Reviewerly/exHarmony huggingface dataset:
Completeness of Composite Refactorings for Smell Removal

Code smells are problems in the internal structural quality. Refactoring is a technique commonly used to remove code smells. A single refactoring rarely suffices to assist developers in achieving a full removal of a code smell. Thus, developers frequently apply composite refactorings (or, simply, composites) with the goal of fully removing a smell. A composite is formed by two or more interrelated single refactorings. Studies report that developers often fail in fully removing code smells through composite refactoring. In this context, a composite refactoring is considered incomplete whenever it does not fully remove a smell. Either incomplete or complete composite is formed by several refactorings; thus, both may inadvertently degrade other parts of the software. However, the literature on (in)complete composites and their effects on structural quality is still scarce. This lack of knowledge hampers the design of empirically-based recommendations to properly assist developers in performing effective complete composites, i.e., those not causing any harm in related parts of the program. This doctoral research investigates the effect of composite (in)completeness on structural quality and proposes a catalog with composite recommendations for the full removal of popular code smell types. We investigated 618 composites in 20 software projects. We found that 58% of incomplete composites did not change the internal structural quality, and 64% of complete composites are formed by refactoring types that were not actually previously recommended in the literature or elsewhere. The expected contributions are a list of findings, guidelines, and a catalog to support developers on how to successfully perform complete composites.
Enhancing Information Preservation in Social Media Text Analytics Using Advanced and Robust Pre-processing Techniques

Data mining has become an essential element of today's information world.Different industries and sources daily produce a huge amount of data.When it comes to textual analysis, internet users produce a large amount of data in the form of Twitter Tweets, updates, posts, and comments from Facebook and blogs, short messages, and emails.Analysis of such data will give more valuable information and insights about the studied subject but the problem with social media text is that it is availbel in very raw form.Social media users usually do not produce text in a particular format required by analytics algorithms.Social Media text contains usually miss-spelt words, links, and hash-tags, mentioning people, word/phrase short forms, word elongations, emotional symbols, and many other raw forms.When available text pre-processing techniques (tokenization, lower case, stemming, lemmatization, stop word removals, and normalization) are applied to this raw and un-cleaned data, the removal of many words/phrases results in information loss or information modification.Hence, the curse of data dimensionality vanished and make it difficult to get as much as possible insights from data.We have proposed some advance and robust pre-processing techniques used to increase information preservation from social media text while preserving the semantics of data remain the same.
Asymob: a platform for measuring and clustering chatbots

Chatbots have become a popular way to access all sorts of services via natural language. Many platforms and tools have been proposed for their construction, like Google’s Dialogflow, Amazon’s Lex or Rasa. However, most of them still miss integrated quality assurance methods like metrics. Moreover, there is currently a lack of mechanisms to compare and classify chatbots possibly developed with heterogeneous technologies.To tackle these issues, we present Asymob, a web platform that enables the measurement of chatbots using a suite of 20 metrics. The tool features a repository supporting chatbots built with different technologies, like Dialogflow and Rasa. Asymob’s metrics help in detecting quality issues and serve to compare chatbots across and within technologies. The tool also helps in classifying chatbots along conversation topics or design features by means of two clustering methods: based on the chatbot metrics or on the phrases expected and produced by the chatbot. A video showcasing the tool is available at https://www.youtube.com/watch?v=8lpETkILpv8.
Developing Healthy Eating Multimedia Courseware for Kindergarten Students: Heuristic and Usability Evaluation

This paper discusses the results of heuristic and usability evaluations of an educational multimedia courseware that contains instructional material in educating kindergarten students about healthy eating. The courseware has been developed to add more digital materials in teaching healthy eating among the kindergarten students. The development of this courseware has applied Gagne’s 9 instructional events, due to its effectiveness and popularity in the instructional design area. In order to carry out the evaluation, heuristic evaluation by courseware experts and usability evaluation by users have been conducted. The evaluation activities exposed some unsuitable courseware elements in the courseware and were used to make improvement. The usability evaluation was carried out to evaluate the effectiveness and usability of the courseware with a group of 30 samples of target respondents. They were students in Tadika Little Khaliph Gong Badak in Kuala Nerus, Terengganu. The respondents were given 30 minutes to try out the courseware and declare their thoughts on the elements of the courseware. The result revealed that the respondents were satisfied with the design and content, and the assessment elements. However, they gave an inadequate response on the retention element in the courseware.
Towards Personalized and Simplified Expository Texts: Pre-trained Classification and Neural Networks Co-Modeling

The goal of automatic text simplification is to reorganize complex text structures into simpler, more comprehendible texts while retaining their original meaning. The automatic text simplification model, coupled with the personalization element, makes it an indispensable tool for assisting students with learning disabilities who struggle to comprehend expository texts found in school textbooks. In recent years, neural networks have been widely embraced in simplified text generation, with most earlier researchers focusing on the Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Transformer models. In general, however, the majority of their efforts resulted in simple, generic texts, and a lack of cognitive-based personalization elements was found in their models. In this paper, we present the concept of generating personalized and simplified expository texts by joining both pre-trained classification and neural networks models. The pre-trained classification aims to predict complex text structures and phrases that give challenges for students with learning disabilities to comprehend, while the neural networks model is then used to generate simplified expository texts based on the predicted text complexity. The advantage of these joint models is the ability to generate simplified expository texts adapted to the cognitive level of students with learning disabilities. This opens up opportunities for continuously personalized learning, makes them less struggling, and increases their motivation to stay competitive with their peers.
Optimal Input Selection for Single Object Tracking using RGB-Thermal Camera

In the modern era, Object tracking has been used in various intelligent applications that include surveillance, autonomous car, smart harvesting, and action recognition systems. In a video-based setting, an object tracking algorithm aims to correlate the object of interest throughout the frames by building the movement trajectory. The most popular sensing input to the tracking algorithm is the RGB channels, yet, it performs relatively poor in low lighting surroundings, especially if the object’s appearance is similar appearance to the background. Therefore, multi-modal input through a combination of RGB and thermal images has been explored to overcome the weakness of a single modality input. For a tracker that is based on the scoring output of convolutional neural networks, pre-trained weights are usually used to represent the feature extraction module. It is the norm that the weights in convolutional layers are frozen, while the parameters fitting is only done in the fully connected layers. Since the weights are pre-trained, the optimal number of channels is only three, which poses a problem for a tracker with RGB-Thermal input. Two schemes have been devised in this work, either to slice the pre-trained weights to accommodate an additional thermal channel, or to duplicate the thermal channel into a three-channel format. Hence, the performance of 4D and 6D inputs are tested on three state-of-the-art trackers, which are MDNet, TCNN, and MMCNN. The best performance result was produced by TCNN-4D with an expected average overlap of 0.2534, accuracy of 0.5963, and reliability of 0.9329. The results indicate that an optimized slicing method to select the best pre-trained weights will produce a significant tracking improvement even if fewer input channels are used.
The best defense is a good defense: adapting negotiation methods for tackling pressure over software project estimates

Software estimation is critical for a software project’s success and a challenging activity. We argue that estimation problems are not restricted to the generation of estimates but also their use for commitment establishment: project stakeholders pressure estimators to change their estimates or to accept unrealistic commitments to attain business goals. In this study, we employed a Design Science Research (DSR) methodology to design an artifact based on negotiation methods, to empower software estimators in defending their estimates and searching for alternatives to unrealistic commitments when facing pressure. The artifact is a concrete step towards disseminating the soft skill of negotiation among practitioners. We present the preliminary results from a focus group that showed that practitioners from the software industry could use the artifact in a concrete scenario when estimating and establishing commitments about a software project. Our future steps include improving the artifact with the suggestions from focus group participants and evaluating it empirically in real software projects in the industry. CCS CONCEPTS • Software and its engineering → Software development process management.
Improved Resolution of Dehazed Images with Dark Channel Prior and Super Resolution GAN

Extracting information from an image becomes difficult when the scene is disrupted due to bad atmospheric conditions. Suspended particles like fog, rain and dust creates haze in the scene. In many real time situations vision algorithms work on clean and sharp images. This makes the task of vision algorithms difficult to operate and get correct results. Moreover, after dehazing an image still a lot of information gets missing because recovery is not perfect. To fill the information gaps resolution of the image needs to be improved. Haze removal and image resolution improvement is a difficult task and this problem is poorly posed. Over the years many methods have come to address the problem of haze and also improvement of resolution of image. However, they have been addressed independently and in many real time environments resolution of dehazed image is low due to which many vision algorithms fail. In this paper, we propose a method combining Dark Channel Prior and Super Resolution GAN removing haze and improving resolution of image simultaneously followed by working principle with proper discussion of algorithm accompanied with visual representation of experimental results.
Learning Assisted Macro Block Selection for IPCM Mode Steganography in H.264 Videos

Among multiple modes for data embedding in H.264 video, IPCM mode has many benefits like higher embedding capacity and easy integration in H.264 encoding process. In most IPCM based data embedding solutions, a macro block is encoded into IPCM block only based on the amount of data to be embedded without considering the resulting quality of distortion after data embedding in the corresponding IPCM block. In this work, a learning assisted macroblock selection algorithm is proposed to select the macroblocks for IPCM encoding with the goal to minimize the quality distortion due to data embedding. In addition to selection of macroblock, the proposed solution also provides the number of least significant bits to be affected in the IPCM block encoding for the selected macroblock. Through performance testing, the proposed solution was found to provide 2.95% higher embedding capacity and 9.2% lower distortion compared to existing works.
Towards Incremental Build of Software Configurations

Building software is a crucial task to compile, test, and deploy software systems while continuously ensuring quality. As software is more and more configurable, building multiple configurations is a pressing need, yet, costly and challenging to instrument. The common practice is to independently build (a.k.a., clean build) a software for a subset of configurations. While incremental build has been considered for software evolution and relatively small modifications of the source code, it has surprisingly not been considered for software configurations. In this vision paper, we formulate the hypothesis that incremental build can reduce the cost of exploring the configuration space of software systems. We detail how we apply incremental build for two real-world application scenarios and conduct a preliminary evaluation on two case studies, namely x264 and Linux Kernel. For x264, we found that one can incrementally build configurations in an order such that overall build time is reduced. Nevertheless, we could not find any optimal order with the Linux Kernel, due to a high distance between random configurations. Therefore, we show it is possible to control the process of generating configurations: we could reuse commonality and gain up to 66% of build time compared to only clean builds.CCS CONCEPTS • Software and its engineering $\rightarrow$ Software configuration management and version control systems.
Distributed System for Recording, Collecting, and Digital Processing of Broadband Geophysical Signals

The paper describes hardware and software implementation of distributed system for recording, collecting and digital processing of broadband geophysical signals. The signals are recorded at observation points that are significantly remote (over 350 km) from data collection and processing point. Structure of the distributed system and its distinctive features are considered. The system consists of two spatially separated hardware and software parts. These are an autonomous part at a remote observation point and a stationary part. The system parts are connected to a single network by a mobile communication channel. The autonomous part records geophysical signals. Hardware and software of the stationary part provide digital filtering, pulse detection, identification and classification, time-frequency analysis and structural linguistic processing of registered signals.
Two-Phases QR Authentication Algorithm (2PQRA) for Digital Energy and IoT Applications

This paper has proposed a simple authentication algorithm utilizing the Quick Response code (QR) to verify information being transferred thru an Internet based medium. Simply, a Two-Phases QR authentication Algorithm (2PQRA) is proposed in order to increase security levels of private information. The 2PQRA can be used with several types of Internet of Things (IoT) based applications e.g., smart home applications, security purposed applications, and digital asset systems. The proposed 2PQRA mainly focuses on two parts which are the sender and the receiver. Meaning, the 2PQRA runs two-phases procedures of protection. The first one is the encryption of information before it is sent while the other one is the authentication while information are decrypted at the receiver. The 2PQRA also performs additional security procedures such as digital codes are generated frequently to be assigned to users, user private key generation, and user’s features extraction. The proposed 2PQRA is evaluated in terms of authentication, computation time, robustness … etc. The proposed 2PQRA is faster than some other competitive techniques in terms of QR code encryption and it is also robust against brute force attack.
Stationary wavelet transformation based video stabilization and stitching

Video stitching has become popular due to recent advancements in technology to provide broad views and high-resolution displays. Comprehensive view or panoramic videos and high-resolution displays are created by stitching videos captured by multiple cameras or by a single camera at different points of time. This paper proposes a video stitching technique with stabilization for moving multi-camera videos adopting the wavelet decomposition technique. This method uses only those feature points that reduce the mismatching and increase the precision in estimating the transformation from among the feature points identified by the Speed-Up Robust Features detector. This work differs from the similar work of others in two directions. Instead of using all selected feature points for the matching purpose, only significant among them are used. Unlike others, the frames are stabilized before they are stitched.
DASH Framework Using Machine Learning Techniques and Security Controls

Interest in video streaming has increased recently, as it constitutes most of the traffic on the Internet and cellular networks. These networks use different video streaming technologies. One of the most famous technologies is DASH (which stands for Dynamic Adaptive Steaming using HTTP). DASH adapts streaming parameters according to network conditions and uses the HTTP protocol to communicate between the user and the server. DASH faces many challenges that may lead to video interruptions and poor quality of user experiences (QoE) such as bad network conditions and buffering level control. In addition to the lack of studies, we cover security issues for these types of services. In this paper, we proposed an integrated framework that consists of four components: quality prediction model, precache model, light web application firewall, and a monitoring system. These four components improve QoE and precache and increase the level of security. The results of the quality prediction model are used to predict the quality of the next segments depending on the user’s network conditions and in the precache model to improve caching to reduce the load on the streaming system and rely more on cache servers. The proposed web application firewall is a light version used to defend against video streaming attacks and verify the existence of necessary HTTP headers. The quality predictor model with the generated dataset achieved 97% classification accuracy using DecisionTree, and this experiment proved the strong relationship between congestion periods and streaming quality, which is s the main key in QoE.
Enabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods

Users around the world rely on software-intensive systems in their day-to-day activities. These systems regularly contain bugs and security vulnerabilities. To facilitate bug fixing, data-driven models of automatic program repair use pairs of buggy and fixed code to learn transformations that fix errors in code. However, automatic repair of security vulnerabilities remains under-explored. In this work, we propose ways to improve code representations for vulnerability repair from three perspectives: input data type, data-driven models, and downstream tasks. The expected results of this work are improved code representations for automatic program repair and, specifically, fixing security vulnerabilities.
An enhanced Hidden Semi-Markov model for outlier detection in multivariate datasets

Outlier detection in multivariate data is one of the critical challenges in preprocessing phase. Many outlier detection methods have been emerged for the past few years to perform outlier detection efficiently in multivariate datasets. The prediction accuracy cannot be improved without proper outlier analysis and the prediction model might not confirm the expected behavior. The generation of huge data in real time applications makes the outlier detection process more crucial and challenging. Most of the currently available detection methods are based on mean and covariance that are not suitable for handling large volume of datasets, they are suitable for handlind static data and simple data to detect outliers. They cannot cope up with large scale data. So, there is a need for an efficient outlier detection model to detect the outliers in multivariate datasets. The primary objective of this research work is to develop a robust model for outlier detection in multivariate data. To achieve this, the work proposed an enhanced Hidden Semi-Markov Model (HSMM) which allows arbitrary time distribution in its states to detect outliers. The proposed work utilized six benchmark datasets and the performance is compared with several outlier detection algorithms such as HMM, iForest, FastABOD, and Expose. The work achieves 98.2 % of accuracy which is significantly better for detecting outliers in multivariate dataset. The proposed work improvised the percentage of acheivements between 2% to 25% than the currently available models.. The experimental analysis shows that the proposed model performs well than the currently available models in terms of accuracy, and receiver operation curve (ROC).
An Overview of RSA and OAEP Padding

Encryption is crucial in information communication. The secret data is transformed into secure form and transferred through various channels. It is important for encryption to prevent unauthorized access to data and the encrypted data can’t be cracked easily. The RSA algorithm was released by Rivest, Shamir and Adleman in 1978. It was the first practical asymmetric cryptosystem and now it is the most widely used asymmetric cryptosystem in the world, covering security of almost everything such as cellphone communication to online banking. In this article, we review the RSA algorithm and the padding schemes used with RSA encryption to avoid semantical insecurity such as optimal asymmetric encryption padding (OSAP).
Digital Competence and the Gender Gap: A Case Study of Hospitality Students

While digital technologies are opening up new avenues for learning, they are also leading to social inequalities; for instance, they have given rise to the digital gender gap.Though a significant effort is being made to make internet access universal, currently, there exists a significant gender gap concerning access, ownership of digital devices, and digital competence (DC).Digital gender equality is one of the critical enablers of sustainable development, so there is a need to assess this gap and develop meaningful indicators for use in designing and implementing effective policies.Accordingly, this study aims to examine this gender divide in DC, specifically among the hospitality students in India.We use the quantitative survey method based on DigiComp framework 2.1 to collect data from a total of 359 hospitality graduates to capture their level of DC in terms of information and data literacy, communication, content creation, safety and problem-solving.Our findings suggest no gender difference in the parameters under study except for problem-solving competence, which includes the ability to solve technical problems, use technology creatively and identify needs and technological responses.