Miracle-dz/UniFAR-FTUs huggingface dataset:
Realistic Facial Expression Reconstruction for VR HMD Users

We present a system for sensing and reconstructing facial expressions of the virtual reality (VR) head-mounted display (HMD) user. The HMD occludes a large portion of the user's face, which makes most existing facial performance capturing techniques intractable. To tackle this problem, a novel hardware solution with electromyography (EMG) sensors being attached to the headset frame is applied to track facial muscle movements. For realistic facial expression recovery, we first reconstruct the user's 3D face from a single image and generate the personalized blendshapes associated with seven facial action units (AUs) on the most emotionally salient facial parts (ESFPs). We then utilize pre-processed EMG signals for measuring activations of AU-coded facial expressions to drive pre-built personalized blendshapes. Since facial expressions appear as important nonverbal cues of the subject's internal emotional states, we further investigate the relationship between six basic emotions - anger, disgust, fear, happiness, sadness and surprise, and detected AUs using a fern classifier. Experiments show the proposed system can accurately sense and reconstruct high-fidelity common facial expressions while providing useful information regarding the emotional state of the HMD user.
Evaluation of Quality and Assurance Parameters of Mulberry Silk Waste and Viscose Blended Knitted Fabrics by Using ‘Fabric Assurance by Simple Testing’ (FAST) Technique

Fabric handle is one of the influential properties for any fabric and is a guiding factor for optimum selection of textile materials for specific end uses. The paper deals with objective analysis of knitted fabrics for fabric hand. Present attempt was made on four knitted fabrics, blended in proportions of 50% mulberry silk: 50% viscose and 40% mulberry silk: 60% viscose, each in two different counts. Fabric Assurance by Simple Testing (FAST) was utilized for determination of properties which is precisely associated with apparel construction and its lastingness. Fabric samples were subjected to tests for obtainment of dimensional stability, formability, low load extensibility, bending rigidity, compression and shear rigidity. Knitted fabric blended in proportion of 50% mulberry silk: 50% viscose in 20 Nm count was found to be most feasible to large scale production and garment construction.
A Robust Scheduling Strategy for Moldable Scheduling of Parallel Jobs.

Moldable job scheduling has been proved to be effective compared to traditionaljob scheduling policies. It is based on the observation that most jobs submitted to a spaceshared parallel system can actually reduce their response times if they were allowed to take any number of processors in a user-specified range. Previous approaches to scheduling of moldable jobs focused on when and how to choose the number of processors for a moldable job. Careful experimental evaluations show that these techniques are not robust. This paper proposes a new strategy for scheduling moldable jobs that outperforms not only the traditional rigid scheme, but also the previous moldable scheduling policies, by doing uniformly well under different load conditions and for jobs of different scalabilities.
The Evolution of Geotechnical Earthquake Engineering Practice in North America: 1954-1994

SYNOPSIS This paper traces the evolution of geotechnical earthquake engineering practice in North America from 1954 to 1994. The development of the state-of-the-art has been shaped strongly by four areas of practice: assessment of seismic hazard, estimation of liquefaction potential, seismic response analysis of earth structures and seismic safety evaluation and remediation of existing dams with potentially liquefiable zones. Evolution of practice in each of these areas will be traced and the current state-of-tl")e-art evaluated. Present capabilities in practice will be illustrated by examples from the areas of seismic response of dams, liquefaction potential and seismic safety evaluation and remediation of potentially liquefiable embankment dams.
Designing Activities for Young Learners in EFL Classrooms

Teaching young learners is quite a hard task for foreign language teachers unless they are equipped with the knowledge appropriate for the subject group. To be able to present the required subject in the classroom adequately, teachers should be able to keep in mind many aspects concerning young learners, such as age, material, interest, level, intelligence, time, and physical conditions in the classroom… etc. Bearing all this in mind, the aim of this article has been to provide necessary information for the EFL teachers so that they can get the utmost feedback from young learners. It is obvious that being knowledgeable in the related field is always of great help to both sides- teachers and learners.
Metrics to select design tasks in experimental creativity research

This paper demonstrates the need for systematic approaches to assess design tasks for experimental studies of idea generation and conceptual design. We first examine a collection of 160 design tasks used in the literature revealing a high variance of task characteristics. Variance across studies is captured by analysing operating variables including task elaboration, task orientation, task selection, participants and time allocated for ideation. Four metrics are then proposed to assist researchers to objectively characterise design tasks. These metrics help identify the semantic and lexical dimensions of problem statements, evaluate precedent ideas and assess task wording. The value of this approach is demonstrated by applying the metrics to a subset of design tasks showing their usefulness to assess the design task objectively as part of the wider experimental design process. Armed with these metrics, researchers can tune both the task and the wider experiment to best match the purposes and the operating variables of their studies. The paper concludes by formulating concrete recommendations to assist in the selection of design tasks using these metrics.
Pragmatic Quotation Use in Online Yelp Reviews and its Connection to Author Sentiment

Pragmatic Quotation Use in Online Yelp Reviews and Its Connection to Author Sentiment Mary Elisabeth Wright Department of Linguistics and English Language, BYU Master of Arts Previous research has established that punctuation can be used to communicate nuances of meaning in online writing (McAndrew & De Jonge, 2011). Punctuation, considered a computer mediated communication (CMC) cue, expresses tone and emotion and disambiguates an author’s intention (Vandergriff, 2013). Quotation marks as CMC cues can serve pragmatic functions and have been understudied. Some of these functions have been generally described (Predelli, 2003). However, no corpus study has specifically focused on the pragmatic uses of quotations in online text. Consumer reviews, a genre of online text, can directly impact business profits and influence customers’ purchasing decisions (Floyd, Freling, Alhoqail, Cho & Freling, 2014). Businesses are investing in sentiment analysis to gauge their target market's opinions (Salehan & Kim, 2016). Sentiment analysis is the computerized appraisal of a text to determine whether its author is expressing a positive or negative opinion (Novak, Smailovic, Sluban & Mozetic, 2015). Sentiment analysis programs are still limited and could be improved in accuracy. Most programs rely on lexicons of words given a pre-determined polarity value (positive or negative) out of context (Novak et al., 2015). However, context is crucial to communication, and sentiment analysis programs could incorporate a better variety of contextual linguistic features to improve their accuracy. Quotations used for pragmatic communication is such a feature. This study discovered seven pragmatic quotation uses in a 2014 Yelp review corpus: Collective Knowledge, Non-standard, Grammatical, Non-literal, Narrative, Idiolect, and Emphasis. An ANOVA and Tukey HSD test were performed, and the results were significant. Pragmatic category accounted for 15% of the variance in review star rating. The Collective Knowledge category and the Narrative and Non-literal categories were significantly different from each other. The Collective Knowledge category showed a correlation with positive sentiment, while the Narrative and Non-literal categories displayed a correlation with negative sentiment. These three categories are likely present in several types of online text, making them valuable for further sentiment analysis research. If these pragmatic patterns could be detected automatically, they could be used in sentiment algorithms to give a more accurate picture of author opinion.
An Optimal Design of the Inlet and Outlet Obstacles at USBR II Stilling Basin

Hydraulic jump is one of the rapidly varied flows that different elements can be employed to control it in stilling basins. The purpose of this study is optimal design of obstacles at USBR II stilling basin of Nazloochay dam model. For this purpose, the obstacles at the end chute with thicknesses and heights of 0.5, 1, 1.5 and 2 m and 2, 3 and 4 steps at the end USBR II stilling basin have been considered. Then for the most optimal state, the pressure, water surface profile, turbulent kinetic energy, and turbulent kinetic energy dissipation investigated for discharging 830 m/s by using K-ε and RNG turbulence model and VOF method. According to changes in water height, obstacles selected with heights of 1 and 1.5 equal of standard state at the end chute and 3 steps at the end stilling basin. The comparison of numerical and experimental values of water surface and pressure profiles showed that numerical model has good agreement with experimental results. Also, hydraulic jump on adverse steps at the end stilling basin increase energy dissipation and stability of hydraulic jump. In addition, numerical model can be used to model stilling basins and measuring non-measurable parameters in laboratory.
Aggregating Deep Pyramidal Representations for Person Re-Identification

Learning discriminative, view-invariant and multi-scale representations of person appearance with different semantic levels is of paramount importance for person Re-Identification (Re-ID). A surge of effort has been spent by the community to learn deep Re-ID models capturing a holistic single semantic level feature representation. To improve the achieved results, additional visual attributes and body part-driven models have been considered. However, these require extensive human annotation labor or demand additional computational efforts. We argue that a pyramid-inspired method capturing multi-scale information may overcome such requirements. Precisely, multi-scale stripes that represent visual information of a person can be used by a novel architecture factorizing them into latent discriminative factors at multiple semantic levels. A multi-task loss is combined with a curriculum learning strategy to learn a discriminative and invariant person representation which is exploited for triplet-similarity learning. Results on three benchmark Re-ID datasets demonstrate that better performance than existing methods are achieved (e.g., more than 90% accuracy on the Duke-MTMC dataset).
Effect of train speed and track geometry on the ride comfort in high-speed railways based on ISO 2631-1

The operational speeds of passenger trains have been increasing and now often exceed 300 km/h. Higher speeds can lead to increased vibration and reduced ride comfort for railway passengers. This study investigates the combined effect of speed and track geometry on vibration discomfort in high-speed trains. Railway vehicle dynamic models with various levels of complexity are used, with the measured geometry of a section of a high-speed track as an input. The models have been calibrated with vibration measurements carried out in a train running over this section of the track and then applied to predict the vibration discomfort at increased speeds. To evaluate the vibration discomfort at speeds up to 400 km/h, information on track geometry should include wavelengths up to at least 150 m. Vertical irregularities have the greatest effect at all speeds but lateral irregularities are also important. Both the vertical and lateral irregularities of a high-speed track should be controlled at wavelengths of 50–100 m that excite rigid modes of the car body, corresponding to frequencies of typically 1–2 Hz. Additionally, vertical irregularities with wavelengths of 5–12 m that excite the fundamental flexible mode of the car body, typically around 10–15 Hz, should also be controlled. The effects of cant, the rates of change of cant, and the radius of vertical curves are also evaluated although they only have a small effect on vibration discomfort.
Upper Elementary Reading Instruction in the Age of Accountability: Balancing Best Practices with Pressures to Achieve on High-Stakes Tests

x Chapter 1: Introduction . 1 Statement of Problem . 1 Rationale for the Study . 2 Statement of Purpose . 3 Literature/Research Background . 3 Reading Instructional Practices . 3 A Changing Educational Landscape . 5 Research Questions . 8 Methodology . 9 Findings . 10 Conclusions and Recommendations . 11 Summary . 12 Chapter 2: Review of Literature . 14 Method for Review of the Literature . 14 Reading Instruction . 16
A Programmable Hyper-Dimensional Processor Architecture for Human-Centric IoT

Hyper-dimensional Computing (HDC), a bio-inspired paradigm defined on random high-dimensional vectors, has emerged as a promising IoT paradigm. It is known to provide competitive accuracy on sequential prediction tasks with much smaller model size and training time compared to conventional ML, and is well-suited for human-centric IoT. In the post-Moore scaling era, where increasing variability has challenged traditional designers, its novel computing method based on randomness can be leveraged for continued performance. This work develops a complete, programmable architecture for ultra energy-efficient supervised classification using HD computing. Its simple construction follows from basic HD operations and its massively parallel, shallow datapath (< 10 logic layers) resembles in-memory computing. The architecture also supports scalability: multiple such processors can be connected pralallely to increase effective HD dimension. A broad evaluation is performed by comparing HDC and 3 conventional ML algorithms on conventional architectures such as CPU and eGPU for instruction count, energy cost and memory requirements. Finally, a 2048-dim ASIC design is synthesized in a 28nm HK/MG process and benchmarked on 9 supervised classification tasks with varying complexity (such as language recognition and human face detection). The simulated chip exhibits energy efficiency $ < 1.5~\mu \text{J}$ /pred. for the entire benchmark at about 2.5ns cycle time, with most applications requiring < 700 nJ/pred. As a first complete design working with high dimensional stochastic signals, the main architectural decisions for similar systems harnessing variability in emerging devices (eg. CNFET and RRAM) are established. A fabricated system could be readily deployed for human-centric IoT applications.
Virtual Network Function Placement for Service Chaining by Relaxing Visit Order and Non-Loop Constraints

Network Function Virtualization (NFV) is a paradigm that virtualizes traditional network functions and instantiates Virtual Network Functions (VNFs) as software instances separate from hardware appliances. Service Chaining (SC), seen as one of the major NFV use cases, provides customized services to users by concatenating VNFs. A VNF placement model for SC that relaxes the visit order constraints of requested VNFs has been considered. Relaxing the VNF visit order constraints reduces the number of VNFs which need to be placed in the network. However, since the model does not permit any loop within an SC path, the efficiency of utilization of computation resources deteriorates in some topologies. This paper proposes a VNF placement model for SC which minimizes the cost for placing VNFs and utilizing link capacity while allowing both relaxation of VNF visit order constraints and configuration of SC paths including loops. The proposed model determines routes of requested SC paths, which can have loops, by introducing a logical layered network generated from an original physical network. This model is formulated as an Integer Linear Programming (ILP) problem. A heuristic algorithm is introduced for the case that the ILP problem is not tractable. Simulation results show that the proposed model provides SC paths with smaller cost compared to the conventional model.
Towards Laws of Visual Attention

Visual attention is a crucial process for humans and foveated animals in general. The ability to select relevant locations in the visual field greatly simplifies the problem of vision. It allows a parsimonious management of the computational resources while catching and tracking coherences within the observed temporal phenomenon. Understanding the mechanisms of attention can reveal a lot about human intelligence. At the same time, it seems increasingly important for building intelligent artificial agents that aim at approaching human performance in real-world visual tasks. For this reasons, in the past three decades, many studies have been conducted to create computational models of human attention. However, these have been often carried over as themere prediction of the saliency map, i.e. topographic map that represents conspicuousness of scene locations. Although of great importance and usefulness in many applications, this type of study does not provide an exhaustive description of the attention mechanism, since it misses to describe its temporal component. In this thesis, we propose three models of scanpaths, i.e. trajectories of free visual exploration. These models share a fundamental idea: the evolution of the mechanisms of visual attention has been guided by fundamental functional principles. Scanpath models emerge as laws of nature, in the framework of mechanics. The approaches are mainly data-driven (bottom-up), defined on video streams and visual properties completely determine the forces that guidemovements. The first proposal (EYMOL) is a theory of free visual exploration based on the general Principle of Least Action. In the framework of analytic mechanics, a scanpath emerges in accordance with three basic functional principles: boundedness of the retina, curiosity for visual details and invariance of the brightness along the trajectories. This principles are given a mathematical formulation to define a potential energy. The resulting (differential) laws of motion are very effective in predicting saliency. Due to the very local nature of this laws (computation at each time step involve only a single pixel and its close surround), this approach is suitable for real-time application. The second proposal (CF-EYMOL) expands the first model with the information coming from the internal state of a pre-trained deep fully convolutional neural network. A visualization technique is presented to effectively extract convolutional features (CF) activations. This information is then used to modify the potential field in order to favour exploration of those pixels that are more likely to belong to an object. This produces incremental results on saliency prediction. At the same time, it suggests how to introduce preferences in the visual exploration process through an external (top-down) signal. The third proposal (G-EYMOL) can be seen as a generalisation of the previous works. It is completely developed in the framework of gravitational (G) physics. No special rule is described to define the direction of exploration, ex-
Say again? Individual articulatory strategies for producing a clearly-spoken minimal pair wordlist

We describe articulatory differences (lingual and labial) between two versions (neutral and clear) of a CVC wordlist of 12 targets (V = /ieaɔoʉ/; C_C = /p_p/ or /m_m/). A companion paper describes the background; the participants, materials and tasks; the impressionistic and acoustic results. Labial measures reflect vowel opening (and edge-spreading) and consonant compression using fleshpoint markers captured by head-mounted video. Consonant closure and total word duration are based on visual judgement of complete closure. Ultrasound data provides the absolute area between neutral and clear mid-sagittal tongue-surface splines at the maximum of each vowel target, and a qualitative description of tongue shape and location. Strong and systematic interspeaker variation was evident in how articulation, acoustics and functional clarity were enhanced. Some large phonologically motivated segmental hyperspeech enhancements were observed, but they were not related straightforwardly to the phonological oppositions in the material nor consistently used by all speakers. Differences in utterance initiation are also discussed.
Deep CM-CNN for Spectrum Sensing in Cognitive Radio

One of the key problems in spectrum sensing is to design the test statistic. Existing methods generally exploit the model-based features as the test statistic, such as energies and eigenvalues. However, these features could not accurately characterize the real environment. Motivated by this, in this paper, we use a deep neural network (DNN) to intelligently explore the data-driven test statistic. Firstly, we introduce a DNN-based detection framework, where a DNN-based likelihood ratio test (DNN-LRT) is derived to guarantee the optimality of the designed test statistic. As a realization of the developed DNN-based framework, we use the sample covariance matrix as the input of a convolutional neural network (CNN), and propose a covariance matrix-aware CNN (CM-CNN)-based spectrum sensing algorithm, which further improves the performance. In addition, we also provide the theoretical analysis of the proposed method. To the best of our knowledge, it’s the first time to analyze the theoretical performance of CNN-based methods. Finally, simulation results demonstrate that the performance of the proposed method is close to that of the optimal detector. Particularly, the proposed method could achieve a detection probability of 96.7% with a false alarm probability of 1.9% at SNR = −18dB, which significantly outperforms the conventional methods.
Metal-Frame Antenna for Big-Screen Smartphones Using Characteristic Mode Analysis

In previously published papers about the metal-frame smartphone antennas, the screen is usually not included in the antenna model. As the big-screen, or even full-screen (also called all-screen or bezel-less) smartphones become the trend in recent and foreseeable future years, the effect of the very close screen to metal-frame antennas becomes prominent, e.g., the antenna’s bandwidth is significantly narrowed. In this paper, a metal-frame antenna for the big-screen smartphone covering 824-960 MHz (GSM850/900), 1452-1496 MHz (LTE1500), 1710–2690 MHz (DCS, PCS, UMTS2100, LTE2300/2500), and 3400-3600 MHz (3.5 GHz 5G) is introduced. For the first time, the screen is fully considered, even designed as part of the antenna. The theory of characteristic modes (TCM) is applied in the design method. A prototype is made and measured for verifying the proposed methodology. Some key parameters of this antenna design are also discussed. This methodology helps to improve the degraded performance of smartphone antennas due to the big-screen industrial design.
A Mathematical Model for Evaluating the Performance of Multicast Systems

© 2008 IEEE. Reprinted, with permission, from Syed S. Rizvi, Aasia Riasat, and Khaled M. Elleith, "A Mathematical Model for Evaluating the Performance of Multicast Systems," The 1st IEEE International Workshop on IP Multimedia Communications (IPMC 2008) August 4 - 7, 2008, St. Thomas U.S. Virgin Islands