Reviewerly/exHarmony huggingface dataset:
Crafting an Undergraduate Course at the Intersection of Machine Learning and Software Engineering

This is a work-in-progress innovative practice short paper. It describes exploratory efforts to craft a new undergraduate course at the juncture of Machine Learning (ML) and Software Engineering (SE). Our motivation comes from recent industry practices and challenges to adopt and deploy ML as part of software development projects.We leverage reported industry experiences by ML practitioners, research findings from academia, as well as student feedback, in order to create a new undergraduate course where ML and SE cross paths. Toward this end, we conducted an empirical study -- student surveys and interviews of ML professionals. Questionnaires helped us measure students’ interest in learning how to leverage SE topics to develop ML-based systems, and to gauge their expectations and concerns about the course. Our interviews with ML professionals helped us solicit and collect recommendations about the content of our course.In summary, our paper presents qualitative results of an empirical study launched to help us craft a new course on ML and SE. We discuss a list of reported challenges from ML professionals and explain how we match them to related academic learning objectives and course content. We also describe how student feedback including their expectations and concerns helped us in this effort. We hope that such a course will not only prepare our students for the workforce but will provide a promising example for other institutions to follow.
Using AI-based NiCATS System to Evaluate Student Comprehension in Introductory Computer Programming Courses

This Research to Practice Full Paper presents the use of data collected by our Non-Intrusive Classroom Attention Tracking System (NiCATS) to evaluate student comprehension. Quantifying students' cognitive processes in classrooms in a non-intrusive way is challenging. By analyzing various aspects of the eye metrics against defined regions of interest (ROI), instructors can better understand students’ cognitive processes as they acquire new knowledge. Eye-tracking studies primarily define ROIs based on commonly used metrics (source code complexity, significant fixation durations, etc.). While helpful, these metrics, when used independently, do not accurately represent their comprehension patterns. This paper contributes an alternative, multilayered approach for calculating gaze metrics against automatically defined ROIs. The work utilizes the AI-based Non-Intrusive Classroom Attention Tracking System (NiCATS - developed by the researchers), collecting raw-gaze data in real-time as information is presented on a computer screen. This paper reports the results of a study in which undergraduate students in a CS programming course were asked to identify defects seeded in Java programs. Each JAVA program included its own unique sets of ROIS defined using two different granularities: lexer-based and line-based. The ROI sets were then used to calculate relevant eye metrics in the context of each ROI layout. The results of the eye metric analysis at specific ROIs w.r.t their code review task provide insights into the cognitive processes students undergo when trying to comprehend new material. Subdividing this region into lexer-based regions, we determined “content topics” students struggled with (e.g., using complex data types) in a specific area. This feedback is valuable to the instructor as it enables the ability to identify hard-to-comprehend content topics post-hoc and gives the ability to validate student learning in the classroom. While this experiment focused on students in introductory programming courses, we intend to conduct experiments in other learning settings where students are expected to read material on a computer screen or solve actual problems. To summarize, the analysis of these eye metrics using more fine-grained ROIs (lexer-based, line-based) as an extension of complexity-based ROIs provides instructors with deeper insights into the cognitive processes used by students when compared to the current state-of-the-art techniques.
Comparing Global Curricula and Local Computing Degree programs using the CC2020 Curriculum Visualization Tool

This special session will introduce the participants to the CC2020 Visualization Tool based on the Landscape of Computing Knowledge Table and its 34 topics areas. It will guide them through the process of assigning a minimum and maximum value to the discipline areas of their own degree programme, then enter them into the application and allow the participants to see where in the landscape of computing their own program fits. It will also allow participants to compare their own programme with any other programme entered into the application. We hope to populate the database with programmes from all over the world over the next few months to ensure a global reach for comparison. There has never been a tool to compare computing degrees and this tool not only allows for comparison with current approved curricula but also allows participants to compare their degree with each other. This tool also enables employers to develop a guideline for potential employees and compare with job definitions to help with alignment of qualifications and competencies.
An Examination of the Personal Technology of Students in STEM Disciplines

As more instructional materials are delivered electronically – particularly within courses in STEM fields – students at institutions of higher education (IHEs) are increasingly expected to be equipped with personal technology that allows them to interact with their academic coursework. Few IHEs directly provide that technology to students, which leaves the students themselves responsible for the selection and purchase of these critical computing devices. The result is that students utilize a wide variety of devices for their academic work – some of which may not meet the expectations of the institutions, instructors, and instructional designers who supervise the selection and implementation of the platforms used to deliver course content. This study will answer the following research questions:1)What computing devices do higher education students in STEM programs own or extensively use?2)What are the barriers higher education students in STEM programs have faced with regards to using their personal technology for coursework?This paper describes a mixed-methods study focusing on students at a large statewide university system in the midwestern United States. The results show that students generally have access to computing devices that allow them to engage in their academic work, but frequently encounter challenges in accessing or using their course materials on the devices they own. Ownership of computing devices varies based on several demographic categories, which results in an immediate divide between subgroups of students. The challenges encountered by students include an inability to perform their academic work on the device of their choice, insufficient computing resources to complete their expected activities, and instructional designs that are sometimes incompatible with the devices students have available.This study hopes to inform the selection of digital platforms used to deliver academic content in courses frequently taken by students enrolled in STEM programs. Further, the study should provide an insight into the students’ computing capabilities so that these factors can be considered during the instructional design and implementation of content delivery platforms.
Investigation of an Outcomes-Driven Assessment model in Programming Course

Course assessment is an important quantitative evaluation for students' learning effect in certifying engineering educations. In this paper, the achievement degree based on learning outcomes is used as the basic indicator to construct a program design course evaluation model. According to the characteristics of the program design course, as well as applying the basic theories for course assessment and previous practical experiences, this study mainly discusses the scope and content of course assessment, assessment principles, assessment index system, evaluation standards and process. In the process of constructing the evaluation index system, more than 40 evaluation indicators were designed from the perspectives of both the teachers and students. The system also takes into account the weightage of evaluation indicators at all levels, determined by analytic hierarchy process, and consistency test is carried out to ensure the design of the evaluation index system and evaluation standards are strictly scientifically determined. In experiment, the controlled program design foundation course provided actual test data, and satisfactory results were obtained. This leads to conclusion that the research and practice conducted can provide reference for curriculum teaching reforms.
Improvements of a Hybrid Syllabus Search Tool by Syllabus-related Heuristics

This Research Full Paper proposes a new method to collect course syllabi. A syllabus is essential information about a course in a university. Students grasp the topics covered by a course through its syllabus, and faculties understand the curriculum offered by the university by a set of syllabi. Thus, a syllabus helps to analyze educational activities. Our previous work proposed a hybrid method that combines Google API as a general keyword search engine and linear support vector machine (SVM) as content-based classification models. We could find more computer science (CS) syllabus pages than using each method alone by employing the hybrid method. This paper extends the hybrid method for finding a directory page with many links to the syllabus pages. We use the hybrid method to collect candidate directory pages and select the true directory pages from the candidates by the three heuristics: (1) Hyperlink-Induced Topic Search (HITS) score. (2) URL pattern, and (3) Content word. (1) HITS score: The relation between directory pages and syllabus pages resembles the relation between the HITS algorithm’s hubs and authorities. We use hub scores to select directory pages. (2) URL pattern: Pages with similar contents and roles share a part of their URLs. We exploit this observation to select syllabus pages. (3) Content word: We expect a syllabus page to include words of the Body of Knowledge (BOK) ‘Computing Science Curricula CS2013,’ released by the ACM and IEEE Computer Society. We use the words extracted from the CS2013 BOK to measure how each candidate syllabus page is related to CS2013. With these three heuristics, we achieved 32%, the percentage of CS syllabus pages included on candidate pages.
Cloud Based Environment for Higher Education Institution in Developing Countries

Current modern technologies are playing a key role in the development of the higher education sector by providing multiple modes of learning delivery, collaboration and communication. Cloud computing is an emerging paradigm that plays an important role and has major impacts on higher education institutions (HEIs) in developing countries. It enables users to access diverse software applications, share data, collaborate more easily, and keep their data safely in the infrastructure. Despite the intensive use of cloud computing, limited research has been conducted regarding the use of cloud-based environments by HEIs in developing countries. The purpose of this work is, first, to provide a coherent understanding of cloud computing models used in HEIs. We distinguish the various opportunities and challenges emerging from using different cloud computing services. Second, we propose a cloud based-environment where several cloud services and deployment models are orchestrated in order to create a coherent environment containing the necessary tools. This environment puts into practice several expertise fields and deepens the knowledge of both students and instructors. In addition, we demonstrate the effectiveness of our proposed environment through the experience and findings of a Tunisian School of Engineering, where our cloud-based environment is implemented. This school is a private higher education institution founded in 2003, it employs around 500 people and hosts more than 8,000 students. Finally, findings from this study will helps academics, instructors, and researchers understand the potential of using cloud computing environments from an engineering school’s perspective.
Towards Application of Speech Analysis in Predicting Learners’ Performance

In this work in progress, we propose a model for analysis of students’ verbal conversation during teamwork to predict their academic performance based on expressed emotions. Our previous studies support the link between an individual’s attitude and emotional states during the cognitive process with their performance in the given context [1], [2]. Traditionally the learners’ affective states were assessed by having them fill out standard surveys. More recently the researchers have been using advanced methods to extract students’ emotions from their writings by using Natural Language Processing (NLP) models. These models are applied to data collected from different sources such as discussion forums, team chats, students’ reflective surveys, and journals. In this research, we take one step further by recording students audio in class as they converse about the course topic in low-stake teams and extract emotions from their conversations by NLP methods. The main contributions of the proposed model are 1) the audio transcription component 2) the multi-class emotion analysis unit and 3) the performance prediction model based on input data. SpeechBrain pre-trained models with transformer language models were applied for automated transcription of audio data and converting them to embedding vectors. NLP methods were applied for sentiment analysis. Next, we formed the feature set by combining the extracted emotions with students’ formative assessment grades during the semester to implement a prediction model. We further analyzed which features in the feature set have a higher impact on the students’ academic performance. The early result of this research is promising as we found high accuracy in the predicted scores of the students.
Improved action proposals using fine-grained proposal features with recurrent attention models

Recent models for the temporal action proposal task show that local properties can be an alternative to the region proposal network (RPN) for generating good proposal candidates on untrimmed videos. In this study, we devise an RPN model with a new two-stage pipeline and a new joint scoring function for temporal proposals. The evaluation of local properties is integrated into our RPN model to search for the best proposal candidates that can be distinguished mainly in fine details of proposal regions. Our network models proposals in multiple scales using two recurrent neural network layers with attention mechanisms. We observe that joint training of the RPN with local clues and multi-scale modeling of proposals with recurrent attention mechanisms improve the performance of the proposal generation task. Our model yields state-of-the-art results on the THUMOS-14 and comparable results on the ActivityNet-1.3 datasets.
NLP-Based Hardware Solution for Censoring Audio on Over-the-Top (OTT) Media Services

Audio censoring or the removal of expletives from audio streams on Over-the-Top (OTT) media services has a growing need for solutions to combat everyday situations like parents ensuring that their kids are not exposed to profanities. Most of the existing solutions focus predominantly on mainstream media and are severely lacking in usability. In this paper, we propose a new application to tackle this prevailing solution gap. Our application includes an NLP-based speech processor that runs on a reliable, low power Single Board Computer (SBC) hardware which requires minimal setup. The application was tested against a dataset of movie clips containing foul language with diverse accents and various contexts. The generated output was then validated by comparing it with the actual number of expletives per audio clip that we obtained by counting manually (also referred to as “ground truth”). The application is designed to be extensible and user-friendly by providing consumers the ability to configure their own dictionaries of foul words that need censoring.
Comments on the Visual Binary QR Code

Quick Response (QR) codes are popular and widespread in real-world applications, and appearance is an interesting and valuable issue. To improve the black and white checkered appearance of QR codes, many designers and researchers have investigated combining QR codes with images to create a new QR style with a visual impression. Unitag is the most common manner, which focuses on a small pattern (such as a logo or a symbol) overlaid on top of the QR code to create an embellished visual image. Cox proposed QArt approach that blend binary image with QR code to improve the semblance. Based on Cox's literature, Chu et al. proposed a halftone QR code to enhance the visual fusion of QR codes. This review will analyze and compare the advantages and disadvantages of these methods in binary QR image fusion techniques. These suggestions can be considered in the related binary QR image design research for future design and user requirements.
Automobile Theft Detection by Driving Behavior Identification Using Deep Autoencoder

Modern vehicles consist of an on-board detection unit that can record a driver’s driving behavior. Detecting anomaly in the driving behavior can be used for theft detection. There are many supervised learning models to detect driving behavior. However, it is impractical to collect the behavior of possible thieves beforehand for training. In this work, we design an unsupervised deep autoencoder model, which can learn the driving behavior only from one or few vehicle owners and it can recognize non-owner driving behavior as the vehicle theft. The model is lightweight and it can achieve high accuracy up to around 98% like a supervised model. The analysis results also show that each driver has different important features for the detection when compared with the other drivers.
A High Capacity Reversible Data Hiding in Encrypted Images Using Multi-MSB Prediction and Huffman Coding

The prosperity of cloud services stimulates the study of reversible data hiding in encrypted images, for which researchers dedicate to find better methods to adopt the new assumption that the ones who provide the cover images, a.k.a. the Content-owner, are no longer the same as the ones who hide the secret message into them, a.k.a. the Data-hider, and the Data-hider might not always be allowed to know the actual content of cover image the Content-owner provided, while the one who receives the stego image must be able to restore the exact cover image after extracting the secret. A method of such reversible data hiding in encrypted images has been proposed and we propose improvements to enhance its performance and make it more generalized and stable. Experimental results show that our proposal improves its performance even in the worst-case scenario.
Lattice-Based Anonymous Multi-Receiver ID-Based Encryption

In ID-based encryption, the public key of each user is set to be his identity, which greatly reduces the overheads of certificate management. Besides, in some scenarios, encrypting a secret message for different receivers is required. Hence, many multi-receiver ID-based encryption schemes have been proposed. Some of them also provide anonymity to protect receivers’ privacy. However, most of them are based on the integer factorization problem or the discrete logarithm problem, which are insecure under quantum attacks. In order to solve the above problem, many researchers are designing cryptosystems that can resist quantum attacks, where a lattice-based cryptoscheme is a mature construction to withstand quantum attacks. This research aims to design a lattice-based anonymous multi-receiver ID-based encryption. The proposed scheme has shorter ciphertexts, guarantees message integrity, protects receivers’ identities, and withstands quantum attacks. Moreover, the proposed scheme is provably secure in both confidentiality and anonymity under the random oracle model.
Urban Surface Solid Waste Detection Based on UAV Images

The monitoring of illegal dumping of urban solid waste requires timeliness. Conventional methods are costly and have difficulty resolving the problem in remote regions timely. In this paper, a method based on semantic segmentation technology, which considers the multiscale characteristics and the disordered distribution of garbage, is proposed for automatic detection of surface solid waste in unmanned aerial vehicle (UAV) images. First, an asymmetrically decomposed atrous convolutional group was embedded to the center of the semantic segmentation network and the residual structure is used to enhance the learning ability of the spatial relationships. Then, a semantic flow alignment module was combined with attention mechanism to solve the disordered edges of information fusion. Experimental results demonstrate that the proposed method achieved better segmentation performance.
Role of Agile Methodologies for Ensuring Quality in Complex Systems: A Systematic Literature Review

In software development, the selection of a software process model set the base for the success of a software product. An inappropriate selection may lead to a delay in project release, introduce defects and make the project difficult to update. This lack of quality characteristics may lead to the risk of losing customer expectations as well as the failure of the project itself. In the case of complex systems, the problems become more severe. To meet such expectations, agile methodologies are used to ensure quality in software and meet customers’ expectations. There is currently no literature that gives insights into the role of agile methodologies in ensuring quality in complex systems. The purpose of this paper is to evaluate the effectiveness and the impact of agile methodology in achieving the quality of complex systems. For this, we perform a Systematic Literature Review (SLR) and define a review protocol. By performing a thorough search and screening, we selected 39 papers related to agile methods and complex systems. Our analysis shows that complex systems have various requirements of quality attributes some of the complex systems mainly focus on security, reliability, and efficiency whereas other emphasizes safety, response time, and maintainability. Our analysis also shows that agile methodologies are widely used for the development of complex systems because ensuring the quality requirements of complex systems is not possible with the use of traditional methods of software development.
Dr.3D: Adapting 3D GANs to Artistic Drawings

While 3D GANs have recently demonstrated the high-quality synthesis of multi-view consistent images and 3D shapes, they are mainly restricted to photo-realistic human portraits. This paper aims to extend 3D GANs to a different, but meaningful visual form: artistic portrait drawings. However, extending existing 3D GANs to drawings is challenging due to the inevitable geometric ambiguity present in drawings. To tackle this, we present Dr.3D, a novel adaptation approach that adapts an existing 3D GAN to artistic drawings. Dr.3D is equipped with three novel components to handle the geometric ambiguity: a deformation-aware 3D synthesis network, an alternating adaptation of pose estimation and image synthesis, and geometric priors. Experiments show that our approach can successfully adapt 3D GANs to drawings and enable multi-view consistent semantic editing of drawings.