Reviewerly/exHarmony huggingface dataset:
An Intelligent Online Judge System for Programming Training

Online judge (OJ) systems are becoming increasingly popular in various applications such as programming training, competitive programming contests and even employee recruitment, mainly due to their ability of automatic evaluation of code submissions. In higher education, OJ systems have been extensively used in programming courses because the automatic evaluation feature can drastically reduce the grading workload of instructors and teaching assistants and thereby makes the class size scalable. However, in our teaching we feel that existing OJ systems should improve their ability on giving feedback to students and teachers, especially on code errors and knowledge states. The lack of such automatic feedback increases teachers' involvement and thus prevents college programming training from being more scalable. To tackle this challenge, we leverage historical student data obtained from our OJ system and implement two automated functions, namely, code error prediction and student knowledge tracing, using machine learning models. We demonstrate how students and teachers may benefit from the adoption of these two functions during programming training.
Immersive and Non-Immersive VR Percutaneous Coronary Intervention Simulation for Acute Myocardial Infarction

Percutaneous Coronary Intervention (PCI) is a standard procedure whereby a hemodynamicist professional eliminates narrowing or blockage of the coronary arteries. The procedure is taught and trained as part of the interventionist cardiology curriculum. The cognitive and motor skills required to complete this procedure are key during the patient's treatment, and are only achieved through extensive training. Currently, there is great concern among physicians regarding the dependency on high-end equipment requiring large investments that can limit the number of practices students can get. This paper presents the development of two virtual reality (VR) simulation prototypes for PCI training for treating Acute Myocardial Infarction. Our approach employs a virtual reality headset and a smartphone to compare their effects on the training experience. The visual immersion and non immersion effects are compared by gathering usability perceptions finding that the simulator has potential as a learning tool for medical education using VR technology.
Blockchain Technologies and Games: A Proper Match?

Not only have virtual currencies in digital games from the pre-Blockchain era helped to understand digital currency systems, but the idea that digital objects can have monetary value is a question of faith that has been expressed primarily through the gaming industry. In the world of business this is now called the ’token economy’. Blockchain as a technology can do much more, besides payment processing with cryptocurrencies, utility tokens can be created to secure in-game currencies and items, gamification systems can be made more transparent while strengthening the privacy of the players and even whole game ecosystems can be secured by Blockchain. However, this is still a very young technology and that there is a certain technological war of faith as well as a big area of scams around and with Blockchain-based systems and tokens. In this paper we will present a bird’s eye view, based on results of the expert interviews, of how Blockchain as technology is connected to the different aspects of games and play.
The Morra Game as a Naturalistic Test Bed for Investigating Automatic and Voluntary Processes in Random Sequence Generation

Morra is a 3,000-years-old hand game of prediction and numbers. The two players reveal their hand simultaneously, presenting a number of fingers between 1 and 5, while calling out a number between 2 and 10. Any player who successfully guesses the summation of fingers revealed by both players scores a point. While the game is extremely fast-paced, making it very difficult for players to achieve a conscious control of their game strategies, expert players regularly outperform non-experts, possibly with strategies residing out of conscious control. In this study, we used Morra as a naturalistic setting to investigate the necessity of attentive control in generation of sequence of items and the ability to proceduralize random number generation, which are both a crucial defensive strategy in Morra and a well-known empirical procedure to test the central executive capacity within the working memory model. We recorded the sequence of numbers generated by expert players in a Morra tournament in Sardinia (Italy) and by undergraduate students enrolled in a course-based research experience (CRE) course at Lawrence Technological University in the United States. Number sequences generated by non-expert and expert players both while playing Morra and in a random number generation task (RNGT) were compared in terms of randomness scores. Results indicate that expert players of Morra largely outperformed non-experts in the randomness scores only within Morra games, whereas in RNGT the two groups were very similar. Importantly, survey data acquired after the games indicate that expert players have very poor conscious recall of their number generation strategies used during the Morra game. Our results indicate that the ability of generating random sequences can be proceduralized and do not necessarily require attentive control. Results are discussed in the framework of the dual processing theory and its automatic-parallel-fast vs. controlled-sequential-slow polarities.
Blockchain-enabled Wireless IoT Networks with Multiple Communication Connections

Blockchain-enabled wireless network has been recognized as an emerging network architecture to be widely employed into the Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without the involvement of a third party. However, the uncertainty and vulnerability of wireless channels among the IoT nodes may pose a serious challenge to facilitate the deployment of blockchain in wireless networks. In this paper, we first present a generic system model for blockchain enabled wireless networks with multiple communication connections, where the number of communication connections between a client IoT node and the blockchain full nodes can be any arbitrary positive integer to satisfy different security requirements. Based on the proposed spatial-temporal network model, we theoretically calculate the transmission successful probability and the required communication throughput to support a wireless blockchain network. Finally, simulation results validate the accuracy of our theoretical analysis.
Online Recommender system for Accessible Tourism Destinations

Traveling for leisure has become an important part of our society. It has proven time and again its benefits for wellbeing and personal growth. There are many types of tourism and one of them is Accessible Tourism (AT), an ongoing endeavor to ensure that everyone, regardless of condition, has the right to benefit from tourism experiences. Recommender systems (RSs) represent a mature technique for generating clear and personalized suggestions. While being widely researched and used by the tourism academic community and the tourism industry in general, Recommender Systems (RSs) can still do much more for Accessible Tourism (AT). This thesis aims to build a recommender system dedicated to recommending accessible tourism destinations and easy the process of e2e trip planning for people with disabilities. With a modular design, use of ontologies, machine learning techniques and a "start small, define expansion, expand" approach, this recommender system, once built, aims to be validated by real users.
Mutual Self Attention Recommendation with Gated Fusion Between Ratings and Reviews

Product ratings and reviews can provide rich useful information of users and items, and are widely used in recommender systems. However, it is nontrivial to infer user preference according to the history behaviors since users always have different and complicated interests for different target items. Hence, precisely modelling the context interaction of users and target items is important in learning their representations. In this paper, we propose a unified recommendation method with mutual self attention mechanism to capture the inherent interactions between users and items from reviews and ratings. In our method, we design a review encoder based on mutual self attention to extract the semantic features of users and items from their reviews. The mutual self attention could capture the interaction information of different words between the user review and item review automatically. Another rating-based encoder is utilized to learn representations of users and items from the rating patterns. Besides, we propose a neural gated network to effectively fuse the review- and rating-based features as the final comprehensive representations of users and items for rating prediction. Extensive experiments are conducted on real world recommendation datasets to evaluate our method. The experiments show that our method can achieve better recommendation performance compared with many competitive baselines.
Blockchain-Based Transaction Management in Smart Logistics: A Sawtooth Framework

Blockchain is a disruptive technology that can be adopted in several business models. However, its applicability in the Supply Chain and in the context of the Logistics 4.0 and Smart Logistics revolution in particular, must still be proved from both an economic and an efficiency standpoint. This paper describes a Hyperledger Sawtooth-based framework for Supply Chain and Smart Logistics. The performance evaluation tests are performed on two Smart Logistics system settings. The results underline the performance decay of the system when concurrent transactions are submitted to multiple nodes.
Improving Code Recommendations by Combining Neural and Classical Machine Learning Approaches

Code recommendation systems for software engineering are designed to accelerate the development of large software projects. A classical example is code completion or next token prediction offered by modern integrated development environments. A particular challenging case for such systems are dynamic languages like Python due to limited type information at editing time. Recently, researchers proposed machine learning approaches to address this challenge. In particular, the Probabilistic Higher Order Grammar technique (Bielik et al., ICML 2016) uses a grammar-based approach with a classical machine learning schema to exploit local context. A method by Li et al., (IJCAI 2018) uses deep learning methods, in detail a Recurrent Neural Network coupled with a Pointer Network. We compare these two approaches quantitatively on a large corpus of Python files from GitHub. We also propose a combination of both approaches, where a neural network decides which schema to use for each prediction. The proposed method achieves a slightly better accuracy than either of the systems alone. This demonstrates the potential of ensemble-like methods for code completion and recommendation tasks in dynamically typed languages.
Software archinaut

In this paper we present Software Archinaut-a tool used to help identify technical debt hotspots in an architecture, and manage the evolution of the architecture once these hotspots are discovered. Archinaut is a platform that integrates analyses from different tools. It supports three main usage scenarios: 1) understanding the architecture, 2) identifying technical debt hotspots, and 3) monitoring and controlling the evolution of the architecture. We illustrate these scenarios by using Apache Kafka as an example.
Increasing the Trust In Refactoring Through Visualization

In software development, maintaining good design is essential. The process of refactoring enables developers to improve this design during development without altering the program's existing behavior. However, this process can be time-consuming, introduce semantic errors, and be difficult for developers inexperienced with refactoring or unfamiliar with a given code base. Automated refactoring tools can help not only by applying these changes, but by identifying opportunities for refactoring. Yet, developers have not been quick to adopt these tools due to a lack of trust between the developer and the tool. We propose an approach in the form of a visualization to aid developers in understanding these suggested operations and increasing familiarity with automated refactoring tools. We also provide a manual validation of this approach and identify options to continue experimentation.
Effective Knowledge-Aware Recommendation via Graph Convolutional Networks

Most existing graph neural network (GNN)-based knowledge-aware recommendation models rely on handcrafted feature engineering and do not allow for end-to-end training. As a state-of-the-art end-to-end framework, the Knowledge-aware Graph Neural Networks with Label Smoothness Regularization (KGNN-LS) model can extend GNNs architecture to knowledge graphs to simultaneously capture semantic relations between entities as well as personalized user preferences for entities/items, thereby making effective recommendation. However, we believe that KGNN-LS still has two weaknesses: (1) In KGNN-LS, the weights of the edges in the graph are determined solely by user preferences for relations without considering user's (potential) personalized interests in entities/items. (2) The sum pooling adopted by KGNN-LS cannot effectively aggregate the most representative information of the neighborhood. In this paper, we propose the improved Knowledge-aware Graph Neural Networks with Label Smoothness Regularization (iKGNN-LS) model, which makes two improvements to KGNN-LS: (1) In iKGNN-LS, by introducing user-specific entity scoring functions, the edge weights are determined jointly by personalized user preferences for relations and for entities. (2) iKGNN-LS uses max pooling instead of sum pooling for neighborhood aggregation. Top-N recommendation experiments on three datasets show that iKGNN-LS outperforms KGNN-LS in terms of Precision@N, Recall@N, and F1-measure@N.
Corona E-Learning Cocktail : Sustainability of University Education in Times of Pandemics

Pandemics like the Corona pandemic in 2020 influence massively the education at presence universities. Because the learning targets have to be achieved, alternative teaching methods are requested - at least temporarily. We present some approaches, which are successfully used at our university. These attempts can be combined to a "learning cocktail". We consider possible fields of applications of electronic teaching (and learning) and will discuss the respective advantages and disadvantages. Based on our positive observations, we propose new learning settings for the time after Corona, in which presence and online learning scenarios can be combined.
A Blind DNA-Steganography Approach using Ciphering and Random Sequence Splicing

The great advances in computers and computations has turned the ancient Greek art of Steganography into a hot research topic. The goal is to hide data into digital media and be imperceptible to a human observer, yet easily detectable by a computer. This paper, proposes a Steganographic method that uses DNA as a carrier of secret information which can be utilized for either authentication, or secret communication purposes. The hiding process of the proposed method consists of two main stages. The first stage uses an 8x8 play-fair ciphering algorithm to encode the secret message into a DNA sequence. The next stage blends the secret sequence into the cover sequence through a random splicing process that depends on the value of a secret key. On the other side of communication, the extraction process starts by: splitting the stego-sequence into chunks to separate both the message and the cover sequences. Then, a deciphering step is carried out to reveal the original message. This process can be done blindly without the need to the original cover sequence. When compared with other methods, the proposed method showed an outstanding performance in terms of both blindness and embedding capacity.
SaveMeNow.AI: A Machine Learning Based Wearable Device for Fall Detection in a Workplace

Slips, trips and falls are among the main causes of accidents in a workplace. For this reason, many fall detection approaches have been proposed in the literature. One of the most important categories of approaches is based on the usage of wearable devices. These devices have many advantages, but they also pose some challenging open issues. In particular, they must not be bulky, must have low power consumption and must be able to optimize the low computational power available. In this paper, we aim at facing these challenges by proposing SaveMeNow.AI, a new wearable device for fall detection. SaveMeNow.AI is based on the deployment of a Machine Learning approach for fall detection embedded in it. This approach exploits data continuously measured by a six-axis IMU present inside the device.
A Personal Assistant Supporting Agriculture Operators

This paper introduces a infrastructure for smart agriculture. The basic components of the infrastructure are also presented. The kernel component is the Operative Center consisting of personal assistants. The purpose of this center is to be the institution for operational management of the whole infrastructure. Furthermore, the development of a personal assistant as a BDI intelligent agent is presented in more details. Some implementation issues of the personal assistant are also discussed in this paper.
Proto-Object Based Saliency Model With Texture Detection Channel

The amount of visual information projected from the retina to the brain exceeds the information processing capacity of the latter. Attention, therefore, functions as a filter to highlight important information at multiple stages of the visual pathway that requires further and more detailed analysis. Among other functions, this determines where to fixate since only the fovea allows for high resolution imaging. Visual saliency modeling, i.e. understanding how the brain selects important information to analyze further and to determine where to fixate next, is an important research topic in computational neuroscience and computer vision. Most existing bottom-up saliency models use low-level features such as intensity and color, while some models employ high-level features, like faces. However, little consideration has been given to mid-level features, such as texture, for visual saliency models. In this paper, we extend a biologically plausible proto-object based saliency model by adding simple texture channels which employ nonlinear operations that mimic the processing performed by primate visual cortex. The extended model shows statistically significant improved performance in predicting human fixations compared to the previous model. Comparing the performance of our model with others on publicly available benchmarking datasets, we find that our biologically plausible model matches the performance of other models, even though those were designed entirely for maximal performance with little regard to biological realism.
The Reproducibility Crisis Is Real

The reproducibility crisis is real, and it is not only the field of psychology that has to deal with it. All the sciences are affected; the field of artificial intelligence is not an exception.
An Encryption-then-Compression Scheme Using Autoencoder Based Image Compression for Color Images

The goal of this work is to develop an encryption and then compression scheme for the efficient transmission of color images over an untrusted channel. An image encryption scheme based on bidirectional diffusion is used to encrypt the 8-bit RGB color image. Then, lossless, autocoder-based compression of the encrypted image is performed to achieve compression. The compression method is used to reduce the size of the images during transmission, thus speeding up the transmission. The performance of the compression algorithm is evaluated in terms of the compression ratio.
Handwritten Signature Authentication Using Smartwatch Motion Sensors

The trade-off between security and ease of use tends to make passwords not necessarily as secure as designers expected. Biometric authentication has been receiving extensive attention and is increasingly used every day, of which signature authentication is one of the most commonly used methods due to the stability of signatures and the high difficulty of imitation. Current solutions often rely on dedicated digitizer consisting of graphic tablets and smartpens. The growth of commercial hand-worn devices such as smartwatches provides an alternative way to digitize signatures. Therefore, it is valuable to explore the feasibility of capturing the uniqueness and stability using hand-worn devices. In this paper, we propose a practical authentication method using smartwatch motion sensor data. It can distinguish whether an unknown signature belongs to the individual that they claimed to be or not. We firstly introduce Siamese Recurrent Neural Networks (RNNs) to deal with smartwatch motion sensor data of signing processes, which can save the task of manual feature design and improves system security. Our method uses a global model instead of a personalized one. Therefore, the trained system dose not require forged signatures from new users. After providing a set of genuine signatures during the enrollment phase, their signatures are irreversibly transformed into representation vectors, which will be used for authentication later while ensuring security. For experiment work, we collected 400 signature-related motion sensor data from 20 subjects and aligned them into 2990 pairs. Our method was evaluated using the collected data and outperformed comparable related work. We achieved an EER of 0.78%.
A 360 Video Editor Framework for Interactive Training

The adoption of consumer-level mobile devices and head-mounted displays (HMDs) has caused an increased consumption of 360° video content. This trend has led to accessible and immersive content creation for training and education in health and safety amongst other areas. Although 360° videos can provide realistic content, they often lack interactivity. We hypothesize that integrating interactive elements into 360° videos will affect its perceived immersion, engagement, and usability. In this paper, we present a framework for adding interactions to 360° videos composed of an editor and a video player. The Editor allows adding interactive content in the form of panels containing information, quizzes, pictures, and 3D models. A three-stage study was conducted with participants from the Ontario Tech University (n=45) to evaluate our VR framework's usability and engagement compared to a non-interactive 360° video by applying the SUS (System Usability Scale) and the GEQ (Game Experience Questionnaire) surveys. Results indicate that the interactions add tension, higher negative affect, and lower positive affect than non-interactive videos that led to higher perceived usability with the non-interactive version. We believe that this is caused by the need to locate and interact with elements while the video is playing.