Reviewerly/exHarmony huggingface dataset:
Teaching Agile in an Agile Way: A Case from the First Iteration in a University

In parallel with the increasing demands for Agile in industry and academia, many lecturers have started teaching Agile Software Development in varying programs. As one of them, in this study, we exhibit our selective course experience at Sakarya University in Turkey with forty-eight final-year Information Systems Engineering undergraduate students using Agile and Lean practices. In this study, we provide a course design, the reasoning behind it, the students’ and lecturer’s experiences, and observations on learning the Agile practices. In particular, we focus on the constraints faced by both the students and lecturer, adaptations made in the course, and the projects during the teaching period. We also present the lessons learned, results of the surveys and interviews performed with the students, and evaluation of these inputs by considering academic literature. Finally, we present recommendations for educators interested in Agile for a higher education course and the lecturers who would give the course at the same university in the upcoming semesters.
Explainable AI at Work! What Can It Do for Smart Agriculture?

Explainable AI (XAI) is gaining the momentum, at now. While the idea is to apply it in different scenarios, including medicine, business analytics, genomics computing and so forth, in this paper we focus the attention on another emerging case, represented by so called smart agricolture. In this paper, we propose the application of some well-known XAI tools on top of the Crop Recommendation dataset. Our research efforts also involve the sensitivity analysis of retrieved results.
Synthesis of Trajectory Planning Algorithms Using Evolutionary Optimization Algorithms

The article considers the problem of planning the optimal trajectory of a delta robot. The workspace of the robot is limited by the range of permissible values of the angles of the drive revolute joints, interference of links and singularities. Additional constraints related to the presence of obstacles have been introduced. Acceptable values of the robot’s input coordinates are obtained based on the inverse kinematics, taking into account the constraints of the workspace, represented as a partially ordered set of integers. For the given initial and final coordinates, a randomly generated family of trajectories belonging to a valid set is obtained. Optimization of each of the trajectories of the family based on evolutionary algorithms is performed. The optimization criterion is a function proportional to the duration of movement along the trajectory. The results of modeling are presented.
GPU-FAN: Leaking Sensitive Data from Air-Gapped Machines via Covert Noise from GPU Fans

Modern computer networks are secured with a wide range of products, including firewalls, intrusion detection and prevention systems (IDS/IPS), and access control mechanisms. But despite the multiple layers of security, these measures can be bypassed by motivated attackers. To cope with this threat, an 'air-gap' is a network security measure that may be taken where highly sensitive information needs to be protected. In this approach, the internal network is isolated from the Internet, physically and logically, to create a physical boundary with the outer digital world. In this paper, we show that attackers can leak data from air-gapped networks via covert acoustic signals. Our method doesn't require speakers on infected computers. Malware running on the computer can use the GPU (graphics processing unit) fans and evasively control its speed. While the slight changes in the RPM (rotation per minute) speed are not noticeable to users, they can be used to modulate and encode binary information. A nearby receiver, such as a compromised smartphone or a laptop, can receive the covert acoustic signals and demodulate and decode the binary information. We discuss the attack model on air-gapped networks and provide relevant technical background and the characteristics of the GPU fans. We also present the covert channel's design, implementation, and evaluation. The results show that a brief amount of sensitive information can be leaked several meters away via covert noises generated from the GPU fans.
Hybrid Scalar/Vector Implementations of Keccak and SPHINCS$$^+$$ on AArch64

This paper presents two new techniques for the fast implementation of the Keccak permutation on the A-profile of the Armarchitecture: First, the elimination of explicit rotations in the Keccak permutation through Barrel shifting, applicable to scalar AArch64 implementations of Keccak-f1600. Second, the construction of hybrid implementations concurrently leveraging both the scalar and the Neon instruction sets of AArch64. The resulting performance improvements are demonstrated in the example of the hash-based signature scheme SPHINCS $$^{+}$$ , one of the recently announced winners of the NIST post-quantum cryptography project: We achieve up to $$1.89\times $$ performance improvements compared to the state of the art. Our implementations target the Arm Cortex-{A55,A510,A78,A710,X1,X2} processors common in client devices such as mobile phones.
Weightwise Almost Perfectly Balanced Functions: Secondary Constructions for All n and Better Weightwise Nonlinearities

The design of FLIP stream cipher presented at Eurocrypt 2016 motivates the study of Boolean function with good cryptographic criteria when restricted to subsets of $$\mathbb {F}_2^n$$ . Since the security of FLIP relies on properties of functions restricted to subsets of constant Hamming weight, called slices, several studies investigate functions with good properties on the slices, i.e. weightwise properties. A major challenge is to build functions balanced on each slice, from which we get the notion of Weightwise Almost Perfectly Balanced (WAPB) functions. Although various constructions of WAPB functions have been exhibited since 2017, building WAPB functions with high weightwise nonlinearities remains a difficult task. Lower bounds on the weightwise nonlinearities of WAPB functions are known for very few families, and exact values were computed only for functions in at most 16 variables. In this article, we introduce and study two new secondary constructions of WAPB functions. This new strategy allows us to bound the weightwise nonlinearities from those of the parent functions enabling us to produce WAPB functions with high weightwise nonlinearities. As a practical application, we build several novel WAPB functions in up to 16 variables by taking parent functions from two different known families. Moreover, combining these outputs, we also produce the 16-variable WAPB function with the highest weightwise nonlinearities known so far.
DPIT: Dual-Pipeline Integrated Transformer for Human Pose Estimation

Human pose estimation aims to figure out the keypoints of all people in different scenes. Current approaches still face some challenges despite promising results. Existing top-down methods deal with a single person individually, without the interaction between different people and the scene they are situated in. Consequently, the performance of human detection degrades when serious occlusion happens. On the other hand, existing bottom-up methods consider all people at the same time and capture the global knowledge of the entire image. However, they are less accurate than the top-down methods due to the scale variation. To address these problems, we propose a novel Dual-Pipeline Integrated Transformer (DPIT) by integrating top-down and bottom-up pipelines to explore the visual clues of different receptive fields and achieve their complementarity. Specifically, DPIT consists of two branches, the bottom-up branch deals with the whole image to capture the global visual information, while the top-down branch extracts the feature representation of local vision from the single-human bounding box. Then, the extracted feature representations from bottom-up and top-down branches are fed into the transformer encoder to fuse the global and local knowledge interactively. Moreover, we define the keypoint queries to explore both full-scene and single-human posture visual clues to realize the mutual complementarity of the two pipelines. To the best of our knowledge, this is one of the first works to integrate the bottom-up and top-down pipelines with transformers for human pose estimation. Extensive experiments on COCO and MPII datasets demonstrate that our DPIT achieves comparable performance to the state-of-the-art methods.
Secure Computations Through Checking Suits of Playing Cards

Abstract Card-based cryptography started with the “five-card trick” designed by Den Boer (EUROCRYPT 1989); it enables Alice and Bob to securely evaluate the AND value of their private bits using a physical deck of five cards. It was then shown that the same task can be done with only four cards, i.e., Mizuki et al. proposed a four-card AND protocol (ASIACRYPT 2012). These two AND protocols are simple and easy even for non-experts, such as high school students, to execute. Their only common drawback is the need to prepare a customized deck consisting of red and black cards such that all cards of the same color must be identical. Fortunately, several existing protocols are based on a standard deck of playing cards (commercially available). Among them, the state-of-the-art AND protocol was constructed by Koch et al. (ASIACRYPT 2019); it uses four playing cards (such as ‘A, J, Q, K’) to securely evaluate the AND value. The protocol is elaborate, while its possible drawback is the need to repeat a shuffling operation six times (in expectation), which makes it less practical. This paper aims to provide the first practical protocol working on a standard deck of playing cards. We present an extremely simple AND protocol that terminates after only one shuffle using only four cards; our proposed protocol relies on a new operation, called the “half-open” action, whereby players can check only the suit of a face-down card without revealing the number on it. We believe that this new operation is easy-to-implement, and hence, our four-card AND protocol working on a standard deck is practical. We formalize the half-open action to present a formal description of our proposed protocol. Moreover, we discuss what is theoretically implied by introducing the half-open action and show that it can be applied to efficiently solving Yao’s Millionaires’ problem with a standard deck of cards.
Fully Dynamic k-Center Clustering with Outliers

We consider the robust version of the classic k-center clustering problem, where we wish to remove up to z points (outliers), so as to be able to cluster the remaining points in k clusters with minimum maximum radius. We study such a problem under the fully dynamic adversarial model, where points can be inserted or deleted arbitrarily. In this setting, the main goal is to design algorithms that maintain a high quality solution at any point in time, while requiring a “small” amortized cost, i.e. a “small” number of operations per insertion or deletion, on average. In our work, we provide the first constant bi-criteria approximation algorithm for such a problem with its amortized cost being independent of both z and the size of the current input.
An End-to-End Formula Recognition Method Integrated Attention Mechanism

Formula recognition is widely used in document intelligent processing, which can significantly shorten the time for mathematical formula input, but the accuracy of traditional methods could be higher. In order to solve the complexity of formula input, an end-to-end encoder-decoder framework with an attention mechanism is proposed that converts formulas in pictures into LaTeX sequences. The Vision Transformer (VIT) is employed as the encoder to convert the original input picture into a set of semantic vectors. Due to the two-dimensional nature of mathematical formula, in order to accurately capture the formula characters’ relative position and spatial characteristics, positional embedding is introduced to ensure the uniqueness of the character position. The decoder adopts the attention-based Transformer, in which the input vector is translated into the target LaTeX character. The model adopts joint codec training and Cross-Entropy as a loss function, which is evaluated on the im2latex-100k dataset and CROHME 2014. The experiment shows that BLEU reaches 92.11, MED is 0.90, and Exact Match(EM) is 0.62 on the im2latex-100k dataset. This paper’s contribution is to introduce machine translation to formula recognition and realize the end-to-end transformation from the trajectory point sequence of formula to latex sequence, providing a new idea of formula recognition based on deep learning.
Country- and app-level factors affecting the adoption and evaluation of COVID-19 mobile apps

Abstract Background Countries across the globe have released many COVID-19 mobile apps. However, there is a lack of systematic empirical investigation into the factors affecting the adoption and evaluation of COVID-related apps. This study explores what factors at the country level and the app levels would influence the adoption and evaluation of COVID-19 apps. Methods We collected data on 267 COVID-19 apps in App Store and Google Play. The number of installs, ratings, reviews and rating scores were used as indicators of adoption and evaluation. Country-level predictors include the number of infected cases and the political system (i.e., democratic vs. non-democratic). App-level predictors include developer (i.e., government vs. non-government) and functions. Four app functions were coded for analysis: providing health information, contact tracing, home monitoring, and consultation. Negative binomial regression and OLS (Ordinary Least Square) regression were used to analyze the data. Results Our analyses show that apps developed by countries with more infected cases (B = 0.079, CI (Confidence Interval) = 0.000, 0.158; P = .049) and by non-governmental institutions (B=-0.369, CI=-0.653, -0.083; P = .01) received more positive rating scores. Apps with home monitoring function received lower rating scores (B=-0.550, CI=-0.971, -0.129; P = .01). Regarding adoption, apps developed by governments were more likely to be installed (IRR (Incident Rate Ratio) = 8.156, CI = 3.389, 19.626; P &lt; .001), to be rated (IRR = 26.036, CI = 7.331, 92.468; P &lt; .001), and to receive user comments (IRR = 12.080, CI = 3.954, 37.568; p &lt; .001). Apps with functions of contact tracing or consultation were more likely to be installed (IRR = 4.533, CI = 2.072, 9.918; p &lt; .001; IRR = 4.885, CI = 1.970, 12.111; p &lt; .001), to be rated (IRR = 11.634, CI = 3.486, 38.827; p &lt; .001; IRR = 17.194, CI = 5.309, 55.680; p &lt; .001), and to receive user comments (IRR = 5.688, CI = 2.052, 5.770; p &lt; .001; IRR = 16.718, CI = 5.363, 52.113; p &lt; .001). Apps with home monitoring functions were less likely to be rated (IRR = 0.206, CI = 0.047, 0.896; P = .04) but more likely to receive user comments (IRR = 3.874, CI = 1.044, 14.349; P = .04). Further analysis shows that apps developed in democratic countries (OR (Odd Ratio) = 3.650, CI = 1.238, 10.758; P = .02) or by governments (OR = 7.987, CI = 4.106, 15.534, P &lt; .001) were more likely to include the function of contact tracing. Conclusion This study systematically investigates factors affecting the adoption and evaluation of COVID-19 apps. Evidence shows that government-developed apps and the inclusion of contact tracing and consultation app functions strongly predict app adoption.
MetaGC-MC: A graph-based meta-learning approach to cold-start recommendation with/without auxiliary information

Collaborative filtering-based methods have achieved distinctive performance in ordinary recommendation tasks. However, they suffer from a cold-start problem when historical interaction is sparse. To carry out cold-start recommendations, many methods assume auxiliary data, such as user/item information and heterogeneous network information, being available. However, considering auxiliary data are not always available in practice, we explore a novel approach, MetaGC-MC, to alleviate cold-start recommendation issues, which can provide more effective cold-start recommendations, regardless of whether auxiliary data is available. MetaGC-MC is based on two emerging techniques, namely, graph convolutional network and meta-learning. In MetaGC-MC, a mechanism of stochastic enclosing subgraph sampling is introduced to randomly sample h-hop enclosing subgraphs as the meta-learning tasks for new users or new items. According to the γ-decaying theory, low-hop enclosing subgraphs contain enough information to learn good high-order graph structure information. Without relying on auxiliary data, MetaGC-MC can capture various subgraph structure information under a meta-learning framework, and encode learned information as a meta-prior that makes rapid adaptions in new subgraphs. MetaGC-MC can also take advantage of auxiliary data to enhance model performance. Extensive experimental results in three real-world datasets illustrate that MetaGC-MC is competitive with other state-of-the-art methods for user and item cold-start scenarios.
An Ethics Framework for Interactive Digital Narrative Authoring

Interactive Digital Narrative (IDN) provides expressive opportunities that can be applied to many serious and non-fiction topics. Such applications, in particular, but also fictional IDN, have an ethicalEthics (ethical) dimension, an aspect in need of increased attention as IDN matures and is more widely deployed. In this chapter, we identify aspects of IDN ethicsEthics (ethical) with a particular concern for IDN authoring, taking into account earlier efforts in related areas, such as more generalized perspectivesPerspective on ethicsEthics (ethical) in computer sciences and considerations pertaining to video games. We use IDN for cultural heritage as a frame for discussing ethical aspects in IDN, since this application area is particularly prone to issues in this regard. Furthermore, we put a focus on VRVirtual Reality (VR), as a topic that most fully divorces audiences from the outside world during the IDN experience and thus poses particular ethicalEthics (ethical) challenges for authoring. Throughout the discussion, we identify questions that an IDN ethics framework needs to address. Then, we introduce such a framework with 12 rulesRules and briefly discuss their application. The IDN ethics framework is meant to be as a first edition, to be further developed by the community.
Distributed Learning in Healthcare

Artificial intelligence and machine learning models are key tools in advancing data-driven healthcare solutions that aim to improve patient care and outcomes. A key step in developing accurate models is to train them on a large quantity and variety of data from several healthcare institutions. However, the standard approach of collecting data into a central database is often undesirable and not feasible in healthcare due to concerns regarding the privacy and security of patient data as well as more general data sharing restrictions. Distributed learning circumvents this need to share sensitive patient data by training models locally. This chapter reviews and discusses the principles and methods of distributed learning and describes its applications in the healthcare domain. Moreover, an overview of the current technical challenges and new advancements in distributed learning is provided, and important considerations in designing a distributed learning framework for healthcare applications discussed.
Multiparty blind quantum computation protocol with deterministic mutual identity authentication

Blind quantum computation (BQC) studies the problem of how a client with limited quantum computation power delegates a computing task to a quantum server while keeping his input, output and algorithm private. To resist attacks from the lack of identity authentication in BQC protocols, we propose a multiparty BQC protocol with deterministic mutual identity authentication. For concreteness, a variant of measurement-device-independent quantum key distribution (MDI-QKD) is first presented, and based on this, a method of deterministically generating the authentication keys of the registered client and the designated server is presented in the registration phase. Then a deterministic mutual identity authentication protocol is proposed to authenticate the identities of the registered client and the designated server by using the variant of MDI-QKD. Finally, in the blind quantum computation phase, the registered client can delegate a computing task to the designated server. Compared with existing BQC protocols with mutual identity authentication, our proposal works in a deterministic and efficient way. Furthermore, the proposed mutual identity authentication method can be applied in other quantum cryptography protocols, thus providing a valuable way for the construction of multiparty quantum communication protocols with identity authentication.
Photovoltaic power prediction under insufficient historical data based on dendrite network and coupled information analysis

In recent years, the installed capacity of photovoltaic solar energy has been increasing year by year. The existing power prediction methods are difficult to achieve reliable power generation prediction for PV equipment that has just been put into service. This poses great difficulties for power system management and dispatching. Therefore, establishing a reliable PV power prediction model for this situation is important for the rapid integration of new-built PV installations into the power system for energy management and dispatching. In this paper, an ultra-short-term PV power prediction method based on coupled information analysis and a dendritic network is proposed. First, an improved Hampel filter is proposed to improve the accuracy of anomaly data processing by analyzing the information between strongly coupled variables. In addition, a dendritic network modeling method is introduced for PV generation prediction. Compared to other solutions, this prediction approach relies on very little historical data to achieve reliable predictions and also features a simple model structure and high generalization. Forty different sets of tests were conducted according to different weather conditions and data conditions. The experimental results show that the proposed method can achieve higher prediction performance and stability compared with the benchmark model.