Reviewerly/exHarmony huggingface dataset:
Investigating Context Adaptation Bugs in Code Clones

The identical or nearly similar code fragments in a code-base are called code clones. There is a common belief that code cloning (copy/pasting code fragments) can introduce bugs in a software system if the copied code fragments are not properly adapted to their contexts (i.e., surrounding code). However, none of the existing studies have investigated whether such bugs are really present in code clones. We denote these bugs as Context Adaptation Bugs, or simply Context-Bugs, in our paper and investigate the extent to which they can be present in code clones. We define and automatically analyze two clone evolutionary patterns that indicate fixing of Context-Bugs. According to our analysis on thousands of revisions of six open-source subject systems written in Java, C, and C#, code cloning often introduces Context-Bugs in software systems. Around 50% of the clone related bug-fixes can occur for fixing Context-Bugs. Cloning (copy/pasting) a newly created code fragment (i.e., a code fragment that was not added in a former revision) is more likely to introduce Context-Bugs compared to cloning a preexisting fragment (i.e., a code fragment that was added in a former revision). Moreover, cloning across different files appears to have a significantly higher tendency of introducing Context-Bugs compared to cloning within the same file. Finally, Type 3 clones (gapped clones) have the highest tendency of containing Context-Bugs among the three major clone-types. Our findings can be important for early detection as well as removal of Context-Bugs in code clones.
How the Experience of Development Teams Relates to Assertion Density of Test Classes

The impact of developers' experience on several development practices has been widely investigated in the past. One of the most promising research fields is software testing, as many researchers found significant correlations between developers' experience and testing effectiveness. In this paper, we aim at further studying this relation, by focusing on how development teams' experience is associated with the assertion density, i.e., the number of assertions per test class KLOC, that has previously been shown as an effective way to decrease fault density. We perform a mixed-methods empirical study. First, we devise a statistical model relating development teams' experience and other control factors to the assertion density of test classes belonging to 12 software projects. This model enables us to investigate whether experience comes out as a statistically significant factor to explain assertion density. Second, we contrast the statistical findings with a survey study conducted with 57 developers, who were asked their opinions on how developer's experience is related to the way they add assertions in test code. Our findings suggest the existence of a relationship: on the one hand, the development team's experience is a statistically significant factor in most of the systems that we have investigated; on the other hand, developers confirm the importance of experience and team composition for the effective testing of production code.
Clozing the gap: How far do cloze items measure?

Originally designed to measure reading and passage comprehension in L1 readers, cloze tests continue to be used for L2 assessment purposes. However, there remain disputes about whether or not cloze items can measure beyond local comprehension information, as well as whether or not they are purely a test of reading alone, or if performance can be generalized to broader claims about proficiency. The current study sets out to address both of these issues by drawing on a large pool of cloze items ( k = 449) taken from 15 cloze passages that were administered to 675 L1 and 2246 L2 examinees. In conjunction with test scores, a large-scale L1 experiment was conducted using Amazon’s Mechanical Turk to determine the level of minimum context required to answer each item. Using Rasch analysis, item function was compared across both groups, with results indicating that cloze items can draw on information at both the sentence and passage level. This seems to suggest further that cloze tests generally tend to measure reading in both L1 and L2 examinees. These findings have important implications for the continued use of cloze tests, particularly in classroom and high-stakes contexts where they are commonly found.
Hidden representations in deep neural networks: Part 1. Classification problems

Deep neural networks have evolved into a powerful tool applicable for a wide range of problems. However, a clear understanding of their internal mechanism has not been developed satisfactorily yet. Factors such as the architecture, number of hidden layers and neurons, and activation function are largely determined in a guess-and-test manner that is reminiscent of alchemy more than of chemistry. In this paper, we attempt to address these concerns systematically using carefully chosen model systems to gain insights for classification problems. We show how wider networks result in several simple patterns identified on the input space, while deeper networks result in more complex patterns. We show also the transformation of input space by each layer and identify the origin of techniques such as transfer learning, weight normalization and early stopping. This paper is an initial step towards a systematic approach to uncover key hidden properties that can be exploited to improve the performance and understanding of deep neural networks.
What Face and Body Shapes Can Tell Us About Height

Recovering a person's height from a single image is important for virtual garment fitting, autonomous driving and surveillance. However, it is also very challenging without absolute scale information. Here, we examine the rarely addressed case, where camera parameters and scene geometry are all unknown. Under this circumstances, scale is inherently ambiguous, and height can only be inferred from those statistics that are intrinsic to human anatomy and can be estimated from images directly, such as articulated pose, bone-length proportions, and facial features. Our contribution is twofold. First, we create a new human-height dataset that is three magnitudes larger than existing ones, by mining explicit height labels and propagating them to additional images through face recognition and assignment consistency. Second, we test a wide range of machine learning models (linear, shallow, and deep models) to capture the relation between image content and human height. We also show that performance is predominantly limited by dataset size. Our central finding is that height can only be estimated with large uncertainty. The remaining high variance demonstrates that the geometrically motivated scale ambiguity persists into the age of deep learning, which has important implications for how to pose monocular reconstruction, such as 3D human pose estimation, in a scale invariant way.
IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things

In this work, we present a new operator, called Instance Mask Projection (IMP), which projects a predicted instance segmentation as a new feature for semantic segmentation. It also supports back propagation and is trainable end-to end. By adding this operator, we introduce a new way to combine top-down and bottom-up information in semantic segmentation. Our experiments show the effectiveness of IMP on both clothing parsing (with complex layering, large deformations, and non-convex objects), and on street scene segmentation (with many overlapping instances and small objects). On the Varied Clothing Parsing dataset (VCP), we show instance mask projection can improve mIOU by 3 points over a state-of-the-art Panoptic FPN segmentation approach. On the ModaNet clothing parsing dataset, we show a dramatic improvement of 20.4% compared to existing baseline semantic segmentation results. In addition, the Instance Mask Projection operator works well on other (non-clothing) datasets, providing an improvement in mIOU of 3 points on “thing” classes of Cityscapes, a self-driving dataset, over a state-of-the-art approach.
Private Information Retrieval with Private Coded Side Information: The Multi-Server Case

In this paper, we consider the multi-server setting of Private Information Retrieval with Private Coded Side Information (PIR-PCSI) problem. In this problem, there is a database of K messages whose copies are replicated across N servers, and there is a user who knows a random linear combination of a random subset of M messages in the database as side information. The user wishes to download one message from the servers, while protecting the identities of both the demand message and the messages contributing to the side information. We assume that the servers know the number of messages contributing to the user's side information in advance, whereas the indices of these messages and their coefficients in the side information are not known to any of the servers a priori. Our goal is to characterize (or derive a lower bound on) the capacity, i.e., the maximum achievable download rate, for the following two settings. In the first setting, the set of messages contributing to the linear combination available to the user as side information, does not include the message demanded by the user. For this setting, we show that the capacity is equal to (1 + 1/N + ··· + 1/N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-M-1</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . In the second setting, the demand message contributes to the linear combination available to the user as side information, i.e., the demand message is one of the messages that form the user's side information. For this setting, we show that the capacity is lower-bounded by (1 + 1/N + ··· + 1/N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-M</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . The proposed achievability schemes and proof techniques leverage ideas from both our recent methods proposed for the single-server PIR-PCSI problem as well as the techniques proposed by Sun and Jafar for multi-server private computation problem.
Cyberbullying Detection on Twitter using Multiple Textual Features

Due to the spread of PCs and smartphones and the rise of user-generated content in social networking service, cyberbullying is also increasing and has become a serious risk that social media users may encounter. In this paper, we focus on the Japanese text on Twitter and construct an optimal model for automatic detection of cyberbullying by extracting multiple textual features and investigating their effects with multiple machine learning models. The experimental evaluation shows that the best model with predictive textual features is able to obtain an accuracy of over 90%.
MigrationMiner: An Automated Detection Tool of Third-Party Java Library Migration at the Method Level

In this paper we introduce, MigrationMiner, an automated tool that detects code migrations performed between Java third-party library. Given a list of open source projects, the tool detects potential library migration code changes and collects the specific code fragments in which the developer replaces methods from the retired library with methods from the new library. To support the migration process, MigrationMiner collects the library documentation that is associated with every method involved in the migration. We evaluate our tool on a benchmark of manually validated library migrations. Results show that MigrationMiner achieves an accuracy of 100%. A demo video of Migration-Miner is available at https://youtu.be/sAlR1HNetXc.
Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis

Due to the lack of large-scale datasets, the prevailing approach in visual sentiment analysis is to leverage models trained for object classification in large datasets like ImageNet. However, objects are sentiment neutral which hinders the expected gain of transfer learning for such tasks. In this work, we propose to overcome this problem by learning a novel sentiment-aligned image embedding that is better suited for subsequent visual sentiment analysis. Our embedding leverages the intricate relation between emojis and images in large-scale and readily available data from social media. Emojis are language-agnostic, consistent, and carry a clear sentiment signal which make them an excellent proxy to learn a sentiment aligned embedding. Hence, we construct a novel dataset of 4 million images collected from Twitter with their associated emojis. We train a deep neural model for image embedding using emoji prediction task as a proxy. Our evaluation demonstrates that the proposed embedding outperforms the popular object-based counterpart consistently across several sentiment analysis benchmarks. Furthermore, without bell and whistles, our compact, effective and simple embedding outperforms the more elaborate and customized state-of-the-art deep models on these public benchmarks. Additionally, we introduce a novel emoji representation based on their visual emotional response which support a deeper understanding of the emoji modality and their usage on social media.
LAMSTAR: For IoT‐based face recognition system to manage the safety factor in smart cities

Abstract Digital transformation played a vital role in smart cities because of its ability to process the different data to providing sustainability, connectivity, and mobility to data effectively. According to the diver operational productivity, preserving public safety government law enforcement integrated that face recognition is more important in smart cities. Traditional face recognition system fails to predict the exact facial features that leads to reduce the facial recognition accuracy. The false facial point detection process maximizes the computation complexity. Therefore, in this work, effective and artificial intelligence internet of things–based facial expression detection system is implemented to predict and match the face from the database. Initially, the facial images are captured from the internet of things sensor device, which is processed by applying the Perona‐Malik diffusion algorithm. Then, face location is cropped from the image, geometric face shape model is created for predicting the exact face from the template face image. From the face shape, different facial features are extracted from various region using Fisher linear discriminant analysis. The derived features are trained with the help of convolution network and the face recognition process is done by using the adaboost large memory storage and retrieval neural network. The network successfully recognizes the face from the template, which is used to eliminate the safety related risk in smart cities.
Digital Learning as a Factor of Professional Competitive Growth

The article considers the problem of developing the university educational environment as the condition for the successful professional activity of a future specialist. The authors emphasize the competitiveness relation with digital competence that is a certain level of mastering new software products and with tacit requirement of high learning ability. In order to study the prospects for the competitive growth of future specialists, the authors have surveyed the attitude of Peter the Great St. Petersburg Polytechnic University students to the digital educational environment and its influence on the parameters of personal and professional competence. The monitoring results show that, as to the majority of students in the reference group, the digital educational environment is the main factor in increasing their competitiveness in the labor market, since it develops independence and responsibility for actions in the information sphere. At the same time, the assessment of the digital technologies impact on various indicators of activity and creativity reveals the ambiguous influence of the digital environment on students’ intellectual self-development.
Hybrid Deep Neural Networks for Friend Recommendations in Edge Computing Environment

With the rising popularity of social networks and service recommendations, research on new methods of friend recommendation have become a key topic, especially when based on quality-driven resource processing in an edge computing environment. Traditional methods seldom systematically combine static attributes (e.g., interests, geographical locations, and common friends), dynamic behaviors (e.g., liking, making comments, forwarding and @), and network structures (e.g., social ties) to recommend a new friend to a target user. Meanwhile, with the advent of deep learning, it has become more challenging to integrate these features into a deep neural network framework for friend recommendation. For example, how do we optimally make use of these features to form a united framework and what type of deep neural network architecture should be introduced into a novel recommendation method in an edge computing environment? In this paper, we propose DFRec++, a hybrid deep neural network framework combining attribute attention and network embeddings to make social friend recommendations with the help of both interactive semantics and contextual enhancement. More specifically, we first utilize the latent dirichlet allocation (LDA) topic model to generate common interest topics between users and compute the similarity of the explicit static attribute vector representation of topics, locations, and common friends. Then we feed dynamic behavior attributes into a convolutional neural network (CNN) to obtain the implicit vector representation of the interactions and context between two users. Subsequently, a multi-attention mechanism is designed to further improve the deep vector representation of the attribute information. Next, the LINE-based network embeddings algorithm is applied to embed the network structure into a low-dimensional vector. Finally, the attribute attention vector and the network embeddings are concatenated to form a deep feature representation, which is subsequently fed to a fully connected neural network (FCNN) to capture the probability of friendship between two users. The output of FCNN indicates the probability of two users becoming friends. We conducted experiments on a real-world Weibo dataset and the results show that DFRec++ outperforms several existing methods.
Agile Project Management Tools: A Brief Comprative View

Abstract Agile methodologies are becoming popular in software development. Managers are required to understand project’s progress and product quality without development documents. During Agile practices of the teams and organizations, Agile project management tools are frequently used. The use of such tools leads to achieving speed and efficiency, affects the quality of the software. The quality of final product is mostly related with to project management. Accordingly, the paper provides brief comparative perspective about the popular project management tools for agile projects. 16 popular Agile project management tools have been presented helping agile developers to plan and manage their tasks in an efficient manner. Taiga, Axosoft, Agielan, Planbox are more appropriate for start-up projects. The most twitted and most appreciated tools are reported as Jira, Trello, and VersionOne. SpiraTeam by Inflectra and Pivotal Tracker are other pricing and popular agile tools, providing flexibility to Agile developers and increase collaboration among team members.
Estimating the prevalence and diversity of words in written language

Recently, a new crowd-sourced language metric has been introduced, entitled word prevalence, which estimates the proportion of the population that knows a given word. This measure has been shown to account for unique variance in large sets of lexical performance. This article aims to build on the work of Brysbaert et al. and Keuleers et al. by introducing new corpus-based metrics that estimate how likely a word is to be an active member of the natural language environment, and hence known by a larger subset of the general population. This metric is derived from an analysis of a newly collected corpus of over 25,000 fiction and non-fiction books and will be shown that it is capable of accounting for significantly more variance than past corpus-based measures.
Computer aided diagnosis system developed for ultrasound diagnosis of liver lesions using deep learning

The Japan Society of Ultrasonics in Medicine (JSUM) is currently constructing an ultrasound image database. This database collects B-mode images of liver tumors and breast tumors, and B-mode videos of heart disease. In the past year, 31,000 liver tumor images have been collected from 11 institutions and 14,000 breast tumor images have been collected from 5 institutions. We are developing computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems for liver and breast tumors based on deep learning using this database. In this paper, we report on CADx to estimate liver tumor types as a first trial. The data used in this study are 159 cyst cases (338 images), 68 hemangioma cases (279 images), 73 hepatocellular carcinoma (HCC) cases (241 images), and 24 metastatic liver cancer cases (122 images), collected at one facility. We developed the CADx system that estimates four types of liver tumor using a convolutional neural network based on VGGNet. The accuracy of the developed 4-class classification CADx was 88.0%. The accuracy by tumor type was 98.1% for cysts, 86.8% for hemangiomas, 86.3% for HCC, and 29.2% for metastatic liver cancer, with increasing accuracy observed for larger data sets. We also developed CADx to estimate whether a liver tumor is benign or malignant. The accuracy of this 2-class classification CADx was 94.8%, the sensitivity was 93.8%, and the specificity was 95.2%. Both 4-class classification and 2-class classification CADx had relatively high accuracy. However, in this study, we used only a small amount data collected from a single facility. In the future, we plan to verify our results using a larger amount of data collected from multiple facilities. In addition, we prototyped CAD software and are currently developing it with feedback from doctors.
Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.