Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning-Based Simultaneous Detection and Characterization of Time Delay Attack in Cyber-Physical Systems

Control and communication technologies are key building blocks of cyber-physical systems (CPSes) that can improve the efficiency of the physical processes. However, they also make a CPS vulnerable to cyberattacks that can cause disruptions or even severe damage. This article focuses on one particular type of CPS cyberattack, namely the time delay attack (TDA), which exploits vulnerabilities in the communication channels to cause potentially serious harm to the system. Much work proposed for TDA detection is tested offline only and under strong assumptions. In order to construct a practical solution to deal with real-world scenarios, we propose a deep learning-based method to detect and characterize TDA. Specifically, we design a hierarchical long short-term memory model to process raw data streams from relevant CPS sensors online and continually monitor embedded signals in the data to detect and characterize the attack. Moreover, various strategies of interpreting the outputs of the model are proposed, which allow the user to tune the performance based on different objectives. We evaluate our model on two representative types of CPS, namely power plant control system (PPCS) and automatic generation control (AGC). Code and dataset can be found at: https://github.com/prakharg24/tda For TDA detection, our solution achieves an accuracy of 92% in PPCS, compared with 81% by random forests (RFs) and 72% by k-nearest neighbours (kNNs). For AGC, our solution achieves 98% accuracy, compared with 74% by RFs and 71% by kNNs. It also reduces the mean absolute error in the delay value characterization from about six to two seconds in the PPCS, and from about three seconds to half a second in the AGC, with about 3x to 4x shorter reaction latency in both systems.
HERA Mission LIDAR Altimeter Implementation

The Asteroid Impact & Deflection Assessment (AIDA) is a collaboration between the NASA DART mission and ESA Hera mission. The aim scope is to study the asteroid deflection through a kinetic collision. DART spacecraft will collide with Didymos-B, while ground stations monitor the orbit change. HERA spacecraft will study the post-impact scenario. The HERA spacecraft is composed by a main spacecraft and two small CubeSats. HERA will monitor the asteroid through cameras, Radar, Satellite-to-Satellite doppler tracking, LIDAR, seismometry and gravimetry. In this paper is reported the first iteration on the LIDAR ENGINEERING MODEL ALTIMETER named HELENA. HELENA is a TOF altimeter that provides time-tagged distances and velocity measurements. The LIDAR can be used for support near asteroid navigation and provides scientific information. The HELENA design comprises a microchip laser and low noise sensor. The synergies between these two technologies enable developing a compact instrument for range measurements of up to 14 km. Thermal-mechanical and radiometric simulations of the HELENA telescope are reported in this paper. The design is subjected to vibrational, static and thermal conditions, and it was possible to conclude by the results that the telescope is compliant with the random vibration levels, the static load and the operating temperatures.
Game Theoretical Approaches for Cooperative UAV NOMA Networks

Wireless communication systems assisted by unmanned aerial vehicles (UAVs) can well extend the coverage of conventional communication networks and quickly provide service wherein the communication infrastructures are destroyed. However, radio resources are always limited and coupled with complex interference in the orthogonal multiple access framework. Thus, non-orthogonal multiple access (NOMA), which can enhance spectrum efficiency and inherently has capability to mitigate interference, is integrated with UAV communication networks in this article. We first propose a cooperative UAV NOMA network (CUNN) architecture and analyze its technical challenges. In addition, with the goal of cooperatively or competitively utilizing the limited resources effectively in a distributed manner, game theory shows its potential in addressing various technical challenges in wireless networks. Thus, we further survey game theoretical approaches for NOMA networks and UAV-based relay networks, and provide insights on how these game theoretical approaches can be adopted to solve technical challenges in CUNNs. Finally, aiming to achieve high spectrum efficiency and ensure fairness among user equipments, we propose a UAV-iUE (isolated user equipment) association, UAVs' position optimization, and power allocation algorithm in a dynamic cooperative game-theoretic framework. In the numerical results and discussions Section, the effectiveness of the proposed dynamic cooperative game-theoretic framework is shown through multiple simulations.
On Model Selection for Scalable Time Series Forecasting in Transport Networks

The transport literature is dense regarding short-term traffic predictions, up to the scale of 1 hour, yet less dense for long-term traffic predictions. The transport literature is also sparse when it comes to city-scale traffic predictions, mainly because of low data availability. In this work, we report an effort to investigate whether deep learning models can be useful for the long-term large-scale traffic prediction task, while focusing on the scalability of the models. We investigate a city-scale traffic dataset with 14 weeks of speed observations collected every 15 minutes over 1098 segments in the hypercenter of Los Angeles, California. We look at a variety of state-of-the-art machine learning and deep learning predictors for link-based predictions, and investigate how such predictors can scale up to larger areas with clustering, and graph convolutional approaches. We discuss that modelling temporal and spatial features into deep learning predictors can be helpful for long-term predictions, while simpler, not deep learning-based predictors, achieve very satisfactory performance for link-based and short-term forecasting. The trade-off is discussed not only in terms of prediction accuracy vs prediction horizon but also in terms of training time and model sizing.
Corporate governance: Theories, mechanisms and the case of oil and gas exporting developing countries

How to cite this paper: Faizabad, A. , Refakar, M. , & Champagne, C. (2021). Corporate governance: Theories, mechanisms and the case of oil and gas exporting developing countries. Corporate Ownership & Control, 18(3), 86-103. https://doi.org/10.22495/cocv18i3art8 Copyright © 2021 The Authors This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). https://creativecommons.org/licenses/by
Semantic Matching for Short Texts: A Cross Attention Mechanism

Semantic matching is a hot topic in the field of NLP. It is widely used in information matching, machine translation, intelligent Q&A, and answering system. With the rapid development of artificial intelligence, people pay more attention to friendly human-computer interaction and how to obtain accurate and effective information. Aiming at the problem of the diversity and structure of Chinese, this paper introduces the cross attention mechanism. Texts provide attention to each other, then to information retrieval. Experiments on aetc-nlp dataset show that the method is effective.
Optimization Research on Abnormal Diagnosis of Transformer Voiceprint Recognition based on Improved Wasserstein GAN

Transformer is an important infrastructure equipment of power system, and fault monitoring is of great significance to its operation and maintenance, which has received wide attention and much research. However, the existing methods at home and abroad are based on model analysis for detection and diagnosis techniques, and their application is limited by the maturity and applicability of expert knowledge. In recent years, deep learning artificial intelligence has made important breakthroughs, however, the relevant research is still mainly focused on the field of speech, and intelligent diagnostic techniques on machine voice patterns have just begun. Therefore, this article adopts an unsupervised learning anomaly detection method based on WGAN-GP to diagnose the electrical and mechanical anomalies of transformer equipment. The experimental results show that the method can effectively identify transformer anomalies and provide an idea for artificial intelligence-based machine voice pattern recognition.
Influence of Visual and Haptic Feedback on the Detection of Threshold Forces in a Surgical Grasping Task

Feedback from sensory modalities is crucial for the precise grasping of tissues during minimally invasive robotic surgery. The aims of the study are to determine the influence of visual and haptic feedback on the detection of threshold forces and to evaluate the applicability of the sensory integration model to a surgical grasping task. A sensorized surgical grasper and a fingertip haptic force feedback device were used. Three types of stimuli were presented (i.e. visual-alone, haptic-alone, and bimodal visual and haptic stimuli). Threshold forces of 100 mN and 87.5 mN were detected for visual and haptic feedback, respectively. When bimodal feedback was provided, the participants detected a threshold force of 75 mN. The threshold force for the bimodal condition was 28.6% lower than the visual-alone feedback and 15.4% lower than the haptic-alone feedback stimuli. Our bimodal condition results showed that there was a 13.1% difference between the experimental result and the predicted value from the sensory integration model. The threshold force discrimination was strongly influenced by the haptic force feedback. It is likely that the tissue stiffness can be more intuitively perceived through the direct force stimulation of the fingertip than just by visual observation alone. Cues like small deformations or changes in the grasping angles of the surgical tool are more difficult to interpret visually as compared to the haptic modality.
Untersuchung von Standorteffekten im seismisch aktiven Gebiet des Odenwaldes in Hessen

Untersuchungen zur Erdbebenaktivität in Hessen zeigen, dass lokale seismische Ereignisse für vergleichbare epizentrische Entfernungen lokal unterschiedliche Intensitäten und Gebäudeschäden erzeugen. Eine mögliche Erklärung für diese Unterschiede ist das Vorhandensein unverfestigter Sedimentschichten. Diese können zu lokalen Verstärkungen der durch seismische Ereignisse verursachten Bodenbewegungen führen. Im Zuge eines Bebens 2014 der Magnitude 4,2 bei Darmstadt sollten Untersuchungen klären, ob solche Verstärkungen lokal auf die seismischen Signale einwirken und ob der Frequenzgehalt der so verstärkten Signale auch mit den Resonanzfrequenzen der Bebauungsstrukturen zusammenfällt. Insgesamt wurden die spektralen Eigenschaften des Untergrundes in drei Gebieten im Odenwald in Südhessen an über 180 einzelnen Messpunkten untersucht. Es konnten Hinweise auf einen Zusammenhang zwischen der Struktur des lokalen Untergrunds und den Informationen ausgewerteter makroseismischer Fragebögen zum beschriebenen Erdbeben ermittelt werden. Die Ergebnisse zeigen eine interessante Korrelation zwischen den spektralen Eigenschaften und der Topographie des Gebiets. Darüber hinaus konnten lokal variierende Untergrundstrukturen bestimmt werden, die möglicherweise Signalverstärkungen verursachen und somit wiederum für die räumliche heterogene Verteilung der berichteten Gebäudeschäden verantwortlich sein könnten.
Ordering Depletion in Inventory Policy with Imperfect Products and Backorder Rebate

This study presents an inventory model for imperfect products with depletion in ordering costs and constant lead time where the price discount in the backorder is permitted. The imperfect products are refused or modified or if they reached to the customer, returned and thus some extra costs are experienced. Lately some of the researchers explicitly present on the significant association between size of lot and quality imperfection. In practical situations, the unsatisfied demands increase the period of lead time and decrease the backorders. To control customers' problems and losses, the supplier provides a price discount in backorders during shortages. Also, an order’s policies may result in including some imperfect products in arrival lots. A discount on price may be offered by the supplier on the out-of-stock products to manage the backorder problems. The study aims to develop a model with imperfect products by permitting the price discount in backorders, and the cost of ordering is considered a decision variable. First, it is assumed that the demand for lead time is followed by a normal distribution and then stops it and assumed that the first two moments of demand for lead time are known. Further, the minimax distribution method is used to solve this model, and a separate algorithm is designed. In this study, two models are discussed with and without a normally distributed rate of demand. The current study verified with the help of some numerical examples over various model parameters.
Advancing Remote Healthcare Using Humanoid and Affective Systems

Social distancing and remote work are becoming more prevalent in the post-covid world. At the same time, there is a huge demand for remote healthcare sessions as well. Although a growing number of such sessions are now utilizing online platforms as a medium of communication, other critical parameters such as the affective state and other feedback opportunities are lost during the transmission of this digital information. This paper presents a solution that leverages a brain-computer interface system for this affective feedback and a humanoid robot for teaching effectively during remote sessions. The solution uses Kinect as a sensing mechanism for the trainer. It utilizes state-of-the-art deep learning algorithms at the back-end to understand the emotional state of the trainee. The training poses (from humanoid’s camera feed and kinect) are calculated using AlphaPose compared using inverse kinematics. To ascertain the trainees’ state (high valence and arousal vs. low valence and arousal), a Capsule Network was used that gives an average accuracy of 90.4% for this classification with a low average inference time of 14.3ms on the publicly available DREAMER and AMIGOS datasets. The system also allows real-time communication through the humanoid, making this experience even more distinct for the trainee.
A streamlined approach to the determination of trace elements in foods

The Nutrition Labeling Education Act of 1990 (NLEA) mandated significant reform of food labels and required extensive chemical testing and analytical method development to implement. Many minerals and nutrient metals were targeted for labeling. Part of the analytical challenge to be met through compliance with NLEA results from the large numbers of samples to be tested and widely divergent levels of the elements of interest within the samples. Ideally, any new methodology developed for food analysis should be generic and applicable to many elements and matrices simultaneously. This work presents one streamlined approach for sample preparation and determination of trace metals that is applicable to all food types as categorized by the Food Matrix Triangle. Inductively coupled plasma optical emission spectrometry (ICP-OES) with an axially-viewed plasma was used to determine the metals. Sample preparation time has traditionally been the primary limitation to sample throughput and this was minimized using microwave digestion. Multiple dilution and analysis iterative steps were not necessary due to the wide linear dynamic range of the ICP-OES. Trace and macro levels of nutrients were determined simultaneously in all samples. Quality assurance (QA) was performed through the analysis of replicate samples, laboratory-fortified samples and blanks, and standard reference materials (SRM). Acceptable precision, spike recoveries, and agreement with certified and U.S. Department of Agriculture values were attained.
CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders

In recent years, deep learning (DL) has attracted increasing attention in hyperspectral unmixing (HU) applications due to its powerful learning and data fitting ability. The autoencoder (AE) framework, as an unmixing baseline network, achieves good performance in HU by automatically learning low-dimensional embeddings and reconstructing data. Nevertheless, the conventional AE-based architecture, which focuses more on the pixel-level reconstruction loss, tends to lose some significant detailed information of certain materials (e.g., material-related properties) in the reconstruction process. Therefore, inspired by the perception mechanism, we propose a cycle-consistency unmixing network, called CyCU-Net, by learning two cascaded AEs in an end-to-end fashion, to enhance the unmixing performance more effectively. CyCU-Net is capable of reducing the detailed and material-related information loss in the process of reconstruction by relaxing the original pixel-level reconstruction assumption to cycle consistency dominated by the cascaded AEs. More specifically, cycle consistency can be achieved by a newly proposed self-perception loss, which consists of two spectral reconstruction terms and one abundance reconstruction term. By taking advantage of the self-perception loss in the network, the high-level semantic information can be well preserved in the unmixing process. Moreover, we investigate the performance gain of CyCU-Net with extensive ablation studies. Experimental results on one synthetic and three real hyperspectral data sets demonstrate the effectiveness and competitiveness of the proposed CyCU-Net in comparison with several state-of-the-art unmixing algorithms.
Biochemical performance of some Sub1 rice (Oryza sativa) genotypes under submergence stress in Odisha

Recurring floods in Asia cause poor crop establishment. Yields decline drastically when plants are completely submerged for a few days. Traditional rice genotypes predominate because they have acquired moderate tolerance to flooding but they carry the penalty of inherently lower grain yields. Genotypes with tolerance to complete submergence were recently developed in the background of popular genotypes by transferring the submergence tolerance gene SUBMERGENCE1 (Sub1) from the highly tolerant Indian landrace FR13A. The present study evaluated thirteen pairs of Sub1 near-isogenic lines (NILs) together with FR13A and other check genotypes in pot culture conditions to assess the survival and growth processes occurring during submergence and recovery that are associated with Sub1. The present experiment was conducted in Department of Plant physiology, OUAT, Bhubaneswar during kharif 2017 and kharif 2019 to screen out the NILs rice genotypes for submergence adaptation traits under coastal regions of Odisha. The present study indicated that among the twenty genotypes, IR-85086-Sub 33-3-2-1(4.87 t ha-1), IR-88760-Sub 93-3-3 (4.7 t ha-1) and Swarna Sub -1 (4.57 t ha-1) contributed highest yield under submergence.
Whither modern business incubation? Definitions, evolution, theory, and evaluation

As chronicled by Adkins (2002), the first business incubator started in early 1959 in Batavia, New York, when Joseph Mancuso acquired an abandoned 850 000 square foot factory from Massey-Ferguson. Due to the closure of the factory, 2,000 people lost their jobs, and Mancuso undertook the task of trying to reduce unemployment by filling the old factory premises with small businesses; this came to be known as Batavia Industrial Center. For a fee, Mancuso offered office space, shared service, help with financing, and some business coaching. Among the first tenants was a company that developed an egg hatchery to artificially produce chickens, which led to the facility being jokingly referred to as the ‘incubator’. Shortly, thereafter in 1964, University City Science Center (UCSC), the nation’s first urban research park and incubator was set up in downtown Philadelphia, in collaboration with a consortium of the area’s 28 academic institutions. The UCSC accepted startup firms, providing them with supportive environment throughout the 1970s and beyond, under the leadership of Randall Whaley. The National Business Incubation Association (NBIA) recognized Mancuso and Whaley as incubation industry founders by honoring them with an award in 1998. They were credited for the initiation and firmly rooting of the business incubation concept respectively through formal programs where new companies in the startup phase were supported to improve their chances of success (Adkins 2002; Bruneel et al. 2012). Application of the concept, however, did not become widespread in the country until well into the 1970s and 1980s. The origin of the technology-oriented business incubation support is generally linked to universities’ involvement in technology development and commercialization efforts undertaken in the US in the post-WWII period, the pioneering example of which is the Stanford Research Park near Silicon Valley in California, which was established in 1951. Affiliated with Stanford University, the Park housed R&D units of large technology firms, university spin-offs, and promising startups, specific to the contemporary research park model. Second, are the concerted efforts at MIT during the post-WWII period, where its president Karl Compton led the drive of university technology transfer through spin-offs to benefit from the commercial use of prior military developments. During this period, the support of spin-offs predominantly originated from MIT’s research labs, many of which were located in MIT owned facilities in the surrounding areas, by early venture capital firms such as the American Research and Development (ARD) corporation, the ‘brainchild’ of Compton. ARD offered business advice and funds to these spin-offs, which shows that MIT had a key role in seeding the technology-based firm creation; some of these startups were later located to the Boston Route 128 (Roberts 1991). This, apparently, was a regionally distributed model of technology business incubation.
Modeling the Mechanisms of Clay Damage by Molecular Dynamic Simulation

We carried out MD simulations to review the mechanisms of clay damage under hydration, the manners clays interact with water and cations, and the structures they form in between aluminosilicate sheets and offered an alternative explanation as to how clay structures become instable after certain water concentration. Water molecules form one, two, and sometimes three layers parallel to the clay surfaces. The cations already present in montmorillonites (the most representative of the smectites) or coming with low salinity water will either be adsorbed by the clay surfaces or become fully hydrated, detached from the surfaces, and located in the midst of the interlayer space between clay surfaces, while water molecules that do not hydrate the cations invade the clay structures, making them unstable. The driving force of this phenomenon happens to be the hydration energy of the cations in question.