Reviewerly/exHarmony huggingface dataset:
Customer acceptance of frontline social robots—Human-robot interaction as boundary condition

From an interactionist perspective, we argue that what happens and how service is delivered during the human-robot interaction may alter the extent to which customers accept service robots. Extending previous research on customer acceptance of service robots and human-robot interaction, we treat the elements during the human-robot interaction process as boundary conditions for the link between service robots' functional and social-emotional capabilities. Specifically, we examine (1) contact frequency between customers and service robots, (2) interdependence among service robots and human service employees, and (3) service complexity, moderate the relationship between service robots' capabilities and customer acceptance. With data collected from 997 customers who have past experience with service robots, we found that the effect of functional and social-emotional capabilities of service robots on customer acceptance are more salient when contact frequency is low rather than high, interdependence among service robots and service employees is high rather than low, and service complexity is low rather than high. Our findings provide new insights into customer acceptance of social robots in the service settings.
Mineral exploration using trace element composition of pyrite grains from till: A case study from the Petäjäselkä Au occurrence, northern Finland

Geochemical anomalies and indicator mineral studies in glacial sediments are commonly used to define mineral exploration targets in glaciated areas. Trace element composition of sediment-hosted heavy minerals, such as pyrite and chalcopyrite, can be potentially used to fingerprint mineral deposits. This study presents results from trace element compositions of pyrite in the heavy mineral concentrates of till and mineralized bedrock samples from the orogenic gold ore occurrences in the Petäjäselkä area, Central Lapland belt, northern Finland. The trace element contents of pyrite grains were evaluated by using a robust compositional data analysis workflow, including symmetric pivot coordinates and robust principal component analysis (PCA). Four different pyrite textures (A-D) were identified from the gold ore occurrences. Compositional data analysis showed that the heavy mineral pyrite correlated with Pyrite D in the ore occurrence and confirmed the discrimination power of pyrite in the heavy mineral grains as a tool for source characterization of transported sedimentary material. The study also discusses the difficulties of finding suitable grains for the LA-ICP-MS, highlighting the importance of proper heavy mineral grain recovery procedures and the factors of oxidization and weathering processes influencing the geochemical compositions of the pyrite grains.
Parallel Acyclic Joins: Optimal Algorithms and Cyclicity Separation

We study equi-join computation in the massively parallel computation (MPC) model. Currently, a main open question under this topic is whether it is possible to design an algorithm that can process any join with load O(N polylog N/p 1/ρ* ) — measured in the number of words communicated per machine — where N is the total number of tuples in the input relations, ρ * is the join’s fractional edge covering number, and p is the number of machines. We settle the question in the negative for the class of tuple-based algorithms (all the known MPC join algorithms fall in this class) by proving the existence of a join query with ρ * = 2 that requires a load of Ω ( N/p 1/3 ) to evaluate. Our lower bound provides solid evidence that the “AGM bound” alone is not sufficient for characterizing the hardness of join evaluation in MPC (a phenomenon that does not exist in RAM). The hard join instance identified in our argument is cyclic, which leaves the question of whether O(N polylog N/p 1/ρ* ) is still possible for acyclic joins. We answer this question in the affirmative by showing that any acyclic join can be evaluated with load O(N / p 1/ρ* ), which is asymptotically optimal (there are no polylogarithmic factors in our bound). The separation between cyclic and acyclic joins is yet another phenomenon that is absent in RAM. Our algorithm owes to the discovery of a new mathematical structure — we call “canonical edge cover” — of acyclic hypergraphs, which has numerous non-trivial properties and makes an elegant addition to database theory.
Re-construction of original face images from plastic surgery images using harmony search algorithm and IRBFN model

Face Reconstruction is the only biometric that allows you to perform passive identification in one to many environments. By changing the geometry and texture of the facial region, there can be an increased intraclass variability between images of an individual before and after surgery. Even so, the nonlinear changes caused by plastic surgery have a hard time being modelled by facial reconstruction systems. An algorithm for plastic surgery face reconstruction is presented here, that uses Harmony Search to confront the challenges involved. A non-disjoint algorithm generates the face granules, each representing different information with varying resolutions and sizes. A Scale-Invariant Feature Transform (SIFT) and Co-occurrence of Adjacent Local Binary Patterns were applied to extract discriminating information from granules of face images. As the concluding step, we used Harmony Search Method (HSM) to combine the various responses to attain the optimal solution. Finally Improved Radial Basis Function Network (IRBFN) algorithm is used for feature matching. The feature matching predict, where the face is recognized or not. The modified method, proposed in this work, has the better performance in classifying more images with higher accuracy. An obtained sensitivity of 100 % and specificity of 97.5 % was achieved by the proposed classifier. The results proved that it is possible to use the developed algorithms understand different pre and post surgical image conditions, evaluate their strength and future development trends, and provide all-facial features basis for detection of pre and post surgical images.
Reweighted robust and discriminative latent subspace projection for face recognition

Subspace projection has been widely studied to implement the feature extraction for face recognition. However, it scarcely facilitates exploring the label information in the subspace projection procedure. The feature extraction and classification are usually divided into two separate procedures. This fails to explore the discriminative information from the training data, and thus degrades the discrimination of the extracted features and lowers the face recognition accuracy. To address this issue, we present a novel reweighted robust and discriminative latent subspace projection (ReRDLSP) method which organically integrates the reweighted latent low-rank representation model and discriminative ridge regression into a union framework. We employ a reweighted nuclear norm minimization to realize the low-rank regularization, which provides adaptive weighted values assignment, regularizes the representation matrix adaptively, and thus boosts the capability and flexibility of the method. Moreover, a discriminative ridge regression method is presented by employing a nonnegative relaxation, which enlarges the margins of the face samples from different classes as much as possible and boosts the freedom of fitting the label information. The computational complexity and convergence analysis are also discussed in detail. Extensive experiments are demonstrated and very promising results are achieved compared with some state-of-the-art face recognition methods.
CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents

In this paper, we focus on inferring whether the given user command is clear, ambiguous, or infeasible in the context of interactive robotic agents utilizing large language models (LLMs). To tackle this problem, we first present an uncertainty estimation method for LLMs to classify whether the command is certain (i.e., clear) or not (i.e., ambiguous or infeasible). Once the command is classified as uncertain, we further distinguish it between ambiguous or infeasible commands leveraging LLMs with situational aware context prompts. For ambiguous commands, we disambiguate the command by interacting with users via question generation with LLMs. We believe that proper recognition of the given commands could lead to a decrease in malfunction and undesired actions of the robot, enhancing the reliability of interactive robot agents. We present a dataset for robotic situational awareness consisting of pairs of high-level commands, scene descriptions, and labels of command type (i.e., clear, ambiguous, or infeasible). We validate the proposed method on the collected dataset and pick-and-place tabletop simulation environment. Finally, we demonstrate the proposed approach in real-world human-robot interaction experiments.
PASTS: Progress-aware spatio-temporal transformer speaker for vision-and-language navigation

Vision-and-language navigation (VLN) is a crucial but challenging cross-modal navigation task. One powerful technique to enhance the generalization performance in VLN is the use of an independent speaker model to provide pseudo instructions for data augmentation. However, current speaker models based on Long-Short Term Memory (LSTM) lack the ability to attend to features relevant at different locations and time steps. To address this, we propose a novel progress-aware spatio-temporal transformer speaker (PASTS) model that uses the transformer as the core of the network. PASTS uses a spatio-temporal encoder to fuse panoramic representations and encode intermediate connections through steps. Besides, to avoid the misalignment problem that could result in incorrect supervision, a speaker progress monitor (SPM) is proposed to enable the model to estimate the progress of instruction generation and facilitate more fine-grained caption results. Additionally, a multifeature dropout (MFD) strategy is introduced to alleviate overfitting. The proposed PASTS is flexible to be combined with existing VLN models. The experimental results demonstrate that PASTS outperforms previous speaker models and successfully improves the performance of previous VLN models, achieving state-of-the-art performance on the standard Room-to-Room (R2R) dataset.
Reactive mobile manipulation based on dynamic dual-trajectory tracking

The letter presents an online obstacle avoidance strategy for a nonholonomic mobile manipulator, including dual-trajectory generation for the mobile base(MB) and end effector(EE), as well as whole-body reactive obstacle avoidance. The feasible region of the manipulator's workspace is generated by traversing the path of the MB, and the spatio-temporal trajectory of the EE is generated by quadratically constrained quadratic programming(QCQP) planning, satisfying the physical constraints and obstacle avoidance constraints. The reactive obstacle avoidance strategy is based on quadratic programming(QP), using singularity avoidance and EE trajectory tracking tasks as soft constraints with different priorities. Considering the nonholonomic constraints and motion differences of MB, we employ a dynamic tracking approach to avoid obstacles of MB. Additionally, an adaptive weight method is utilized to adjust the priority of MB path tracking task, effectively preventing joint velocity jitter during the insertion process. Our strategy has been proven to generate obstacle avoidance trajectories for EE in real-time and ensure safe joint velocities for MB and the manipulator through simulation and real-world scenarios.
A state-of-the-art on federated learning for vehicular communications

With the increasing number of connected vehicles on the road, vehicular communications have become an important research area. Federated learning (FL), a distributed machine learning technique that enables learning from distributed data sources while preserving data privacy and efficient learning, has the advantages of privacy preservation, low latency, and energy efficiency, making it a suitable technique for vehicular communications. In vehicular communications, where dynamic data privacy, real-time decision-making, and energy conservation are paramount, FL's inherent benefits find unique relevance. The preservation of data privacy is of utmost concern in vehicular networks, where sensitive information is exchanged in real time. FL addresses this concern by allowing vehicles to collaboratively train machine learning models without sharing raw data, thus ensuring the privacy and security of vehicular users. Furthermore, FL's decentralized nature enables low-latency model updates, crucial for Intelligent Transportation Systems (ITS) real-time applications like collision avoidance and traffic management, where split-second decisions can be life-saving. Additionally, FL can contribute to energy efficiency by reducing the need for centralized data aggregation, which is particularly beneficial in resource-constrained vehicular environments. The paper provides a comprehensive survey of the state-of-the-art in FL for vehicular communications, including existing frameworks and architectures, challenges, and applications in various aspects of vehicular communications. The survey analyzes various FL techniques, their strengths and weaknesses, and the performance metrics of FL in vehicular networks. The paper concludes by presenting the open research issues and future directions for FL in vehicular communications. Our survey provides a timely and comprehensive overview of the recent advances in this emerging research field and serves as a valuable reference for researchers and practitioners in both FL and vehicular communications.
Few-shot Incremental Event Detection

Event detection tasks can enable the quick detection of events from texts and provide powerful support for downstream natural language processing tasks. Most such methods can only detect a fixed set of predefined event classes. To extend them to detect a new class without losing the ability to detect old classes requires costly retraining of the model from scratch. Incremental learning can effectively solve this problem, but it requires abundant data of new classes. In practice, however, the lack of high-quality labeled data of new event classes makes it difficult to obtain enough data for model training. To address the above mentioned issues, we define a new task, few-shot incremental event detection, which focuses on learning to detect a new event class with limited data, while retaining the ability to detect old classes to the extent possible. We created a benchmark dataset IFSED for the few-shot incremental event detection task based on FewEvent and propose two benchmarks, IFSED-K and IFSED-KP. Experimental results show that our approach has a higher F1-score than baseline methods and is more stable.
Analyzing transparency and malicious insiders prevention for cloud computing environment

The emerging cloud technology and its integration with cloud services from an enterprise standpoint have provided a gateway for Cloud Service Providers (CSPs) to garner consumers and their data. Due to the growing increase of Cloud Service Users (CSUs), it is pertinent to provide an adequate amount of cloud security to prevent detrimental impacts to businesses. Two large challenges CSPs face for cloud security include transparency and malicious insiders. The primary objective of this work is to provide a framework that analyzes CSPs based on evaluation metrics such as data breaches, data loss, account hijacking, insecure APIs, DoS, malicious insiders, abuse of cloud services, insufficient due diligence, and shared technology concerns. The security index of a CSP is then computed based on the listed evaluation metrics and given a score that translates to a linguistic rating of the CSP. Factors within transparency and malicious insiders are further broken down within the framework to emphasize various components. Transparency factors include published audit certifications, consumer base, membership of cloud organizations, and published incidents. Malicious insiders are sub-sectioned into personnel-related prevention, policy-related prevention, third-party CSP prevention, technical-related prevention, and hypervisor-related prevention. With this scoring contribution, analysis is able to be done on a CSP's potential threats within the network, which can then be visualized into recommended security controls or preventative measures. Finally, we conducted case studies to demonstrate the application of the scoring analysis using our proposed framework and to evaluate the security levels of two extensively employed CSPs.
Data Mining for Cyber Biosecurity Risk Management – a comprehensive review

The world is currently facing the era of Cyberbiosecurity, also known as Biocybersecurity, or Digital Biosecurity, which poses a few unique security vulnerabilities. A significant percentage of the scientific, agricultural, and health communities are still unaware of the unique security complexities that have resulted from fusion of the supply chain, infrastructure, cyber, and life and medical sciences. Measurement, analysis, and mitigation of cyberattacks on biological systems are the goals of Cyberbiosecurity. Data mining is a promising avenue for further investigation as a means of mitigating cyber-attacks for research purposes. Data mining is the process of extracting useful patterns, information, and expertise from massive datasets. In the domain of Cyberbiosecurity, data mining techniques have received little attention. The purpose of this survey on data mining in cybersecurity is to determine the state of the art in cybersecurity issues, including different types of assaults and data mining methods that could be used to address these issues. This review's findings shed insight on the characteristics of security issues as well as gaps in Cyberbiosecurity. Furthermore, the review's conclusions show that most responses to Cyberbiosecurity attacks advocate for national or international government/private organizations to raise public awareness of these attacks and equip their clients/dealers with secure methods to transmit data online. These attempts to raise awareness can be divided into four distinct levels, each with its own set of techniques and objectives, resulting in a holistic strategy to addressing the difficulties of Cyberbiosecurity. A notable gap found in this review is the absence of appropriate countermeasures against cyberattacks employing data mining in the biological systems domain such as healthcare, agriculture, biomedical research, and other domains. The goal of this review is to increase knowledge among Cyberbiosecurity experts and to develop innovative solutions by utilizing cutting-edge data mining techniques in this sector.
Improved mixture differential attacks on 6-round AES-like ciphers towards time and data complexities

AES is the most widely used symmetric cipher. Many cryptographic primitives are designed following the structure of AES, called AES-like ciphers. Mixture differential cryptanalysis is a structural cryptanalysis technique for AES, which gave the best key recovery attack on 5-round AES in the chosen-plaintext setting and attacks with practical data and memory complexities on round-reduced AES. In this paper, we study the mixture differential attacks on 6 rounds of AES-like ciphers, and propose two improved attacks towards optimizing time and data complexities respectively. As an application to AES, we give the improved mixture differential attack on 6 rounds of AES-128 with time complexity 262.74, reducing by a factor of 210.26 compared with the previous mixture differential attack. For low data complexity, we show the improved 6-round mixture differential attack on AES-128 with data complexity 224.05, reducing by a factor of about 4. We also apply two attacks to 6 super-rounds of Saturnin, one of the second-round candidates in NIST lightweight cryptography standardization process, which is the first security evaluation of Saturnin against mixture differential cryptanalysis.
Practical implications to becoming agile organizations: NASA case study

Many organizations have adopted agile values and principles in their products and services to provide increasing value to their customers. Although agile started with software development, it has been applicable and used in different industries and disciplines, including the space industry. This article presents the practical implications of teams transitioning to be agile teams using a general inductive analysis of 33 individuals from different NASA agile teams. The findings and recommendations are part of a larger effort performed as part of the development of a model for agile leadership. Some organizations may see agile as very risky and costly, and this article provides some guidance derived from the data to help support planning for agile transformation efforts.
ِA new occurrence of rift-related damtjernite (ultramafic) lamprophyre, Gebel Anweiyib area, Arabian Nubian shield: Insights from bulk rock geochemistry and remote sensing data analysis

The occurrence of lamprophyre dikes is being reported first time in the Gebel Anweiyib area, south Eastern Desert, Egypt. The investigation of the ultramafic lamprophyre (damtjernite) dikes intruded into the crustal younger granites and metasediments based on their spectral characteristics, mineralogical and geochemical compositions. Three multispectral sensors of Landsat-8, ASTER and S2 have been used to detect and characterize the lithologic units and the lamprophyre dikes. The reflectance and absorption signatures of the granitic rocks and lamprophyre dikes were the basis for detecting them from the remotely sensed data using the application of advanced image processing algorithms including FCC, BR, PCA, MNF, and DS. Geochemical studies inferred that the damtjernite dikes are under-saturated with SiO2 (<38.89 wt%), alkaline with total alkalis (avg. 4 wt%), metaluminous with A/CNK (0.22–0.51), and ultramafic with MgO (13.88–19.23 wt%). They exhibit unique geochemical characteristics of mantle (with high MgO, Ni, Cr, and Mg#) coupled with crustal (high Sr, Ba, Zr, Ga and REEs) sources, similar to global ultramafic lamprophyres. High TiO2 and highly fractionated REEs with low HREEs (avg. 23 ppm) relative to LREEs (avg. 354 ppm) contents, yielding residual garnet in melts. Low partial melting of primitive garnet lherzolite can account the obtained REEs in the ultramafic lamprophyres. These features signify their derivation from a deeper enriched asthenospheric (plume-type) magma like OIB, which is attested by the high Nb/La (avg. 1.32), Nb/U (avg. 102), La/Yb (avg. 43), and low Y/Nb < 0.52, Rb/Sr < 1, Rb/Ba (<0.14), and Sr/Ba (<0.9), suggesting their derivation from a mantle-derived source rather than crustal rocks. High concentrations of TiO2 and Fe2O3 in the ultramafic lamprophyres, reflecting their formation from a fertile peridotite by mantle plume-derived melts with K-residual phlogopite rather than amphibole as obtained by low Ba/Rb and high Rb/Sr ratios. The Anweiyib lamprophyres are anorogenic and confined to being derived from rifting regions, and their magma is not contaminated with crustal sources during emplacement.