Reviewerly/exHarmony huggingface dataset:
Text Spotting for Curved Metal Surface: Clustering, Fitting, and Rectifying

In the machining system, how to recognize text on the curved metal surface of parts is still a challenging question. In this article, a text spotting framework for curved metal surface based on image rectification and CNN is proposed. First, a curved text rectification method through CNN-based detection and text curve fitting is investigated. The CNN model for text detection is trained on the original images, which is used to obtain the location of characters of the text. The characters are clustered based on location and size, and the text line is fitted based on the centers of characters that belong to the same cluster. The text image is expanded according to the fitting text line, which finishes the rectification of the curve text image. Second, plenty of original text images are rectified by the proposed rectification method to construct the training data set. Third, in the recognition stage, an original text image is rectified and used as the input of the CNN recognition model. In the experiments, after comparing the detection performance between YOLOv3, Faster R-CNN, SSD-ResNet101, and RetinaNet101, RetinaNet101 was chosen as the text detector. Also, the recognition results on the rectification training set and test set generated by the proposed framework are better than those on the original image data sets. The experimental results show that the proposed method can achieve competitive results text spotting for curved metal surface.
Multi-language Design Smells

Context: Multi-language systems became prevalent with technological advances. Developers opt for the combination of programming languages to build an application. Problem: Software quality is achieved by following good practices and avoiding bad ones. However, most of the practices in the literature are applied to a single programming language and do not consider the interaction between programming languages. Objective: We previously defined a catalog of bad practices i.e., design smells related to multi-language systems. This paper aims to provide empirical evidence on the relevance of our catalog and its impact on software quality. Method: We analysed 262 snapshots of nine open source projects to detect occurrences of multi-language design smells. We also extracted information about the developers that contributed to those systems. We plan to perform an open and a closed survey targeting developers in general but also developers that contributed to those systems. We will survey developers about the perceived prevalence of those smells, their severity and impact on software quality attributes.
Collaboratively augmented UIP – Filtered RIP with relevancy mapping for personalization of web search

Personalized information retrieval has become more crucial and challenging in today’s time, as every user is demanding information according to their own perspectives. To achieve personalization, this paper proposes a methodology with a focus on the selection of an effective composition corresponding to various supporting modules of a personalization methodology. Collaborative tagging can be quite helpful in constructing User Interest Profile (UIP) and Resource Illustration Profile (RIP). The proposed methodology also focuses on UIP augmentation using multiple strategies; and a novel approach has also been designed to handle outlier tags which caused ambiguity in collective RIP. Even a good UIP and RIP alone cannot create an efficient personalization methodology; they also require a suitable mapping with user’s query requirement. Therefore, in the proposed methodology, the fuzzy satisfaction requirement-based novel mapping functions have been designed to measure query relevance score and user interest relevance score for a web resource. These scores have been further used to calculate the post-relevance score of a web resource after a suitable trade-off. Experiments using the del.icio.us dataset show that the proposed methodology has outperformed each and every baseline by a considerable margin.
Agile Software Development – Do We Really Calculate the Costs? A Multivocal Literature Review

Agile software development methods, in their various different forms, have become the basis for most software projects in today's world. The methodology is present in almost all organisations today. However, despite the popularity, failure rates in software projects remain high. This paper identifies why agile methodologies have become so successful. In addition, the paper discusses certain factors that may often be overlooked in organisations that have adopted agile methods, such as rework, maintainability, adoption, turnover rates and the potential costs associated with each. The research carried out was a multivocal literature review (MLR). Multiple white and grey literature which was deemed to be relevant was selected. 32 contributions from white literature were selected for use in the review as well as 8 from grey literature sources. We find that while agile has many advantages, organisations may overlook the potential downsides of using an agile methodology. If not managed or implemented correctly, organisations risk taking on more hidden and expensive costs, for example in relation to rework. It is important that organisations are sufficiently trained in agile methods in order to succeed.
Fingerprinting Voice Applications on Smart Speakers over Encrypted Traffic

The popularity of smart speakers with virtual personal assistants such as Amazon Alexa and Google Assistant has been growing. However, the security and privacy of users using these devices have not been thoroughly examined which can lead to severe security and privacy concerns. In this poster, we present a fingerprinting attack of skills on Amazon Echo by analyzing the encrypted network traffic and show the potential privacy leakage of users using skills on smart speakers. Skills are voice applications that can be accessed through Amazon Alexa. Skills increase the diversity of using Amazon smart speakers by providing a variety of applications that can be used from a single platform with the use of voice commands without accessing a phone or a computer. In this fingerprinting attack, an adversary can eavesdrop on the network traffic of the smart speaker and predict the skill that is used by the user without decrypting the encrypted network traffic. For this attack, we collect the dataset consisting of 10,000 traces of encrypted network traffic related to 100 popular skills. We perform feature selection to investigate the top features that contribute to privacy leakage. We implement the attack by leveraging multiple machine learning algorithms. Among the different machine learning algorithms we implement, Random Forest Classifier performs the best by achieving an accuracy of 68.32% (compared to a random guess of 1%). The experimental results show that there is a need for developers to focus on the privacy leakages of users.
Generalized Net Model Simulation of Cluster Analysis Using CLIQUE: Clustering in Quest

Cluster analysis searches for similarities between data objects according to their characteristics and groups the similar objects into clusters. One of the techniques which combines subspace grid-based clustering and density-based cluster analysis, namely Clustering In Quest (CLIQUE), is studied in the present research. The main steps performed in the process of detecting groups of objects with similar behaviour are: dividing the data space into a finite number of cells, forming a grid-based structure, detecting groups of similar objects and defining the clusters. Generalized Nets (GNs) have been introduced by Atanassov as an extension of the ordinary Petry nets and other their extensions and modifications. They are a powerful tool for modelling real processes. A GN-model of the CLIQUE real-time data clustering process is constructed here and a simulation of the model is performed using a platform independent software, called GN Integrated Development Environment (GN IDE). An open-source version of the RapidMiner software is used for performing the cluster analysis on real datasets.
The Identification of Negative Content in Websites by Using Machine Learning Approaches

The massive spread of negative contents on the internet has become a national problem in Indonesia. Website is one of the media which is used to spread the negative contents. This research aims to classify the negative contents into several categories such as gambling, pornography, and fraud based on existing resources from TRUST+™ Positif database. The classification is done by utilizing the keywords from URLs and the entire contents of the website. The research will also generate a collection of the lexicon in unigram and bigram of Bahasa Indonesia which characterizes gambling, pornography, and fraud category. Therefore, the classification process can be completed faster and also will reduce the dependency on human labors. The methodology of this research consists of web crawling, web scraping, feature extraction, training, testing, and data validation. Among three classification algorithms (Naive Bayes, K-Nearest Neighbor, and Support Vector Machine) which are experimented in this research, the results show that the Support Vector Machine (SVM) model has the highest level of accuracy (95.3886%). The data validation is done by trying out 20 new URLs that are not part of the training dataset. Among those URLs, there are 17 or 85% URLs that display the correct results. According to the experiments, the system has made many mistakes in gambling and fraud content prediction. Because those categories have similar characteristics in words representations. Based on the result, this research concludes that there is a number of improvements that can be conducted to improve the negative content identification.
Which monads Haskell developers use: An exploratory study

Monads are a mechanism for embedding and reasoning about notions of computation such as mutable state, I/O, exceptions, and many others. Even though monads are technically language-agnostic, they are mostly associated with the Haskell language. Indeed, one could argue that the use of monads is one of the defining characteristic of the Haskell language. In practical terms, monadic programming in Haskell relies on the standard mtl package library, which provides eight core notions of computation: identity, error, list, state, reader, writer, RWS, and continuations. Despite their widespread use, we are not aware of any empirical investigations regarding which monads are the most used by developers. In this paper we present an empirical study that covers a snapshot of available packages in the Hackage repository—covering 85135 packages and more than five million Haskell files. To the best of our knowledge this is the first large-scale analysis of Hackage with regards to monads and their usage as dependencies. Our results show that around 30.8% of the packages depend on the mtl package, whereas only 1.2% depend on alternative, yet compatible implementations. Nevertheless, usage patterns for each specific monad remain similar both for mtl and alternatives. Finally, the state monad is by far the most popular one, although all of them are used. We also report on the distribution of packages that use mtl, regarding their category and stability level.
In-Band Secret-Free Pairing for COTS Wireless Devices

Many IoT devices lack the necessary interfaces (keyboards, screens) for entering passwords or changing default ones. For these devices, bootstrapping trust can be challenging. We address the problem of device pairing in the absence of any shared secrets. Pairing is a two-phase process that requires mutual authentication between the two parties and the agreement to a common key that can be used to further bootstrap essential cryptographic mechanisms. We propose a secret-free and in-band trust establishment protocol that achieves the secure pairing of commercial off-the-shelf (COTS) wireless devices with a hub. As compared to the state-of-the-art, our protocol does not require any hardware/firmware modification to the devices, or any out-of-band channels, but can be applied to any COTS device. Furthermore, our protocol is resistant to active signal manipulations attacks that include recently demonstrated signal nullification at an intended receiver. These security properties are achieved in-band with the assistance of a helper device such as a smartphone and by exploiting hard-to-forge signal propagation laws. We perform extensive theoretical analysis to verify the security of the proposed protocol. In addition, we validate our theoretical results with experiments using COTS devices and USRP radios.
Integrating Approaches in Software Development: A Case Analysis in a Small Software Company

There are a myriad of software development methods, methodologies, frameworks, techniques and practices in both traditional and agile software development. Seeking synergy between these approaches has become necessary for the evolution of a software development process. Software companies deal with that challenge by combining well-structured comprehensive methods and flexible agile practices. In fact, some studies have revealed that mixed approaches in software industry are not uncommon. This paper analyzes a case study of the evolution of a software development process in a small company, which is based on Design Thinking, PMBOK and SCRUM. Results show the natural evolution of the software process over 15 years and lessons learned from a pragmatic process selection. The evolution in the company depends on its adaptability which captures the capacity of the company to learn, combine experience and knowledge, and adjust its software process to a changing context. Despite the results are promising further studies should be done.
Design and Evaluation of Proof of Work Based Anti-Spam Solution

According to recent reports, most of the overall email traffic consists of spam, which represents an abuse for the purpose of mass distribution of unwanted messages. Spam can lead to serious attempts at a data breach or email consumer's identity theft, also. There are a lot of different anti-spam solutions. The aim of this paper is to design and evaluate an anti-spam solution based on a novel Proof of Work concept. The proposed solution requires a certain amount of work from a sender prior to the transfer of an email message. The extended SMTP protocol will be designed in order to enable the evaluation of client credibility using the Proof of work algorithm. The design of the proposed solution will be presented in detail. The implemented system will be evaluated in distributed spam attempt, simulated by Seagull tool. An evaluation of the client work, the impact on the overall amount of sent spam messages, as well as the impact of distributed spam attempt on the attacked server resource usage will be shown. We will show that the proposed solution helps in reducing spam traffic and server load, while it doesn't diminish the consumer experience of legitimate email users.
Visualization, Monitoring and Control Techniques for Use in Scrum Software Development: An Analytic Hierarchy Process Approach

Scrum is the most widely used agile development framework that guides the development process with its ability to create customer-valued software artifacts iteratively and incrementally, whilst seeking best practices to provide continuous measurement during production. However, measuring success in Scrum can be a challenging endeavor. In particular, it is hard to select the best fitting agile metrics during consecutive Scrum sprints. The goal of this industrial case study was to utilize a systematic selection process for identifying the appropriate scrum metrics tools addon component within the TBİTAK SAGE software development group. Moreover, the distribution of software developers’ preferences of process metrics were analyzed according to their characteristic features and defense industry structure, and are presented using various distribution charts. Finally, alternatives to the software development process measurement component, which was integrated into the agile software process tool employed by the TBİTAK SAGE software development group, were efficiently determined by using the Analytic Hierarchy Process approach. Among the options discussed, our results suggest that the Actionable Agile Addon scored the highest followed by the Screenful Addon. The present study presents a rigorous approach that ultimately have improved community participation in metric planning, implementation and monitoring, thus moving towards sustainable software development goals.
OVERSEE: Outsourcing Verification to Enable Resource Sharing in Edge Environment

Multi-tenant (or colocation) data centers are good solutions to support edge computing, since each enterprise or organization usually has limited servers at an edge site. When any data center tenant faces a burst of workload, renting resources from the other tenants in the same data center can provide the required resources while keeping the merits of edge computing, but its challenges of reliability and performance are daunting. In this paper, we propose OVERSEE, an outsourcing verification mechanism that enables resource sharing in multi-tenant data centers, fully exploiting the benefits of edge computing. OVERSEE addresses the above two challenges by making skillful use of Intel SGX suite. OVERSEE consists of two sub schemes, the Report-Proof mechanism and Sampling-Challenging mechanism. The Report-Proof mechanism guarantees a task outsourced by a tenant can be executed correctly, i.e., completely and without modification, in the operating environment provided by another tenant. The Sampling-Challenging mechanism can be used to verify that sufficient computing capacity is provided to achieve the required QoS according to the resource lease agreement between the tenants. The theoretical analysis shows the effectiveness of OVERSEE and the experimental results show that it brings minimal overhead.
Optimized Arithmetic Operations for Isogeny-Based Cryptography on Huff Curves

Up to now, the state-of-the-art implementations of Supersingular Isogeny Diffie-Hellman (SIDH) work with Montgomery curves or Edwards curves, due to the facts that such curve models provide high efficiency for elliptic curve arithmetic operations. In this work, we propose a new w-coordinate method to optimize the arithmetic operations on Huff curves. Specifically, for the optimal computations of addition operation and doubling operation proposed by Orhon and Hisil on a fixed Huff curve, the costs of these operations can be further improved by about 40%. For the evaluations of odd-degree isogeny and 2-isogeny on variable Huff curves proposed by Moody and Shumow, the costs of evaluating $$\ell $$ -isogeny ( $$\ell $$ is odd) point and $$\ell $$ -isogeny curve can be further improved by about 50%. The computations of evaluating 2-isogeny point and 2-isogeny curve can be separately replaced by computing 4-isogeny point and 4-isogeny curve, which need $$6M+2S$$ and 4S, respectively, and avoid square root calculation mentioned in Moody and Shumow’s work. Interestingly, the desired computational issues on variable Huff curves have the same computational costs as those on variable Montgomery curves, as well supported by our implementations.
Long-distance disorder-disorder relation extraction with bootstrapped noisy data

Artificial intelligence in healthcare increasingly relies on relations in knowledge graphs for algorithm development. However, many important relations are not well covered in existing knowledge graphs. We aim to develop a novel long-distance relation extraction algorithm that leverages the article section structure and is trained with bootstrapped noisy data to identify important relations for diagnosis, including may cause, may be caused by, and differential diagnosis. Known relations were extracted from semistructured web pages and a relational database and were paired with sentences containing corresponding medical concepts to form training data. The sentence form was extended to allow one concept to be in the title. An attention mechanism was applied to reduce the effect of noisily labeled sentences. Section structure embedding was added to provide additional context for relation expressions. Graph information was further incorporated into the model to differentiate the target relations whose expressions were often similar and interwoven. The extended sentence form allowed 1.75 times as many relations and 2.17 times as many sentences to be found compared to the conventional form. The various components of the proposed model all added to the accuracy. Overall, the positive sample accuracy of the proposed model was 9 percentage points higher than baseline deep learning models and 13 percentage points higher than naïve Bayes and support vector machines. Our bootstrap data preparation method and the extended sentence form could form a large training dataset to enable algorithm development and data mining efforts. Section structure embedding and graph information significantly increased prediction accuracy.
Effectiveness of Mobile Learning Media on Computer Assembly at Vocational High School

Abstract The purpose of this study was to determine the effectiveness of mobile learning media in computer assembly subjects in vocational high schools. Previously this media had been declared valid by media experts and material experts, which was then used by students. The research method used in this study is quasi-experimental, with time-series research design. Data collection techniques with pretest and posttest. The effectiveness obtained from the results of the post-test seen from classical completeness, all students declared complete and through the gain score in the medium category. Based on the findings of this study it was concluded that the mobile learning media is effective to be used as a learning media and increase the learning outcomes. This research recommends to be tested more broadly and to provide more complete content for the sake of improvement in the development of this mobile learning media.
Compositional Procedures in Electronic Music and the Emergence of Time Continuum

We introduce an analytical methodology to approach the perception of time in the electronic works Thema: Omaggio a Joyce (1958), by Luciano Berio, and Gesang der Jünglinge (1955–6), by Karlheinz Stockhausen. Such works have already been widely analysed and discussed. Moreover, similarities between them have been pointed out, such as the use of the voice as their main compositional material and the search for a continuum between the voice and electronic sounds. Despite their similarities, we argue that the perception of time in those works is significantly different. For that purpose, we bring theoretical references such as time concepts related to complex dynamic systems, and the perception of time according to the Gestalt theory. We discuss segmentation and texture evolution in time of both works employing graphical representations based on perceptual audio descriptors such as the mel scale and the volume. In addition, aiming to find recurrences, repetitions and variations of the spectral material in time, we apply phase space graphs addressing the values of the descriptors employed in the analysis. The features found will lead to conclusions on the emergence of time perception in which the continuity depends on the presence of similar events, periodicities and pregnancies, while discontinuity is given by the presence of more variation, instability and saliences. We emphasise the differences of form perception in those pieces, arguing that they are the result of the manipulation of sound materials and organisation in time by the composers.