Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating and Comparing Game-controller based Virtual Locomotion Techniques

The incremental hardware costs of virtual locomotion are minimized when the technique uses interaction capabilities available in controllers and devices that are already part of the VE system, e.g., gamepads, keyboards, and multi-function controllers. We used a different locomotion technique for each of these three devices: gamepad thumb-stick (joystick walking), a customized hybrid keyboard for gaming (speedpad walking), and an innovative technique that uses the orientation and triggers of the HTC Vive controllers (TriggerWalking). We explored the efﬁcacy of locomotion techniques using these three devices in a hide and seek task in an indoor environment. We measured task performance, simulator sickness, system usability, perceived workload, and preference. We found that users had a strong preference for TriggerWalking, which also had the least increase in simulator sickness, the highest performance score, and highest perceived usability. However, participants using TriggerWalking also had the most object and wall-collisions. Overall we found that TriggerWalking is an effective locomotion technique and that is has signiﬁcant and important beneﬁts. Future research will explore if TriggerWalking can be used with equal beneﬁts in other virtual-environments, on different tasks, and types of movement.
vStream: Virtual Stream Management for Multi-streamed SSDs

Recently, multi-streamed SSDs have been proposed to reduce the cost of garbage collection in NAND ﬂash-based SSDs. In multi-stream SSDs, application developers can control data placement within the SSD by specifying a stream ID along with the WRITE command. However, commercial SSDs support only a small number of streams due to the device’s limitation in hardware resources. This makes it difﬁcult to fully utilize the beneﬁt of the multi-streamed SSDs. In this paper, we propose a new concept of virtual streams (vStreams) for the multi-streamed SSDs. vStreams allow application developers to manage a suf-ﬁcient number of streams regardless of the physical streams supported by the device. We also present novel mechanisms to monitor the lifetime of each stream with a negligible memory overhead and to map one or more vStreams into a physical stream at run time. Our evaluations with RocksDB show that the proposed vStream SSD improves the throughput by 70% compared to the legacy SSD with no stream support.
Stochastic PCA with (cid:96) 2 and (cid:96) 1 Regularization

We revisit convex relaxation based methods for stochastic optimization of principal component analysis (PCA). While methods that directly solve the nonconvex problem have been shown to be optimal in terms of statistical and computational efﬁciency, the methods based on convex relaxation have been shown to enjoy comparable, or even superior, empirical performance – this motivates the need for a deeper formal understanding of the latter. Therefore, in this paper, we study variants of stochastic gradient descent for a convex relaxation of PCA with (a) (cid:96) 2 , (b) (cid:96) 1 , and (c) elastic net ( (cid:96) 1 + (cid:96) 2 ) regularization in the hope that these variants yield (a) better iteration complexity, (b) better control on the rank of the intermediate iterates, and (c) both, respectively. We show, theoretically and empirically, that compared to previous work on convex relaxation based methods, the proposed variants yield faster convergence and improve overall runtime to achieve a certain user-speciﬁed (cid:15) -suboptimality on the PCA objective. Furthermore, the proposed methods are shown to converge both in terms of the PCA objective as well as the distance between subspaces. However, there still remains a gap in computational requirements for the proposed methods when compared with existing nonconvex approaches.
Automatic Question Generation using Relative Pronouns and Adverbs

This paper presents a system that automatically generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. Our system is syntax-based, runs on dependency parse information of a single-sentence input, and achieves high accuracy in terms of syntactic correctness, semantic adequacy, fluency and uniqueness. One of the key advantages of our system, in comparison with other rule-based approaches, is that we nearly eliminate the chances of getting a wrong wh-word in the generated question, by fetching the requisite wh-word from the input sentence itself. Depending upon the input, we generate both factoid and descriptive type questions. To the best of our information, the exploitation of wh-pronouns and wh-adverbs to generate questions is novel in the Automatic Question Generation task.
Ontology Summit 2018 Communiqu´e Contexts in Context

It is generally agreed that the interpretation of information, in any form, is context-dependent. The goal of the recent Ontology Summit 2018 was to explore the various relationships spanning ontology and context. This article is the end product of the summit and associated symposium. It describes motivation for creating explicit, formal context speciﬁcations, and discusses approaches for ﬁnding, understanding and formalizing context. We present this work and associated materials with the goal to foster the research and development of approaches to contexts and to drive towards context-aware solutions which can be incorporated into both knowledge engineering processes and ontology design best practices. The
Oscillation Suppression in the Sense Mode of a High-Q MEMS Gyroscope Using a Simplified Closed-Loop Control Method

The oscillation of the sense mode of the micro-machined Coriolis vibratory gyroscope (MCVG) with high quality factor (Q) is analyzed in this study and the corresponding force feedback control scheme is presented to suppress this oscillation. The controller consists of integrator and some filters, instead of the common but complicated demodulation and remodulation modules. Compared with using no oscillation suppression scheme, the proposed simplified oscillation suppression control scheme can achieve an improvement of the sense mode of the MCVG. The inband spectrum ripple of the angular rate output are improved from 51.4 dB to 4.23 × 10−4 dB. Correspondingly, these two performance parameters are improved by 370.4 and 186.2 times, which are higher than two orders of magnitude, respectively. Bias stability is improved from 9.72 deg/h to 2.5 deg/h. Test results prove that the proposed control scheme is effective in suppressing the oscillation.
Geriatrix: Aging what you see and what you don't see. A file system aging approach for modern storage systems

File system performance on modern primary storage devices (Flash-based SSDs) is greatly affected by aging of the free space, much more so than were mechanical disk drives. We introduce Geriatrix, a simple-to-use profile driven file system aging tool that induces target levels of fragmentation in both allocated files (what you see) and remaining free space (what you don't see), unlike previous approaches that focus on just the former. This paper describes and evaluates the effectiveness of Geriatrix, showing that it recreates both fragmentation effects better than previous approaches. Using Geriatrix, we show that measurements presented in many recent file systems papers are higher than should be expected, by up to 30% on mechanical (HDD) and up to 80% on Flash (SSD) disks. Worse, in some cases, the performance rank ordering of file system designs being compared are different from the published results. Geriatrix will be released as open source software with eight built-in aging profiles, in the hopes that it can address the need created by the increased performance impact of file system aging in modern SSD-based storage.
System Modeling for Priority Schemes in Managed Peer-to-Peer Networks

In Peer-to-Peer (P2P) networks it is not uncom- mon to find users that have initiated a file transfer and decide to leave the system before the end of the download. This is a particularly harmful behavior due to the resources (bandwidth or energy) wasted in such an aborted process. This negative effect is amplified when the systems conditions are such that the peers are not downloading at the maximum capacity. This is because in these conditions, there are not enough peers uploading the file, and a part of the bandwidth is wasted on peers that do not share their resources to the network once they leave the system. In this paper, the case of managed P2P networks is of special interest. As such, the variability in the transfer rates, such as the download and upload rates, is expected to be much less than in the case of the unmanaged P2P network. The limit case, where these transfer rates are constant is considered as an alternative to the exponential distribution. Additionally, two priority schemes are presented for a BitTorrent-based P2P network where the peers that are more likely to remain longer in the system are served first.
Asymptotic estimates for the number of permutations without short cycles

We explore the probability κ(n, r) that a permutation sampled uniformly at random from the symmetric group Sn has only cycles of length greater than r, where 1 ≤ r < n. As the order of the group increases, new asymptotic formulas valid in the specified regions are obtained using the saddle-point method in combination with ideas originated in analytic number theory. Some results for permutations without long cycles are employed.
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning

In this paper we propose Reward Machines – a type of finite state machine that supports the specification of reward functions while exposing reward function structure to the learner and supporting decomposition. We then present Q-Learning for Reward Machines (QRM), an algorithm which appropriately decomposes the reward machine and uses off-policy q-learning to simultaneously learn subpolicies for the different components. QRM is guaranteed to converge to an optimal policy in the tabular case, in contrast to Hierarchical Reinforcement Learning methods which might converge to suboptimal policies. We demonstrate this behavior experimentally in two discrete domains. We also show how function approximation methods like neural networks can be incorporated into QRM, and that doing so can find better policies more quickly than hierarchical methods in a domain with a continuous state space.
Second-order consensus of multi-agent systems with noises via intermittent control

ABSTRACT Second-order consensus of multi-agent systems with noises via intermittent control is investigated in this paper. First, we study the mean-square consensus problem with communication noises by intermittent control. In order to reach consensus, under the strong directed interacted topology, by using the tools of graph theory and Lyapunov method, a distributed control protocol is proposed based on the noises and periodical intermittent information. The upper bound of noise strength in the sense of matrix norm and the lower bound of communication time duration are obtained. Second, a class of coupled system models which include delay-terms in their nonlinearities in the noisy environment is discussed. Under the balanced strongly connected topology, the sufficient conditions to achieve the mean-square average-consensus are obtained. Finally, simulations are given to illustrate the effectiveness of our results.
Knockdown of timeless Disrupts the Circadian Behavioral Rhythms in Laodelphax striatellus (Hemiptera: Delphacidae)

Abstract Most living organisms developed the innate clock system to anticipate daily environmental changes and to enhance their chances of survival. timeless (tim) is a canonical clock gene. It has been extensively studied in Drosophila melanogaster (Diptera: Drosophilidae) as a key component of the endogenous circadian clock, but its role is largely unknown in some agriculture pests. Laodelphax striatellus (Fallén) (Hemiptera: Delphacidae), an important rice pest, exhibits a robust locomotor rhythm. In the present study, we cloned tim gene (ls-tim) from L. striatellus and investigated its function in the regulation of behavioral rhythms. Quantitative real-time polymerase chain reaction revealed a circadian expression pattern of ls-tim under different light conditions with a trough in the photophase and a peak in the late scotophase. After the knockdown of ls-tim via RNA interference (RNAi), the adults showed an earlier onset of locomotor activity under light/dark cycles and became arrhythmic in constant darkness. ls-tim RNAi also abolished the timing of adult emergence that normally occurs in the early photophase. These results suggest that ls-tim is essential for the light-entrained circadian rhythms in L. striatellus and provide more insights into the endogenous clock network underlying the behavioral and physiological rhythms of this insect.
Learning Disentangled Representation from 12-Lead Electrograms: Application in Localizing the Origin of Ventricular Tachycardia

The increasing availability of electrocardiogram (ECG) data has motivated the use of data-driven models for automating various clinical tasks based on ECG data. The development of subject-specific models are limited by the cost and difficulty of obtaining sufficient training data for each individual. The alternative of population model, however, faces challenges caused by the significant inter-subject variations within the ECG data. We address this challenge by investigating for the first time the problem of learning representations for clinically-informative variables while disentangling other factors of variations within the ECG data. In this work, we present a conditional variational autoencoder (VAE) to extract the subject-specific adjustment to the ECG data, conditioned on task-specific representations learned from a deterministic encoder. To encourage the representation for inter-subject variations to be independent from the task-specific representation, maximum mean discrepancy is used to match all the moments between the distributions learned by the VAE conditioning on the code from the deterministic encoder. The learning of the task-specific representation is regularized by a weak supervision in the form of contrastive regularization. We apply the proposed method to a novel yet important clinical task of classifying the origin of ventricular tachycardia (VT) into pre-defined segments, demonstrating the efficacy of the proposed method against the standard VAE.
Dynamic Balance Gait for Walking Assistance Exoskeleton

Purpose Powered lower-limb exoskeleton has gained considerable interests, since it can help patients with spinal cord injury(SCI) to stand and walk again. Providing walking assistance with SCI patients, most exoskeletons are designed to follow predefined gait trajectories, which makes the patient walk unnaturally and feels uncomfortable. Furthermore, exoskeletons with predefined gait trajectories cannot always maintain balance walking especially when encountering disturbances. Design/Methodology/Approach This paper proposed a novel gait planning approach, which aims to provide reliable and balance gait during walking assistance. In this approach, we model the exoskeleton and patient together as a linear inverted pendulum (LIP) and obtain the patients intention through orbital energy diagram. To achieve dynamic gait planning of exoskeleton, the dynamic movement primitive (DMP) is utilized to model the gait trajectory. Meanwhile, the parameters of DMP are updated dynamically during one step, which aims to improve the ability of counteracting external disturbance. Findings The proposed approach is validated in a human-exoskeleton simulation platform, and the experimental results show the effectiveness and advantages of the proposed approach. Originality/Value We decomposed the issue of obtain dynamic balance gait into three parts: (1) based on the sensory information of exoskeleton, the intention estimator is designed to estimate the intention of taking a step; (2) at the beginning of each step, the discrete gait planner utilized the obtained gait parameters such as step length S and step duration T and generate the trajectory of swing foot based on (S, T); (3) during walking process, continuous gait regulator is utilized to adjust the gait generated by discrete gait planner to counteract disturbance.
Accuracy assessment of single viewing techniques for metric measurements on single images

Single view methodology has been a long-standing issue of extracting metric information from a single image in the relevant disciplines. To perform metric measurements on a single image, two methods, which are based on identifying vanishing points (VPs), were employed in the study. One of the two methods is based on the cross ratio and VPs computation and the other integrates robust statistical estimators and close range techniques in a multidisciplinary concept. The estimation of object distance from a single image taken in an outdoor environment was the main concern for a practical application in the study. As the main concern of the study, accuracy analysis of distance measurements was performed to compare the performance of two techniques and experimental results are presented.
TR-IDS: Anomaly-Based Intrusion Detection through Text-Convolutional Neural Network and Random Forest

As we head towards the IoT (Internet of Things) era, protecting network infrastructures and information security has become increasingly crucial. In recent years, Anomaly-Based Network Intrusion Detection Systems (ANIDSs) have gained extensive attention for their capability of detecting novel attacks. However, most ANIDSs focus on packet header information and omit the valuable information in payloads, despite the fact that payload-based attacks have become ubiquitous. In this paper, we propose a novel intrusion detection system named TR-IDS, which takes advantage of both statistical features and payload features. Word embedding and text-convolutional neural network (Text-CNN) are applied to extract effective information from payloads. After that, the sophisticated random forest algorithm is performed on the combination of statistical features and payload features. Extensive experimental evaluations demonstrate the effectiveness of the proposed methods.
Honey Bee and Bumble Bee Antiviral Defense

Bees are important plant pollinators in both natural and agricultural ecosystems. Managed and wild bees have experienced high average annual colony losses, population declines, and local extinctions in many geographic regions. Multiple factors, including virus infections, impact bee health and longevity. The majority of bee-infecting viruses are positive-sense single-stranded RNA viruses. Bee-infecting viruses often cause asymptomatic infections but may also cause paralysis, deformity or death. The severity of infection is governed by bee host immune responses and influenced by additional biotic and abiotic factors. Herein, we highlight studies that have contributed to the current understanding of antiviral defense in bees, including the Western honey bee (Apis mellifera), the Eastern honey bee (Apis cerana) and bumble bee species (Bombus spp.). Bee antiviral defense mechanisms include RNA interference (RNAi), endocytosis, melanization, encapsulation, autophagy and conserved immune pathways including Jak/STAT (Janus kinase/signal transducer and activator of transcription), JNK (c-Jun N-terminal kinase), MAPK (mitogen-activated protein kinases) and the NF-κB mediated Toll and Imd (immune deficiency) pathways. Studies in Dipteran insects, including the model organism Drosophila melanogaster and pathogen-transmitting mosquitos, provide the framework for understanding bee antiviral defense. However, there are notable differences such as the more prominent role of a non-sequence specific, dsRNA-triggered, virus limiting response in honey bees and bumble bees. This virus-limiting response in bees is akin to pathways in a range of organisms including other invertebrates (i.e., oysters, shrimp and sand flies), as well as the mammalian interferon response. Current and future research aimed at elucidating bee antiviral defense mechanisms may lead to development of strategies that mitigate bee losses, while expanding our understanding of insect antiviral defense and the potential evolutionary relationship between sociality and immune function.
Utilitarian Performance Isolation in Shared SSDs

This paper proposes a utilitarian performance isolation (UPI) scheme for shared SSD settings. UPI exploits SSD's abundant parallelism to maximize the utility of all tenants while providing performance isolation. Our approach is in contrast to static resource partitioning techniques that bind parallelism, isolation, and capacity altogether. We demonstrate that our proposed scheme reduces the 99th percentile response time by 38.5% for a latency-critical workload, and the average response time by 16.1% for a high-throughput workload compared to the static approaches.
VoC-DL: Revisiting Voice Of Customer Using Deep Learning

In the field of digital marketing, understanding the voice of the customer is paramount. Mining textual content written by visitors on websites or social media can offer new dimensions to marketers and CX executives. Traditional tasks in NLP like sentiment analysis, topic modeling etc. can solve only certain specific problems but don’t provide a generic solution to identifying/understanding the intention behind a text. In this paper we consider higher dimensional extensions to the sentiment concept by incorporating labels like product enquiry, buying intent, seeking help, feedback and pricing query which give us a deeper understanding of the text. We show how our model performs in a real-world enterprise use case. Word2Vec embeddings are used for word representations and later we compare three algorithms for classification. SVM’s provide us with a strong baseline. Two deep learning models viz. vanilla CNN and RNN’s with LSTM are compared. With no use of hard-coded or hand engineered features, our generic model can be used in a variety of use cases where text mining is involved with ease.
Intracellular Dynamics in Cuneate Nucleus Neurons Support Self-Stabilizing Learning of Generalizable Tactile Representations

How the brain represents the external world is an unresolved issue for neuroscience, which could provide fundamental insights into brain circuitry operation and solutions for artificial intelligence and robotics. The neurons of the cuneate nucleus form the first interface for the sense of touch in the brain. They were previously shown to have a highly skewed synaptic weight distribution for tactile primary afferent inputs, suggesting that their connectivity is strongly shaped by learning. Here we first characterized the intracellular dynamics and inhibitory synaptic inputs of cuneate neurons in vivo and modeled their integration of tactile sensory inputs. We then replaced the tactile inputs with input from a sensorized bionic fingertip and modeled the learning-induced representations that emerged from varied sensory experiences. The model reproduced both the intrinsic membrane dynamics and the synaptic weight distributions observed in cuneate neurons in vivo. In terms of higher level model properties, individual cuneate neurons learnt to identify specific sets of correlated sensors, which at the population level resulted in a decomposition of the sensor space into its recurring high-dimensional components. Such vector components could be applied to identify both past and novel sensory experiences and likely correspond to the fundamental haptic input features these neurons encode in vivo. In addition, we show that the cuneate learning architecture is robust to a wide range of intrinsic parameter settings due to the neuronal intrinsic dynamics. Therefore, the architecture is a potentially generic solution for forming versatile representations of the external world in different sensor systems.