Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Navigation System for Vehicle Communication Performing Conditional Automation Using Deep Learning

Intelligent Transport System applications play a significant role in accident prevention at the roadside. ITS system was introduced to find the solutions in vehicle parameter failures. Nowadays the transport security system can automate the vehicle driving task that means to give proper instruction to human, as well as to provide automated communication to control the vehicle. In this paper, the vehicle was connected with the internet environment that can interact with other vehicles with its range using the method as mobility services. All the communicated information stored at the cloud database server, that will help to retrieve the data and also aids to produce the data when the vehicle parameter gets failed. The interruption will found when it get disturbs with its normal functionalities. So the system can predict the vehicles at the roadside environmental conditions and to counter the deeds of other accidental incidents. The acquired value is analyzed with the stored value to check whether the vehicle parameter gets failed or not through an EVNS emergency vehicle notification system. If Vehicle failure was initiated, the alert system informs the vehicle and automatically gets slowdown at the road site.
The Complex Indoor Localization Technique B ased on Ontology and SLAM-M ethod

— One of the problems associated with methods using to determine the location of users of mobile devices within indoors, based on Wi-Fi radio signals, is the time-consuming procedure for setting up and placing equipment, which includes building a map of the room, creating a map of radio signals, or calibrating the radio signal propagation model. In solving this problem, it is planned to use complex indoor localization technique based on the usage of ontology and the SLAM method, which includes the phase of forming a training sample, as well as the phase of simultaneous navigation and mapping. The SLAM method is based on The Gaussian Process Latent Variable Model (GP-LVM) and includes requirements for correlation of the signal level values of the nearest points of the user's localization, for which the parameters of the correlation function are configured based on the training sample. The proposed method is based on solving the regression problem using machine learning methods to form a training sample, as well as solving the problem of reducing the dimension for simultaneous navigation and map construction. As a training sample, the smartphone's internal sensor readings (steps and rotation angles) and Wi-Fi received signal strength values obtained using crowd calculations are used. The resulting training sample is used to determine the parameters of the correlation function that sets the correlation between the user's localization points. The proposed ontology is intended to determining the user's entrance to the room and searching for Wi-Fi access points.
The effect of promoted self-efficacy through induced success performance in junior high school students

Kudo and Mori (2015) used a presentation trick to covertly present a set of easier anagram tasks to 20 junior high school students while their 60 classmates observed more difficult tasks. These two samples were selected randomly, and their IQ scores counterbalanced. The target students outperformed their classmates, and showed greater self-efficacy after their success. However, they did not examine the relationship between this self-efficacy and performance two months later on another set of anagram tasks. Here, we analyzed their data in this regard. We found those students whose self-efficacy was raised by the former task performed better than the control students whose self-efficacy ratings were in the middle range. These findings suggest that students with high self-efficacy performed better than their classmates did on new anagram tasks, even when adjusting for IQ. (133 words)
A Graph Embedding Technique for Weighted Graphs Based on LSTM Autoencoders

A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each node-weight sequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.
Two-Attribute Skew Free, Isolated CP Theorem, and Massively Parallel Joins

This paper presents an algorithm to process a multi-way join with load $\tO(n/p^2/(α φ) )$ under the MPC model, where n is the number of tuples in the input relations, α the maximum arity of those relations, p the number of machines, and φ a newly introduced parameter called the \em generalized vertex packing number. The algorithm owes to two new findings. The first is a \em two-attribute skew free technique to partition the join result for parallel computation. The second is an \em isolated cartesian product theorem, which provides fresh graph-theoretic insights on joins with α \ge 3$ and generalizes an existing theorem on α = 2$.
Unveiling of Potential Zika Virus RNA polymerase Inhibitors Using Structure based Virtual Screening

Zika virus (ZIKV) infection has been associated with Guillain-Barre syndrome in adults and Microcephaly in infants. However, there is no currently approved antiviral drug against ZIKV. The 103-kDa NS5 protein is the largest ZIKV protein whose C-terminal portion has RNA-dependent RNA polymerase activity and N-terminal RNA cap-processing activity (methyltransferase domain). Therefore, it is an important target for synthesis/discovery of novel anti-ZIKV drugs. A database consisting of 720 marine and plant derived compounds of South Africa was used for virtual screening against a potential inhibitor binding site of ZIKV’s NS5 protein. All its cataloged compounds were screened for their drug likeliness using Lipinski’s parameters. Thereafter, 200 selected compounds were used for virtual screening for their affinities for the target site. Finally, top 5 ligands were redocked to get a detailed insight into their interaction with conserved amino acids of the target site. An additional screening was mounted to filter out the potential toxic compounds among the top 5 likely candidates by AdmetSAR web-server. Two lead compounds, SANC00235 and SANC00257 fulfilled all the criteria laid down by us. These compounds can be potential candidates for further in vitro and in vivo studies in order to validate their anti-ZIKV activity.
A neural dynamic process model of combined bottom-up and top-down guidance in triple conjunction visual search

The surprising efficiency of triple conjunction search has created a puzzle for modelers who link visual feature binding to selective attention, igniting an ongoing debate on whether features are bound with or without attention. Nordfang and Wolfe (2014) identified feature sharing and grouping as important factors in solving the puzzle and thereby established new constraints for models of visual search. Here we extend our neural dynamic model of scene perception and visual search (Grieben et al., 2020) to account for these constraints without the need for preattentive binding. By demonstrating that visual search is not only guided top-down, but that its efficiency is affected by bottom-up salience, we address a major theoretical weakness of models of conjunctive visual search (Proulx, 2007). We show how these complex interactions emerge naturally from the underlying neural dynamics.
Characterization of Electrode with Cu2O-ZnO/C and Pt-Ru/C Catalyst for Electrochemical Reduction CO2 to CH3OH

Electrode characterization has been carried out with Cu 2 O-ZnO/C and Pt-Ru/C catalysts to convert carbon dioxide to methanol. Characterization are carried out with XRD analysis, Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS). The electrodes are made by distributing Cu 2 O-ZnO/C and/or Pt-Ru/C catalyst by spraying method. The results of XRD analysis showed that the characteristic peak of platinum was 2θ = 39. 7⁰ - 40. 74⁰ with an intensity of 970 cps and 1384 cps and the diffraction peak of Ru oxide was found at 47. 02⁰ with an intensity of 923 cps. The peak of Cu 2 O characteristics appeared at 36. 12⁰ with an intensity of 88 cps and the peak for ZnO characteristics at 68. 2⁰ with an intensity of 13 cps. The test results with the cyclic voltammetry method showed that the electrode with a Cu 2 O-ZnO/C catalyst obtained the highest ECSA value which was 26.044 cm 2 /g, with an electrical conductivity value of 3.4 x 10 -3 S/cm and a total real resistance of 5.9425 Ω .
The need for comparative evaluation of online clustering approaches for topic and event detection on Twitter

With the advent of Twitter as the undisputed market leader in social micro-blogging and the steady growth of user-generated content (over 500 million tweets daily), the use of Twitter as news reporting mechanism is constantly increasing. As a response to this developing trend a number of topic and event detection techniques have been proposed in order to meet the challenging task of detecting valid interesting information in a continuous stream of short natural language text. According to a broad classification based on their underlying methods, existing topic and event detection approaches can be grouped in two main categories. The former discovers events by clustering documents if the semantic distance between documents and clusters of documents is within a similarity threshold [Yiming98], while the latter analyzes word distributions and detects events by clustering words together [Kleinberg02]. Although some form of evaluation as to the quality of the underlying techniques has been conducted for event detection approaches in general, no sufficient comparative evaluation effort can be documented for online clustering approaches falling in the first category.
GNSS NAVIGATION IN DIFFICULT ENVIRONMENTS: HYBRIDIZATION AND RELIABILITY

Satellite navigation is critical in signal-degraded environments where GNSS signals are strongly corrupted. In these cases the use of a single GNSS does not guarantee an accurate and continuous positioning. A possible approach to solve this problem is the use of multi-constellation that provides additional measurements improving the redundancy of the system. Measurements in urban scenario are strongly affected by gross errors, degrading navigation solution; hence a quality check on redundant measurements, defined as RAIM is necessary. The classical RAIM algorithms, developed for aviation, need to be redesigned for urban applications, considering frequent multiple blunders. The FDE schemes analyzed in this research are the Subset, the Forward-Backward and the Danish; they are obtained by combining different basic statistical tests. Specifically a so-called Global Test is adopted to verify the measurement self-consistency. A Local Test is used to identify and reject a blunder into a data set declared not-consistent. The considered FDE methods are modified to optimize their behavior in urban scenario. Specifically a preliminary check based on the WARP parameter, generalization of the classical ARP, is implemented to screen out bad geometries. Moreover a large blunder could cause multiple test failures, inducing incorrect measurements exclusions; hence a separability index is implemented to avoid the incorrect exclusion of blunder-free measurements. Development and testing RAIM algorithms for multi-constellation in multiple blunder case is a main target of this work. For indoor navigation pseudolites are adopted and different positioning algorithms are developed. Indoor positioning has been demonstrated with meter level of accuracy.
To hold or not to hold? - Reducing Passenger Missed Connections in Airlines using Reinforcement Learning

Missed connections at transit airports are a source of both poor customer experience and reduced airline operational efficiency. Air-lines typically handle missed connections by rebooking customers. Recently, airlines have started holding departing flights for some time in a rule-based manner to avoid missed connections. However, rule-based heuristics typically use information local to a flight and do not learn in a globally informed way across the entire network. We complement existing approaches by learning a policy for holding a flight to avoid misconnections, using reinforcement learning (RL). The state presented to the RL agent uses forecasted flight-specific context; and measured network-wide context. The reward uses components that trade off the decrease in on-time performance due to the hold decisions, for a decrease in missed connections. We attribute the global rewards to individual local hold actions through a novel delay tree that approximates the network interactions. Multiple flights are handled through the same instance of the agent handling them in sequence with varying state information. We evaluate our approach for two different airlines with training and testing over a microsimulator that uses real-world data for calibration. Across different algorithms (DQN, AC, A2C, DDPG), we find that the best performing RL-based agent is able to reduce significantly more (up to 50%) missed connections for a minimal decrease (˜5%) in on-time performance; when compared with a current rule-based heuristic. Further, the approach is tunable and able to transfer learn across different airlines
Inbreeding depression and heterosis vary in space and time in the serpentinophyte perennial Minuartia smejkalii

Heterosis has been used occasionally in attempts to save endangered populations of plants. However, there is a lack of knowledge on how inbreeding, heterosis and outbreeding depression jointly influence fitness of species with small, fragmented populations. Understanding the joint action of these processes is further complicated by variation among populations and environments in time and the stochastic effects of genetic drift. We assayed offspring of hand pollinated plants from three natural populations of Minuartia smejkalii, an endemic serpentinophyte perennial, in two soil treatments and three competitive environments for two consecutive years. We detected no early-acting inbreeding or outbreeding depression in a greenhouse. Late-acting inbreeding depression in the common garden was low in small populations and could be counteracted by heterosis. Both inbreeding depression and heterosis varied among traits, between years and with environmental stress. Notably inbreeding depression declined as environmental stress increased and phenotypic variation in the population decreased. Moreover, heterosis increased with environmental stress. Based on our results, we recommend a conservation approach in which between-population outbred plants are introduced into very small populations to maximise the benefits of heterosis in M. smejkalii. Nevertheless, extrapolation to other species, or even other populations of M. smejkalii of limited size, should be done with caution because of the stochastic effects of genetic drift that result in unique genetic consequences of outbreeding for each population.
Is Carbon Risk Priced in the Cross-Section of Corporate Bond Returns?

This paper examines the pricing of a firm's carbon risk in the corporate bond market. Contrary to the "carbon risk premium" hypothesis, bonds of more carbon-intensive firms earn significantly lower returns. This effect cannot be explained by a comprehensive list of bond characteristics and exposure to known risk factors. Investigating sources of the low carbon alpha, we find the underperformance of bonds issued by carbon-intensive firms cannot be fully explained by divestment from institutional investors. Instead, our evidence is most consistent with investor underreaction to the predictability of carbon intensity for firm cash-flow news, creditworthiness, and environmental incidents.
Feasibility of Leum Pua glutinous rice substrate for sugar syrup and vinegar production by raw starch degrading enzyme hydrolysis

Raw starch degrading enzyme (RSDE) from Laceyella sacchari LP175 was applied for hydrolysis of Leum Pua glutinous rice powder (200 g/L) and compared with other brown and black rice varieties in 250 mL Erlenmeyer flasks at 50°C for 12 h. Results showed that Leum Pua glutinous rice gave the highest liberated sugar syrup and anthocyanin contents at 11°Brix and 82.27 mg/L, respectively. Leum Pua glutinous rice powder (250 g/L) was up-scaled for hydrolysis in 5.0 L glass jar by simultaneous liquefaction and saccharification at 50°C for 12 h and obtained liberated sugar syrup at 13°Brix. Sugars were fermented by two mixed strains of Saccharomyces cerevisiae for 10 d without shaking at room temperature. During fermentation, total soluble solids decreased from 22 to 10°Brix while alcohol content increased to 15% (w/v). Leum Pua glutinous rice wine was subjected to acetic acid surface culture fermentation (SCF) with Acetobacter aceti TISTR 354 at room temperature. After 6 d of fermentation, acidity of Leum Pua glutinous rice vinegar increased to 5.7%. Antioxidant activity and vinegar concentration were determined during acetic acid fermentation for 6 d. Results suggested the feasibility of producing Thai rice vinegar using cold hydrolysis with raw starch degrading enzyme without the heating process.
Corporate Ownership and Firm Performance

All the approaches of the theory of the firm are based on the fundamental hypothesis that the main source of value from the firm’s performance is the ownership of the physical and monetary capital. This approach has always been challenging already from the decade of ’30s, mainly after the appearance of the path breaking work of Berle and Means (1932), claiming that in the big corporations there exists explicit separation between ownership and control and that the decision are made by multiple hierarchical levels of administration where ownership and management form a complex mixture. Nowadays, where the performance of the firm acquired high degree of complexity the problem of the relation between ownership and control has become decisive. The present article is an effort to statistically test the effectsif anyof the ownership structure, as measured by the distribution of the shares in the shareholders, the book value and the market value of the firm, the recovery costs of the firm’s assets and the capital expenditure on the firm’s performance, particularly on the firm performance, as measured by the Tobin’s q. The concrete statistical analysis on the existing data does not reveal any clear connection between corporate ownership and performance.
Dynamic Hand Gesture Recognition using 2D Convolutional Neural Network

—In recent years, many algorithms arise in the ﬁeld of dynamic gesture recognition. Traditional methods lack of accuracy and rely heavily on hand-crafted features. Due to powerful ability of feature extraction, deep learning methods show amazing performance, especially Convolutional Neural Network (CNN) that has been addressed lately in video analysis. However, some CNN-based methods such as C3D and Two-Stream are time-consuming and a great deal of computation is needed. In this paper we propose a novel method for hand gesture recognition based on 2D CNN. For a given video sequence, the input of the network is no longer a number of sampled images. We encode each sampled image ﬁrstly and encoding vector of each image is just got. For each feature vector encoded, we stack them to generate a new image that contains rich spatio-temporal information of gesture. The new image instead of origin video is then sent in traditional 2D CNN model and the classiﬁcation result of gesture is ﬁnally obtained. 3D spatio-temporal information has been compressed into 2D presentation in the course of classiﬁcation, and the computation and time to be consumed are reduced. At the same time, it reduces the risk of overﬁtting to a certain extent. Based on proposed method, the performance is evaluated on the Microsoft Research 3D dataset (MSR3D). The experiment shows that our approach is highly effective and efﬁcient at classifying a wide variety of actions on MSR3D.
IT FLEXIBILITY, CAPABILITIES AND IT-BUSINESS ALIGNMENT: DO ORGANIZATIONAL CHARACTERISTICS AND CONTEXT MATTER?

The relationship between IS flexibility, IT capability, and IT-Business alignment is well recognized in both theoretical and practitioner’s literature. However, there is much less understanding upon what factors these relationships might be contingent. At the same time, there is growing evidence that both internal and external factors may be important and influential. Therefore, to enhance the existing IT-Business alignment research, this study presents and empirically tests a moderator framework of IS flexibility, IT capability, and IT-Business alignment. The influence of both endogenous (organizational size, strategic orientation) and exogenous (environmental uncertainty, industry environment) moderators is considered. The research confirmed the positive relationships between IT flexibility, IT capability, and IT-business alignment. System infrastructure, connectivity, and IT relationship to organizational tasks showed individual statistically significant influences. Environmental uncertainty and, to a lesser degree, firm’s strategic orientation demonstrated significant moderating effects. Theoretical and practical implications of the findings are discussed
of Birmingham Maritime moving target long time integration for GNSS-based passive bistatic radar

— The paper addresses the exploitation of Global Navigation Satellite Systems (GNSS) as transmitters of opportunity in passive bistatic radar (PBR) systems for maritime surveillance. The main limitation of this technology is the restricted power budget provided by navigation satellites, which makes it necessary to define innovative moving target detection techniques specifically tailored for the system under consideration. To this aim, this paper puts forward long integration time techniques able to collect the signal energy over long time intervals (tens of seconds), allowing the retrieval of suitable levels of signal-to-disturbance ratios for detection purposes. A local plane based technique is first considered, providing target detection in a plane that represents the section of maritime area covered by the radar antenna. As a suboptimum solution in terms of achievable integration gain, but more efficient from a computational point of view, a second technique is considered working in the conventional bistatic range&Doppler plane (basic plane based). Results against synthetic and experimental datasets show the effectiveness of the proposed techniques.