Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Generic Simulator for Continuum Robot Control*

Nowadays, it became extremely difficult to deny the importance of continuum robotics, especially in medical applications [1]. In contrast to classical industrial robots, that are typically characterized by a series of discrete rigid links, continuum robots (CR) have a particular structure going from robotic arm inspired by elephant trunk to concentric tube robots (CTR). However, there is a common property which defines them as an actuated mechanism whose backbone forms curves with continuous tangent vectors. For developing advanced control and achieving complex tasks with CR, a generic simulator would be helpful. Indeed, building a realistic experimental platform containing continuum robots may be a challenging task both in terms of time and cost. One of the most popular CR for medical applications is CTR that is obtained by assembling elementary precurved tubes with different diameters in telescopic manner. Generally, these tubes are made of Nickel Titanium alloy but more recently 3D printed ones were introduced [2]. The tube assembly constitutes the effective part of the CTR and in order to get a functional robot, it is necessary to have an actuation unit including angular and linear stages. Various challenges remain to be tackled to obtain lightweight, compact, and high degrees of freedom actuation unit. Therefore, the core of this paper is the development of a generic simulation platform for CR, especially a CTR. Comparing to widelyused simulation platforms such as V-Rep or Gazebo, our simulator has an important advantage: it is possible, and easy, to add new deformable robots. Actually, the simulator can be used, for instance, to optimize the CTR mechanism design, to develop and validate advanced controls by using various feedbacks, and to achieve complex tasks by integrating anatomical models, etc. In the reminder of this paper, at first the software architecture is presented. Secondly, we discuss the capabilities of the simulator from the user point of view, i.e., what a person with no background in programming can achieve with it.
Microsoft Word-SNU-UT Joint Seminar_Nam Kanghyun__Hori Lab_.docx

In this paper, robust yaw stability control based on active steering control is proposed for electric vehicles (EVs). A two degree of freedom control method by a disturbance observer is applied to control system for yaw stabilization. Moreover, the feed-forward disturbance compensator is designed to compensate unexpected yaw moment caused by torque differences between left and right driving motors. Since the vehicle control system has a large model variation due to road conditions, the disturbance observer is designed based on a robust control method. The proposed control system is verified by computer simulations using CarSim.
Effective and efficient community search over large heterogeneous information networks

Recently, the topic of community search (CS) has gained plenty of attention. Given a query vertex, CS looks for a dense subgraph that contains it. Existing studies mainly focus on homogeneous graphs in which vertices are of the same type, and cannot be directly applied to heterogeneous information networks (HINs) that consist of multi-typed, interconnected objects, such as the bibliographic networks and knowledge graphs. In this paper, we study the problem of community search over large HINs; that is, given a query vertex q, find a community from an HIN containing q, in which all the vertices are with the same type of q and have close relationships. To model the relationship between two vertices of the same type, we adopt the well-known concept of meta-path, which is a sequence of relations defined between different types of vertices. We then measure the cohesiveness of the community by extending the classic minimum degree metric with a meta-path. We further propose efficient query algorithms for finding communities using these cohesiveness metrics. We have performed extensive experiments on five real large HINs, and the results show that the proposed solutions are effective for searching communities. Moreover, they are much faster than the baseline solutions.
Defective TiO2 with intrinsic point defects for photocatalytic hydrogen production: A review

Titanium dioxide (TiO2) has been intensively investigated for solar hydrogen conversion via photocatalytic and photoelectrochemical water splitting. However, the performance of TiO2 photocatalyst for hydrogen generation is low due to the rapid electron-hole recombination and its wide band gap (3.0-3.2 eV) which is only feasible for UV light absorption of solar spectrum. There are various strategies have been used for TiO2 modification to harvest the sunlight and improve the surface chemical reaction between TiO2 and H2O for water splitting. Among the TiO2 modification strategies, intrinsic point defects such as vacancies and interstitials in TiO2 have been proven to improve the TiO2 properties for photo-catalytic activity. In this article, the recent progress achieved in defective TiO2 with intrinsic point defects for photocatalytic hydrogen evolution and photoelectrochemical water splitting has been reviewed. The fabrication methods of defective TiO2 along with their structural, optical, and charge carrier properties have been introduced. It has appeared that the intrinsic point defects are a promising strategy to improve the performance of TiO2 for photocatalytic hydrogen production, and more efforts are still needed to improve the TiO2 performance for photocatalytic hydrogen production in this area of research.
FACTORS DETERMINING STUDENTS’ INTENTION TO ADOPT E -LEARNING AN EVIDECE FROM INDONESIA HIGHER EDUCATION CONTEXT

The study aimed to address the question: what are the factors determining students’ intention to adopt e -learning in higher education and what are the relationships among these factors? This study investigates and identifies factors affecting higher education students ’ intention to adopt e-learning in higher education institutions in Indonesia context. The data drawn from university students in West Sumatra Province, Indonesia. There are four universities which has applied computer and internet in the management information system to participate in the study. E-learning adoption is approached from the information systems acceptance point of view. This study an extended version of the Technology Acceptance Model (TAM) was developed to investigate the underlying factors determining intention to adopt e-learning in higher education students. Those factors are management support, perceived ease of use, and perceived usefulness. The data gathered from a survey of 200 undergraduate universities students, who were using the e-learning system at their universities. The model was estimated using Structural Equation Modeling (SEM). A path model was developed to analyze the relationships between the factors to explain higher education students’ intention to adopt e -learning system. Implications of the study also discussed in this research.
University of Glasgow Terrier Team and Naver Labs Europe at TREC 2019 Fair Ranking Track

In our participation to the TREC 2019 Fair Ranking Track, the University of Glasgow Terrier Team and Naver Labs Europe joined forces to investigate (1) a novel probabilistic retrieval strategy that maximises the utility of the ranking, (2) two greedy brute-force re-ranking approaches that build on our novel probabilistic retrieval strategy and enforce individual fairness before adopting a particular trade-off between the utility and the fairness of the ranking, and (3) two approaches that deploy search results diversification as a fairness component to diversify over multiple possible dimensions of the task’s unknown author groupings.
Learning Effective Sparse Sampling Strategies using Deep Active Sensing

: Registering a known model with noisy sample measurements is in general a difﬁcult task due to the problem in ﬁnding correspondences between the samples and points on the known model. General frameworks exist, such as variants of the classical iterative closest point (ICP) method to iteratively reﬁne correspondence estimates. However, the methods are prone to getting trapped in locally optimal conﬁgurations, which may be far from the true registration. The quality of the ﬁnal registration depends strongly on the set of samples. The quality of the set of sample measurements is more noticeable when the number of samples is relatively low ( ≈ 20). We consider sample selection in the context of active perception , i.e. an objective-driven decision-making process, to motivate our research and the construction of our system. We design a system for learning how to select the regions of the scene to sample, and, in doing so, improve the accuracy and efﬁciency of the sampling process. We present a full environment for learning how best to sample a scene in order to quickly and accurately register a model with the scene. This work has broad applicability from the ﬁelds of geodesy to medical robotics, where the cost of taking a measurement is much higher than the cost of incremental changes to the pose of the equipment.
THE RELATIONSHIPS BETWEEN SOCIAL NETWORKING AND INDIVIDUAL JOB PERFORMANCE OF LAW SECTOR IN MALAYSIA

Social media has become a very critical segment of the average professional person’s life. Even a profession as a lawyer, should appreciate the impact of social media on their job performance. Failure to comprehend how social media can impact their individual job performance could result in a severe harm to their career. Furthermore, lawyers are benefitting from social media to grow their practices through the networking and publicity prospects provided by the numerous social media sites and applications. The aim of this current study is to examine the correlation between usefulness and ease of use of social media for lawyers with their individual job performance. This study applies a Technology Acceptance Model (TAM) to conceptualize this framework. 370 questionnaires were administered to lawyers identified through simple random sampling from which 185 usable responses were returned. The findings of this study reveal that a majority of lawyers in Malaysia agree that social media is useful and easy to use to influence their individual job performance. Specifically, the findings of the study prove that there are significant and positive relationships between social media usefulness and lawyer’s task performance and contextual performance except negative relationship between social media usefulness and counterproductive work behavior. Furthermore, social media ease of use also influences positively with lawyer’s task performance, contextual performance and counterproductive work behavior. In regards to the implication to the theory that was used in this research, the TAM affords a theoretical perception to comprehend the elements in an extensive array of user orientated technological mechanisms. This archetype supports the researcher by providing ground work and basic foundation to cognize the usage of social media by lawyers.
ARTEMIS: An Intrusion Detection System for MQTT Attacks in Internet of Things

The Internet of Things (IoT) is now being used increasingly in transportation, healthcare, agriculture, smart home and city systems. IoT devices, the number of which is expected to reach 25 billion all over the world by 2021, are required to be deployed very fast, taking into account commercial pressures. This results in a very important layer, i.e. security, being either completely neglected or having significant shortcomings. Since IoT has a heterogeneous structure, there is a need for intrusion detection systems (IDSs) that take into account the specifics of an IoT system architecture, including the computing power limitations, variety of protocols and prevalence of zero-day attacks. In this paper, we describe ARTEMIS, an IDS for IoT, which processes data from IoT devices using machine learning to detect deviations from the normal behavior of the system and generates alerts in case of anomalies. We have implemented a prototype of the system using IoT devices subscribed to topics at an MQTT broker and provide experimental evaluation of the system under MQTT-related attacks.
On the Eﬀect of User Mobility and Density on the Performance of Routing Protocols for Ad-hoc Mobile Networks

—In this paper we demonstrate the signiﬁcant impact of (a) the mobility rate and (b) the user density on the performance of routing protocols in ad-hoc mobile networks. In particular, we study the effect of these parameters on two different approaches for designing routing protocols: (a) the route creation and maintenance approach and (b) the “support” approach, that forces few hosts to move acting as “helpers” for message delivery. We study one representative protocol for each approach, i.e. AODV for the ﬁrst approach and RUNNERS for the second. Wehaveimplemented the two protocols and performed a large scale and detailed simulation study of their performance. For the ﬁrst time, we study AODV (and RUNNERS ) in the 3D case. The main ﬁndings are: the AODV protocol behaves well in networks of high user density and low mobility rate, while its performance drops for sparse networks of highly mobile users. On the other hand, the RUNNERS protocol seems to tolerate well (and in fact beneﬁt from) high mobility rates and low densities. Thus, we are able to partially answer an important conjecture of [7].
Automatic generation of specialized direct convolutions for mobile GPUs

Convolutional Neural Networks (CNNs) are a powerful and versatile tool for performing computer vision tasks in both resource constrained settings and server-side applications. Most GPU hardware vendors provide highly tuned libraries for CNNs such as Nvidia's cuDNN or ARM Compute Library. Such libraries are the basis for higher-level, commonly-used, machine-learning frameworks such as PyTorch or Caffe, abstracting them away from vendor-specific implementation details. However, writing optimized parallel code for GPUs is far from trivial. This places a significant burden on hardware-specific library writers which have to continually play catch-up with rapid hardware and network evolution. To reduce effort and reduce time to market, new approaches are needed based on automatic code generation, rather than manual implementation. This paper describes such an approach for direct convolutions using Lift, a new data-parallel intermediate language and compiler. Lift uses a high-level intermediate language to express algorithms which are then automatically optimized using a system of rewrite-rules. Direct convolution, as opposed to the matrix multiplication approach used commonly by machine-learning frameworks, uses an order of magnitude less memory, which is critical for mobile devices. Using Lift, we show that it is possible to generate automatically code that is X10 faster than the direct convolution while using X3.6 less space than the GEMM-based convolution of the very specialized ARM Compute Library on the latest generation of ARM Mali GPU.
Noise Learning for Weakly Supervised Segment Classiﬁcation in Video

This paper describes our solution for the 3rd YouTube-8M video understanding challenge. The challenge of this year is different from the previous challenge. Given a large scale video dataset with video-level labels and a small scale video dataset with segment-level labels, we are asked to recognize segments in videos this year. It can be regarded as a weakly supervised learning problem. To answer the challenge, we propose a solution consists of three different models, i.e., segment-level classiﬁer, self-attention mechanism, noise learning classiﬁer. Among them, the noise learning classiﬁer performs the best. By noise learning, it can reduce the noise of label and sample for training, and improve the performance. Moreover, we achieve the MAP of 0.78878 in the private leaderboard by model ensemble based on introduced models, ranking the 8th place on the challenge.
WATER RESOURCES GOVERNANCE IN GHANA: DO POLICIES PROMOTE WOMEN INVOLVEMENT?

There have been several policies as well as empowerment platforms that are meant to increase women involvement in water resources all over the globe. One of the major barriers to women involvement in water resources governance has been arguably claimed to be the cultural attributes which emanate from patriarchal structures. This study dwelt on other factors besides traditional attributes as hindrance to women’s involvement in water resources governance. Ten traditional authorities were interviewed as well as sixteen FGDs involving community members, males and females were conducted in four urban and four rural communities within the Wa Central Municipal Assembly and the Bosomtwi District. It was found that involvement in water resources governance was found to be both active and passive for women in these communities regardless of policies and empowerment projects. Women were found to be more active in water resources governance in water-stressed communities but were found to be passive in water resource endowed areas. It was also found out that women were more active in patriarchal communities and passive in matriarchal communities. It is therefore recommended that policies designed to empower women in water resources governance should be place specific to address specific condition of women.
MAUI: Tele-Assistence for Maintenance of Cyber-Physical Systems

— In this paper, we present the maintenance assistance user interface (MAUI), a novel approach for providing tele-assistance to a worker charged with maintenance of a cyber-physical system. Such a system comprises both physical and digital interfaces, making it challenging for a worker to understand the required steps and to assess work progress. A remote expert can access the digital interfaces and provide the worker with timely information and advice in an augmented reality display. The remote expert has full control over the user interface of the worker in a manner comparable to remote desktop systems. The worker needs to perform all physical operations and retrieve physical information, such as reading physical labels or meters. Thus, worker and remote expert collaborate not only via shared audio, video or pointing, but also share control of the digital interface presented in the augmented reality space. We report results on two studies: The ﬁrst study evaluates the beneﬁts of our system against a condition with the same cyber-physical interface, but without tele-assistance. Results indicate signiﬁcant beneﬁts concerning speed, cognitive load and subjective comfort of the worker. The second study explores how interface designers use our system, leading to initial design guidelines for tele-presence interfaces like ours.
Low Intrinsic Dimension Implies Generalization

Neural networks’ ability to generalize to unseen data has proven difﬁcult to explain by statistical learning theory. Recent works have used PAC-Bayes bounds to derive non-trivial generalization bounds for deep neural networks applied to realistic problems. In other works, it has been found that neural networks can obtain high performance even when projected onto random low-dimensional subspaces of their parameter space. In this paper I use this property and PAC-Bayes theory to obtain the tightest generalization bounds yet for neural networks applied to MNIST
MPII at TREC CAsT 2019: Incoporating Query Context into a BERT Re-ranker

MPII participated in the Conversational Assistance Track (CAsT) at TREC 2019. Our approach consists of an initial stage ranker followed by a BERT-based [3] neural document re-ranking model. BM25 with query expansion based on external knowledge (i.e., Wikipedia and ConceptNet) serves as the ﬁrst stage ranking method, while the neural model uses BERT embeddings and a kernel-based ranking module (KNRM) to predict a document-query relevance score. We repurpose and modify subtopics from the TREC Web Track’s diversity task to train the neural module. We ﬁnd that the neural re-ranking module substantially improves upon the initial ranking approach.
Dynamically Evolving

Peer-to-peer (P2P) systems, which are realized as overlays on top of the underlying Internet routing architecture, contribute a signiﬁcant portion of today’s Internet trafﬁc. While the P2P users are a good source of revenue for the Internet Service Providers (ISPs), the immense P2P trafﬁc also poses a signiﬁcant trafﬁc engineering challenge to the ISPs. This is because P2P systems either implement their own routing in the overlay topology or may use a P2P routing underlay [1], both of which are largely independent of the Internet routing, and thus impedes the ISP’s trafﬁc engineering capabilities. On the other hand, P2P users are primarily interested in ﬁnding their desired content quickly, with good performance. But as the P2P system has no access to the underlying network, it either has to measure the path performance itself or build its overlay topology agnostic of the underlay. This situation is disadvantageous for both the ISPs and the P2P users. To overcome this, we propose and evaluate the feasibility of a solution where the ISP offers an “oracle” to the P2P users. When the P2P user supplies the oracle with a list of possible P2P neighbors, the oracle ranks them according to certain criteria, like their proximity to the user or higher bandwidth links. This can be used by the P2P user to choose appropriate neighbors, and therefore improve its performance. The ISP can use this mechanism to better manage the immense P2P trafﬁc, e.g., to keep it inside its network, or to direct it along a desired path. The improved network utilization will also enable the ISP to provide better service to its customers.
In-Situ Analysis of Ultrashort Pulsed Laser Ablation with Pulse Bursts

Laser ablation using ultrashort pulsed (USP) laser sources enables contact free materials processing with very high precision at negligible thermal influence for the processed workpiece. However, the achieved productivity is still too low for industrial applications in many cases. To increase the throughput of USP laser processes, three approaches are followed: high repetition rates up to several MHz using fast beam deflection techniques, pulse bursts or parallel processing with multiple laser foci. In case of high repetition rates and pulse bursts the temporal and spatial distance of consecutive pulses becomes so small that heat accumulation and shielding effects of plasma and particles affect the processing quality and achieved efficiency significantly. In order to gain a better insight of these still barely investigated effects we apply in this work in situ imaging with an ICCD camera during ablation processes of copper and stainless steel with pulse bursts to evaluate the excitation of process luminescence as well as its relation to ablation efficiency and shielding effects.
English to Sinhala Machine Translation: Towards Better information access for Sri Lankans

Statistical Machine Translation is a well established data driven approach for automatic translation between different languages. However considerably few re-searches have taken place on Sri Lankan as well as Asian Languages. We research on possible English-Sinhala Translation us-ing Statistical Machine Translation. Special attention is paid for the impact of parameter tuning on Sinhala Language. Re-sults indicate that adequate parameter tuning to overcome structural differences existing in the language pair yields a satisfactory performance, providing valuable insight towards further research in this area.