Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Learning of Geometric Sampling Invariant Representations for 3D Point Clouds

Point clouds consist of a discrete set of points irregularly sampled from continuous 3D objects. Most existing approaches for point cloud learning are in (semi)-supervised fashions, which nevertheless require costly human annotations. To this end, we propose a novel unsupervised learning of geometric sampling invariant representations, aiming to learn intrinsic feature representations of point clouds on graphs based on that the geometry of one object can be sampled in various patterns and densities into different forms of point clouds. In particular, we represent point clouds on graphs and exploit invariant representations at multiple hierarchies: the low-resolution invariance and original-resolution invariance. To learn invariance at a lower resolution, we subsample the input point cloud in distinct patterns, and maximize the mutual information among the subsampled variants. Further, to learn invariance at the original resolution, we increase the resolution of the subsampled point clouds to the original resolution of the input based on the learned features, and minimize the distance between the input and each of the upsampled versions. In experiments, we apply the learned representations to representative downstream tasks of point clouds, and results on point cloud classification, segmentation and upsampling demonstrate the superiority of the proposed model.
Research on the Application of Artificial Intelligence Technology in Teaching Introduction to Hotel Management in the Context of Smart Hotel

The rapidly developing information technology has made smart hotels one of the development trends in the hospitality industry. In the transition of pursuing digital transformation of hotel enterprises, artificial intelligence technology is increasingly applied to various fields of hotel services and management. The development of smart hotels and the application of artificial intelligence technology have put forward new demands and standards for hotel professionals. it is necessary to explore the role of artificial intelligence technology in the teaching of relevant courses in hotel management. Combining the construction of the course Introduction to Hotel Management and the analysis of the demand for talents from hotels under the background of the development of smart hotels, this paper explores the application of artificial intelligence technology in teaching Introduction to Hotel Management.
Unsourced Random Access Based on List Recoverable Codes Correcting t Errors

We consider the unsourced random access based on a coded compressed sensing approach. The main idea is to replace the outer tree code proposed by Amalladinne et al. with the code capable of correcting t errors. We derive a finite-length random coding bound for such codes and suggest a practical code construction. We have conducted numerical experiments in the single antenna quasi-static Rayleigh fading MAC. The results show that transition to list-recoverable codes correcting t errors allows performance improvement of coded compressed sensing scheme by 7–10 dB compared to the tree code-based scheme.
A Preliminary Evaluation of a Privacy-Preserving Dialogue System

Dialogue systems are AI applications widely used in many contexts requiring user interaction. However, unconstrained interaction may lead to users communicating sensitive data. This raises concerns about how these systems handle personal data, and about their compliance with relevant laws, regulations, and ethical principles. We propose to integrate advanced natural language processing techniques in a dialogue system architecture based on computational argumentation, ensuring that user data are ethically managed and regulations are respected. A preliminary experimental evaluation of our proposal over a COVID-19 vaccine information case study shows promising results. Copyright © 2021 Copyright for this paper by its authors.
Evaluating Transformer Models for Punctuation Restoration in Italian

In this paper, we propose an evaluation of a Transformer-based punctuation restoration model for the Italian language. Experi-menting with a BERT-base model, we perform several ﬁne-tuning with diﬀerent training data and sizes and tested them in an in-and cross-domain scenario. Moreover, we oﬀer a comparison in a multilingual setting with the same model ﬁne-tuned on English transcriptions. Finally, we conclude with an error analysis of the main weaknesses of the model related to speciﬁc punctuation marks.
Leveraging CLIP for Image Emotion Recognition

Multi-modal neural models that are able to encode and process both visual and textual data are becoming more and more common in the last few years. Such models enable new ways to learn the interaction between vision and text, and thus can be successfully applied to tasks of varying complexity in the domain of image and text classification. However, such models are traditionally oriented to learn grounded properties of images and of the objects they depict and less suited to solve tasks involving subjective characteristics, such as the emotions they can convey in viewers. In this paper, we provide some insights in the performances of the recently released OpenAI CLIP model for an emotion classification task. We evaluate the model both under zero-shot settings and via fine tuning on an image-emotion dataset. We compare the performances of CLIP both in a zero-shot and fine-tuning setting on (i) a standard benchmark dataset for object recognition (ii) an image-emotion dataset. Moreover, we evaluate to which extent a CLIP model adapted to emotions is able to retain general knowledge and generalization capabilities.
A Sequence to Sequence Approach for Knowledge Base Relation Linking

Currently, Question Answering (QA) has taken a central role in the area of the Semantic Web , allowing access to a large amount of information in knowledge bases in a simple way. In this ﬁeld, this is known as Knowledge Base Question Answering (KBQA). The most recent KBQA systems allow to abstract all those lay users from the complexities of the query languages like SPARQL and the ontology deﬁnition by posing questions in natural language to retrieve information. Relation Linking (RL) is an essential component in end-to-end KBQA systems, allowing to map relations in natural language questions to corresponding relations in knowledge bases. In this paper, we propose an approach for RL that leverages the strength of the Deep Learning Sequence to Sequence models . We demonstrate the potential of our system by presenting its results on DBNQA and QALD-9, which are well-known datasets for Question Answering over DBpedia.
Step-Based Data Sharing and Exchange in One-of-a-Kind Product Collaborative Design for Cloud Manufacturing

With the trend for global collaboration, there is a need for collaborative design between geographically distributed teams and companies. In particular, this need is inevitable in the companies doing their business based on one-of-a-kind production (OKP). One important problem is the lack of interoperability and compatibility of data between different CAx systems. This problem is further highlighted in data exchange in cloud manufacturing. To the best of authors' knowledge, current studies have limitations in achieving the interoperability and compatibility of data. In this paper, a STEP-based data model is proposed to represent OKP product data/knowledge, which contains four categories of product knowledge (i.e., customer, product, manufacturing, and resource resp.). A STEP-based data modelling approach is proposed to describe each category of knowledge separately and then connect them to form the final integrated model. Compared with most current product models, this model includes the more complete product data/knowledge involved in OKP product development (OKPPD), and thus it can provide more adequate knowledge support for OKPPD activities. Based on the proposed STEP-based data model, a product data exchange and sharing (DES) framework is proposed and developed to enable DES in collaborative OKPPD in the cloud manufacturing environment. Case studies were carried out to validate the proposed data model and DES framework.
Improving Efficiency, Extending the Maximum Load Limit and Characterizing the Control-Related Problems Associated With Higher Loads in a 6-Cylinder Heavy-Duty Natural Gas Engine

High EGR rates combined with turbocharging has been identified as a promising way to increase the maximum load and efficiency of heavy duty spark ignition Natural Gas engines. With stoichiometric conditions a three way catalyst can be used which means that regulated emissions can be kept at very low levels. Most of the heavy duty NG engines are diesel engines which are converted for SI operation. These engine's components are in common with the diesel-engine which put limits on higher exhaust gas temperature. The engines have lower maximum load level than the corresponding diesel engines. This is mainly due to the lower density of NG, lower compression ratio and limits on knocking and also high exhaust gas temperature. They also have lower efficiency due to mainly the lower compression ratio and the throttling losses. However performing some modifications on the engines such as redesigning the engine's piston in a way to achieve higher compression ratio and more turbulence, modifying EGR system and optimizing the turbocharging system will result in improving the overall efficiency and the maximum load limit of the engine. This paper presents the detailed information about the engine modifications which result in improving the overall efficiency and extending the maximum load of the engine. Control-related problems associated with the higher loads are also identified and appropriate solutions are suggested. (Less)
Constitutive modeling and control of 1D smart composite structures

Homogenization techniques for determining effective properties of composite materials may provide advantages for control of stiffness and strain in systems using hysteretic smart actuators embedded in a soft matrix. In this paper, a homogenized model of a 1D composite structure comprised of shape memory alloys and a rubber-like matrix is presented. With proportional and proportional/integral feedback, using current as the input state and global strain as an error state, implementation scenarios include the use of tractions on the boundaries and a nonlinear constitutive law for the matrix. The result is a simple model which captures the nonlinear behavior of the smart composite material system and is amenable to experiments with various control paradigms. The success of this approach in the context of the 1D model suggests that the homogenization method may prove useful in investigating control of more general smart structures. Applications of such materials could include active rehabilitation aids, e.g. wrist braces, as well as swimming/undulating robots, or adaptive molds for manufacturing processes.
Interleaved high step-up DC–DC converter based on three-winding high-frequency coupled inductor and voltage multiplier cell

This study presents an interleaved high step-up DC–DC converter based on three-winding high-frequency coupled inductor and voltage multiplier cell (VMC) techniques. The primary and secondary windings of each coupled inductor are inserted in the same phase and the third winding is inserted in the other phase. The VMC in each phase consists of two diodes, two capacitors, the secondary winding of the same phase coupled inductor and the third winding of the other phase coupled inductor. The voltage gain is increased and the output voltage is clamped across the capacitors of the VMCs. Then, the voltage across the power metal oxide semiconductor field effect transistors (MOSFETs) is decreased. The leakage inductance of the coupled inductors controls the output diode falling rate, which alleviates reverse recovery problems. The power MOSFETs are turned-on under zero current switching that helps to conversion efficiency improvement. Three modes of operation named as continuous conduction mode, discontinuous conduction mode and boundary conduction mode are investigated for the proposed converter. The carried mathematical analysis and satisfying operation of the proposed converter are verified via experimental results of an 870 W 60 V-input to 590 V-output laboratory prototype with 95.2% conversion efficiency.
SS-JIRCS: Self-Supervised Joint Image Reconstruction and Coil Sensitivity Calibration in Parallel MRI without Ground Truth

Parallel magnetic resonance imaging (MRI) is a widely-used technique that accelerates data collection by making use of the spatial encoding provided by multiple receiver coils. A key issue in parallel MRI is the estimation of coil sensitivity maps (CSMs) that are used for reconstructing a single high-quality image. This paper addresses this issue by developing SS-JIRCS, a new self-supervised model-based deep-learning (DL) method for image reconstruction that is equipped with automated CSM calibration. Our deep network consists of three types of modules: data-consistency, regularization, and CSM calibration. Unlike traditional supervised DL methods, these modules are directly trained on undersampled and noisy k-space data rather than on fully sampled high-quality ground truth. We present empirical results on simulated data that show the potential of the proposed method for achieving better performance than several baseline methods.
ToFNest: Efficient normal estimation for time-of-flight depth cameras

In this work, we propose an efficient normal estimation method for depth images acquired by Time-of-Flight (ToF) cameras based on feature pyramid networks (FPN). We perform the normal estimation starting from the 2D depth images, projecting the measured data into the 3D space and computing the loss function for the point cloud normal. Despite its simplicity, our method called ToFNest proves to be efficient in terms of robustness and runtime. In order to validate ToFNest we performed extensive evaluations using both public and custom outdoor datasets. Compared with the state of the art methods, our algorithm is faster by an order of magnitude without losing precision on public datasets. The demo code is available on https://github.com/molnarszilard/ToFNest
Computation of the cycle state-variable sensitivity matrix of PWM DC/DC converters and its applications

This paper presents a generalized technique for computation of cycle state-variable sensitivity matrix of pulse-width-modulated (PWM) dc/dc converters. It determines the sensitivity of the state variable variation, with the inclusion of the effects of changing the topology duration on the sensitivity matrix. A basic sensitivity graph for each circuit topology is derived. By cascading corresponding graphs, a sensitivity matrix of multiple topologies is formulated. The correctness of the proposed method is compared with other methods in the performance of the steady-state determination and stability analysis.
A dual‐resonance CMOS voltage‐controlled oscillator with enhanced performance through new varactor topology

A new fully integrated, dual‐band CMOS voltage controlled oscillator (VCO) is presented. The VCO is composed of a p‐core cross‐coupled and an n‐core cross‐coupled Colpitts oscillator with dual‐resonance LC tank and was implemented in 90‐nm CMOS technology with 1.15 V supply voltage. The circuit allows the VCO to operate at two resonant frequencies with a common LC tank. This VCO is configured with 6.3 and 10 GHz frequency bands with differential outputs. The dual‐band VCO operates at 6.11–6.413 GHz and 9.72–10.24 GHz. The phase noises of the VCO operating at 9.72 and 6.4 GHz are −116.31 dBc/Hz and −122.79 dBc/Hz at 1‐MHz offset, respectively, while the VCO draws 4/4.28 mA and 4.6/4.92 mW consumption at high/low frequency band from a 1.15‐V supply. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:1590–1593, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26882
Workforce training initiatives for megaproject success

The recent trend of mega construction projects has resulted in some major challenges and opportunities for construction management and execution. One of these challenges is in construction workforce renewal and training. With a rapidly aging workforce, the challenge is to attract new workers while simultaneously providing adequate training for the influx of young and sometimes inexperienced people. Client–owners are now recognizing the economic need to implement advanced training on their projects, to improve productivity and minimize losses. With workforces ranging in the thousands, skilled labour has one of the most significant impacts on project outcomes. Alberta has seen a significant increase in workforce training initiatives, particularly since 2000. The objectives of this paper are to provide an overview of the recent advances and initiatives in workforce training in Alberta within the unionized building trades sector of the industrial construction industry and to highlight the economic significanc.
Control strategy and site selection of a shunt active filter for damping of harmonic propagation in power distribution systems

This paper deals with a shunt active filter which will be installed by an electric utility, putting much emphasis on the control strategy and the best point of installation of the shunt active filter on a feeder in a power distribution system. The objective of the shunt active filter is to damp harmonic propagation, which results from harmonic resonance between many capacitors for power factor improvement and line inductors in the feeder, rather than to minimize voltage distortion throughout the feeder. Harmonic mitigation is a welcome "by-product" of the shunt active filter, which comes from damping of harmonic propagation. This paper concludes that the shunt active filter based on detection of voltage at the point of installation is superior in stability to others, and that the best site selection is not the beginning terminal but the end terminal of the primary line in the feeder. Computer simulation is performed to verify the validity and effectiveness of the shunt active filter by means of an analog circuit simulator, which is characterized by installing it on a feeder of a radial distribution system in a residential area.
Thermal and reverse base current effects on heterojunction bipolar transistors and circuits

Device modeling of the heterojunction bipolar transistor (HBT) has been examined. The Gummel-Poon model derived from silicon bipolar transistor is able to predict the base and collector currents of the HBT with good accuracy for negligible self- heating. The present model extends the Gummel-Poon model equations to account for self-heating using a nonlinear thermal resistance. The present model also accounts for base current reversal due to impact ionization in the collector-base depletion region. Effects of self-heating on the performance of analog circuits such as current mirrors and small-signal amplifiers are evaluated.
Putting the Aero Back into Aeroelasticity

The lack of progress in understanding the physics of rotorcraft loads and vibration over the last 30 years is addressed in this paper. As befits this extraordinarily difficult problem, the reasons for the lack of progress are complicated and difficult to ascertain. It is proposed here that the difficulty lies within at least three areas: 1) a loss of perspective as to what are the key factors in rotor loads and vibration, 2) the overlooking of serious unsolved problems in the field, and 3) cultural barriers that impede progress. Some criteria are suggested for future research to provide a more concentrated focus on the problem.
Application of UHF partial discharge monitoring and expert system diagnosis

This paper handles the practical application of the ultra high frequency (UHF) partial discharge (PD) measuring technique in on-site tests using a resonant test set-up. Application during the service life of gas-insulated switchgear (GIS) is also addressed (on-line monitoring). Tests made during commissioning proved that even small PD defects can be detected. Even at a test voltage frequency of 122 Hz (produced by a resonant test system), the type of PD defect can be recognised on the basis of PRPD patterns from 50 Hz laboratory measurements. Furthermore, the application of PD expert system diagnosis is discussed in terms of reliability. Results based on more than a thousand measurements made at a 420/550 kV GIS laboratory set-up are presented.
Content development in an indigenous digital library: A case study in community participation

Africa and African libraries and information centres are poorly equipped to make a meaningful contribution to the current global digital knowledge economy. Low local content on the Web retards buy-in from local communities into digital resources and inhibits development of digital skills. A library with content of local relevance will encourage communities to make use of library services, especially if they are empowered to participate in development of the content. Public libraries serving ethnic communities whose histories are locked up in written, pictorial and oral traditions are well positioned to provide a platform for public engagement in the collecting and disseminating of indigenous knowledge in the communities they serve. This paper presents a case study in community participation in developing content for a digital library of local indigenous knowledge. Description of the programme highlights interaction between the library, the community and the technology used. Implementation challenges, results and lessons learnt are discussed and benefits to the community pointed out. In providing an online, contextually-based information service to local communities, public libraries in Africa will ensure future-oriented access to cultural heritage resources through 21st century information communication technologies (ICTs). The potential to reduce the digital divide will be enhanced and African communities will be introduced to the global information society.
Compliant Control for the Redundant Dual-Arm Live-Working Robot

Two arms are attached on the live-working robot for cooperating jobs and some task requiring larger stiffness such as pushing and butting when replacing cross arm or insulator. The different kinematic structure of this two arms brings many difficulties to get the dynamic model of the total system. This paper proposes a force control method to solve this problem. Two arms are considered as one arm firstly, then the general stiffness matrix is obtained. And based on the compliant relationship between the dual arms and the environment, the force control method for the exact force control is presented. The scheme is experimentally tested on the live-working robot, and the effectiveness and rapidity is validated .
A Proposed Silicon Optical Electronic Integrated Circuit with Monolithic Integration of LED, OPFET and Receiver Circuit

The paper presents an improved model of Silicon Optical Electronic Integrated Circuit (SOIC). The objective is to improve our OEIC model i.e. OEIC Circuit with Two Metal Layer Silicon Waveguide and Low Power Photonic Receiver Circuit by increasing responsitivity by increasing the quantum efficiency without changing the basic approach. This can be done by using optically controlled silicon MESFET also called OPFET. We also introduced the transparent indium tin oxide gate which when combines with MESFET, acts as a pre-amplifier in OEIC. The optically controlled silicon MESFET is integrated here having improved responsitivity, low gate to source capacitance, less signal delay, reduced noise and increased quantum efficiency.