Miracle-dz/UniFAR-FTUs huggingface dataset:
Welcoming Robot Behaviors for Drawing Attention

Drawing the attention of passersby is a basic task of a social robot to initiate an interaction in a public environment (e.g., shopping malls, museums or hospitals). Humans use several social cues, both verbal and nonverbal, to draw the attention of others. In this study, we investigate whether similar behaviors can also be effectively used by a social robot for drawing attention. To this end, we setup a humanoid robot (Pepper) to act as a welcoming robot at the entrance of a university building. The behaviors selected for Pepper include one or a combination of behavioral modalities (i.e., a waving gesture, utterance and movement). These behaviors are triggered automatically using the output of people detection software which tracks passersby and monitors their head keypoints (nose, eyes, and ears). The reactions of people toward Pepper are observed and recorded by means of an observation sheet. For several weeks, we deployed Pepper at the entrance with the aim of wearing off the novelty effect. In our final study, we collected data from several hundreds of passersby $(\mathbf{N}=364)$ and conducted post-interviews with randomly selected ones $(\mathbf{N}=28)$. Passersby noticed Pepper at the entrance and clearly recognized its role as a welcoming robot. In addition, Pepper was able to draw more attention when displaying a combination of behavioral modalities. However, passersby did not recall the robot utterance as they, for example, were unable to reproduce it or mistakenly claimed that the robot said something when it was only waving.
Explanation-Based Reward Coaching to Improve Human Performance via Reinforcement Learning

For robots to effectively collaborate with humans, it is critical to establish a shared mental model amongst teammates. In the case of incongruous models, catastrophic failures may occur unless mitigating steps are taken. To identify and remedy these potential issues, we propose a novel mechanism for enabling an autonomous system to detect model disparity between itself and a human collaborator, infer the source of the disagreement within the model, evaluate potential consequences of this error, and finally, provide human-interpretable feedback to encourage model correction. This process effectively enables a robot to provide a human with a policy update based on perceived model disparity, reducing the likelihood of costly or dangerous failures during joint task execution. This paper makes two contributions at the intersection of explainable AI (xAI) and human-robot collaboration: 1) The Reward Augmentation and Repair through Explanation (RARE) framework for estimating task understanding and 2) A human subjects study illustrating the effectiveness of reward augmentation-based policy repair in a complex collaborative task.
Dual-Band Spiral Printed Quadrifilar Helical Antenna Miniaturized by Surface and Inner Dielectric Loading

The design and implementation of a spiral-folded printed quadrifilar helical antenna (FPQHA) is presented for a global positioning system (GPS) <inline-formula> <tex-math notation="LaTeX">$L1$ </tex-math></inline-formula>-/<inline-formula> <tex-math notation="LaTeX">$L2$ </tex-math></inline-formula>-band application. The proposed FPQHA can be miniaturized by inserting dielectric loading inside the helix, covering the radiation elements by the dielectric sealant and turning the radiation elements into the form of parallelogram spirals. The manufactured antenna’s radiation elements are printed on a hollow rod ceramic substrate with a diameter of 12 mm and a height of 24.7 mm. The measurement results of the proposed FPQHA mounting on unmanned platform fit very well with the simulations, which validates the design methods and suggests that the proposed FPQHA is suitable for GPS <inline-formula> <tex-math notation="LaTeX">$L1$ </tex-math></inline-formula>-/<inline-formula> <tex-math notation="LaTeX">$L2$ </tex-math></inline-formula>-band applications.
Pricing, no-arbitrage bounds and robust hedging of instalment options

An instalment option is a European option in which the premium, instead of being paid up-front, is paid in a series of instalments. If all instalments are paid the holder receives the exercise value, but the holder has the right to terminate payments on any payment date, in which case the option lapses with no further payments on either side. We discuss pricing and risk management for these options, in particular the use of static hedges, and also study a continuous-time limit in which premium is paid at a certain rate per unit time.
TSDL: A Two-Stage Deep Learning Model for Efficient Network Intrusion Detection

The network intrusion detection system is an important tool for protecting computer networks against threats and malicious attacks. Many techniques have recently been proposed; however, these techniques face significant challenges due to the continuous emergence of new threats that are not recognized by the existing detection systems. In this paper, we propose a novel two-stage deep learning model based on a stacked auto-encoder with a soft-max classifier for efficient network intrusion detection. The model comprises two decision stages: an initial stage responsible for classifying network traffic as normal or abnormal using a probability score value. This is then used in the final decision stage as an additional feature for detecting the normal state and other classes of attacks. The proposed model is able to learn useful feature representations from large amounts of unlabeled data and classifies them automatically and efficiently. To evaluate and test the effectiveness of the proposed model, several experiments are conducted on two public datasets: an older benchmark dataset, the KDD99, and a newer one, the UNSW-NB15. The comparative experimental results demonstrate that our proposed model significantly outperforms the existing models and methods and achieves high recognition rates, up to 99.996% and 89.134%, for the KDD99 and UNSW-NB15 datasets, respectively. We conclude that our model has the potential to serve as a future benchmark for deep learning and network security research communities.
Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments.
A graphene resonator as an ultrasound detector for generalized Love waves in a polymer film with two level states

We have investigated surface shear waves at 22 MHz in a 0.5 micron thick polymer film on a SiO2/Si substrate at low temperatures using suspended and non-suspended graphene as detectors. By tracking ultrasound modes detected by oscillations of a trilayer graphene membrane both in vacuum and in helium superfluid, we assign the resonances to surface shear modes, generalized Love waves, in the resist/silicon-substrate system loaded with gold. The propagation velocity of these shear modes displays a logarithmic temperature dependence below 1 K, which is characteristic for modification of the elastic properties of a disordered solid owing to a large density of two level state systems. For the dissipation of the shear mode, we find a striking logarithmic temperature dependence, which indicates a basic relation between the speed of the surface wave propagation and the mode dissipation.
Chaos-Induced and Mutation-Driven Schemes Boosting Salp Chains-Inspired Optimizers

Salp swarm algorithm (SSA) is a newly developed meta-heuristic algorithm, which is mainly developed based on the swarming behavior of salps sailing and foraging in the ocean. An improved salp swarm-based optimizer is proposed in this paper to overcome the potential shortcomings of original SSA, including being easily trapped in local or deceptive optima and its slow convergence rates in dealing with some high-dimensional and multimodal landscapes. The designed variant is called CMSSA that combines two strategies simultaneously. First, a chaotic exploitative mechanism with “shrinking” mode is introduced into the basic SSA to improve the exploitative tendencies of the algorithm. Then, a combined mutation scheme is adapted to make full use of the strong intensification capabilities of Gaussian mutation and the strong exploratory leanings of Cauchy mutation. In addition, the embedded strategies can achieve a more stable equilibrium between the core searching patterns of the SSA, which are diversification and intensification. We thoroughly studied the optimization advantages of the improved CMSSA using several representative benchmark cases, including unimodal, multimodal, and fixed-dimension multimodal functions, and three well-regarded engineering cases. The obtained experimental results, statistical tests, and comparative simulations indicate that the exploratory and exploitative proclivities of the SSA and its convergence patterns are vividly improved. The results indicate that the proposed CMSSA is a promising algorithm and shows superior efficacy compared with other algorithms.
Why put up with immunity when there is resistance: an excursion into the population and evolutionary dynamics of restriction–modification and CRISPR-Cas

Bacteria can readily generate mutations that prevent bacteriophage (phage) adsorption and thus make bacteria resistant to infections with these viruses. Nevertheless, the majority of bacteria carry complex innate and/or adaptive immune systems: restriction-modification (RM) and CRISPR-Cas, respectively. Both RM and CRISPR-Cas are commonly assumed to have evolved and be maintained to protect bacteria from succumbing to infections with lytic phage. Using mathematical models and computer simulations, we explore the conditions, under which selection mediated by lytic phage will favor such complex innate and adaptive immune systems, as opposed to simple envelope resistance. The results of our analysis suggest that when populations of bacteria are confronted with lytic phage: (i) In the absence of immunity, resistance to even multiple bacteriophage species with independent receptors can evolve readily. (ii) RM immunity can benefit bacteria by preventing phage from invading established bacterial populations and particularly so when there are multiple bacteriophage species adsorbing to different receptors. (iii) Whether CRISPR-Cas immunity will prevail over envelope resistance depends critically on the length of the co-evolutionary arms race between the bacteria acquiring spacers and the phage generating CRISPR-escape mutants. We discuss the implications of these results in the context of the evolution and maintenance of RM and CRISPR-Cas and highlight fundamental questions that remain unanswered. Summary The two most widely used tools for manipulating and editing DNA restriction and Cas9 endonucleases both originate from studies of mechanisms that provide bacteria with immunity to infections with lytic bacteriophage (phage): restriction modification and CRISPR-Cas. Using mathematical and computer simulations, we explore the a priori conditions under which selection mediated by lytic phage will favor the evolution and maintenance of restriction-modification and CRISPR-Cas immunity in bacteria that, by mutation, can generate envelope resistance to these phage. The results of our analysis make predictions and raise testable-hypotheses about the genetic and ecological conditions under which these immune systems, rather than envelope resistance, will evolve and be maintained as the dominant mechanism of protecting bacteria from succumbing to infections with these viruses.
Investigation of Near-accident Car-driving Scenario using Deep Imitation Learning and Reinforcement Learning

According to [1], the road traﬃc can be divided into three categories in terms of human driver’s responsibility: navigation, guidance and control. In this work, we focus on the guidance level in the high risk scenarios, which is responsible for the output of desired trajectory and/or speed. First, We take the approach of implementing deep imitation learning to obtain the driver agent model using data generated by predeﬁned dominant control law. Then, reinforcement learning is applied to ﬁnd the policy in high risk scenarios via switching control model considering both eﬃciency and safety
Self-Paced Convolutional Neural Network for PolSAR Images Classification

Fully polarimetric synthetic aperture radar (PolSAR) can transmit and receive electromagnetic energy on four polarization channels (HH, HV, VH, VV). The data acquired from four channels have both similarities and complementarities. Utilizing the information between the four channels can considerably improve the performance of PolSAR image classification. Convolutional neural network can be used to extract the channel-spatial features of PolSAR images. Self-paced learning has been demonstrated to be instrumental in enhancing the learning robustness of convolutional neural network. In this paper, a novel classification method for PolSAR images using self-paced convolutional neural network (SPCNN) is proposed. In our method, each pixel is denoted by a 3-dimensional tensor block formed by its scattering intensity values on four channels, Pauli’s RGB values and its neighborhood information. Then, we train SPCNN to extract the channel-spatial features and obtain the classification results. Inspired by self-paced learning, SPCNN learns the easier samples first and gradually involves more difficult samples into the training process. This learning mechanism can make network converge to better values. The proposed method achieved state-of-the-art performances on four real PolSAR dataset.
Digital Forensic Readiness for Financial Network

Major bank hacking cases such as the Carbanak and the Bangladesh bank robbery are reported constantly. As a result of a global joint investigation, Carbanak’s leader has been caught in March 2018, but in the case of the Bangladesh bank robbery, the damage has not been restored to this day due to lack of digital forensic evidence. As shown in the Bangladesh bank robbery, digital forensic evidence is the most important thing for incident response. Therefore, in this paper we propose IP (Internet Protocol) traceback and visualization techniques for better digital forensic.
Dielectric Resonator Antenna for Wide Millimeter-Wave band

A rectangular dielectric resonator antenna (DRA) is excited by properly combine two higher-order modes, TE<inf>113</inf> and TE<inf>115</inf> to provide a wide 10-dB impedance bandwidth of about 25% in millimeter wave band. The variation of antenna gain is less than 1.5 dB across impedance passband (|S<inf>11</inf>|<-10dB).
A High Gain UWB Vivaldi Antenna Loaded with Elliptical Slots

A high gain and ultra-wide band(UWB) planar printed Vivaldi antenna loaded with elliptical slots is presented in this paper. The antenna consists of a layer of substrate and two exponentially tapered slot patches, which forms an antipodal structure. A set of symmetrical elliptical slots are loaded on the two arms of the patches, respectively. The antenna gain is increased by about 1 to 2.6dB within the frequency range of 7 to 25GHz, while the matching performance at the low frequency band is also improved. Metal vias and semi-circular slots are loaded on both sides of the microstrip feedline to improve the transmission performance at the high frequency band. According to the simulated results, the relative bandwidth of the reflection coefficient less than -10dB is 145% (6.4GHz-40GHz) with the center frequency of 23.0GHz. The simulated gain of the antenna is about 8dBi in the 6.4–17.5GHz band, and greater than 10dBi in the 17.5–40GHz band with the maximum gain of 11.3dBi. Within above band, the cross polarization is less than -15dB.
Developing Leaders

Avolio begins this project with an ambitious goal: defining a ‘‘universal model of leadership development’’ (p. 7). Additionally, from the book’s title, Avolio proposes to consider the old chicken-and-egg question: in leadership development, are leaders born or made? In terms of both of these targets the book falls short. There is no real discussion of ‘‘born or made’’ as this is a book about the making of leadership skills and competencies. While recognizing that each of us has differing strengths, there is no effort at a sustained argument that leadership is in the genes of the leader. And why would there be such an argument? After many years of study, the academic side of the argument has long ago been settled* leadership is not born, it is an experiential compilation of additives that results in contextual competence. There is also the oft-renewed traits argument, which resurfaced again in the last decade in the discussion of charismatic leadership, but that argument represents more good fiction than sound theory. Avolio is correct in not spending time pursuing this exercise in discounted speculation. Still, one must wonder why he chose this particular book title for a work that is anything but a recognition of great traits directing gifted individuals to leadership. It terms of the development of a universal model, Avolio actually makes a compelling case for personal development on a path that leads to self-actualization and is then situated within context as leadership. This argument, threaded throughout the book, is artfully explored and carefully explained. But the ‘‘universal’’ element is not sustained, since there is no evidence that self-actualization is a personal development path available to all who seek it. Self-actualization is not an empiricallymeasured accomplishment that metrics could certify the attainment of. And context presents an equally challenging target to capture empirically. Unfortunately, for the universal argument to work, both of these components of leadership would have to
Statistical criticality arises in most informative representations

We show that statistical criticality, i.e. the occurrence of power law frequency distributions, arises in samples that are maximally informative about the underlying generating process. In order to reach this conclusion, we first identify the frequency with which different outcomes occur in a sample, as the variable carrying useful information on the generative process. The entropy of the frequency, that we call relevance, provides an upper bound to the number of informative bits. This differs from the entropy of the data, that we take as a measure of resolution. Samples that maximise relevance at a given resolution—that we call maximally informative samples—exhibit statistical criticality. In particular, Zipf’s law arises at the optimal trade-off between resolution (i.e. compression) and relevance. As a byproduct, we derive a bound of the maximal number of parameters that can be estimated from a dataset, in the absence of prior knowledge on the generative model. Furthermore, we relate criticality to the statistical properties of the representation of the data generating process. We show that, as a consequence of the concentration property of the asymptotic equipartition property, representations that are maximally informative about the data generating process are characterised by an exponential distribution of energy levels. This arises from a principle of minimal entropy, that is conjugate of the maximum entropy principle in statistical mechanics. This explains why statistical criticality requires no parameter fine tuning in maximally informative samples.
Is Worse Attention a Risk Factor for or a Consequence of Depression, or Are Worse Attention and Depression Better Accounted for by Stress? A Prospective Test of Three Hypotheses

It is unclear whether impaired cognition is a risk factor for depression or a consequence of depression, or whether both depression and impaired cognition are caused by a third underlying process (e.g., stress). These three hypotheses were tested in 523 adolescents assessed annually for depression, attentional functioning, and childhood/recent life stress. Baseline switching, sustained, and selective attention did not predict first onset of depression or depressive symptoms. Divided attention predicted depressive symptoms only. Piecewise growth modeling indicated that the trajectory of switching attention declined prior to first onset of depression; there was evidence of significant recovery in switching attention following first onset of depression. Structural equation modeling indicated that impaired switching attention prospectively predicted higher depressive symptoms and that higher depressive symptoms predicted worse selective and switching attention. Further, childhood stress prospectively predicted higher depressive symptoms via switching attention and worse switching attention via depressive symptoms.
ANTI-HIV ACTIVITY OF TERMINALIA CHEBULA CRUDE EXTRACTS AND GALLIC ACID

Herbal drug products have special place in the pharmaceutical world. Terminalia chebula is been used in various traditional medicines for treatment of various diseases. The objective of the current research work was to study the antiHIV I activity of the crude extract of Terminalia chebula and its chemical constituent gallic acid. Terminalia chebula fruit extract and gallic acid showed the IC50 was of 84.81 μg/ml and 71.99 μM respectively for the HIV integrase strand transfer inhibition activity. The crude extract of Terminalia chebula and gallic acid showed dose dependent inhibition on different HIV-1 strains such as NARI-VB 28, NARI-29, NARI-VB 30, NARI-VB 39, NARI-VB 49 and HIV-1 92 HT599. The Terminalia chebula fruit extract and gallic acid did not show the cytotoxicity at the concentration used in the study. Terminalia chebula fruit extract and its constituents can be further exploited for future plant based anti-HIV.
Modeling Polycentric Urbanization Using Multisource Big Geospatial Data

Understanding the dynamics of polycentric urbanization is important for urban studies and management. This paper proposes an analytical model that uses multisource big geospatial data to characterize such dynamics to facilitate policy making. There are four main steps: 1) main centers and subcenters are identified using spatial cluster analysis and geographically weighted regression (GWR) based on Visible Infrared Imaging Radiometer Suite (VIIRS)/NPP and social media check-in data; 2) the built-up areas are extracted by using Defense Meteorological Satellite Program – Operational Linescan System (DMSP/OLS) gradient images; 3) the economic corridors that connect the main center and subcenters are constructed using road network data from Open Street Map (OSM) with the least-cost distance method; and 4) the major urban development direction is identified by analyzing the changes in built-up areas within the economic corridors. The model is applied to three major cities in northeastern, central, and northwestern China (Shenyang, Wuhan, and Xi'an) from 1992 to 2012.