Reviewerly/exHarmony huggingface dataset:
ASEST framework: a proposal for improving teamwork by making cohesive software engineering student teams

Teamwork is an important aspect of software engineering education. This paper presents ASEST (Agile Software Engineers Stick Together), a teaching framework whose aim is to improve teamwork in terms of team performance and team learning by developing team cohesion at the graduate level of software engineering student teams. The basis of ASEST was established by identifying the most current trends in teamwork in software engineering education. The framework itself combines these approaches in a procedure consisting of eight steps through which students are expected to go through. A study of one group of students that applied the ASEST framework indicated that their perceptions of team cohesion, team performance and team learning increased significantly compared with the perceptions of the students in a group that did not use the framework. Also, a survey showed students' acceptance of our proposal.
Knowledge-based strategies for multi-agent teams playing against Nature

We study teams of agents that play against Nature towards achieving a common objective. The agents are assumed to have imperfect information due to partial observability, and have no communication during the play of the game. We propose a natural notion of higher-order knowledge of agents. Based on this notion, we define a class of knowledge-based strategies, and consider the problem of synthesis of strategies of this class. We introduce a multi-agent extension, MKBSC, of the well-known knowledge-based subset construction applied to such games. Its iterative applications turn out to compute higher-order knowledge of the agents. We show how the MKBSC can be used for the design of knowledge-based strategy profiles, and investigate the transfer of existence of such strategies between the original game and in the iterated applications of the MKBSC, under some natural assumptions. We also relate and compare the “intensional” view on knowledge-based strategies based on explicit knowledge representation and update, with the “extensional” view on finite memory strategies based on finite transducers and show that, in a certain sense, these are equivalent.
Situation-Driven Context-Aware Safety Model for Risk Mitigation Using LTL in a Smart Home Environment

Early-stage dementia impacts aging adults' cognitive functioning including the initiative and performance of instrumental activities of daily living (IADLs) that require a series of actions to be completed (e.g., meal preparation, medication management). Thus, cognitive impairment predisposes adults to temporal organization of action sequence error in IADLs that may constitute life-threatening injuries or accidents [11]-[13]. To mitigate this inherent risk in the performance of IADLs, a linear temporal logic (LTL)-based safety model is proposed for detection of risky behaviors inherent IADLs in smart home environments.
A State Representation Dueling Network for Deep Reinforcement Learning

In recent years there have been many successes in boosting the performance of Deep Q-Networks (DQN). Dueling DQN uses simple dueling architecture but significantly improves the performance of DQN [1]. However, Dueling DQN is only concerned about dueling in estimating Q-values. In this paper, we introduce a state representation dueling network, which provides an auxiliary task designed to be combined with other reinforcement learning algorithms to improve the performance of Deep RL. The state representation dueling network is designed to be beneficial for solving reinforcement learning tasks with high dimensional observation, such as camera input. The experiment shows that adding the state representation dueling network to Dueling DQN improves both the training speed and performance of Dueling DQN in CartPole environment.
A longitudinal Bayesian mixed effects model with hurdle Conway‐Maxwell‐Poisson distribution

Dental caries (i.e., cavities) is one of the most common chronic childhood diseases and may continue to progress throughout a person's lifetime. The Iowa Fluoride Study (IFS) was designed to investigate the effects of various fluoride, dietary and nondietary factors on the progression of dental caries among a cohort of Iowa school children. We develop a mixed effects model to perform a comprehensive analysis of the longitudinal clustered data of IFS at ages 5, 9, 13, and 17. We combine a Bayesian hurdle framework with the Conway-Maxwell-Poisson regression model, which can account for both excessive zeros and various levels of dispersion. A hierarchical shrinkage prior distribution is used to share the temporal information for predictors in the fixed-effects model. The dependence among teeth of each individual child is modeled through a sparse covariance structure of the random effects across time. Moreover, we obtain the parameter estimates and credible intervals from a Gibbs sampler. Simulation studies are conducted to assess the accuracy and effectiveness of our statistical methodology. The results of this article provide novel tools to statistical practitioners and offer fresh insights to dental researchers on effects of various risk and protective factors on caries progression.
Cost Optimization in Neural Network using Whale Swarm Algorithm with Batched Gradient Descent Optimizer

Abstract Optimization algorithms are liable for sinking the losses and to give the most precise outcomes conceivable. Optimizers are utilized to modify the properties of neural network, for example, training rate and weights are used to reduce the losses. Optimization means a procedure of obtaining a global optimal solution for a given problem under given conditions. The real-world problems in the scientific fields, such as engineering design and economic planning, mostly are multimodal, high-dimensional, disconnected, and oscillated optimization problems. These complex problems cannot be solved well within reasonable time using traditional method based on gradient. Nature-inspired algorithms are becoming delightful in resolving mathematical optimization problems, like multiprocessor scheduling problem, vehicle routing and classification problems etc. In this manuscript, Whale Swarm Optimization algorithm on optimizing the neural networks, one of the meta-heuristic algorithms is applied to analysis of the cardiovascular disease dataset and compares the performance with Gradient Descent and RMSprop optimization techniques.
Chinese Short Text Entity Linking Based On Semantic Similarity and Entity Correlation

Nowadays, more and more corpora in Internet consist of short texts, such as QA queries, posts in social media and news titles. Entity linking for these short texts is quite important. However, due to the serious colloquialism and insufficient contexts, the entity linking task of Chinese short text is more difficult. In this paper we propose an entity linking model named BERT-RWR. BERT-RWR aims at improving the accuracy of predicting the right target entity in candidate set by integrating deep neural network and graph model. More specifically, deep neural network based on fine-tune BERT is designed to calculate the mention-entity semantic similarity and Random Walk with Restart (RWR) algorithm can further capture the correlation between candidate entities of different mentions. In BERT-RWR, we leverage (1) semantic similarity score between each mention and its candidate entities and (2) the prior probability and (3) the correlation between different candidate entities to select the target entity. To improve the recall rate of candidate entity, we put forward three-method fusion strategy for candidate generation. Experimental results demonstrate that our model outperforms the state-of-the-art results for entity linking in Chinese short text datasets.
Pose-Guided Part-Based Adaptive Pyramid Features for Occluded Person Reidentification

Reidentifying an occluded person across nonoverlapping cameras is still a challenging task. In this work, we propose a novel pose-guided part-based adaptive pyramid neural network for occluded person reidentification. Firstly, to alleviate the impact of occlusion, we utilize pose landmarks to generate pose-guided attention maps. The attention maps will help the model focus on the nonoccluded regions. Secondly, we use pyramid pooling to extract multiscale features in order to address the scale variation problem. The generated pyramid features are then multiplied by attention maps to achieve pose-guided adaptive pyramid features. Thirdly, we propose a pose-guided body part partition scheme to deal with the alignment problem. Accordingly, the adaptive pyramid features are divided into partitions and fed into individual fully connected layers. In the end, all the part-based matching scores are fused with a weighted sum rule for person reidentification. The effectiveness of our method is clearly validated by the experimental results on two popular occluded and holistic datasets, i.e., Occluded-DukeMTMC and the Market-1501.
Precision protection through cosmic string in quantum metrology

Abstract The dynamics of the quantum Fisher information of the parameters of the initial atomic state and atomic transition frequency is studied, in the framework of open quantum systems, for a static polarizable two-level atom coupled in the multipolar scheme to a bath of fluctuating vacuum electromagnetic fields in cosmic string space-time. Our results show that with the presence of cosmic string, the quantum Fisher information becomes position and atomic polarization dependent. It may be enhanced or depressed as compared to that in flat space-time case. Remarkably, when the atom is extremely close to the cosmic string and the polarization direction of the atom is perpendicular to the direction of the cosmic string, the quantum Fisher information has been totally protected from the fluctuating vacuum electromagnetic fields. So on the one hand, near a cosmic string, precision of estimation can be enhanced by ranging the radial distance between the probe atom and the cosmic string; on the other hand, the cosmic string can be sensed by studying the distribution of parameter induced state-separation.
A Thrifty Annotation Generation Approach for Semantic Segmentation of Biofilms

Recent advances in semantic segmentation using deep learning methods have achieved promising results on several benchmark datasets. However, the primary challenge involved in such segmentation approaches is the availability of applicable training data. Since only experts are equipped to effectively annotate (or label) any available data for training semantic segmentation networks, the effort and cost involved can be considerable, especially for larger datasets. In this paper, we aim to address this problem by proposing a Thrifty Annotation Generation approach that records high performance on segmentation networks with minimal expert effort and cost (intervention). We present a deep active learning framework that combines the use of marker-controlled watershed (MC-WS) algorithm to generate pseudo labels for segmentation networks (U-Net) and active learning to significantly minimize effort and cost by selecting only the most impactful training data for labeling. We built the initial U-Net model by generating pseudo labels for the training data using MC-WS. We then make use of the uncertainty information (entropy) of each image provided by the U-Net to determine the most uncertain or effective images for expert labeling. We evaluated the TAG approach using the 2012 ISBI Challenge dataset for 2D segmentation and a novel Biofilm dataset. Our approach achieved promising segmentation accuracy (IoU) and classification accuracy with minimal expert intervention. The results of our experiments also indicate that the TAG approach can be generalized to achieve high-performance segmentation results on any dataset using minimal expert effort and cost.
Physical Exercise Form Correction Using Neural Networks

Monitoring and correcting the posture during physical exercises can be a challenging task, especially for beginners that do not have a personal trainer. Recently, successful mobile applications in this domain were launched on the market, but we are unable to find prior studies that are general-purpose and able to run on commodity hardware (smartphones). Our work focuses on static exercises (e.g. Plank and Holding Squat). We create a dataset of 2400 images. The main technical challenge is achieving high accuracy for as many circumstances as possible. We propose a solution that relies on Convolutional Neural Networks to classify images into: correct, hips too low or hips too high. The Neural Network is used in a mobile application that provides live feedback for posture correction. We discuss limitations of the solution and ways to overcome them.
Special Session: Quantum Carry Lookahead Adders for NISQ and Quantum Image Processing

Progress in quantum hardware design is progressing toward machines of sufficient size to begin realizing quantum algorithms in disciplines such as encryption and physics. Quantum circuits for addition are crucial to realize many quantum algorithms on these machines. Ideally, quantum circuits based on fault-tolerant gates and error-correcting codes should be used as they tolerant environmental noise. However, current machines called Noisy Intermediate Scale Quantum (NISQ) machines cannot support the overhead associated with fault-tolerant design. In response, low depth circuits such as quantum carry lookahead adders (QCLA)s have caught the attention of researchers. The risk for noise errors and decoherence increase as the number of gate layers (or depth) in the circuit increases. This work presents an out-of-place QCLA based on Clifford+T gates. The QCLAs optimized for T gate count and make use of a novel uncomputation gate to save T gates. We base our QCLAs on Clifford+T gates because they can eventually be made fault-tolerant with error-correcting codes once quantum hardware that can support fault-tolerant designs becomes available. We focus on T gate cost as the T gate is significantly more costly to make fault-tolerant than the other Clifford+T gates. The proposed QCLAs are compared and shown to be superior to existing works in terms of T-count and therefore the total number of quantum gates. Finally, we illustrate the application of the proposed QCLAs in quantum image processing by presenting quantum circuits for bilinear interpolation.
Am I going to Heaven?

Context: Nowadays, software development organizations have adopted agile practices and data-driven software development aiming at competitive advantage. Moving from traditional to agile and data-driven software development requires changes in the organization culture and structure, which may not be easy. Stairway to Heaven Model (StH) describes this evolution path in five stages. Objective: We aimed to investigate how Systems Theory tools, GUT Matrix and reference ontologies can help organizations in the first transition of StH, i.e., moving from traditional to agile development. Method: We performed a participative case study in a Brazilian organization that develops software in partnership with a European organization. We applied Systems Theory tools (systemic maps and archetypes) to understand the organization and identify undesirable behaviors and their causes, and also GUT matrices to decide about which ones should be addressed first; we defined strategies to change the undesirable behaviors by implementing agile practices, and we used reference ontologies to share a common understanding about agile concepts. Results: By understanding the organization, a decision was made to implement a combination of agile and traditional practices. The implemented strategies improved software quality and project time and cost. Problems due to misunderstanding agile concepts were solved, allowing the organization to experience agile culture and foresee changes in its business model. Conclusion: Systems Theory tools and GUT Matrix aid organizations to move from traditional to agile development by supporting better understand the organization, find leverage points of change and enabling to define strategies aligned to the organization characteristics and priorities. Reference ontologies can be useful to establish a common understanding about agile, enabling teams to be aware of and, thus, more committed to agile practices and concepts.
Timely Publication of Transaction Records in a Private Blockchain

Blockchain technology has been successfully applied in many different application domains. However, in some time-sensitive applications, e.g., a COVID-19 tracking system with case and testing information recorded in blockchains, it is critical to publish information or transactions in blockchains in a timely manner. In this paper, we introduce an approach to timely publishing transaction records in private blockchains, which are small-scale permissioned ones with limited number of transactions broadcast per unit of time. In our approach, when a transaction is broadcast by a peer, it is published immediately in a temporary block after being approved using a consensus mechanism. When a predefined number of temporary blocks have been published, the system will combine them into a permanent one and publish it upon approval using the same consensus mechanism. The experimental results show that our proposed approach is feasible and effective for timely publication of transaction records in a private blockchain.
Modelling Medical Devices with Honeypots

Cyber security is one of the key priorities in the modern digitalised and complex network totality. One of the major domains of interest is the healthcare sector where a cyber incident may cause unprecedented circumstances. In the healthcare domain there are abundant networked systems, software and hardware, which may be vulnerable for a cyber intrusion or incident. For cyber resilience, it is important to know the status of the valuable assets under attention. Sensor information has a significant role for achieving the comprehension of the valuable assets in the cyber domain. While networked medical devices form an important asset group in healthcare environment, one interesting solution to gather sensor information are the honeypots. In this paper, honeypot technology is studied for the healthcare domain. Especially typical characteristics of medical devices are considered from the perspective of modelling the medical devices with honeypots. The technical priorities are studied and concluded with the discovered future research topics.
3D Map Experience for Youth with Virtual/Augmented Reality Applications

Human beings have started using maps since prehistoric times. The first men has made maps to the cave walls but nowadays the maps are being used with virtual/augmented reality (VR/AR) applications. With the use of 3D VR/AR applications, traditional map education can be given to the youth explicitly. With this technology, students can learn the maps enthusiastically and develop their spatial abilities. In this study, a 3D model of the Harran University Osmanbey Campus was created and prepared for the use of VR/AR applications. This model was then transferred to the Unity Game Engine Software for augmented reality applications that students can see on tablets and smartphones. The resulting application was presented in a science festival where students experienced these technologies. It has been observed that students approached more enthusiastically to these technologies as a learning material because of the interactive nature of these technologies.
Post-quantum Hash-Based Signatures for Secure Boot

The potential development of large-scale quantum computers is raising concerns among IT and security research professionals due to their ability to solve (elliptic curve) discrete logarithm and integer factorization problems in polynomial time. All currently used, public-key cryptography algorithms would be deemed insecure in a Post-quantum setting. In response, the United States National Institute of Standards and Technology has initiated a process to standardize quantum-resistant cryptographic algorithms, focusing primarily on their security guarantees. Additionally, the Internet Engineering Task Force has published two quantum-secure signature schemes and has been looking into adding quantum-resistant algorithms in protocols. In this work, we investigate two post-quantum, hash-based signature schemes published by the Internet Engineering Task Force and submitted to the National Institute of Standards and Technology for use in secure boot. We evaluate various parameter sets for the use-cases in question and we prove that Post-quantum signatures would not have material impact on image signing. We also study the hierarchical design of these signatures in different scenarios of hardware secure boot.
Compressing Piecewise Smooth Images with the Mumford-Shah Cartoon Model

Compressing piecewise smooth images is important for many data types such as depth maps in 3D videos or optic flow fields for motion compensation. Specialised codecs that rely on explicitly stored segmentations excel in this task since they preserve discontinuities between smooth regions. However, current approaches rely on ad hoc segmentations that lack a clean interpretation in terms of energy minimisation. As a remedy, we derive a generic region merging algorithm from the Mumford- Shah cartoon model. It adapts the segmentation to arbitrary reconstruction operators for the segment content. In spite of its conceptual simplicity, our framework can outperform previous segment-based compression methods as well as BPG by up to 3 dB.
Using Commonsense Knowledge and Text Mining for Implicit Requirements Localization

This paper addresses identification of implicit requirements (IMRs) in software requirements specifications (SRS). IMRs, as opposed to explicit requirements, are not specified by users but are more subtle. It has been noticed that IMRs are crucial to the success of software development. In this paper, we demonstrate a software tool called COTIR developed by us as a system that integrates Commonsense knowledge, Ontology and Text mining for early identification of Implicit Requirements. This relieves human software engineers from the tedious task of manually identifying IMRs in huge SRS documents. Our evaluation reveals that COTIR outperforms existing IMR tools. This demo paper would be useful to Software Engineers since it deals with automation in the requirements analysis phase, thus contributing to Requirements Engineering. It would interest AI scientists as it entails multi-disciplinary work encompassing text mining, ontology and commonsense knowledge. It makes a broader impact on Smart Cities, because automated identification of IMRs would offer inputs to Smart City Tools, where requirements may often be implicit given that Smart Cities are an emerging and growing paradigm.
Nonparametric Different-Feature Selection Using Wasserstein Distance

In this paper, we propose a feature selection method that characterizes the difference between two kinds of probability distributions. The key idea is to view the feature selection problem as a sparsest k-subgraph problem that considers Wasserstein distance between the studied two probability distributions. Our method does not presume any specific parametric models on the data distribution and is non-parametric. It outperforms existing Kullback-Leibler divergence based approaches, since we do not require two distributions to overlap. This relaxation makes our method work in many problems in which Kullback-Leibler divergence based methods fail. We also design a fast calculation algorithm using dynamic programming. Our experimental results show that our method outperforms the current method in both computation accuracy and speed.