Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource: A Framework for Community Prediction

Online user communities exhibit distinct temporal dynamics in response to popular topics or breaking events. Despite abundant community detection libraries, there is yet to be one that provides access to the possible user communities in future time intervals. To bridge this gap, we contribute SEERa , an open-source end-to-end community prediction framework to identify future user communities in a text streaming social network. SEERa incorporates state-of-the-art temporal graph neural networks to model inter-user topical affinities at each time interval via streams of temporal graphs and learns temporal vector representations for users. This all takes place while users’ topics of interest and hence their inter-user topical affinities are changing over time. SEERa predicts yet-to-be-seen user communities based on the final positions of users’ vectors in the latent space. Notably, our framework serves as a one-stop-shop to future user communities for Social Information Retrieval and Social Recommendation systems. While there are strong research papers on the community prediction problem, SEERa is the first framework to be publicly released for this purpose.
Identiﬁcation and Location of Multiple Weeds in Grassland for Removal Operation

Weeds are a common issue in agriculture. Image-based weed identiﬁcation has regained popularity in recent years as computing power increases. Researchers have successfully applied weed detection in the crop ﬁeld and have combined the sensor (e.g.camera) and mechanical such as robotic weeders to get the location of the weeds. Meanwhile, many studies also have been conducted on the two classiﬁcations between grass and weed. However, there is no excellent and comprehensive weed dataset in reality because weeds are always similar and diﬃcult to obtain by non-specialists. Moreover, it is challenging to identify weeds from grasslands for their similar colors, sizes, and shapes. In this paper, our goal is to build a natural and eﬀective dataset to train the classiﬁer by extracting features of weeds so that weeds can be accurately identiﬁed and quickly located. We investigate three weeds (Bitter Gentian, Hawk’s Beard, Pedunculate) relatively common in grasslands. Then, we select the typical grassland dominated by the above weeds for data collection. A natural and eﬀective dataset is built and has generality in the scene of actual grassland. Secondly, we extract image features, including Color, Histogram, and orientation gradient histogram(HOG), and make various combinations to accurately and comprehensively reﬂect the actual characteristics of weeds. Thirdly, we propose a ”core zone” algorithm to locate the weeds. The algorithm mainly adopts technology in image processing, such as threshold segmentation and morphological transformations. Experiments show that our binary classiﬁer is more accurate than the comparison method, and the accuracy of the multi-classiﬁer is also high. In addition, the algorithm for weeds location is more eﬃcient than the comparative method.
Virtual Team Leadership and Operation in the Automotive Industry: Profile of a Research Case Study

: The impact of digitalisation on industry and society at large is huge and has parallels with the advent of the internet more than twenty years ago. In the automotive sector, companies are also confronted with the implications of the socalled megatrends of connected car, autonomous vehicles, sharing/subscription, and electrification, which are challenging current business models and working practices. This has brought about new approaches to project management practices, notably those relating to collaborating over distance between and within dispersed teams. Researchers and practitioners have started to think more comprehensively about the complexity of projects with virtual teams, and how best to manage them. This article is the result of the distillation of relevant literature relating to virtual teams and the analysis of in-depth interviews undertaken with industry experts. It puts forward a model (V-CORPS) for virtual team leadership and management. The authors believe these results can be of value in providing guidance for practitioners working in virtual teams, and as an analytical framework for further research studies in this field.
Detection and classification of breast cancer using transfer learning

Breast cancer is among the leading cause of mortality among women in developing as well as under-developing countries. The detection and classification of breast cancer in the early stages of its develop-ment may allow patients to have proper treatment. In this article, we proposed a novel deep learning framework for the detection and classification of breast cancer in breast cytology images using the con-cept of transfer learning. In general, deep learning architectures are modeled to be problem specific and is performed in isolation. Contrary to classical learning paradigms, which develop and yield in isolation, transfer learning is aimed to utilize the gained knowledge during the solution of one problem into an-other related problem. In the proposed framework, features from images are extracted using pre-trained CNN architectures, namely, GoogLeNet, Visual Geometry Group Network (VGGNet) and Residual Networks (ResNet), which are fed into a fully connected layer for classification of malignant and benign cells using average pooling classification. To evaluate the performance of the proposed framework, experiments are performed on standard benchmark data sets. It has been observed that the proposed framework outclass all the other deep learning architectures in terms of accuracy in detection and classification of breast tumor in cytology images.
Disruptive Business Model Innovation in Indonesia Digital Startups

Many studies have investigated how incumbents react to disruptive business model innovation. However, how digital (tech) startups as the initiator performs business model innovation that consciously or not disrupts incumbents from various industries, has not yet been widely analyzed empirically. This study employed a quantitative method with Partial Least Squares-Structural Equation Modeling (PLS-SEM) using SmartPLS 3 software to present and analyze the data. The sample of Indonesia digital startups was taken from the list managed by DailySocial, an Indonesia digital media startup. A self-administered questionnaire was distributed among the Founders and or C-Level of Indonesia startups adopting a random sampling technique. The findings of this study showed that startups apply disruptive business model innovation to survive and scale-up. This study also suggested some predictors of disruptive business model innovation. As an alternative to implementing dynamic capabilities, startups need to have transformation capability in the form of continuous reconfiguration capability; leadership aspects, especially entrepreneurship mentality, which are embodied in strategic orientation; and stakeholder management support.
CHANDRA OBSERVATIONS OF WR 147 REVEAL A DOUBLE X-RAY SOURCE

We report the first results from deep X-ray observations of the Wolf–Rayet (WR) binary system WR 147 with the Chandra High Energy Transmission Gratings. Analysis of the zeroth-order data reveals that WR 147 is a double X-ray source. The northern counterpart is likely associated with the colliding wind region, while the southern component is certainly identified with the WN star in this massive binary. The latter is the source of high-energy X-rays (including the Fe Kα complex at 6.67 keV) whose production mechanism is yet unclear. For the first time, X-rays are observed directly from a WR star in a binary system.
ON SYNTHESIZING DANISH SHORT VOWELS

Danish has a distinctive vowel length opposition which is realized with little differences in vowel qualities. This paper evaluates the concept of synthesizing short vowels from the corresponding long vowels. Different strategies for synthesizing the short vowels from their long counterpart were tested. The first strategy used the original (homogeneous) time-scaling technique built into the signal generation module, the second relied on a new (steady state) time-scaling algorithm. The two time-scaling strategies for synthesizing short vowels from their long counterparts, were compared to using pre-recorded short vowels in a comprehensive listening test. As a reference for the intelligibility score, a set of identical natural stimuli were included. The results showed a great deviation in intelligibility. The steady state time-scaling algorithm reduced the error rate by 50%, but still introduced a significant reduction in intelligibility compared to pre-recorded short vowels.
A contribution to definitions of some fractal concepts

Background. The aim of the present study was to address the dimensional imbalances in fractal geometry, define the modified capacity dimension, calculate its value, relate its value with the scaling exponent and show that such a definition satisfies basic demands of physics, before all the dimensional balance in mathematical equations used in applied sciences. Methods. The study was performed using the basic rules from fractal geometry, l'Hopital's theorem and other relevant facts from geometry and calculus. Results. It was offered the quantitative determination of the self-similarity using the von Koch fractal set as an example. The main result was also the formula for the modified capacity dimension and its relationship to the scaling exponent and fractal dimension. Conclusion. The text includes some important modifications and advantages in fractal theory and allows better communication between fractal geometry and wide readership. It is important to notice that these modifications and quantifications did not affect already known facts in fractal geometry.
HUMAN FACTORS EVALUATION OF A SUBMARINE SPATIAL AWARENESS TRAINING TOOL

SubSafe is a prototype interactive 3D spatial awareness training tool, designed to supplement legacy training media currently in use by Royal Navy (RN) submarine qualification (SMQ) instructors. SubSafe also provides a virtual vessel for SMQ student “visits”, for those occasions when no real boats are alongside, or, when they are, they are not in an appropriate condition to support onboard learning, due to refit status. Navigating decks and compartments in a “first-person” mouse-and-keyboard game style, SMQ students have access to all decks forward of Bulkhead 59 (i.e. from the Control Room forward), comprising over 30 compartments and 500 objects – including major safety-critical items, such as fire extinguishers, hose units, high-pressure air valves, and emergency breathing system masks. A statistical analysis of knowledge transfer data, collated over a year of experimental trials undertaken in collaboration with the RN’s Submarine School, revealed that use of the simulation during classroom training significantly improves the final “walk-round” performance of students (onboard an actual submarine) when compared to that of a control group. Further experiments have investigated the optimum presentation times for SubSafe, comparing a single exposure of the simulation to SMQ students at the end of their 6-week training régime to course-specific exposures during weeks 2 to 4. Significant attitudinal changes have also been recorded, with participants responding more positively to the notion of using games-based simulation in mainstream RN training after exposure to SubSafe.
Development of Wearable Device for Hearing Impaired people Using Arduino

Hearing impaired people are apt to be in danger because they can’t detect danger with sound. Hearing impaired people have less risk-detection ability than non-disabled people because of lack of hearing. There are many devices to help the hearing impaired, such as hearing aids. A hearing aids can be helpful, but it may not be available depending on the degree or type of hearing loss for example, to the hearing-impaired people with little remaining hearing of high frequencies, ordinary hearing aids are not very useful for understanding the high frequency consonants and it requires a high cost, from thousands to tens of thousands of dollars. Also, it is difficult for the underprivileged, such as the low-income bracket and the elderly, to use them because they are difficult to manage. Therefore, this paper describes the development of low-cost wearable device to assistant a hearing-impaired people using Arduino. Also, it accepts values from switches or sensors and can control external electronic devices such as LEDs and motors to create objects that can interact with the environment. In this is paper, through sound sensors, the ambient sound was taken as an analogue value and transmitted to the aduino board, and the vibration motor was operated when the noise was generated, so that the user could be aware of the occurrence of danger.
GALGO: Scalable Graph Analytics Engine Exploiting Parallel DBMSs.

We present GALGO, a system for large scale graph analytics. GALGO promotes easy access to complex graph analytics in a parallel cluster, exploiting parallel database systems as a computation engine for R. The current DBMS technologies are supported: columnar, row and array. In this demonstration we show that fundamental graph algorithms including all pairs shortest path (APSP), single source shortest path (SSSP), PageRank, triangle counting, connected components and reachability are solved completely with queries dynamically generated by our system. Our system presents performance that is very competitive to a state-of-the-art graph system. Furthermore, our out-of-core graph computation can process graphs larger than the system RAM, without compromising performance. GALGO allows the user to execute iteratively complex graph algorithms and simple graph analytics.
Obscured accretion from AGN surveys

Abstract Recent models of super-massive black hole (SMBH) and host galaxy joint evolution predict the presence of a key phase where accretion, traced by obscured Active Galactic Nuclei (AGN) emission, is coupled with powerful star formation. Then feedback processes likely self-regulate the SMBH growth and quench the star-formation activity. AGN in this important evolutionary phase have been revealed in the last decade via surveys at different wavelengths. On the one hand, moderate-to-deep X-ray surveys have allowed a systematic search for heavily obscured AGN, up to very high redshifts (z≈5). On the other hand, infrared/optical surveys have been invaluable in offering complementary methods to select obscured AGN also in cases where the nuclear X-ray emission below 10 keV is largely hidden to our view. In this review I will present my personal perspective of the field of obscured accretion from AGN surveys.
Research on marine organism detection algorithm based on improved YOLOv5

In order to better grasp the distribution of underwater marine organism, this paper combines YOLOv5s and ShuffleNetV2 to propose a more lightweight object detection model SL-YOLO for marine organism. Firstly, ShuffleNetV2 module is used to replace the C3 module of feature extraction in YOLOv5s. In order to further improve the detection accuracy, BiFPN module is used for feature fusion. Finally, DIOU-NMS is used to replace NMS to solve the missing detection. The results show that compared with the original YOLOv5s model, SL-YOLO is equivalent to the original YOLOv5s model in the mAP, which reduces the number of parameters by 39 %, and achieves the balance between accuracy and real-time performance.
Joint full-waveform ground-penetrating radar and electrical resistivity inversion applied to field data acquired on the surface

We exploit the different but complementary data sensitivities of ground-penetrating radar (GPR) and electrical resistivity (ER) by applying a multiphysics, multiparameter, simultaneous 2.5D joint inversion without invoking petrophysical relationships. Our method joins full-waveform inversion (FWI) GPR with adjoint derived ER sensitivities on the same computational domain. We incorporate a stable source estimation routine into the FWI-GPR. We apply our method in a controlled alluvial aquifer using only sur-face-acquired data. The site exhibits a shallow groundwater boundary and unconsolidated heterogeneous alluvial deposits. We compare our recovered parameters to individual FWI-GPR and ER results, and we compare them to log measurements of capacitive conductivity and neutron-derived porosity. Our joint inversion provides a more representative depiction of subsurface structures because it incorporates multiple intrinsic parameters, and it is therefore superior to an interpretation based on log data, FWI-GPR, or ER alone.
Modeling Extremes with d -max-decreasing Neural

The main idea behind extreme value theory (EVT) is to establish a form of the central limit theorem for the maxima of appropriately scaled random variables. EVT characterizes the behavior of the maxima of n independent and identically distributed (i.i.d.) random variables X1, · · · , Xn with continuous distribution function F . More precisely, let Mn = max1≤i≤n Xi, then there exists sequences of real numbers an > 0 and bn such that the limit P [(Mn − bn) /an ≤ x] → H(x) as n → ∞ is non-degenerate. We then say that F is in the maximum domain of attraction of H or equivalently F ∈ MDA(H). This limit is fully identified by the generalized extreme value (GEV) distribution given by:
Simulation and Analysis of Pull-Up Manoeuvre during In-Air Capturing of a Reusable Launch Vehicle

The innovative ‘In-Air-Capturing (IAC)’ recovery method involves winged rocket stages being captured mid-air and towed back to the launch site. This patented approach by German Aerospace Center (DLR), shows potential for substantial cost reduction by eliminating the need for an additional propulsion system during descent. A critical operation in IAC involves the Towing Aircraft (TA) and Reusable Launch Vehicle (RLV) performing a coordinated pull-up, such that the configuration transitions from a descending glide to a powered ascending flight. During this maneouvre, the two vehicles are connected to each other by a rope, allowing the TA to use its propulsion system to tow the RLV and gain altitude. The final goal at the end of this maneouvre is to reach a suitable cruise altitude for the tow-back to the launch site. This paper presents the modelling and simulation of a full-scale RLV and TA performing the Pull-Up Maneouvre of IAC. Important subsystems like aerodynamics, propulsion, guidance and control, as well as external disturbances from the wake of the aircraft are discussed in detail. The RLV and TA are assumed to be connected by a simplified rope, modelled as a rigid link. An analysis is performed to determine the optimal cruise flight conditions for the mated configuration. Trajectory simulations are then performed to identify the challenges and additional requirements for the system. From the simulations, it could be concluded that the configuration could reach the commanded cruise conditions despite external disturbances from the wake. To sum up, the potential improvements and future simulations are discussed.
Black Widow Optimization (BWO) Algorithm in Cloud Brokering Systems for Connected Internet of Things

The Internet of Things (IoT) now connects over nine billion devices. This number is predicted to approach 20 billion in the near future, and the number of things is rapidly expanding, implying that a large amount of data will be created. To handle the connected things, an infrastructure must be built. Cloud computing (CC) has become necessary in the analysis and data storage for IoT. A cloud broker, which is an intermediate in the infrastructure that controls connected things in cloud computing, is discussed in this study. An optimization problem is examined for maximizing the broker's profit and system availability while minimizing request response time and energy consumption. For this purpose, an objective function is proposed and solved using the Black Widow Optimization (BWO) algorithm. Subsequently, the obtained results are compared with the particle swarm optimization (PSO) algorithms. The results indicate that the BWO algorithm could outperform the PSO algorithm, and it can provide much better results considering different scenarios.
Nonlinear Spherical Shells for Approximate Principal Curves Skeletonization

We present Nonlinear Spherical Shells (NSS) as a non-iterative model-free method for constructing approximate principal curves skeletons in volumes of d dimensional data points. NSS leverages existing model-free techniques for nonlinear dimension to remove nonlinear artifacts in data. With nonlinearities removed and topology preserved, data embedded by such procedures are assumed to have properties amenable to simple skeletonization procedures. Given these assumptions, NSS is able extract points in the “middle” of the volume data and hierarchically link them into principal curves, or a set of 1-manifolds connected at junctions.
Can Income Inequality Affects Household Debt?

The present study aims to evaluate the influence of income inequality on household debt by applying the dynamic GMM estimator to a database covering advanced and emerging countries over the period 1994 to 2019. The result shows that income inequality promotes the growth of household debt. Furthermore, higher house prices and financial development increase the household debt. Meanwhile, the economic growth, interest rate and unemployment have negative and significant effect on household debt. The finding of the study suggests the involved authorities formulate suitable policies and initiatives in order to monitor the increase in household debt. Indirectly, this measure can be useful to consider as an early warning signal for crises.
Teachers’ changed perceptions of pupils’ motivation for learning through professional development: the moderating role of classroom ethnic composition

ABSTRACT This study assessed the effect of a teacher professional development (PD) programme that incorporated culturally responsive teaching on Chinese kindergarten teachers’ perceptions of learning motivation of ethnic minority (EM) vs. Chinese pupils in Hong Kong. The moderating role of classroom ethnic composition was examined. Forty-five teachers in the intervention group attended 20 sessions of PD courses over 10 months, whereas 40 teachers who did not attend the current programme formed a control group. Pre- and post-tests were conducted to assess teacher perceptions of learning motivation and engagement of Chinese and EM pupils. Hierarchical structure of data was accounted for with multilevel modelling. Results showed that the intervention resulted in positive changes in teacher perceptions of learning motivation of EM (vs. Chinese) pupils among teachers working in classrooms with low proportions of EM pupils when compared to teachers in the control group. The findings highlighted that teachers’ cultural diversity training programmes should be evaluated in relation to classroom ethnic composition and other contextual factors that influence the quality and frequency of interethnic contact.
Two-particle dark state in the transport through a triple quantum dot

We study transport through a triple quantum dot in a triangular geometry with applied bias such that both singly- and doubly-charged states participate. We describe the formation of electronic dark states\char22{}coherent superpositions that block current flow\char22{}in the system, and focus on the formation of a two-electron dark state. We discuss the conditions under which such a state forms and describe the signatures that it leaves in transport properties such as the differential conductance and shot noise.