Reviewerly/exHarmony huggingface dataset:
Using topic-noise models to generate domain-specific topics across data sources

Domain-specific document collections, such as data sets about the COVID-19 pandemic, politics, and sports, have become more common as platforms grow and develop better ways to connect people whose interests align. These data sets come from many different sources, ranging from traditional sources like open-ended surveys and newspaper articles to one of the dozens of online social media platforms. Most topic models are equipped to generate topics from one or more of these data sources, but models rarely work well across all types of documents. The main problem that many models face is the varying noise levels inherent in different types of documents. We propose topic-noise models, a new type of topic model that jointly models topic and noise distributions to produce a more accurate, flexible representation of documents regardless of their origin and varying qualities. Our topic-noise model, Topic Noise Discriminator (TND) approximates topic and noise distributions side-by-side with the help of word embedding spaces. While topic-noise models are important for the types of short, noisy documents that often originate on social media platforms, TND can also be used with more traditional data sources like newspapers. TND itself generates a noise distribution that when ensembled with other generative topic models can produce more coherent and diverse topic sets. We show the effectiveness of this approach using Latent Dirichlet Allocation (LDA), and demonstrate the ability of TND to improve the quality of LDA topics in noisy document collections. Finally, researchers are beginning to generate topics using multiple sources and finding that they need a way to identify a core set based on text from different sources. We propose using cross-source topic blending (CSTB), an approach that maps topics sets to an s-partite graph and identifies core topics that blend topics from across s sources by identifying subgraphs with certain linkage properties. We demonstrate the effectiveness of topic-noise models and CSTB empirically on large real-world data sets from multiple domains and data sources.
Keyboard Layout Optimization and Adaptation

Since the keyboard is the most common method for text input on computers today, the design of the keyboard layout is very significant. Despite the fact that the QWERTY keyboard layout was designed more than 100 years ago, it is still the predominant layout in use today. There have been several attempts to design better layouts, both manually and automatically. In this paper we improve on previous works on automatic keyboard layout optimization, by using a deep neural network to assist in a genetic search algorithm, which enables the use of a sophisticated keyboard evaluation function that would otherwise take a prohibitive amount of time. We also show that a better choice of crossover routine greatly improves the genetic search. Finally, in order to test how users with different levels of experience adapt to new keyboard layouts, we conduct some layout adaptation experiments with 300 participants to examine how users adapt to new keyboard layouts.
Incorporating Normalized L1 Penalty and Eigenvalue Constraint for Causal Structure Learning

Inferring causal relationships is key to data science. Learning causal structures in the form of directed acyclic graphs (DAGs) has been widely adopted for uncovering causal relationships, nonetheless, it is a challenging task owing to its exponential search space. A recent approach formulates the structure learning problem as a continuous constrained optimization task that aims to learn causal relation matrix. Following it are nonlinear variants that can uncover nonlinear causal relationships. However, the nonlinear variant which considers the ℓ 1 penalty as part of its optimization objective may not effectively eliminate false predictions. In this paper, we investigate the defect of the model that the ℓ 1 penalty cannot effectively make the relation matrix sparse, thus introduces false predictions. Besides, the acyclicity constraint is unable to identify large circles within the margin of identification error, thus is unable to guarantee acyclicity of inferred causal relationships. Based on the theoretical and empirical analysis of the defects, we propose the normalized ℓ 1 penalty which replaces the original ℓ 1 penalty with a normalized first-order matrix norm, and propose a constraint based on eigenvalue to substitute the original acyclicity constraint. We then compare our proposed model NEC with three models to show considerable performance improvement. We further conduct experiments to show the effectiveness of the normalized ℓ 1 penalty and the eigenvalue constraint.
RF AND IOT SECURITY

Where are the key risks in RF and IOT systems and what steps do developers need to take to address them?
Byzantine Agreement with Optimal Resilience via Statistical Fraud Detection

Since the mid-1980s it has been known that Byzantine Agreement can be solved with probability 1 asynchronously, even against an omniscient, computationally unbounded adversary that can adaptively corrupt up to f < n/3 parties. Moreover, the problem is insoluble with f ≥ n/3 corruptions. However, Bracha's [Bra87] 1984 protocol (see also Ben-Or [Ben83]) achieved f < n/3 resilience at the cost of exponential expected latency 2θ(n), a bound that has never been improved in this model with f = ⌊(n- 1)/3⌋ corruptions.In this paper we prove that Byzantine Agreement in the asynchronous, full information model can be solved with probability 1 against an adaptive adversary that can corrupt f < n/3 parties, while incurring only polynomial latency with high probability. Our protocol follows earlier polynomial latency protocols of King and Saia [KS16, KS18] and Huang, Pettie, and Zhu [HPZ22], which had suboptimal resilience, namely f ≈ n/109 [KS16, KS18] and f < n/4 [HPZ22], respectively.Resilience f = (n - 1)/3 is uniquely difficult as this is the point at which the influence of the Byzantine and honest players are of roughly equal strength. The core technical problem we solve is to design a collective coin-flipping protocol that eventually lets us flip a coin with an unambiguous outcome. In the beginning the influence of the Byzantine players is too powerful to overcome and they can essentially fix the coin's behavior at will. We guarantee that after just a polynomial number of executions of the coin-flipping protocol, either (a) the Byzantine players fail to fix the behavior of the coin (thereby ending the game) or (b) we can “blacklist” players such that the blacklisting rate for Byzantine players is at least as large as the blacklisting rate for good players. The blacklisting criterion is based on a simple statistical test of fraud detection.* Supported by NSF grants CCF-1815316 and CCF-2221980.
Optimal Square Detection Over General Alphabets

Squares (fragments of the form xx, for some string x) are arguably the most natural type of repetition in strings. The basic algorithmic question concerning squares is to check if a given string of length n is square-free, that is, does not contain a fragment of such form. Main and Lorentz [J. Algorithms 1984] designed an 𝒪(n log n) time algorithm for this problem, and proved a matching lower bound assuming the so-called general alphabet, meaning that the algorithm is only allowed to check if two characters are equal. As an open question, they asked if there is a faster algorithm if one restricts the size of the alphabet. Crochemore [Theor. Comput. Sci. 1986] designed a linear-time algorithm for constant-size alphabets, and combined with the more recent results his approach in fact implies such an algorithm for linearly-sortable alphabets. Very recently, Ellert and Fischer [ICALP 2021] significantly relaxed this assumption by designing a linear-time algorithm for general ordered alphabets, that is, assuming a linear order on the characters. However, the open question of Main and Lorentz from 1984 remained unresolved for general (unordered) alphabets. In this paper, we show that testing square-freeness of a length-n string over general alphabet of size σ can be done with 𝒪(n log σ) comparisons, and cannot be done with o(n log σ) comparisons. We complement this result with an 𝒪(n log σ) time algorithm in the Word RAM model.
Nonlinear codes exceeding the Gilbert-Varshamov and Tsfasman-Vlăduţ-Zink bounds

The Gilbert-Varshamov (GV for short) bound has been a benchmark for good Hamming-metric codes. It was even conjectured by some coding theorists that the asymptotic Gilbert-Varshamov bound is tight. The GV bound had remained to be the best asymptotic lower bound for thirty years before it was broken by the Tsfasman-Vlăduţ-Zink bound via algebraic geometry codes. The discovery of algebraic geometry codes by Goppa was a breakthrough in coding theory. After another twenty years, no any improvements on the Tsfasman-Vlăduţ-Zink bound took place before the work by Xing-Elkies [14, 15, 1, 2] in the early of 2000 via tools from algebraic geometry. By using the similar ideas as in [14, 15, 9], some further improvements were given in [7, 17]. Since then, no further progress on asymptotic lower bounds has been made. The main result of this paper is to show that all previous asymptotic lower bounds can be improved in an interval.We present two types of constructions of Hamming-metric codes. Both constructions involve algebraic geometry and need insights on applications of algebraic geometry to coding theory. In order to obtain good codes, one construction requires a larger number of positive divisors of fixed degree, while other construction requires a smaller number of positive divisors of fixed degree. As a result, no matter how large the number of positive divisors of fixed degree is, we can always obtain codes with good parameters. It turns out that all previous asymptotic lower bounds are improved.
Che-Yong(體用) Logic and Research Methodology

Today's Eastern philosophers try to use the formal logic organized by Aristotle, saying that there was no logic in the East. This researcher found that Confucius and other Asians used Che-Yong logic. The Che-Yong logic is based on the Che-Yong law, which is a natural law. The Che-Yong law consists of the Che-Yong principle and the Hyeon-Mi principle. The Hyeon-Mi principle is that if there is an appearance on the outside, there is a corresponding cause in it. The Che-Yong principle is that the highest common cause of various appearances is Che, and the Che grows and changes on its own to become a Yong. Identifying Che and predicting Yong is Che-Yong logic. Here, I'd like to introduce Che-Yong logic and suggest a new research methodology to apply it.
Leveraging multi-level embeddings for knowledge-aware bug report reformulation

Software bug analysis based on the information retrieval (IR) technology is widely studied and used for bug understanding, localization and fixing. IR technology with various textual feature extraction methods formulates the textual information in a given new bug report (i.e., title and description) as an initial query. However, due to the low-quality content in the new bug report and improper representation to be used as a query, the retrieval results are usually not satisfactory. To alleviate these problems, we propose a novel knowledge-aware bug report reformulation approach (a.k.a, KABR) by leveraging multi-level embeddings from the bug data. First, we construct a bug-specific knowledge graph (KG) to manage and reuse prior knowledge extracted from historical bug reports. Then, we extract word embedding from the original bug data, entity embedding and context embedding from the bug-specific KG to enhance the initial query. Finally, a new query representation is generated by leveraging multi-level embeddings through Convolutional Neural Networks (CNN) with the self-attention mechanism. We evaluate KABR based on the duplicate bug report detection task, and the experimental results show that KABR achieves 6%–11% F1-measure improvement over the state-of-the-art approaches.
KennyRiMr: An Eclipse Plug-in to Improve Correctness of Rename Method Refactoring in Java

Rename Instance Method Refactoring (RiMr) is a behavior-preserving code transformation that changes the name of a non-static method declaration along with its references (i.e., method calls) while preserving all method bindings over an entire program. RiMr checks a set of preconditions to ensure that the original method bindings will be preserved after rename. Only when all preconditions are satisfied, are the method declaration and references transformed. A decade ago, however, it was found that RiMr offered by Java Integrated Development Environment (IDE) tools may change existing method bindings due to incorrect precondition checks, which consequently cause program behavior changes. Surprisingly, we found that none of the current Java IDEs have corrected those flaws in their RiMr preconditions.We created a Java RiMr tool (called KennyRiMr) as an Eclipse JDT plug-in that addresses the method rebinding issues in RiMr. We verified the correctness of KennyRiMr with thirteen nontrivial programs in terms of precondition checks and code transformations. Our experiments demonstrated that KennyRiMr fixed all known flaws in RiMr preconditions, requiring merely a few more seconds to process the additional precondition checks that we introduced. With KennyRiMr, correctness remains consistent regardless of program size.
Design and Implementation of a Dockerized, Cross Platform, Multi-Purpose Cryptography as a Service Framework Featuring Scalability, Extendibility and Ease of Integration

Following cybersecurity standards nowadays is becoming one of the highest priorities to the digital specialists. Due to the global direction to apply digital transformation, data security is a concern. It becomes crucial to ensure data confidentiality, integrity, and availability whether while transmitting, at rest or even while processing it. The difficulty being faced by organizations, is the challenge of applying the needed security measures. Also, implementing, and maintaining the cryptographic algorithms that ensure the wellness of the data encryption. Having a crypto library or a server that can fit multiple use-cases is either too costly to implement, or expensive to buy (including licensing options, per user/server/year…etc.). The goal of our work is to identify the data protection challenges, by implementing a solution that could match a theoretical hypothesis of having cryptography as a service framework. The term “as a service” has been promoted lately due to its capabilities to provide a ready-made solution by the vendors to satisfy their customer base. In this paper, we are proposing a framework that works cross-platform with ease. It is a scalable, extendible solution with multiple hosting options, from an on-premises hosting to cloud hosting. The proposed framework is implemented and evaluated. The results show that the proposed framework can efficiently process enormous amounts of data. In addition, it could be easily accessed by standard HTTPS requests using JSON format. Also, proving the used deployment technique, we were able to evaluate it on-premises and on cloud with the same allocated resources, getting matching results.
Classification of Medicinal Wild Plants Using Radial Basis Function Neural Network with Least Mean Square

Wild plants are considered as plants whose growth interferes with other plants. However, in its development it turns out that wild plants contain ingredients for medicines. However, information and knowledge about wild plant species, especially plants with herbaceous vegetation, needs to be carried out with various technologies, including through digital image processing. This study aims to develop a classification model for medicinal plant species using color and texture extraction using the Radial Basis Function Neural Network (RBFNN) algorithm with a combination of the Least Mean Square (LMS) algorithm. Color feature extraction is obtained by calculating the RGB average value and texture feature extraction using a Gabor filter obtained from the mean, entropy, and variance parameters. The result of feature extraction becomes input for RBFNN with LMS. RBFNN has three layers that have feedforward properties that can assist in solving classification or pattern recognition problems. The LMS algorithm is used for learning or updating neural network weights. Based on the test results by calculating precision, recall, and accuracy, it gets a precision value of 92.50%, recall gets a value of 91.74, accuracy gets a value of 92.08. These results indicate that the developed model can classify wild plant species with medicinal properties well.
An Evaluation of General-Purpose Static Analysis Tools on C/C++ Test Code

In recent years, maintaining test code quality has gained more attention due to increased automation and the growing focus on issues caused during this process.Test code may become long and complex, but maintaining its quality is mostly a manual process, that may not scale in big software projects. Moreover, bugs in test code may give a false impression about the correctness or performance of the production code. Static program analysis (SPA) tools are being used to maintain the quality of software projects nowadays. However, these tools are either not used to analyse test code, or any analysis results on the test code are suppressed.This is especially true since SPA tools are not tailored to generate precise warnings on test code. This paper investigates the use of SPA on test code by employing three state-of-the-art general-purpose static analysers on a curated set of projects used in the industry and a random sample of relatively popular and large open-source C/C++ projects. We have found a number of built-in code checking modules that can detect quality issues in the test code. However, these checkers need some tailoring to obtain relevant results. We observed design choices in test frameworks that raise noisy warnings in analysers and propose a set of augmentations to the checkers or the analysis framework to obtain precise warnings from static analysers.
Exploiting dynamic analysis for architectural smell detection: a preliminary study

Architectural anomalies, also known as architectural smells, represent the violation of design principles or decisions that impact internal software qualities with significant negative effects on maintenance, evolution costs and technical debt. Architectural smells, if early removed, have an overall impact on reducing a possible progressive architectural erosion and architectural debt. Some tools have been proposed for their detection, exploiting different methods, usually based only on static analysis. This work analyzes how dynamic analysis can be exploited to detect architectural smells. We focus on two smells, Hub-Like Dependency and Cyclic Dependency, and we extend an existing tool integrating dynamic analysis. We conduct an empirical study on ten projects. We compare the results obtained comparing a method featuring dynamic analysis and the original version of Arcan based only on static analysis to understand if dynamic analysis can be successfully used. The results show that dynamic analysis helps identify missing architectural smells instances, although its usage is hindered by the lack of test suites suitable for this scope.
Developing Voice Chatbot for Learning Maritime English

This article aims at explaining the design decisions of a voice chatbot for learning Maritime English. Even though Maritime English is a rather conservative conventional system to a great extent, practicing real life English is a key factor for language fluency. Voice chatbots can provide more practice for mastering listening and comprehension skills. Through the critical analysis of existing systems for learning the English language approaches to design and developing voice chatbot for learning Maritime English are worked out. The practical significance lies in creating an effective tool for learning and mastering Maritime English. Major advantages of the developed system are familiar environment and user-friendly interface.
WALTS: Walmart AutoML Libraries, Tools and Services

Automated Machine Learning (AutoML) is an upcoming field in machine learning (ML) that searches the candidate model space for a given task, dataset and an evaluation metric and returns the best performing model on the supplied dataset as per the given metric. AutoML not only reduces the man-power and expertise needed to develop ML models but also decreases the time-to-market for ML models substantially. In Walmart, we have designed an enterprise-scale AutoML frame-work called WALTS to meet the rising demand of employing ML in the retail business, and thus help democratize ML within our organization. In this work, we delve into the design of WALTS from both algorithmic and architectural perspectives. Specfiically, we elaborate on how we explore models from a pool of candidates along with describing our choice of technology stack to make the whole process scalable and robust. We illustrate the process with the help of a business use-case, and finally underline how WALTS has impacted our business so far.
Intelligent Classification Procedure for Plasmodium Knowlesi Malaria Species

Plasmodium knowlesi (PK) is the fifth most prevalent malarial parasite species that causes serious health problems. Generally, PK present in a thin blood smear is observed using a microscope to differentiate between trophozoites (PKT), schizonts (PKS), gametocytes (PKG), and white blood cells (WBCs). This process is time-consuming and strenuous for the human eye. This study developed an intelligent classification procedure for PK using image processing and classification methods. The processes involved starting from image acquisition, and contrast enhancement based on Combination Local and Global Statistical Data (CLGSD), and local contrast stretching (LCS). Subsequently, a segmentation procedure was developed to segment the malaria images into two regions, namely malarial parasites and background regions. The proposed 16 feature sets were extracted, which consisted of the size of the object, size ratio of the object per infected RBC, and seven moments for each object shape based on size and perimeter. Finally, to validate the procedure performance, the proposed procedure was tested using 800 malarial parasites and WBC images. The results showed that the proposed procedure can classify three stages of PK, namely PKT, PKS, and PKG, as well as WBCs with an accuracy of 99.56% for training and 98.84% for validation, using a multi-layer perceptron (MLP) trained using the Levernberg-Marquardt (LM) algorithm.
Sentiment Analysis From Subjectivity to (Im)Politeness Detection: Hate Speech From a Socio-Pragmatic Perspective

Although sentiment analysis by definition is that field of Natural Language processing which focuses on analyzing texts that tackle evaluating, analyzing and detecting the state of mind of the human beings towards a range of domains, most of the studies limit it to opinion mining. Yet, opinion mining is just one sub-field of three others under the umbrella of sentiment analysis which are; opinion mining, emotion mining and ambiguity detection. Noticeably, ambiguity detection is considered to be a combination of the other two sub-fields thanks to its linguistic nature that considers statistical and/or syntactic-semantic levels of analysis are not adequate to reach a satisfying level of disambiguating human language. Henceforth, the current paper proposes digging deeply to reach pragmatic and socio-pragmatic levels of analysis in order to eliminate ambiguity and avoid misjudgments over texts and social media posts specifically in the sub-tasks of detecting hate speech. Accordingly, it suggests utilizing an eclectic linguistic model of analysis includes speech act theory and the theory of (im)politeness.