two key methods involving classifier ranking and combination with the hierarchical structure are devised to mitigate the error propagation produced by the classifier. The effectiveness of the developed framework is validated through experiments over two real-world traffic datasets in both stationary and nonstationary environments. The experimental results demonstrate that our proposed methods outperform the state-of-the-art approaches in terms of classification accuracy and running time. The proposed methods are particularly effective in the nonstationary imbalanced environment.
Noise-Resistant Demosaicing with Deep Image Prior Network and Random RGBW Color Filter Array

In this paper, we propose a deep-image-prior-based demosaicing method for a random RGBW color filter array (CFA). The color reconstruction from the random RGBW CFA is performed by the deep image prior network, which uses only the RGBW CFA image as the training data. To our knowledge, this work is a first attempt to reconstruct the color image with a neural network using only a single RGBW CFA in the training. Due to the White pixels in the RGBW CFA, more light is transmitted through the CFA than in the case with the conventional RGB CFA. As the image sensor can detect more light, the signal-to-noise-ratio (SNR) increases and the proposed demosaicing method can reconstruct the color image with a higher visual quality than other existing demosaicking methods, especially in the presence of noise. We propose a loss function that can train the deep image prior (DIP) network to reconstruct the colors from the White pixels as well as from the red, green, and blue pixels in the RGBW CFA. Apart from using the DIP network, no additional complex reconstruction algorithms are required for the demosaicing. The proposed demosaicing method becomes useful in situations when the noise becomes a major problem, for example, in low light conditions. Experimental results show the validity of the proposed method for joint demosaicing and denoising.
A New Security Notion for PKC in the Standard Model: Weaker, Simpler, and Still Realizing Secure Channels

Encryption satisfying CCA2 security is commonly known to be unnecessarily strong for realizing secure channels. Moreover, CCA2 constructions in the standard model are far from being competitive practical alternatives to constructions via random oracle. A promising research area to alleviate this problem are weaker security notions—like IND-RCCA secure encryption or IND-atag-wCCA secure tag-based encryption—which are still able to facilitate secure message transfer (SMT) via authenticated channels. In this paper we introduce the concept of sender-binding encryption (SBE), unifying prior approaches of SMT construction in the universal composability (UC) model. We furthermore develop the corresponding non-trivial security notion of IND-SB-CPA and formally prove that it suffices for realizing SMT in conjunction with authenticated channels. Our notion is the weakest so far in the sense that it generically implies the weakest prior notions—RCCA and atag-wCCA—without additional assumptions, while the reverse is not true. A direct consequence is that IND-stag-wCCA, which is strictly weaker than IND-atag-wCCA but stronger than our IND-SB-CPA, can be used to construct a secure channel. Finally, we give an efficient IND-SB-CPA secure construction in the standard model from IND-CPA secure double receiver encryption (DRE) based on McEliece. This shows that IND-SB-CPA security yields simpler and more efficient constructions in the standard model than the weakest prior notions, i.e., IND-atag-wCCA and IND-stag-wCCA.
Electronic Guides on the Website of the Library of Congress: Types, Subjects, Structure

The article is a continuation of a comprehensive study of domestic and foreign electronic guides (EG). As the previous analysis showed, they are the most important form of electronic information resources created by modern libraries. The purpose of this article is to analyze EG presented on the official website of the Library of Congress (LC). The types of EG presented on the website of this Library in terms of multidimensional classification have been identified. The three-part structure of these EG was analyzed, including an introductory article, the main part and auxiliary pointers and/or a search string. It has been established that both Russian and American EG are created on such fields of knowledge as social sciences and humanities, history, literature and, to a lesser extent, on health, agriculture and technical sciences. The advantages and disadvantages of Russian and American EG are identified, the most important are the following: the lack of unity in the interpretation of the concepts “electronic guide” and “guide” is characteristic of both Russian-language and English-language terminology; American EG from the point of view of the information medium of of reflected objects have mainly a mixed nature, they include information about both printed and electronic information resources; The advantage of EG presented on the LC website is the active use of internal hyperlinks to the library’s own resources.
BBServerless: A Bursty Traffic Benchmark for Serverless

Serverless is a mainstream computing mode in modern cloud native systems. Different from traditional monolithic cloud, workloads for Serverless architecture are disaggregated into short-lived and fine-grained functions. In Serverless, functions are usually invoked with a bursty pattern, which means the system needs to deliver these functions at high throughput to meet SLA (Service-level agreement) requirements. To explore bursty traffic implications on Serverless platforms, in this paper, we propose a novel benchmarking suite for serverless systems - BBServerless. BBServerless is designed to capture end-to-end and system-level performance in bursty-traffic workloads which help reveal performance bottlenecks of Serverless platform and guide better architecture design for cloud systems. To demonstrate performance variations on Serverless platforms, we also design a traffic generating algorithm (based on Poisson distribution) for four mainstream cloud workloads, i.e. BigData, Stream processing (STREAM), Web Applications (WebApps), and Machine Learning Inference (MaLI). We conduct experiments with trace-driven simulations in a private cloud environment. With data collected from evaluations, we observe that the performance of time-localized components like CPU migration, branch prediction, and cache is highly correlated with end-to-end workload performance. and publicly available at Github ( https://github.com/whoszus/BurstyServerlessBenchmark ).
Polynomial IOPs for Linear Algebra Relations

This paper proposes new Polynomial IOPs for arithmetic circuits. They rely on the monomial coefficient basis to represent the matrices and vectors arising from the arithmetic constraint satisfaction system, and build on new protocols for establishing the correct computation of linear algebra relations such as matrix-vector products and Hadamard products. Our protocols give rise to concrete proof systems with succinct verification when compiled down with a cryptographic compiler whose role is abstracted away in this paper. Depending only on the compiler, the resulting SNARKs are either transparent or rely on a trusted setup.
Random forest for spatial prediction of censored response variables

The spatial prediction of a continuous response variable when spatially exhaustive predictor variables are available within the region under study has become ubiquitous in many geoscience fields. The response variable is often subject to detection limits due to limitations of the measuring instrument or the sampling protocol used. Consequently, the response variable's observations are censored (left-censored, right-censored, or interval-censored). Machine learning methods dedicated to the spatial prediction of uncensored response variables can not explicitly account for the response variable's censored observations. In such cases, they are routinely applied through ad hoc approaches such as ignoring the response variable's censored observations or replacing them with arbitrary values. Therefore, the response variable's spatial prediction may be inaccurate and sensitive to the assumptions and approximations involved in those arbitrary choices. This paper introduces a random forest-based machine learning method for spatially predicting a censored response variable, in which the response variable's censored observations are explicitly taken into account. The basic idea consists of building an ensemble of regression tree predictors by training the classical regression random forest on the subset of data containing only the response variable's uncensored observations. Then, the principal component analysis applied to this ensemble allows translating the response variable's observations (uncensored and censored) into a linear equalities and inequalities system. This system of linear equalities and inequalities is solved through randomized quadratic programming, which allows obtaining an ensemble of reconstructed regression tree predictors that exactly honor the response variable's observations (uncensored and censored). The response variable's spatial prediction is then obtained by averaging this latter ensemble. The effectiveness of the proposed machine learning method is illustrated on simulated data for which ground truth is available and showcased on real-world data, including geochemical data. The results suggest that the proposed machine learning technique allows greater utilization of the response variable's censored observations than ad hoc methods.
Research on Localization and Path Planning of Indoor Robot Based on ROS

To meet the needs of monitoring the indoor environment, a mobile robot that patrols indoors is designed. The mobile robot uses Raspberry Pi equipped with a ROS system as the control core, uses lidar to collect environmental information, and uses various function packages to develop algorithms under the ROS distributed framework. It is based on the Karto algorithm to build maps and uses the A* algorithm and DWA algorithm to realize autonomous navigation. The robot uses a two-wheel independent drive robot chassis to cope with the complex environment. After the indoor environment test, the goal of indoor map construction and path planning can be completed, which is of great significance to the research in the indoor environment in the future.
Crossmodal knowledge distillation hashing

由于计算和存储的高效性, 哈希被广泛地用于大规模跨模态检索. 现有跨模态哈希方法分别对单模态数据生成哈希码, 忽略了模态内和模态间的上下文信息, 无法充分挖掘多媒体数据的潜在关联信息. 为此, 本文提出一种基于知识蒸馏的跨模态哈希方法. 该方法首先利用基于Transformer的教师网络从图像和文本数据中捕获模态内和模态间的上下文信息, 进而得到包含丰富视觉-语义关联信息的联合表示, 并将联合表示投影到低维的汉明空间以得到判别性较高的二值哈希码. 此外, 该方法利用知识蒸馏技术将教师网络学到的多模态数据潜在关联信息迁移到学生网络, 从而让学生网络生成的哈希码最大程度保留多模态关联信息. 该方法在MIRFLICKR-25K和NUS-WIDE数据集上进行验证, 实验结果表明该方法的跨模态检索性能优于目前的主流方法.
Cryptographic pointers for fine-grained file access security

We present a paradigm for fine-grained access security in a protection environment featuring files and records. Files are allocated at increasing addresses in a virtual space whose size is extremely large, so that virtual space reuse is never necessary. A record is a portion of a file. A subject certifies possession of an access privilege for a given object, file, or record, by presenting a cryptographic pointer (c-pointer) referencing that object. The c-pointer includes a key, and the composition of the access privilege expressed in terms of the two access rights, to read and to write. The c-pointer is valid if the key descends from a master key indicated in the c-pointer, by application of a universally known, symmetric algorithm. Records can be encrypted, and the key is specific to the given record. A set of security primitives forms the user interface of the security system. The resulting environment is evaluated from a number of viewpoints that include key proliferation, weakening and revocation, selective encryption, file directories, and robustness against security attacks aimed at c-pointer forging.
Demonstrating a Memory Orb — Cylindrical Device Inspired by Science Fiction

We present the design and prototype of a memory orb, a cylindrical device inspired from an artifact depicted in the science fiction movie Blade Runner 2049. The memory orb is a handheld input device, which allows users to control and manipulate three-dimensional, virtual content by combining pushing, pulling and rotating motor actions, facilitating muscle memory and eyes-free interaction. Our prototype integrates a large number of electronic components while maintaining a small form factor to allow ease of control and simple handling. The described implementation of this prototype aims to showcase the application potential of this cylindrical device in mixed, or virtual reality systems.
Automatic Chinese Financial Knowledge Graph Constructing Framework

As rapid growth of complexity of finance, building a Chinese Financial Knowledge Graph (CFKG) from scratch become more and more important. The key of construction of CFKG is the argument relationship extracting method. There are many researches focusing on extracting argument relationship from text based on open domain methods or closed domain methods. However, closed domain methods rely on vast tagged data source which is difficult to acquire and most open domain methods have the problem of high false extracting rate on financial data. So we propose a financial domain open domain extractor which not only adopt the advantages of syntax and semantic dependence but also adopt the advantages of Siamese Network for classification. Experimental results show that our solution outperforms the existing best method DSNFs in terms of precision and recall measure and our improved Siamese Network can reduce the number of false relations. In the end, we build an experimental graph from news data, in which we can explore personnel flow, implicit relationship, etc.