Reviewerly/exHarmony huggingface dataset:
Automatic Identification of Idling Reasons in Excavation Operations Based on Excavator–Truck Relationships

Excavators and trucks are important equipment for earthwork operations, which make major contributions to construction productivity. To control the work efficiency and productivity of earthwork equipment, computer vision (CV) methods have been proposed to monitor equipment operations from site surveillance videos. Existing methods can recognize equipment activities to estimate the working and idling times. Idling time is an important factor that influences equipment productivity; however, the causes of equipment idling have not been considered in previous CV methods. Therefore, this research proposes a method to identify the main causes of excavator and truck idling by analyzing their interactive operations. First, the activities of the excavators and trucks are identified using convolutional neural networks. Then, work groups of excavators and trucks are clustered. Finally, the relationships between each excavator and the surrounding trucks are analyzed to identify the potential reason for idling. The proposed method was validated with videos from several construction sites, and the results were promising.
Predicting success in United States Air Force pilot training using machine learning techniques

The chronic pilot shortage that has plagued the United States Air Force over the past three years poses a national-level problem that senior military members are working to overcome. Unfortunately, not all pilot candidates successfully complete the necessary training requirements to become fully qualified Air Force pilots, which wastes critical time and resources and only further exacerbates the pilot shortage problem. Therefore, it is important for the Air Force to carefully consider whom they select to attend pilot training. This research examines historical specialized undergraduate pilot training (SUPT) candidate data leveraging a variety of machine learning techniques to obtain insights on candidate success. Computational experimentation is performed to determine how selected machine learning techniques and their respective hyperparameters affect solution quality. Results reveal that the extremely randomized tree machine learning technique can achieve nearly 94% accuracy in predicting candidate success. Additional analysis indicates degree type and commissioning source are the most important features in determining candidate success. Ultimately, this research can inform the modification of future SUPT candidate selection criteria and other related Air Force personnel policies. • This research examines specialized undergraduate pilot training candidate data. • Machine learning techniques are utilized to obtain insights on candidate success. • Results show tree-based machine learning techniques outperform neural networks. • This work informs future selection criteria for pilot training candidates.
A flexible attribute based data access management scheme for sensor-cloud system

Sensor-cloud combines the merits of WSN with cloud computing and has been deployed in many practical applications. Data security is the prerequisite for the implementation of a sensor-cloud system, but conventional security mechanisms cannot fully satisfy the several new security demands arisen in this scenario. To address the challenge of flexible and secure data sharing, in this paper, we design an attribute based data access management scheme for sensor-cloud (ABDM-SC). We take the advantages of attribute based cryptography and hash proof primitive to realize fine-grained access control and efficient user authentication. The data captured by the sensors are labeled by attributes and can only be accessed by users processing the valid credentials. During the data sharing procedure, the cloud side and user side only need to conduct one round of interaction. By security proof and performance evaluation, our ABDM-SC is equipped with high security level and satisfactory efficiency.
Bounding passive light-source side channels in quantum key distribution via Hong-Ou-Mandel interference

Passive side-channel attacks in quantum key distribution (QKD) aim at obtaining information about the quantum signals without disturbing them and hence compromise real-world QKD security. Currently, there are no reliable tools for the assessment of QKD signal generation imperfections. In this work we propose a generic experimental method which allows to upper-bound QKD light-source imperfections and directly integrate them into the modern security proofs. The method relies on Hong-Ou-Mandel interference between different emitted signals: the maximum interference visibility reveals overall signal distinguishability that could lead to passive side-channel information leakage. We apply it for the standard decoy-state BB84 protocol and calculate a lower bound on the secret key rate for realistic values of interference visibility. The method can be readily implemented in practical QKD setups and is especially relevant for multiple-laser QKD systems such as the one installed on the Micius satellite.
A CNN-BiLSTM based hybrid model for Indian language identification

Automatic language identification (LID) is the practice of detecting the language through linguistic content of speech spoken by certain anonymous speaker. In multi-lingual based Indian society, the capability to identify and classify a spoken language is an imperative task. In this work, convolutional neural network (CNN) based bidirectional long short-term memory (BiLSTM) model has been proposed for Indian language identification with special emphasis on Northeastern languages. Initially, speech samples are transformed into spectrogram images, which comprises the multi-frequency band signals with respect to time. Deep linguistic features for the said task have been extracted from the spectrogram images using CNNs. Furthermore, BiLSTM network is used to extract more temporal information for language identification. On top of it, attention mechanism has been used to find the context for each frame. At the end, softmax function is applied to calculate language score for each language. The simulation is conducted on recorded database consists of four Indian languages. The languages are Assamese, Bengali, Indian English, and Hindi respectively. The performance of proposed LID model is tested on two different CNN architectures ResNet-50 and VGG-16. Simulation results show that the ResNet-50 based model has achieved accuracy up to 98.10% as compare to 97.70% for VGG-16 based model.
Quantum Error Mitigation Relying on Permutation Filtering

Quantum error mitigation (QEM) is a class of promising techniques capable of reducing the computational error of variational quantum algorithms tailored for current noisy intermediate-scale quantum computers. The recently proposed permutation-based methods are practically attractive, since they do not rely on any a priori information concerning the quantum channels. In this treatise, we propose a general framework termed as permutation filters, which includes the existing permutation-based methods as special cases. In particular, we show that the proposed filter design algorithm always converge to the global optimum, and that the optimal filters can provide substantial improvements over the existing permutation-based methods in the presence of narrowband quantum noise, corresponding to large-depth, high-error-rate quantum circuits.
BEAT: Blockchain-Enabled Accountable and Transparent Network Sharing in 6G

Infrastructure sharing is a widely discussed and implemented approach and is successfully adopted in telecommunications networks today. In practice, it is implemented through prior negotiated service level agreements (SLAs) between the parties involved. However, it is recognized that these agreements are difficult to negotiate, monitor, and enforce. For future 6G networks, resource and infrastructure sharing is expected to play an even greater role. It will be a crucial technique for reducing over-all infrastructure costs and increasing operational efficiencies for operators. More efficient SLA mechanisms are thus crucial to the success of future networks. In this work, we present BEAT, an automated, transparent, and accountable end-to-end architecture for network sharing based on blockchain and smart contracts. This work focuses on a particular type of blockchain, permissioned distributed ledger, due to its permissioned nature allowing for industry-compliant SLAs with stringent governance. Our architecture can be implemented with minimal hardware changes and with minimal overheads.
Boolean logic algebra driven similarity measure for text based applications

In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean logic algebra basics (BLAB-SM), which aims at effectively reaching the desired accuracy at the fastest run time as compared to the recently developed state-of-the-art measures. Using the term frequency–inverse document frequency (TF-IDF) schema, the K-nearest neighbor (KNN), and the K-means clustering algorithm, a comprehensive evaluation is presented. The evaluation has been experimentally performed for BLAB-SM against seven similarity measures on two most-popular datasets, Reuters-21 and Web-KB. The experimental results illustrate that BLAB-SM is not only more efficient but also significantly more effective than state-of-the-art similarity measures on both classification and clustering tasks.
Humanized Recommender Systems: State-of-the-art and Research Issues

Psychological factors such as personality, emotions, social connections , and decision biases can significantly affect the outcome of a decision process. These factors are also prevalent in the existing literature related to the inclusion of psychological aspects in recommender system development. Personality and emotions of users have strong connections with their interests and decision-making behavior. Hence, integrating these factors into recommender systems can help to better predict users’ item preferences and increase the satisfaction with recommended items. In scenarios where decisions are made by groups (e.g., selecting a tourism destination to visit with friends), group composition and social connections among group members can affect the outcome of a group decision. Decision biases often occur in a recommendation process, since users usually apply heuristics when making a decision. These biases can result in low-quality decisions. In this article, we provide a rigorous review of existing research on the influence of the mentioned psychological factors on recommender systems. These factors are not only considered in single-user recommendation scenarios but, importantly, also in group recommendation ones, where groups of users are involved in a decision-making process. We include working examples to provide a deeper understanding of how to take into account these factors in recommendation processes. The provided examples go beyond single-user recommendation scenarios by also considering specific aspects of group recommendation settings.
PRAAT scripts to measure speed fluency and breakdown fluency in speech automatically

Fluency in terms of speed of speech and (lack of) hesitations such as silent and filled pauses (‘uhm’s) is part of oral proficiency. Language assessment rubrics therefore include aspects of fluency. Measuring fluency, however, is highly time-consuming because of the manual labour involved. The current paper aims to automatically measure aspects of L2 fluency, including filled pauses, in both Dutch and English. A revised existing script and a new script for filled pauses are tested on accuracy. We also gauged whether the outcomes of the new script could be used for language assessment purposes by relating the outcomes to human judgements. Without further investigations, the current script should not (yet) be used for the purpose of assessing fluency automatically in (high-stakes) oral proficiency assessment. However, the performance of the scripts for measuring aspects of fluency globally and quickly are promising, especially given their stability in accuracy on new corpora.
Development of ‘ibuki’ an electrically actuated childlike android with mobility and its potential in the future society

Abstract In this paper, we present an electrically driven childlike android named ibuki equipped with a wheeled mobility unit that enables it to move in a real environment. Since the unit includes a vertical oscillation mechanism, the android can replicate the movements of the human center of mass and can express human-like upper-body movements even when moving by wheels. Moreover, providing 46 degrees of freedom enables it to perform various human-like physical expressions. The development of ibuki , as well as the implementation and testing of several functions, is described. Finally, we discuss the potential advantages and future research direction of a childlike mobile android.
Blood cell classification using interpretable shape features: A Comparative study of SVM models and CNN-Based approaches

Background and Objective: Models to count blood cells have been extensively studied, but there are very few for classifying potential blood cells. This is crucial for verifying that what these systems are counting are in fact the correct object of interest. Furthermore, we have very little insight into which features are important for differentiating these classes in computational models. Methods:This paper presents a competitive solution for classifying red blood cells, white blood cells, and platelets from each other. Results:We were able to achieve an overall classification rate of about 99% and outperformed convolutional neural networks by about 10% by using the Blood Cell Count Dataset with a support vector machine, a polynomial kernel, and interpretable and explainable features. Further, we are also able to identify that the important features for classifying the three classes are the number of corners, the proportion of the area of the cell over the relevant area the cell occupies, the major axis relative length of the cell, and the relevant area surrounding the cell. Conclusion: We built a model for blood cell classification that is more interpretable and explainable while also outperforming the state-of-the-art.
Complex Behavior of a Hyperchaotic TNC Oscillator: Coexisting Bursting, Space Magnetization, Control of Multistability and Application in Image Encryption Based on DNA Coding

In this work, several new aspects of the dynamics of the well-known TNC hyperchaotic oscillator are investigated. Numerous novelties appear in this work, namely particular structures of space magnetization, the coexistence of bursting patterns, the coexistence of up to four asymmetric different attractors, offset-boosting, and antimonotonicity. In addition to this interesting and particular combination of features in the TNC oscillator, the control of multistability phenomenon is carried out using linear augmentation control scheme. Finally, knowing that the protection of digital images is of particularly great interest, the complexity of pseudo-random hyperchaotic sequences of the TNC oscillator is combined with the similarity that the DNA coding shares with the binary code to build a new image encryption algorithm with strong robustness and high speed. This algorithm is successfully evaluated using cropping attack, noise attack and differential attack. The results obtained demonstrate that the proposed algorithm is efficient and of good quality.
Self-Supervised Representation Learning for Evolutionary Neural Architecture Search

Recently proposed neural architecture search (NAS) algorithms adopt neural predictors to accelerate architecture search. The capability of neural predictors to accurately predict the performance metrics of the neural architecture is critical to NAS, but obtaining training datasets for neural predictors is often time-consuming. How to obtain a neural predictor with high prediction accuracy using a small amount of training data is a central problem to neural predictor-based NAS. Here, a new architecture encoding scheme is first devised to calculate the graph edit distance of neural architectures, which overcomes the drawbacks of existing vector-based architecture encoding schemes. To enhance the predictive performance of neural predictors, two self-supervised learning methods are proposed to pre-train the architecture embedding part of neural predictors to generate a meaningful representation of neural architectures. The first method designs a graph neural network-based model with two independent branches and utilizes the graph edit distance of two different neural architectures as a supervision to force the model to generate meaningful architecture representations. Inspired by contrastive learning, the second method presents a new contrastive learning algorithm that utilizes a central feature vector as a proxy to contrast positive pairs against negative pairs. Experimental results illustrate that the pre-trained neural predictors can achieve comparable or superior performance compared with their supervised counterparts using only half of the training samples. The effectiveness of the proposed methods is further validated by integrating the pre-trained neural predictors into a neural predictor guided evolutionary neural architecture search (NPENAS) algorithm, which achieves stateof-the-art performance on NASBench-101, NASBench-201, and DARTS benchmarks
The impact of SETA event attributes on employees’ security-related Intentions: An event system theory perspective

Current research recommends security education, training, and awareness (SETA) programs as an effective way to counter internal security threats and promote employees’ security behaviors. Contrary to the dominant approach, which views SETA as a simple, single construct, we argue that the attributes of SETA are the key determinants of the programs’ impact on individuals. More specifically, we bridge this gap by using event system theory (EST) to conceptualize SETA programs as “organization events” and empirically test the role of SETA attributes in affecting employees’ intentions to comply with security policy (i.e., in-role behavioral intentions) and extra-role behavioral intentions. The results of this research advance the understanding of SETA attributes and their impacts on employees’ compliance intentions and extra-role behavioral intentions from the EST perspective. The results suggest that the novelty of SETA event is more effective in fueling extra-role behavioral intention than compliance intention. The results also suggest that criticality exerts a similar positive influence on both compliance and extra-role behavioral intention, while the disruption of SETA event has a significant negative effect on these two intentions. Further, there is evidence that the negative relationship between SETA event disruption and two security behavioral intentions is stronger when the SETA event is dispersed throughout a wide range of organizational hierarchy levels. The expected research and practical implications are also discussed in the paper.
Variational Gridded Graph Convolution Network for Node Classification

The existing graph convolution methods usually suffer high computational burdens, large memory requirements, and intractable batch-processing. In this paper, we propose a high-efficient variational gridded graph convolution network (VG-GCN) to encode non-regular graph data, which overcomes all these aforementioned problems. To capture graph topology structures efficiently, in the proposed framework, we propose a hierarchically-coarsened random walk (hcr-walk) by taking advantage of the classic random walk and node/edge encapsulation. The hcr-walk greatly mitigates the problem of exponentially explosive sampling times which occur in the classic version, while preserving graph structures well. To efficiently encode local hcr-walk around one reference node, we project hcr-walk into an ordered space to form image-like grid data, which favors those conventional convolution networks. Instead of the direct 2-D convolution filtering, a variational convolution block (VCB) is designed to model the distribution of the random-sampling hcr-walk inspired by the well-formulated variational inference. We experimentally validate the efficiency and effectiveness of our proposed VG-GCN, which has high computation speed, and the comparable or even better performance when compared with baseline GCNs.
Wearable technologies as extensions: a postphenomenological framework and its design implications

Wearable technologies are increasing both in number and variety enabling new ways for collecting personal data, as well as novel interaction modalities. Even though the Human-Computer Interaction (HCI) community has widely explored the potential applications of wearables, its theoretical contribution on this research field has been far from impressive. Most scholars and designers seem to rely on a series of dominant assumptions that look at wearables "from the outside" by focusing on their "external properties." When these assumptions are fully embraced at design-time, however, they may cloud opportunities for designing for the "internal aspects" of our everyday experience. In this article, I propose a theory that looks at wearables "from the inside,"giving a theoretical backdrop to all those wearable designs that pay attention to the internal aspects of interaction. By adopting a postphenomenological approach, I conceptualize wearable devices as "extensions" of our intentionality and introduce the "extension relation" to explain how wearables may alter how we relate to the world. In doing so, I propose a series of design considerations that aim to trace future research lines for all those wearables that are currently designed from an "externalistic" perspective.
Auction-and-Learning Based Lagrange Coded Computing Model for Privacy-Preserving, Secure, and Resilient Mobile Edge Computing

We design a novel encoding model based on Lagrange coded computing (LCC) for private, secure, and resilient distributed mobile edge computing (MEC) systems, where multiple base stations (BSs) act as “masters” offloading their computations to edge nodes acting as “workers”. A two-fold objective of the scheme is: i) efficient allocation of computing tasks to the workers; ii) providing the workers with appropriate incentives to complete their tasks. As such, each master must decide on its offloading requests to the workers including the allocated tasks and service fees to be paid. This problem is complex due to the following reasons: i) masters can be privately-owned or managed by different operators, i.e., there is no communication and no coordination among them; ii) workers are heterogeneous non-dedicated nodes with limited and nondeterministic transmission and computing resources. As a result, the masters must compete for constrained resources of workers in a stochastic partially-observable environment. To address this problem, we define the interactions between masters and workers as a direct stochastic first-price-sealed-bid (FPSB) auction. To analyze the auction, we represent it as a stochastic Bayesian game and develop a Bayesian learning framework to perfect the auction solution.