Reviewerly/exHarmony huggingface dataset:
Virtual Training System for an Industrial Pasteurization Process

This article presents the development of a training system in a virtual environment, a pasteurization plant, which allows interaction with the industrial process. The environment has structures, equipment and other instrumentation that is presented in a real pasteurizer plant. Thus, the proposed system manages textures and movements in a Unity 3D graphic environment, specially designed to develop monitoring and manipulation skills of this industrial process. The interface and the animation of the process are developed in Unity 3D software, together with the modeling of all the elements; in CAD design software. The experimental tests allow the operator to interact with the virtual environment and have knowledge of the different stages of pasteurization in the dairy industry.
Measuring Difficulty of Introductory Programming Tasks

Quantification of the difficulty of problem solving tasks has many applications in the development of adaptive learning systems, e.g., task sequencing, student modeling, and insight for content authors. There are, however, many potential conceptualizations and measures of problem difficulty and the computation of difficulty measures is influenced by biases in data collection. In this work, we explore difficulty measures for introductory programming tasks. The results provide insight into non-trivial behavior of even simple difficulty measures.
Fast event-driven incremental learning of hand symbols

This paper describes a hand symbol recognition system that can quickly be trained to incrementally learn to recognize new symbols using about 100 times less data and time than by using conventional training. It is driven by frames from a Dynamic Vision Sensor (DVS) event camera. Conventional cameras have very redundant output, especially at high frame rates. Dynamic vision sensors output sparse and asynchronous brightness change events that occur when an object or the camera is moving. Images consisting of a fixed number of events from a DVS drive recognition and incremental learning of new hand symbols in the context of a RoShamBo (rock-paper-scissors) demonstration. Conventional training on the original RoShamBo dataset requires about 12.5h compute time on a desktop GPU using the 2.5 million images in the base dataset. Novel symbols that a user shows for a few tens of seconds to the system can be learned on-the-fly using the iCaRL incremental learning algorithm with 3 minutes of training time on a desktop GPU, while preserving recognition accuracy of previously trained symbols. Our system runs a residual network with 32 layers and maintains 88.4% after 100 epochs or 77% after 5 epochs overall accuracy after 4 incremental training stages. Each stage adds an additional 2 novel symbols to the base 4 symbols. The paper also reports an inexpensive robot hand used for live demonstrations of the base RoShamBo game.
Radar Waveform Recognition based on Deep Residual Network

This article presents our initial results in deep learning for the complex multiple radar waveforms recognition. The method is composed of time-frequency analysis and deep residual network (ResNet). Firstly, we transform one-dimensional radar signals into two-dimensional time-frequency images (TFIs), which can reveal more characteristics of the signals. And then, we preprocess these images by grayscale, image opening operation and image resizing. Meanwhile, we design a ResNet and sent these preprocessed images into the network for off-line training. Finally, we use the trained model to recognize different modulation radar signals on-line and test the performance of the method. From our simulation results, the approach can achieve considerable performances that the overall recognition rate of 14 types radar signal is close to 96% when the SNR is -2dB.
Formal Models of Bitcoin Contracts: A Survey

Although Bitcoin is mostly used as a decentralized application to transfer cryptocurrency, over the last ten years there have been several studies on how to exploit Bitcoin to execute smart contracts. These are computer protocols which allow users to exchange bitcoins according to complex pre-agreed rules. Some of these studies introduce formal models of Bitcoin contracts, which specify their behaviour in non-ambiguous terms, in some cases providing tools to automatically verify relevant contract properties. In this paper we survey the formal models proposed in the scientific literature, comparing their expressiveness and applicability in the wild.
Integrating Mobile Learning to Learning Management System in Community College

This article reports findings from a study that aims to understand how to integrate mobile-learning into Learning Management System (LMS) in a community college in Hong Kong. In this study, a mobile-enabled LMS named SOUL was adopted to improve students’ learning engagement and academic performance. Participating students were segregated into two groups where one was prompted by the instructor to use mobile access while another group was not prompted. A survey was conducted to investigate students’ use of SOUL via mobile access and the factors influencing their adoption of mobile access to SOUL based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Semi-structured interviews were conducted with students to collect in-depth explanations regarding their experience with SOUL via mobile access. The study reveals that many students used SOUL with their mobile devices despite not being prompted. It was also found that students most frequently accessed SOUL via their mobile devices for retrieving learning resources and information owing to immediate availability and convenience brought by the notification of its native mobile app. Multiple linear regression analyses revealed that facilitating conditions and performance expectancy were the only significant predictor for unprompted and prompted mobile access, respectively. Implications on integrating mobile learning to LMS in community colleges are discussed.
Product development of mechanical practice: Augmented reality (AR) approach

Presently, the quick innovative development has prompted technology assessment that is in accordance with the presence of industrial revolution 4.0. Notwithstanding, the field of education is also not on left behind in the current 4.0 industry development which directed to education 4.0. There are 9 pillars of revolution 4.0 and essentially, augmented reality and internet of things (IoT) are the important pillars that has been used for this research. Likewise, revolution 4.0 is an innovation that moves from the digital age to the physical cyber era. With the launching of Malaysia Education Development Plan 2015-2025 for higher education steps have been taken to push the Technical and Vocational Education and Training (TVET) which includes special intervention in teaching and learning in this field. Similarly, augmented reality being the interactive method of technology that has been hypnotize peoples especially generation Z. In this regard, 21st Century learning methods are subjected to the technology based in order to attract the student. Consequently, the researchers have made a study on augmented reality technology approach towards Mechanical Workshop practice 1 especially in welding workshop, machine workshop and fitting workshop among the Mechanical engineering students in Politeknik Tuanku Syed Sirajuddin, Arau, Malaysia in order to produce an innovation that can be impacted and interactive to the students. As a result, the innovation of Mechanical Workshop Practice with Augmented Reality approach has been created. Product development of mechanical practice has been done using ADDIE model approach. Hence, this innovation shows that augmented technology was suitable to adapt in 21th century learning method that can be impacted to the students. This innovative product has wide opportunity to be marketed to all Polytechnics in Malaysia and all Malaysian institutes of higher education which have Mechanical Engineering department.
Improved Background Subtraction-based Moving Vehicle Detection by Optimizing Morphological Operations using Machine Learning

Object detection represents the most important component of Automated Vehicular Surveillance (AVS) systems. Moving vehicle detection based on background subtraction, with fixed morphological parameters, is a popular approach in AVS systems. However, the performance of such an approach deteriorates in the presence of sudden illumination changes in the scene. To address this issue, this paper proposes a method to adjust in real-time the morphological parameters to the illumination changes in the scene. The method is based on machine learning. The features used in the machine learning models are first, second, third and fourth-order statistics of the grayscale images, and the outputs are the appropriate morphological parameters. The resulting background subtraction-based object detection is shown to be robust to illumination changes, and to significantly outperform the conventional approach. Further, artificial neural network (ANN) is shown to provide better performance than Naive Bayes and K-Nearest Neighbours models.
Graded Team Assignments in MOOCs

The ability to work in teams is an important skill in today's work environments. In MOOCs, however, team work, team tasks, and graded team-based assignments play only a marginal role. To close this gap, we have been exploring ways to integrate graded team-based assignments in MOOCs. Some goals of our work are to determine simple criteria to match teams in a volatile environment and to enable a frictionless online collaboration for the participants within our MOOC platform. The high dropout rates in MOOCs pose particular challenges for team work in this context. By now, we have conducted 15 MOOCs containing graded team-based assignments in a variety of topics. The paper at hand presents a study that aims to establish a solid understanding of the participants in the team tasks. Furthermore, we attempt to determine which team compositions are particularly successful. Finally, we examine how several modifications to our platform's collaborative toolset have affected the dropout rates and performance of the teams.
Using Product Line Engineering in a Globally Distributed Agile Development Team to Shorten Release Cycles Effectively

We introduced Product Line Engineering (PLE) with the objectives of 1) achieving shorter release cycles, 2) improving product quality, and 3) increasing cost-effectiveness. This paper shares our experiences in adopting PLE, describing the challenges faced in fulfilling these objectives. It also outlines the approaches that were used to overcome the architectural constraints, foster a culture of reuse, and enhance test automation. The resulting changes in team dynamics of globally distributed teams are also mentioned. We present the results and the extent to which the objectives were met. Finally, few challenges still faced by teams to move to shorter release cycle are highlighted.
Cross Platform Web-based Smart Tourism Using Deep Monument Mining

Tourism is one of the largest sources of economic revenue for many countries around the world. The historical and cultural treasures of Iran made it one the main destinations for international tourists. One of the biggest problems encountered by the tourists during the visit to monuments of Iran is the lack of information about the visited landmark. Given that cameras can be found in all of the smart phones, the use of the landmark's photos can be very important for obtaining information about the tourism sites. The detection of the landmarks in an image taken by the mobile phone camera can be a very complex task depending on the angle and the light situation in which the photo is taken. In this paper, a web based cross platform mobile framework based on deep neural networks for autonomous identification of the historical landmarks of Iran is presented. The images recorded by the mobile phone is sent to the decentralized servers in order to be processed and the information about the landmark is determined and transferred to the mobile device of the tourist. The proposed framework is evaluated on the tourism attractions of Iran and the experimental results show that the proposed system can recognize the historical landmarks with precision of 95%.
Virtual unfolding of folded papyri

The historical importance of ancient manuscripts is unique since they provide information about the heritage of ancient cultures. Often texts are hidden in rolled or folded documents. Due to recent improvements in sensitivity and resolution, spectacular disclosures of rolled hidden texts were possible by X-ray tomography. However, revealing text on folded manuscripts is even more challenging. Manual unfolding is often too risky in view of the fragile condition of fragments, as it can lead to the total loss of the document. X-ray tomography allows for virtual unfolding and enables non-destructive access to hidden texts. We have recently demonstrated the procedure and tested unfolding algorithms on a mockup sample. Here, we present results on unfolding ancient papyrus packages from the papyrus collection of the Musée du Louvre, among them objects folded along approximately orthogonal folding lines. In one of the packages, the first identification of a word was achieved, the Coptic word for “Lord”.
Using temporal information for recognizing actions from still images

In this paper we raise two important question, “1. Is temporal information beneficial in recognizing actions from still images? 2. Do we know how to take the maximum advantage from them?”. To answer these question we propose a novel transfer learning problem, Temporal To Still Image Learning (i.e., T2SIL) where we learn to derive temporal information from still images. Thereafter, we use a two-stream model where still image action predictions are fused with derived temporal predictions. In T2SIL, the knowledge transferring occurs from temporal representations of videos (e.g., Optical-flow, Dynamic Image representations) to still action images. Along with the T2SIL we propose a new action still image action dataset and a video dataset sharing the same set of classes. We explore three well established transfer learning frameworks (i.e., GANs, Embedding learning and Teacher Student Networks (TSNs)) in place of the temporal knowledge transfer method. The use of derived temporal information from our TSN and Embedding learning improves still image action recognition.
Cyber Resilient Behavior: Integrating Human Behavioral Models and Resilience Engineering Capabilities into Cyber Security

Cybercrime is on the rise. With the ongoing digitization of our society, it is expected that, sooner or later, all organizations have to deal with cyberattacks; hence organizations need to be more cyber resilient. This paper presents a novel framework of cyber resilience, integrating models from resilience engineering and human behavior. Based on a pilot study with nearly 60 small and medium-sized enterprises (SMEs) in the Netherlands, this paper shows that the proposed framework holds the promise for better development of human aspects of cyber resilience within organizations. The framework provides organizations with diagnostic capability into how to better prepare for emerging cyber threats, while assuring the viability of human aspects of cyber security critical to their business continuity. Moreover, knowing the sources of behavior that predict cyber resiliency may help in the development of successful behavioral intervention programs.
On Side Channel Vulnerabilities of Bit Permutations in Cryptographic Algorithms

Lightweight block ciphers rely on simple operations to allow compact implementation. Thanks to its efficiency, bit permutation has emerged as an optimal choice for state-wise diffusion. It can be implemented by simple wiring in hardware or shifts in software. However, efficiency and security often go against each other. In this paper, we show how bit permutations introduce a side-channel vulnerability that can be exploited to extract the secret key from the cipher. Such vulnerabilities are specific to bit permutations and do not occur in other state-wise diffusion alternatives. We propose side-channel assisted differential-plaintext attack (SCADPA) which targets this vulnerability in the bit permutation operation. SCADPA is first experimentally demonstrated on PRESENT-80 on an 8-bit microcontroller, with the best case key recovery in 17 encryptions. In Addition, we adjust SCADPA to state-of-the-art bit sliced implementation from CHES'17 with experimental evaluation on a 32-bit microcontroller. The attack is then extended to latest bit-permutation-based cipher GIFT, allowing full key recovery in 36 encryptions. Application for reverse engineering of secret S-boxes in PRESENT-like proprietary ciphers is also shown.
Sentiment Analysis and Visualization of Amazon Books' Reviews

Nowadays user-generated content is growing in a digital world, where many individuals can post texts about various products in order to express their feedback. Reviews about products are critical as they guide others to use it or not which affect the production profit, and popularity. Amazon is one of the companies that provide reviews of users regarding all its products. Since reviews are textual and varying; an interactive representation would be supportive to users through summarizing opinions and providing feedback. In this paper, a visual analytics tool proposed; as a dashboard; a user-friendly analysis of books' reviews on Amazon. Interactive Packed bubbles, Linear chart, Stacked bars, and Word-cloud are four visualization techniques incorporated together enabling users to explore relationships between multiple objectives at the same time in an interactive manner. Besides, visual sentiment analysis of opinions extracted from the user reviews of books whether positive, neutral or negative is provided to support users' decision.
Speculative Symbolic Graph Execution of Imperative Deep Learning Programs

The rapid evolution of deep neural networks is demanding deep learning (DL) frameworks not only to satisfy the requirement of quickly executing large computations, but also to support straightforward programming models for quickly implementing and experimenting with complex network structures. However, existing frameworks fail to excel in both departments simultaneously, leading to diverged efforts for optimizing performance and improving usability. This paper presents JANUS, a system that combines the advantages from both sides by transparently converting an imperative DL program written in Python, a de-facto scripting language for DL, into an efficiently executable symbolic dataflow graph. JANUS can convert various dynamic features of Python, including dynamic control flow, dynamic types, and impure functions, into elements of a symbolic dataflow graph. Our experiments show that JANUS can achieve fast DL training by exploiting the techniques imposed by symbolic graph-based DL frameworks, while maintaining the simple and flexible programmability of imperative DL frameworks at the same time.
An Adaptive Affinity Graph with Subspace Pursuit for Natural Image Segmentation

Graph-based segmentation methods have become a major trend in computer vision. Due to the advantages of assimilating different graphs, a multi-scale fusion graph have a better performance than a single graph with single-scale. However, it is not reliable to determine a principle of graph combination. In this paper, we propose an adaptive affinity graph with subspace pursuit (AASP-graph) for natural image segmentation. The input image is first over-segmented into superpixels at different scales. An improved affinity propagation clustering method is proposed to select global nodes of these superpixels adaptively. Then, a L0-graph at each scale is obtained by a sparse representation of global nodes based on subspace pursuit. The adjacency-graph is finally built upon all superpixels of each scale and updated by the L0-graph. Experimental results on the Berkeley segmentation database show the effectiveness of the proposed AASP-graph in comparison with state-of-the-art approaches.
Reading the tea leaves: A comparative analysis of threat intelligence

The term has swiftly become a staple buzzword in the computer security industry. The entirely reasonable premise is that, by compiling up-to-date information about known threats (i.e., IP addresses, domain names, file hashes, etc.), recipients of such information may be able to better defend their systems from future attacks. Thus, today a wide array of public and commercial sources distribute threat intelligence data feeds to support this purpose. However, our understanding of this data, its characterization and the extent to which it can meaningfully support its intended uses, is still quite limited. In this paper, we address these gaps by formally defining a set of metrics for characterizing threat intelligence data feeds and using these measures to systematically characterize a broad range of public and commercial sources. Further, we ground our quantitative assessments using external measurements to qualitatively investigate issues of coverage and accuracy. Unfortunately, our measurement results suggest that there are significant limitations and challenges in using existing threat intelligence data for its purported goals.
A Longitudinal Study on the Maintainers' Sentiment of a Large Scale Open Source Ecosystem

Software development is a collaborative activity in which feelings and emotions can affect the developer's productivity, creativity, and contribution satisfaction. For example, the Linux Kernel Mailing List (LKML), which is used by subsystem maintainers to review patches sent by contributors, is known for its direct communication style, which is sometimes blamed as having a negative impact on contributors. In September 28, 2018, the kernel's lead maintainer, Linus Torvalds, announced that he would take a temporary break from the community, which led numerous members of the kernel community and observers from other communities to wonder to what extent this unexpected event could raise awareness about respectful interactions between community members. This paper performs an exploratory study in which we use an off-the-shelf sentiment mining tool to assess whether the maintainers' sentiment changed after Linus Torvalds' temporary break from his maintainer role. Based on the data available thus far, we did not find any high-level changes in maintainer sentiment. In future work, we will perform more fine-grained sentiment analysis.
Investigating the Adoption and Application of Large-Scale Scrum at a German Automobile Manufacturer

Over the last two decades, agile methods have been adopted by an increasing number of organizations to improve their software development processes. In contrast to traditional methods, agile methods place more emphasis on flexible processes than on detailed upfront plans and heavy documentation. Since agile methods have proved to be successful at the team level, large organizations are now aiming to scale agile methods to the enterprise level by adopting and applying so-called scaling agile frameworks such as Large-Scale Scrum (LeSS) or Scaled Agile Framework (SAFe). Although there is a growing body of literature on large-scale agile development, literature documenting actual experiences related to scaling agile frameworks is still scarce. This paper aims to fill this gap by providing a case study on the adoption and application of LeSS in four different products of a German automobile manufacturer. Based on seven interviews, we present how the organization adopted and applied LeSS, and discuss related challenges and success factors. The comparison of the products indicates that transparency, training courses and workshops, and change management are crucial for a successful adoption.