Reviewerly/exHarmony huggingface dataset:
Finding Smelly or Non-smelly Using Injected and Revision Method

Code smells are simple programmatic qualities, which can indicate a code or plan problem that makes programming difficult to develop and maintain, and which can cause code refactoring. Late research is dynamic in characterizing programmed discovery instruments to help people in discovering smells when code size gets unmanageable for manual audit. Since the meanings of code smells are casual and emotional, evaluating how viable code smell identification apparatuses are is both significant and difficult to accomplish. This paper audits the present scene of the devices for programmed code smell identification. It characterizes explore inquiries regarding the consistency of their reactions, their capacity to uncover the locales of code generally influenced by basic rot, and the importance of their reactions concerning future programming development. It offers responses to them by breaking down the yield of four agent code smell identifiers applied to six unique forms of Gantt Project, an open-source framework written in Java. The aftereffects of these trials illuminate what current code smell location instruments can do and what the pertinent zones for additional improvement.
Natural Language Controlled Real-Time Object Recognition Framework for Household Robot

Assistive robot systems are designed to help individuals with physical disabilities perform routine daily living activities. Current assistive robot systems are increasingly capable of performing a variety of tasks that benefit the human user. This has created the need to specify the particular task that the disabled human user desires the system to perform. Enhanced user interfaces such as joysticks can provide fine-grained control commands, but this is time-consuming and mentally and physically taxing. This article describes an ongoing project that aims to provide people with physical disabilities a method to control a robot arm using voice commands. The goal is to enable the user to control the system using natural language, i.e., without learning a special robot control vocabulary. The work describes the design and evaluation of a real-time framework that combines speech recognition, camera-based object detection, and an inference module that matches the potentially ambiguous results of speech recognition with object detection outputs to generate a unique control input for a computer vision-based robot arm. Thus, the integration of natural language and object detection systems reduces the ambiguity in specifying tasks, one of the major bottlenecks in voice-based user interfaces. A modified version of the deep learning-based object detection network YOLO (You only look once) is used to identify all potential objects of interest in the environment. Evaluating this integrated voice and object recognition-based user interface indicates that tasks can be specified accurately in different settings.
Improving Object Detection in Real-World Traffic Scenes

Single Shot Multi-Box Detector (SSD) is a well-known object detection algorithm. It can detect 20 different types of objects making it suitable for an object detector for traffic scenes. In a real-world traffic scene, objects can appear in different sizes and pose different details. This can potentially lead to false detections made by an SSD. Depending on how input information (image) is provided to SSD (leading to a proposed SSD model), the accuracy of the proposed model can vary. The overall objective of this study is to evaluate different SSD models while examining accuracy of object detection where the object type is only a vehicle. This study is derived from human vision. Where, an object is easily identifiable in a sharper image with brightness than a blurry one with darkness. Based on these assumptions hypotheses were created, based on which SSD based models were proposed. Comparison based on true positives and false positives was performed and the winner was identified by using the Enpeda. Image Sequence Analysis Test Site (EISATS) stereo image barriers dataset set 9.
Twitter's Big Data Analysis Using RStudio

This work is an application of tweets analytics using the R programming language [1] and its interface Rstudio. In this work, we have used many packages such as “twitteR,” “tm,” and “wordcloud.” Two political figures – the US President Donald Trump and the Canadian Prime Minister Justin Trudeau – were chosen because they are among the most influential leaders in the last four years. This research aims to reach a big collection of tweets that talk about each one. After that, data obtained through different steps are revised. Finally, we created a cloud of words (which is a visual representation of words in which the size is proportional to the frequency of that word in a given text) for each one and analyzed them.
Texture Based Image Retrieval Using GLCM and LBP

In the technical period, things will enrich it every day, one of which is content-based image retrieval. The comparative study of texture-dependent algorithms based on a spatial domain with feature extraction and distance metrics was explored in this paper. Texture attribute extraction uses the gray level co-occurrence matrix and local binary pattern for statistical analysis. The correlation test shows that contrast, correlation, energy, entropy, and homogeneity feature of gray level co-occurrence matrix and local binary pattern features taken from the LBP histogram have a significant correlation. For the study of the gray level co-occurrence vector and local binary pattern on content-based image retrieval system, the precision, accuracy, and recall as well as F-score are measured.
Facilitating Integration of IoT Ecosystems via Universal Cloud Interface

The siloed state of IoT ecosystem is a well-recognized issue. Companies that operate IoT ecosystems have participated in standardization bodies to address this concern. One such organization that has attracted many industry members is the Open Connectivity Foundation (OCF), which recently published a new standard, Universal Cloud Interface, to facilitate the integration of IoT clouds. As one of the companies who have been actively involved in developing this standard and the first to implement it, we share our experiences in hope of creating IoT ecosystems that are seamlessly integrated and providing new multi-device experiences to users without imposing the burden of extensive development efforts on device manufacturers.
Towards Personalized Hybrid Recommender System using Average Visit Intervals

The overload of online data creates a problem in filtering information that is appropriate to a user, which is why many companies and websites utilize recommendation systems. Finding products and contents that match users' interests has become crucial for online content-providers, and users are always in search for content that suit their preferences. It is becoming more and more difficult for users to find the content they are looking for with the rate of content increasing in the world, thus the importance of recommender systems has increased more than ever. Yet, recommendation systems have not been perfected and shows much room for research and development. This paper therefore presents a new hybrid method, integrating a parallel collaborative and content-based filtering system with a ranking system based on a user profile with the addition of a time variable to represent the time sensitive characteristic of content and interest. The evaluation of our approach shows promising results in the cinematographic field and indicates further potential for development.
Aleksander Tsinkalovsky an epistolary legacy for the study of scientific and social activities

Aleksander Tsinkalovsky an epistolary legacy for the study of scientific and social activitiesThis category refers to information on how the letter was originally sent and how it ultimately arrived.Regarding the former, there are references in all three corpora to letters being sent by an 'express'.This term had multiple meanings, referring not only to the fast delivery of a single letter, or the package in which various letters were contained, but to the person entrusted with quickly delivering the letter.The research topic is a study of the epistolary heritage, a little-studied and very important material.This category of sources more obviously reveals the political, social, economic and cultural condition of the epoch, than other type of historical material (sources with official narrative, court's chroniclers historical works); The facts and events of the private and public life are described in the official and informal letters.The exploration analyses the epistolary sources of life and work of the famous Ukrainian archaeologist, historian, volynologist Alexander Mykolayovych Tsinkalovsky .It is shown that the epistolary contains biographical information about the scientist, reveals his inner world, ideological and aesthetic views, individual character traits.A detailed analysis of the correspondence makes it possible to shed light on Alexander Tsinkalovsky's connections with the environment and to more accurately and more adequately determine his place in the current scientific and historical process.The prospect of the study is to consider the collection of a complete set of epistolary sources concerning the figure of Alexander Tsinkalovsky.The article is considered a collection of unread letters A .Tsinkalovsky.Is outlined history of epistolyaries returning home.Epistolary sources analysed in species characteristics, giving emphasis to their informative potential.
Examination of NoSQL Transition and Data Mining Capabilities

An estimated 2.5 quintillion bytes of data are created every day. This data explosion, along with new datatypes, objects, and the wide usage of social media networks, with an estimated 3.8 billion users worldwide, make the exploitation and manipulation of data by relational databases, cumbersome and problematic. NoSQL databases introduce new capabilities aiming at improving the functionalities offered by traditional SQL DBMS. This paper elaborates on ongoing research regarding NoSQL, focusing on the background behind their development, their basic characteristics, their categorization and the noticeable increase in popularity. Functional advantages and data mining capabilities that come with the usage of graph databases are also presented. Common data mining tasks with graphs are presented, facilitating implementation, as well as efficiency. The aim is to highlight concepts necessary for incorporating data mining techniques and graph database functionalities, eventually proposing an analytical framework offering a plethora of domain specific analytics. For example, a virus outbreak analytics framework allowing health and government officials to make appropriate decisions.
Reconstruct and Visualise Hierarchical Relationships in Whole Slide Images

Extracting hierarchical properties from Whole Slide Images automatically and expanding the possibilities of visualising data from digital pathology will not only drastically improve speed and accuracy of the pathologists' work but also simplify the necessary pre-processing steps for machine learning tool-chains. The introduced pipeline identifies and converts areas of interest into binary masks and finds groups of areas that share similar locations using k-Means. This grouping is evaluated by the Silhouette Score which serves as a measure of confidence for the separability of clusters. Found objects are compared using structural similarities and HU-Moments. These results are then stored as measures of similarities creating virtual groups of the most similar objects. Finally, the information on similarities combined with further structural parameters are visualised.
Cyber Resilience Using Self-Discrepancy Theory

Threats take many forms, and understanding them in order to make organisations more cyber resilient remains challenging. Many resilience management models and standards exist. They can help enterprises recover from harmful incidents. No approach today comprehensively examines perspectives of resilience concerns. In this paper, I argue it is necessary to consider a much broader spectrum of threats and harms to better understand the complex dependencies and interactions between an enterprise (‘self’) and the environment (‘world’). This paper adapts Self-Discrepancy Theory from psychology to help establish and reason about multiple views of the ‘self’: the actual, ideal and ought enterprise, as viewed by the enterprise/risk owner and others (e.g. competitors). The paper investigates how changes in priorities and operations can affect the self (again, as viewed by the self and others). This framework does not compete with existing models and standards. Instead, the purpose of this work is to complement them by exhaustively considering different perspectives (views) of enterprises with the aim to re-contextualise resilience concerns. By using this framework, risk-owners can start making decisions with new insights, akin to: what would my opponent do in my position?; or, if I change my mission, what effect might this have on my security (and vice versa)? Viewing enterprise resilience from different perspectives is an underexplored topic in resilience and security research, and is a key motivation of this article. This is a position paper, and further studies will be necessary to provide empirical evidence of feasibility in real-world settings.
Refactoring the UX of a popular voice application

Commercial voice services like Google Assistant and Amazon Alexa are reaching extreme popularity. While Natural Language Processing (NLP) and Artificial Intelligence (AI) techniques -- applied to the aural channel -- deliver high quality voice recognition, the voice channel still lacks a good methodology to design user experiences. For instance, The Amazon Alexa team suggests gathering the information model of Alexa skills by talking with test users behind a curtain, pretending to be the machine. In our opinion, such kind of bottom-up strategy is not effective because it overfits the UX to very specific cases. A top-down approach could provide the right answer also in unseen and unpredictable situations instead. Our work aims to propose a novel model driven approach that allows authors to design from scratch the overall vocal UX as well as rethink existing visual UX before porting them to the aural channel. Our approach, which is inherently top-down, is based on Aural IDM, an UX design method thought for screen readers modelling in the early '00. In this paper we've refactored the Spotify Alexa skill to demonstrate the validity of Aural IDM for designing vocal UXs. The experience of Spotify on Alexa is quite primordial and does not reflect the richness of the desktop app. A prototype is currently under development, and the result of a comparison between the AS-IS and TO-BE voice skill will be subject of a future work.
M-NUQAM Modulations with Constant Symbol Mean Energy

Currently with the growth in traffic in wireless networks and with services that require higher speed, the challenge of reaching Shannon’s limit remains a topic of great interest, so that, seeking to increase the capacity BICM of a channel allows the use of better performing channel codes approaching the Shannon limit. The article presents an algorithm proposal to find the positions of the symbols in QAM modulations that maximize capacity, generating the Non Uniform QAM modulations. Additionally, symbol positions are presented to maximize capacity and keep average symbol energy constant. It is validated that this increase in capacity doesn’t affect the bit error rate in an AWGN channel.
Technology Intelligence Map: Twitter and Currency

Social media is becoming the backbone of virtual communication in our modern society. It is widely used to spread information, voice opinions and foster relationships. It also plays a major role in politics where people are using social media as a way to make their voices heard. The vast amount of data on these platforms can be used to analyse public engagement and criticism. This chapter aims at exploring the means of social media to understand and predict the economy. We are using sentiment analysis and regression techniques to capture the wider sentiment of the public and verify if these changes in sentiment lead to fluctuations in the economy as well. To estimate the economy, we will check the historic rates of Indian National Rupee (INR) and its conversion value to the United States Dollar (USD). The results show that public sentiment might be correlated to the INR value.
The Identification and Geological Significance of Fault Buried in the Gasikule Salt Lake in China based on the Multi‐source Remote Sensing Data

Abstract The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not, the fault buried in the salt lake could control the abundance of the salinity. Therefore, it is of great geological importance to identify the fault buried in the salt lake. Taking the Gasikule Salt Lake in China for example, the paper established a new method to identify the fault buried in the salt lake based on the multi‐source remote sensing data including Landsat TM, SPOT‐5 and ASTER data. It includes the acquisition and selection of the multi‐source remote sensing data, data preprocessing, lake waterfront extraction, spectrum extraction of brine with different salinity, salinity index construction, salinity separation, analysis of the abnormal salinity and identification of the fault buried in salt lake, temperature inversion of brine and the fault verification. As a result, the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal. Because the level of the salinity is positively correlated to the mineral abundance, the result provides the important reference to identify the water body rich in mineral resources in the salt lake.
Interpreting Adjuncts: Processing English<i>As-</i>Clauses

Four experiments probed the interpretation of sentence-final as-clauses (e.g., Close the book as a librarian would/would do) ambiguous between a manner interpretation and a "propositional" interpretation. Experiment 1, an interpretation study, found a predominance of manner interpretations for sentences containing would and would do as the elliptical predicate inside the as-clause, biased by which form participants were initially exposed to. In Experiment 2, we assumed that a comma may be present before the as-clause for both interpretations, but that when the contrast between a comma and no comma is called to the reader's attention it will favor the propositional interpretation. The expectation was confirmed. In Experiment 3 a would-sentence was preceded by a How question or by a What's with question: propositional interpretations were rare but more prevalent following the What's with question than the manner question. Experiment 4 added a What did question and tested both no-comma would (NoComma) sentences and comma would do (CommaDo) sentences. CommaDo sentences received more propositional interpretations than NoComma sentences, and were read faster following the What's with question than the How question, whereas the NoComma were read faster after the How question. All four studies showed manner interpretations prevail, though would do, a (contrastive) comma or a non-manner question increase the frequency of propositional interpretations. Two possibilities are considered for what underlies the manner preference: a general preference for an adjunct to be part of the event description in cases of ambiguity, or the availability of a pre-existing event-"slot" for manner. The reading time results favor the former possibility.
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks

Over recent years, a myriad of novel convolutional network architectures have been developed to advance state-of-the-art performance on challenging recognition tasks. As computational resources improve, a great deal of effort has been placed on efficiently scaling up existing designs and generating new architectures with Neural Architecture Search (NAS) algorithms. While network topology has proven to be a critical factor for model performance, we show that significant gains are being left on the table by keeping topology static at inference-time. Due to challenges such as scale variation, we should not expect static models configured to perform well across a training dataset to be optimally configured to handle all test data. In this work, we expose the exciting potential of inference-time-dynamic models. We show that by allowing just four layers to dynamically change configuration at inference-time, off-the-shelf models like ResNet-50 have an upper bound accuracy of over 95% on ImageNet. This level of performance currently exceeds that of models with over 20x more parameters and significantly more complex training procedures. While this upper bound of performance may be practically difficult to achieve for a real dynamic model, it indicates a significant source of untapped potential for current models.
Lenguaje juvenil, autenticidad y resistencia entre los <i>youtubers</i> españoles

In recent years, so-called YouTubers have become new media celebrities who have managed to attract the attention of the public, especially young audiences. Drawing on an empirical study based on interviews with teenagers and young people and an online observation of the YouTube channels they followed (September 2016–May 2017), this paper focuses on a particularly significant issue in the case of some Spanish YouTubers that attract these young audiences: the regular use of swear words and bad language. We will argue that for these YouTubers, this bad language constitutes their distinctiveness and a hallmark of “authenticity” that helps them connect with their young audiences. As we will show in our analysis, they express tension when required to change their speech by the media corporations that host or sponsor them, feeling that this is an attack against their persona, so the bad language becomes a form of resistance.
A Polynomial-Time Approximation Scheme for Sequential Batch Testing of Series Systems

The paper studies a recently introduced generalization of the classic sequential testing problem for series systems, consisting of multiple stochastic components. The conventional assumption in such settings is that the overall system state can be expressed as an AND function, defined with respect to the states of individual components. However, unlike the classic setting, rather than testing components separately, one after the other, we allow aggregating multiple tests to be conducted simultaneously, while incurring an additional set-up cost. This feature is present in numerous practical applications, where decision makers are incentivized to exploit economy of scale by testing subsets of components in batches. The main contribution of this paper is to devise a polynomial-time approximation scheme for the sequential batch-testing problem, by leveraging a number of techniques in approximate dynamic programming, based on a synthesis of ideas related to efficient enumeration methods, state-space collapse, and charging schemes.
Basic Commands in R

Abstract This chapter is an introduction to the basics of object-oriented programming in R. It covers how to create and view objects in R, as well as how to perform basic calculations, manipulate objects, and identify the “class” of different objects. Finally, it addresses how to create and subset data frames, change row and column names, and cover matrix multiplication.