Reviewerly/exHarmony huggingface dataset:
API-misuse detection driven by fine-grained API-constraint knowledge graph

API misuses cause significant problem in software development. Existing methods detect API misuses against frequent API usage patterns mined from codebase. They make a naive assumption that API usage that deviates from the most-frequent API usage is a misuse. However, there is a big knowledge gap between API usage patterns and API usage caveats in terms of comprehensiveness, explainability and best practices. In this work, we propose a novel approach that detects API misuses directly against the API caveat knowledge, rather than API usage patterns. We develop open information extraction methods to construct a novel API-constraint knowledge graph from API reference documentation. This knowledge graph explicitly models two types of API-constraint relations (call-order and condition-checking) and enriches return and throw relations with return conditions and exception triggers. It empowers the detection of three types of frequent API misuses - missing calls, missing condition checking and missing exception handling, while existing detectors mostly focus on only missing calls. As a proof-of-concept, we apply our approach to Java SDK API Specification. Our evaluation confirms the high accuracy of the extracted API-constraint relations. Our knowledge-driven API misuse detector achieves 0.60 (68/113) precision and 0.28 (68/239) recall for detecting Java API misuses in the API misuse benchmark MuBench. This performance is significantly higher than that of existing pattern-based API misused detectors. A pilot user study with 12 developers shows that our knowledge-driven API misuse detection is very promising in helping developers avoid API misuses and debug the bugs caused by API misuses.
Quantum teleportation with imperfect quantum dots

Abstract Efficient all-photonic quantum teleportation requires fast and deterministic sources of highly indistinguishable and entangled photons. Solid-state-based quantum emitters—notably semiconductor quantum dots—are a promising candidate for the role. However, despite the remarkable progress in nanofabrication, proof-of-concept demonstrations of quantum teleportation have highlighted that imperfections of the emitter still place a major roadblock in the way of applications. Here, rather than focusing on source optimization strategies, we deal with imperfections and study different teleportation protocols with the goal of identifying the one with maximal teleportation fidelity. Using a quantum dot with sub-par values of entanglement and photon indistinguishability, we show that the average teleportation fidelity can be raised from below the classical limit to 0.842(14), adopting a polarization-selective Bell state measurement and moderate spectral filtering. Our results, which are backed by a theoretical model that quantitatively explains the experimental findings, loosen the very stringent requirements set on the ideal entangled-photon source and highlight that imperfect quantum dots can still have a say in teleportation-based quantum communication architectures.
Sensitive integration of multilevel optimization model in human activity recognition for smartphone and smartwatch applications

This study proposes an intelligent data analysis model for finding optimal patterns in human activities on the basis of biometric features obtained from four sensors installed on smartphone and smartwatch devices. Theproposed model, referred to as Scheduling Activities of smartphone and smartwatch based on Optimal Pattern Model(SA-OPM), consists of four main stages. The first stage relates to the collection of data from four sensors in real time (i.e., two smartphone sensors called accelerometer and gyroscope and two smartwatch sensors of the same name). The second stage involves the preprocessing of the data by converting them into graphs. As graphs are difficult to deal with directly, a deterministic selection algorithm is proposed as a new method to find the optimal root to split the graphs into multiple subgraphs. The third stage entails determining the number of samples related to each subgraphby using the optimization technique called the lion optimization algorithm. The final stage involves the generation of patterns from the optimal subgraph by using the association pattern algorithm called gSpan. The pattern finder based on Forward-Backward Rules (FBR) generates the optimal patterns and thus aids humans in organizing their activities. Results indicate that the proposed SA-OPM model generates robust and authentic patterns of human activities.
Scattering in the Ising model with the quantum Lanczos algorithm <sup>*</sup>

Abstract Time evolution and scattering simulation in phenomenological models are of great interest for testing and validating the potential for near-term quantum computers to simulate quantum field theories. Here, we simulate one-particle propagation and two-particle scattering in the one-dimensional transverse Ising model for 3 and 4 spatial sites with periodic boundary conditions on a quantum computer. We use the quantum Lanczos algorithm to obtain all energy levels and corresponding eigenstates of the system. We simplify the quantum computation by taking advantage of the symmetries of the system. These results enable us to compute one- and two-particle transition amplitudes, particle numbers for spatial sites, and the transverse magnetization as functions of time. The quantum circuits were executed on various IBM Q superconducting hardware. The experimental results are in very good agreement with the values obtained using exact diagonalization.
Post-Quantum Era Privacy Protection for Intelligent Infrastructures

As we move into a new decade, the global world of Intelligent Infrastructure (II) services integrated into the Internet of Things (IoT) are at the forefront of technological advancements. With billions of connected devices spanning continents through interconnected networks, security and privacy protection techniques for the emerging II services become a paramount concern. In this paper, an up-to-date privacy method mapping and relevant use cases are surveyed for II services. Particularly, we emphasize on post-quantum cryptography techniques that may (or must when quantum computers become a reality) be used in the future through concrete products, pilots, and projects. The topics presented in this paper are of utmost importance as (1) several recent regulations such as Europe's General Data Protection Regulation (GDPR) have given privacy a significant place in digital society, and (2) the increase of IoT/II applications and digital services with growing data collection capabilities are introducing new threats and risks on citizens' privacy. This in-depth survey begins with an overview of security and privacy threats in IoT/IIs. Next, we summarize some selected Privacy-Enhancing Technologies (PETs) suitable for privacy-concerned II services, and then map recent PET schemes based on post-quantum cryptographic primitives which are capable of withstanding quantum computing attacks. This paper also overviews how PETs can be deployed in practical use cases in the scope of IoT/IIs, and maps some current projects, pilots, and products that deal with PETs. A practical case study on the Internet of Vehicles (IoV) is presented to demonstrate how PETs can be applied in reality. Finally, we discuss the main challenges with respect to current PETs and highlight some future directions for developing their post-quantum counterparts.
Hey Alexa, is this Skill Safe?: Taking a Closer Look at the Alexa Skill Ecosystem

Amazon's voice-based assistant, Alexa, enables users to directly interact with various web services through natural language dialogues.It provides developers with the option to create third-party applications (known as Skills) to run on top of Alexa.While such applications ease users' interaction with smart devices and bolster a number of additional services, they also raise security and privacy concerns due to the personal setting they operate in.This paper aims to perform a systematic analysis of the Alexa skill ecosystem.We perform the first largescale analysis of Alexa skills, obtained from seven different skill stores totaling to 90,194 unique skills.Our analysis reveals several limitations that exist in the current skill vetting process.We show that not only can a malicious user publish a skill under any arbitrary developer/company name, but she can also make backend code changes after approval to coax users into revealing unwanted information.We, next, formalize the different skillsquatting techniques and evaluate the efficacy of such techniques.We find that while certain approaches are more favorable than others, there is no substantial abuse of skill squatting in the real world.Lastly, we study the prevalence of privacy policies across different categories of skill, and more importantly the policy content of skills that use the Alexa permission model to access sensitive user data.We find that around 23.3 % of such skills do not fully disclose the data types associated with the permissions requested.We conclude by providing some suggestions for strengthening the overall ecosystem, and thereby enhance transparency for end-users.1 Example of a skill that interacts with cars: https://amazon.com/Alexa-
Quantum Teleportation between Remote Qubit Memories with Only a Single Photon as a Resource

Quantum teleportation enables the deterministic exchange of qubits via lossy channels. While it is commonly believed that unconditional teleportation requires a preshared entangled qubit pair, here we demonstrate a protocol that is in principle unconditional and requires only a single photon as an ex-ante prepared resource. The photon successively interacts, first, with the receiver and then with the sender qubit memory. Its detection, followed by classical communication, heralds a successful teleportation. We teleport six mutually unbiased qubit states with average fidelity $\overline{F}=(88.3\ifmmode\pm\else\textpm\fi{}1.3)%$ at a rate of 6 Hz over 60 m.
Threat Defense: Cyber Deception Approach and Education for Resilience in Hybrid Threats Model

This paper aims to explore the cyber-deception-based approach and to design a novel conceptual model of hybrid threats that includes deception methods. Security programs primarily focus on prevention-based strategies aimed at stopping attackers from getting into the network. These programs attempt to use hardened perimeters and endpoint defenses by recognizing and blocking malicious activities to detect and stop attackers before they can get in. Most organizations implement such a strategy by fortifying their networks with defense-in-depth through layered prevention controls. Detection controls are usually placed to augment prevention at the perimeter, and not as consistently deployed for in-network threat detection. This architecture leaves detection gaps that are difficult to fill with existing security controls not specifically designed for that role. Rather than using prevention alone, a strategy that attackers have consistently succeeded against, defenders are adopting a more balanced strategy that includes detection and response. Most organizations deploy an intrusion detection system (IDS) or next-generation firewall that picks up known attacks or attempts to pattern match for identification. Other detection tools use monitoring, traffic, or behavioral analysis. These reactive defenses are designed to detect once they are attacked yet often fail. They also have some limitations because they are not designed to catch credential harvesting or attacks based on what appears as authorized access. They are also often seen as complex and prone to false positives, adding to analyst alert fatigue. The security industry has focused recent innovation on finding more accurate ways to recognize malicious activity with technologies such as user and entity behavioral analytics (UEBA), big data, artificial intelligence (AI), and deception.
Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.Inspired by recent work on evaluating factual consistency in abstractive summarization, we propose an automatic evaluation metric for factual consistency in knowledge-grounded dialogue using automatic question generation and question answering.Our metric, denoted Q 2 , compares answer spans using natural language inference (NLI), instead of token-based matching as done in previous work.To foster proper evaluation, we curate a novel dataset of dialogue system outputs for the Wizard-of-Wikipedia dataset, manually annotated for factual consistency.We perform a thorough meta-evaluation of Q 2 against other metrics using this dataset and two others, where it consistently shows higher correlation with human judgements.
Transformational leadership and human capital within the disruptive business environment of academia

Abstract Purpose – The purpose of this paper is to examine the impact of transformational leadership on the cultivation of human capital in an academic setting from the perspective of university employees Design/methodology/approach – Quantitative data was collected from a range of staff working within three universities located in Jordan using an online questionnaire survey. The focus was on identifying which factors of transformational leadership were perceived as having the greatest potential contribution towards developing human capital and if there was a consensus amongst staff about these factors Results – 131 staff members submitted a completed questionnaire giving a response rate of 93%. Respondents generally had a good understanding of the concepts of human capital and transformational leadership, but the way in which transformational leadership was perceived to impact on human capital differed. Conclusion – Overall, the findings from the survey, consistent with other similar studies, confirmed that there was a correlation between transformational leadership and human capital in the context of academia from the perspective of staff. Research limitations/implications – The study confirms the need for leaders in academia to consider the leadership approach that is adopted to ensure that human capital, which is one of its main assets, is supported and developed appropriately Practical implications – The paper describes implications for leaders in higher education, as well as more broadly for educators who are attempting to develop human capital in their organisations Originality/value – This paper expands upon a developing field in leadership in higher education&#x0D; &#x0D; Keywords: Transformational Leadership; Human Capital; Academia; Disruptive Business Environment
Multi-Modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training

Recently a number of studies demonstrated impressive performance on diverse vision-language multi-modal tasks such as image captioning and visual question answering by extending the BERT architecture with multi-modal pre-training objectives. In this work we explore a broad set of multi-modal representation learning tasks in the medical domain, specifically using radiology images and the unstructured report. We propose Medical Vision Language Learner (MedViLL), which adopts a BERT-based architecture combined with a novel multi-modal attention masking scheme to maximize generalization performance for both vision-language understanding tasks (diagnosis classification, medical image-report retrieval, medical visual question answering) and vision-language generation task (radiology report generation). By statistically and rigorously evaluating the proposed model on four downstream tasks with three radiographic image-report datasets (MIMIC-CXR, Open-I, and VQA-RAD), we empirically demonstrate the superior downstream task performance of MedViLL against various baselines, including task-specific architectures.
Blockchain-integrated technologies for solving supply chain challenges

Purpose Blockchain technology was developed to synchronize the data and transactions over the supply chain network and connected nodes. This paper aims to show how blockchain technology can enhance flexibility and agility in supply chain operations. The integration of blockchain and other recently developed technology can help deal with supply chain uncertainties and other challenges being faced by the industry. Design/methodology/approach Through an extensive literature review of existing research papers and conversation with supply chain managers, barriers and challenges in the supply chain were identified. Some elements were researched of blockchain technology that can be used to resolve some challenges. Blockchain technology and other technologies integration is developed for implementation in supply chain for better visibility and efficiency of supply chain. Findings The challenges in the supply chain are categorized, and the solution is given through the integration of blockchain and other technologies like Internet of Things and artificial intelligence. The integration shows the execution of tasks through blockchain and various technologies in supply chain. Research limitations/implications Blockchain in supply chain is finding its strong place in India when compared to developing nations. There is a need for technology experts, supply chain managers and consumers to understand blockchain’s importance. Challenges faced by industries to use blockchain may be analyzed further with real-life industry case studies. Practical implications This research helps enterprises in successful execution of smart technologies in their supply chains. This research helps enterprises in successful execution of smart technologies in their supply chains. Managers and practitioners may use the models developed in real-time implementation. The technologies are described in detail to help the practitioners select the best suitable for their organization. Social implications Digital supply chains are finding the way in industries due to lean and efficient nature. It is beneficial to use the smart technologies to make supply chain green and sustainable. Originality/value The implementation of the digital supply chain and its challenges are discussed in the research paper. This will work as a platform for research in the area of technologies for supply chain.
A Spherical Video-Based Immersive Virtual Reality Learning System to Support Landscape Architecture Students’ Learning Performance during the COVID-19 Era

During the COVID-19 pandemic, campuses are closed, and many schools are forced to transition to online instruction. There are many problems in landscape architecture education during the pandemic such as students’ decreased attention, poor self-regulation, low proactivity, and difficulty in grading. In addition, situational effects are required for landscape architecture education, but online education lacks the necessary case scenarios for courses during the pandemic. In order to solve these problems, the present study adopted a more accessible, and interactive spherical video-based immersive virtual reality (SV-IVR) approach, developed a landscape architecture SV-IVR learning system, and conducted a quasi-experimental study to examine its effectiveness. The results indicated that the experimental group students showed more positive results in their learning achievements, learning attitudes, and self-regulation than the control group. Besides, it was found that students required more time to develop their self-efficacy and that this system would not have an effect on students’ cognitive load.
Knowledge Enhanced Masked Language Model for Stance Detection

Detecting stance on Twitter is especially challenging because of the short length of each tweet, the continuous coinage of new terminology and hashtags, and the deviation of sentence structure from standard prose.Finetuned language models using large-scale indomain data have been shown to be the new state-of-the-art for many NLP tasks, including stance detection.In this paper, we propose a novel BERT-based fine-tuning method that enhances the masked language model for stance detection.Instead of random token masking, we propose using a weighted log-odds-ratio to identify words with high stance distinguishability and then model an attention mechanism that focuses on these words.We show that our proposed approach outperforms the state of the art for stance detection on Twitter data about the 2020 US Presidential election.
Effects of Digitalisation on Higher Education in a Sustainable Development Framework—Online Learning Challenges during the COVID-19 Pandemic

Throughout the planet, the medical challenges posed by the pandemic caused by the SARS-Cov-2/COVID-19 coronavirus have overlapped, inter alia, with the necessity to continue the academic process on every level. Romania was no exception. With the new vaccines against COVID-19, the hope of resuming face-to-face activity, considered as ‘normal’ before 2020, has emerged. In these circumstances, not at all far-fetched, certain questions have arisen, such as: should and must the online university education be completely removed? Should this form of education be continued? If so, to what extent? We have used econometric methods related to ARDL (auto regressive distributed lag models) such as pooled mean group (PMG) and mean group (MG) and used different tests for unit roots for the stationarity check of the series implied. The results show the positive effect of digitalisation on tertiary education and also the positive impact of the latter on sustainable development, as a base for future stimulation in public policies. The present study also aims to harness the university experience of these times, from some of the main Romanian university centres; the method used was a quantitative and qualitative research based on a questionnaire, which was answered by a number of 258 university teachers and 1569 students from prestigious public and private universities. The results of this analysis allowed us to conclude that most of the participants in the university educational process have adapted to the online activity, and the latter ‘saved’ the academic years 2019–2020 and, respectively, 2020–2021. The present study is useful for tertiary education institution and policymakers in terms of formulating strategies and policy recommendations to support teachers and students during any future pandemics.
Detecting Beneficial Feature Interactions for Recommender Systems

Feature interactions are essential for achieving high accuracy in recommender systems. Many studies take into account the interaction between every pair of features. However, this is suboptimal because some feature interactions may not be that relevant to the recommendation result and taking them into account may introduce noise and decrease recommendation accuracy. To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy. The automatic feature interaction detection is achieved via edge prediction with an L0 activation regularization. Our proposed model is proved to be effective through the information bottleneck principle and statistical interaction theory. Experimental results show that our model (i) outperforms existing baselines in terms of accuracy, and (ii) automatically identifies beneficial feature interactions.