Reviewerly/exHarmony huggingface dataset:
Software Defect Prediction on Unlabelled Datasets: A Comparative Study

Background: Defect prediction on unlabelled datasets is a challenging and widespread problem in software engineering. Machine learning is of great value in this context because it provides techniques - called unsupervised - that are applicable to unlabelled datasets. Objective: This study aims at comparing various approaches employed over the years on unlabelled datasets to predict the defective modules, i.e. the ones which need more attention in the testing phase. Our comparison is based on the measurement of performance metrics and on the real defective information derived from software archives. Our work leverages a new dataset that has been obtained by extracting and preprocessing its metrics from a C++ software. Method: Our empirical study has taken advantage of CLAMI with its improvement CLAMI+ that we have applied on high energy physics software datasets. Furthermore, we have used clustering techniques such as the K-means algorithm to find potentially critical modules. Results: Our experimental analysis have been carried out on 1 open source project with 34 software releases. We have applied 17 ML techniques to the labelled datasets obtained by following the CLAMI and CLAMI+ approaches. The two approaches have been evaluated by using different performance metrics, our results show that CLAMI+ performs better than CLAMI. The predictive average accuracy metric is around 95% for 4 ML techniques (4 out of 17) that show a Kappa statistic greater than 0.80. We applied K-means on the same dataset and obtained 2 clusters labelled according to the output of CLAMI and CLAMI+. Conclusion: Based on the results of the different statistical tests, we conclude that no significant performance differences have been found in the selected classification techniques.
Security Analysis of a New FPE Stream Cipher

In some encryption systems it is necessary to preserve the format and length of the encrypted data. This kind of encryption is called FPE (Format Preserving Encryption) or DPE (Datatype Preserving Encryption). For example, in the case of PCS (Physical Coding Sublayer) at Gigabit Ethernet optical communications, the 8b10b symbol flow can be encrypted at line rate thanks to a symmetric encryption scheme that must preserve the format and coding properties of 8b10b symbols. In this case, since a high speed data flow needs to be encrypted preserving its format, the usage of an FPE stream cipher could be advantageous. For this purpose, a new keystream generator scheme is proposed in this work. It is based on a secure block cipher working in CTR (Counter) mode whose output is subjected to a modulo operation. Moreover, a security analysis for this mode is carried out, deriving an expression for the IND-CPA (Indistinguishability under Chosen Plaintext Attack) advantage of any adversary, concluding that this mode can be considered secure in the same way as traditional modes are. Furthermore, the proposed structure has been implemented over an FPGA (Field Programmable Gate Array) device and adapted to a Gigabit Ethernet application.
An Improved Intelligent Scissors Algorithm for the Segmentation of Vessels Segments in Coronary Angiography Imaging

Interactive segmentation is widely used to precisely segment the parts of interest in medical images. Among them, the intelligent scissors algorithm is an efficient interactive segmentation algorithm, but it has poor performance in processing images with a high similarity between foreground and background. And it is easily affected by the strong edge, which leads to more seed points and longer time to segment the interested vessel segments in the coronary angiography images. In order to improve the segmentation efficiency, we redesign the cost function, using Canny operator, Scharr operator, to replace the Laplace operator in the traditional algorithm. Moreover, gradient magnitude adjustment function and histogram adjustment function are introduced to improve the segmentation effect. Experiment results show that the improved algorithm can shorten the segmentation time and improve the segmentation efficiency without the loss of performance compared with the traditional algorithm.
One-Shot Unsupervised Domain Adaptation for Object Detection

The existing unsupervised domain adaptation (UDA) methods require not only labeled source samples but also a large number of unlabeled target samples for domain adaptation. Collecting these target samples is generally time-consuming, which hinders the rapid deployment of these UDA methods in new domains. Besides, most of these UDA methods are developed for image classification. In this paper, we address a new problem called one-shot unsupervised domain adaptation for object detection, where only one unlabeled target sample is available. To the best of our knowledge, this is the first time this problem is investigated. To solve this problem, a one-shot feature alignment (OSFA) algorithm is proposed to align the low-level features of the source domain and the target domain. Specifically, the domain shift is reduced by aligning the average activation of the feature maps in the lower layer of CNN. The proposed OSFA is evaluated under two scenarios: adapting from clear weather to foggy weather; adapting from synthetic images to real-world images. Experimental results show that the proposed OSFA can significantly improve the object detection performance in target domain compared to the baseline model without domain adaptation.
Design and development wire cutter CNC for Styrofoam product

Abstract Cutting Styrofoam manually will result in inaccurate cutting and dissimilar shape. To upgrade the quality of the product, a Styrofoam cutting machine which can cut automatically is needed. Thus, the aim of this research is to create a Styrofoam cutting machine using CNC 2 Axis Wire Cutter which can cut the material according to the CAD drawing module, and accelerate the cutting process. The control system of the machine uses the Arduino UNO microcontroller. The cutting process is performed by sending G-Code file to the microcontroller through the Universal G-Code Sender software. Afterwards, the microcontroller sends a signal to activate the stepper motor, and it is generated actuator movement according to the drawing in G-code file. The optimal Styrofoam cutting resulted by using CNC Wire Cutter with 0.2 mm nickel wire, 220oC, and 2 cm thickness.
A Bayesian Nonparametric Approach to Differentially Private Data

The protection of private and sensitive data is an important problem of increasing interest due to the vast amount of personal data collected. Differential Privacy is arguably the most dominant approach to address privacy protection, and is currently implemented in both industry and government. In a decentralized paradigm, the sensitive information belonging to each individual will be locally transformed by a known privacy-maintaining mechanism Q. The objective of differential privacy is to allow an analyst to recover the distribution of the raw data, or some functionals of it, while only having access to the transformed data. In this work, we propose a Bayesian nonparametric methodology to perform inference on the distribution of the sensitive data, reformulating the differentially private estimation problem as a latent variable Dirichlet Process mixture model. This methodology has the advantage that it can be applied to any mechanism Q and works as a “black box” procedure, being able to estimate the distribution and functionals thereof using the same MCMC draws and with very little tuning. Also, being a fully nonparametric procedure, it requires very little assumptions on the distribution of the raw data. For the most popular mechanisms Q, like Laplace and Gaussian, we describe efficient specialized MCMC algorithms and provide theoretical guarantees. Experiments on both synthetic and real dataset show a good performance of the proposed method.
Do preparatory programming lab sessions contribute to even work distribution in student teams?

Unfair work distribution is common in project-based learning with teams of students. One contributing factor is that students are differently skilled developers. To mitigate the differences in a course with group work, we introduced mandatory programming lab sessions. The intervention did not affect the work distribution, showing that more is needed to balance the workload. Contrary to our goal, the intervention was very well received among experienced students, but unpopular with students weak at programming.
Comparative Analysis of Random Generators

We report on experiments with selected software and hardware (pseudo)random generators under controlled conditions and compare their throughput and consumed entropy. In software, we distinguish between number theoretic generators (which come with corresponding security reductions) and generators based on AES. True random generators covered by our study are the physical hardware generator PRG310-4 and the generators /dev/random and /dev/urandom as implemented in the Linux kernel.
Generating Compact Tree Ensembles via Annealing

Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretable manner. Most algorithms for obtaining tree ensembles are based on versions of boosting or Random Forest. Previous work showed that boosting algorithms exhibit a cyclic behavior of selecting the same tree again and again due to the way the loss is optimized. At the same time, Random Forest is not based on loss optimization and obtains a more complex and less interpretable model. In this paper we present a novel method for obtaining compact tree ensembles by growing a large pool of trees in parallel with many independent boosting threads and then selecting a small subset and updating their leaf weights by loss optimization. We allow for the trees in the initial pool to have different depths which further helps with generalization. Experiments on real datasets show that the obtained model has usually a smaller loss than boosting, which is also reflected in a lower misclassification error on the test set.
Modelling Data Visualization Interactions

This paper makes a point of current perspectives on Data Visualization research that were essentially conceived to provide guidelines for finding the best mapping between data and visual representations. Going back to foundational concepts of HCI that rely on manipulation of visual symbols, we propose a new perspective, with the aim to focus on a different configuration, that considers visual signs, professional contexts and user practices. We argue that, so far, user practices have been neglected or left behind in design, evaluation and recommendation scenarios, reducing them to the pure relational focus among kind of data, kind of charts and in lab tasks. This may underestimate the potential of the pragmatic side of this relation, where humans manipulate and interpret signs on the basis of their "practical knowledge, a factor that should be considered to improve human interactions with Data Visualization tools. The perspective discussed here would bring into light and help frame open problems such as interactions in routine tasks and the interpretation of data through visual interactive tools in daily professional practices. By proposing a light but formal model of investigation of these pragmatic interactions, we would like to contribute to the current debate around data visualization as the new strategic tool for dealing with the growing complexity of big data streams, digitization of life, sensor and hardware-embedded intelligence.
Introduction—Feelings Matter

Emotions are a hot topic in design, human–computer interaction and any area of business these days. Their significance in areas in which people make choices, decisions and engage in action has been undeniable for at least the last 40 years of psychology and consumer scholarship. What once was an extremely contested, fuzzy and (almost) easily scientifically avoidable area, is now at the centre of everyone’s interest. In an era of cognitive computing, artificial intelligence (so-called learning and thinking machines), and optimization, all attention is placed on what makes us human, and the ways in which human thought actually operates. This emotional logic, intentionality and consciousness itself, drive not simply the ways in which individuals process (cognitise) information, but also ways in which society and the built world are structured. Emotions play as much a role in shaping technology design, as they do in the way we experience it. This introduction presents a book that takes many angles towards concretely understanding what it is in design that makes people emotionally experience it in the ways that they do. It introduces the main themes and concepts of the book that include ethics, culture, measurement and design methods. It additionally demonstrates a broader understanding of technology in chapters that investigate graffiti, urban and art experience, filmic experience, architecture and cultural movements. It is hoped that combining this broader cultural-emotional insight into one package will enable readers to connect their design practice and research to the broader system of emotions, culture, ethics, lived experience and technology.
Critiques of CDA/CDS and Responses

Chapter 5 addresses various critiques of CDA/CDS along with the responses to them, including recent changes in focus and direction. We begin with a discussion of Widdowson’s well-known critique of CDA and Fairclough’s (and later, Wodak’s) response to it, followed by critiques of what ‘critical’ in ‘critical discourse analysis/studies’ means, as well as the need for reflexivity within the field, particularly in terms of what it actually accomplishes in the world. Additionally, we address methodological and theoretical shortcomings, the need for more contextually oriented analyses that attend to cultural influences, relationships between readers, text producers, researchers and texts, and the need to pay attention to other fields and modalities. In response to this, we discuss Positive Discourse Analysis (PDA) and the move toward generative critique, more attention to culture in the construction of discourse, researcher reflexivity, and how the field is continually redefining itself.
Classification of Carcass Fatness Degree in Finishing Cattle Using Machine Learning

Nowadays, there is an increase in world demand for quality beef. In this way, the Government of the State of Mato Grosso do Sul has created an incentive program (Precoce MS) that stimulates producers to fit into production systems that lead to the slaughter of animals at young ages and superior carcass quality, towards a more sustainable production model. This work aims to build a classification model of carcass fatness degree using machine learning algorithms and to provide the cattle ranchers with indicators that help them to early finishing cattle with better carcass finishing. The dataset from Precoce MS contains twenty-nine different features with categorical and discrete data and size of 1.05 million cattle slaughter records. In the data mining process, the data were cleaned, transformed and reduced in order to extract patterns more efficiently. In the model selection step, the data was divided into five different datasets for performing cross-validation. The training set received \(80\%\) of the data and the test set received the other \(20\%\), emphasizing that both had their data stratified respecting the percentage of each target class. The algorithms analyzed and tested in this work were Support Vector Machines, K-Nearest Neighbors, AdaBoost, Multilayer Perceptron, Naive Bayes and Random Forest Classifier. In order to obtain a better classification, the recursive feature elimination and grid search techniques were used in the models with the objective of selecting better characteristics and obtaining better hyperparameters, respectively. The precision, recall and f1 score metrics were applied in the test set to confirm the choice of the model. Finally, analysis of variance ANOVA indicated that there are no significant differences between the models. Therefore, all these classifiers can be used for the construction of a final model without prejudice in the classification performance.
Acceleration and energy reduction of object detection on mobile graphicsprocessing unit

The evolution of high performance computing in today’s smartphones is enabling their use in compute-intensive applications. As the compute requirement increases, the energy required to do the computation cannot increase in proportion because the cost of providing that energy available and cooling would become prohibitive. An alternative, potentially power-reducing approach is to use graphics processing units or special accelerator cores. Today’s smartphones are equipped with systemon-chip (SoC) devices that house many cores such as graphics processing units, digital signal processors, and special multimedia encoder/decoder hardware alongside multi-core central processing units. Their inclusion enables applications that require greater computational power such as real-time computer vision. In this work, we study the capability of the recently introduced general-purpose graphics processing unit (GPU) in a smartphone SoC to enable energy-efficient object detection. This will include understanding the architecture of the recent GPUs that will be used (the Adreno 320 and Adreno 420 from Qualcomm), the implementation and optimization of the object detection algorithm used in the Open Computer Vision library (OpenCV) using these GPUs and measuring the energy consumption of this implementation. We implemented the Viola- Jones based object detection on the GPU in an Android tablet. The implementation is 35% faster on average than the same algorithm running on the CPU on the same device. The implementation also reduces the average energy consumption by 68% compared to the CPU on the same device. An application that utilized the object detector on the mobile GPU to detect Ringworm skin disease was developed. A classifier was trained for this application and it has an accuracy of 75%.
Bayesian Personalized Ranking-Based Rank Prediction Scheme (BPR-RPS)

Cloud service capabilities can always be clearly indicated by Quality of Service (QoS) properties. The quality and effectiveness of cloud services can be measured at both client and server sides. The parameters such as response time, throughput and failure rate influence the QoS properties at the client side. These parameters may differ with the utilization of the application for the same service. The proposed Bayesian Personalized Ranking-based Rank Prediction Scheme (BPR-RPS) provides rank prediction of QoS properties on the client side, since client-side properties vary with the usage of the application. The proposed rank prediction framework provides ranking to the services when particular cloud services are requested by the client. Personalization of cloud providers and customers is made possible by concentrating on the item recommendation imposed on the cloud by creating user-specific ranking for a set of items. The past response of the user with respect to the item aids in decision-making. The proposed ranking framework includes the following modules: Computation of similarity index between the active and ad hoc users and finding of identical users based on similarity index values.
Understanding Concepts of Blockchain Technology for Building the DApps

Blockchains are the digital ledgers with tamper resistance and are implemented in the distributed networks with the consensus protocols which were introduced by Satoshi Nakamoto. A high-level secured technology for users both in the public and private networks. The peers in the network who share their transactions with the other peers are recorded and once recorded transactions cannot be modified by any of the peers in the blockchain technology. Here the digital signatures are used to provide the trusted third party to prevent the double-spending attacks. Due to the longest chain generated by the user by using the high computational power, there may be possibility of an attacker to access the whole network and this is prevented by the proof of work consensus protocol as forking concept. This paper provides a review of the blockchain network and helps the readers to understand the working of blockchain technology.
CAPTCHA Techniques of Secure Web Authentication: A Survey

Today, web security is the foremost concern due to the requirement of a validation and verification process that not only authenticates the end user but also prevents the data from various malicious programs. Due to the huge use of internet and electronic devices, the computer bots try to access the authentic data resided at the servers. The CAPTCHA is generally meant for authentication of the websites during login, for securing and getting access of data. A CAPTCHA challenge is kept for doing the conformation whether the process is initiated by an authentic user or by an attacker. If the challenge is successfully chased, it is signified as a human or else a computer bot, and the access will be provided to the right user. Our aim is to make a natural interface between authentic user and web servers so that the communication between the entities will take place in a secured manner. This paper presents a brief summary on CAPTCHA and its necessity in current scenarios. We have discussed a study on various types of CAPTCHAs available, along with their methodologies and the implementation details which help to secure the web data. The paper, moreover, presents some of the important advantages, disadvantages and applications of these techniques and technologies used in different research in addition to the different future scope of CAPTCHAs.
Passive Batch Injection Training Technique: Boosting Network Performance by Injecting Mini-Batches from a different Data Distribution

This work presents a novel training technique for deep neural networks that makes use of additional data from a distribution that is different from that of the original input data. This technique aims to reduce overfitting and improve the generalization performance of the network. Our proposed technique, namely Passive Batch Injection Training Technique (PBITT), even reduces the level of overfitting in networks that already use the standard techniques for reducing overfitting such as L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization and batch normalization, resulting in significant accuracy improvements. Passive Batch Injection Training Technique (PBITT) introduces a few passive mini-batches into the training process that contain data from a distribution that is different from the input data distribution. This technique does not increase the number of parameters in the final model and also does not increase the inference (test) time but still improves the performance of deep CNNs. To the best of our knowledge, this is the first work that makes use of different data distribution to aid the training of convolutional neural networks (CNNs). We thoroughly evaluate the proposed approach on standard architectures: VGG, ResNet, and WideResNet, and on several popular datasets: CIFAR-10, CIFAR-100, SVHN, and ImageNet. We observe consistent accuracy improvement by using the proposed technique. We also show experimentally that the model trained by our technique generalizes well to other tasks such as object detection on the MS-COCO dataset using Faster R-CNN. We present extensive ablations to validate the proposed approach. Our approach improves the accuracy of VGG-16 by a significant margin of 2.1% over the CIFAR-100 dataset.