Miracle-dz/UniFAR-FTUs huggingface dataset:
Classifying Confusion: Autodetection of Communicative Misunderstandings using Facial Action Units

Classification of secondary emotions via facial expression analysis or other forms of social signal processing is a topic which has received more attention recently due to the increasing computational and algorithmic power of emotion expression and detection software. Of the social emotions (or affective states), confusion detection is of paramount importance in interactive tasks that require communicating from different viewpoints. Confusion has only fairly recently been the subject of computational modelling approaches owing to the contention as to what expressive components are foundational. One aspect of confusion that has been perhaps de-emphasized in such models is the temporal component. This includes facial expression and non-expression data and the temporal context (state) within which they are embedded. Using the Facial Action Coding schema of Ekman, this article reports the findings of a Long-Short Term Memory (LSTM neural network) modelling approach to (video) instances of confusion as expressed in the context of a “instructor-follower” interactive (map directions) task. The LSTM neural network is able to encode the temporal context of expressed micro instances of positive, negative, neutral and confusion based affective states and is compared against a simpler algorithm used in FaceReader 7.1. that does not account for temporal context (memory state). The LSTM neural network, when trained on time series data of a number of theoretically relevant Action Units, performed at a level comparable to the chosen benchmark highlighting its potential utility. Lesioning trials of the pre-trained best model showed that Action Units 25, 26 and 27, involving open mouth expressions, were the most individually important contributing factors to the model's performance. Testing on novel and larger datasets, as well as possible incorporation of multimodal social signals, would be expected to enhance generalizability and provide potential avenues for future work.
Supporting Mood Introspection from Digital Footprints

There is an urgent need to understand how technology impacts psychological health. This is challenging because the relationship between digital behaviour, emotions and wellbeing is complex, individual, and ethically sensitive. This study describes a mood detection system which solely utilises digital usage data from a commercial digital usage tracker tool. Using 813 days of digital behaviour data, and 807 mood self-reports, from 47 users, the system achieved maximum accuracies varying between 81–82%. The result indicates that digital footprints are useful as features to detect mood. We discuss ethical issues, and an approach to address them.
THEORETICAL APPROACHES TO VIOLENT CRIMINAL ACTS OF STREET GANGS IN USA

Gang membership and criminal activity continue to f lourish in the US communities. There are approximately 1.4 million gang members in the USA operating in over 33,000 gang groups such as street gang, prison gang, outlaw mot orcycle gang, and neighborhood/local street gang. Street gangs could be defined as criminal org anizations formed on the street operating throughout the United States. This study examines t h concepts of gang membership, gang delinquency/violence in the light of some selected theoretical approaches and models such as developmental and life course perspective, risk fac tors model, selection, social facilitation and enhancements models, theory of normative ambiguity, anomie/strain theory, control theories/social bonding theory, social learning the ory, social disorganization theory, integrated theory, and organizational theory/network approach. It is not aimed to make in-depth analysis of each theoretical approach/model or list all finding s in the literature; this study aims at explaining gang membership and violence by linking these conce pts to selected theories and models.
Improving Automatic Pain Level Recognition using Pain Site as an Auxiliary Task

Pain is an unpleasant sensory and distressing feeling usually induced by physical damages, and the intensity is further modulated by the experienced pain site. Objective assessment of pain is critical in a variety of clinical practices, however, the status quo in medical practices is based solely on self-report. Recent advancements have been observed in automatic assessment of pain using audio-video recordings, but most do not consider the complex clinical dependency between pain level and pain site. In this study, we propose a Task Specific Encoder with Soft Layer Ordering structure (TSEN-SLO) that utilizes a learnable tensor to flexibly share information between pain level and pain site while still keeping the representations of each task in their self-encoding layers to improve pain level recognition. Our network learns from both face and voice data and achieves accuracy of 70% and 48.1% in a binary and ternary self-report pain level classification in a challenging in-the-wild setting. The approach improves a relative of 6.5% and 9.1% compare to previous work on the same dataset. Further analysis also demonstrates the variation in the self-reported pain level as observed in the facial and acoustic features for different pain sites, which points toward a potential relationship between the neural-mechanism behind internal pain sensation and its effect on expressive facial/vocal behaviors.
Joint Precoding Using Successive Over-relaxation Matrix Inversion and Newton Iteration for Massive MIMO Systems

In massive multiple-input multiple-output (MIMO) systems, Newton iteration can effectively reduce the computation complexity of regularized zero forcing (RZF) precoding algorithm in which complicated matrix inversion is required. However, the Newton iteration has slow convergence speed and it needs complicated calculation to search initial values. In this paper, by using successive over-relaxation (SOR) iteration, a matrix inversion method, namely successive over-relaxation matrix inverse (SORMI) iteration, is derived to obtain the initial values for Newton iteration. Correspondingly, the SORMI-Newton joint precoding algorithm is presented. Moreover, convergence and complexity are analyzed for the investigated joint precoding algorithm. Simulation results show that compared with the conventional Newton, the joint precoding algorithm possesses have faster convergence speed because of its appropriate initial values, at the same time, it can achieve a bit error rate (BER) performance comparable to that of the RZF.
Coordinating the Modes of the Axial Roll Shifting and Roll Bending Systems of a Roll Mill Stand

Automatic control of a rolled workpiece profile is recognized as a powerful tool that helps improve the quality of rolling mill products. The paper reviews a CVCPlus technology with a WRB system of work roll bending in the process of rolling. It considers the functions of the axial shifting system for S-shaped work rolls (WRS) within CVC. It is noted that the interconnected operation of these systems has been studied insufficiently. The authors present a functional diagram for the automatic system of gap profile and roll bending adjustment on a 5,000 mm plate mill. They analyze the oscillograph records that describe operation of the WRB and WRS systems. The authors point out the flaws in the settings of these systems and provide rationalization for ways of their improvement. A new algorithm ensuring enhanced performance and higher accuracy of force control in the hydraulic cylinders of the WRB system is proposed. An algorithm for reducing the force in the WRS hydraulic cylinders in CVC-motion mode is introduced. The oscillograph records obtained in the process of implementation of the proposed algorithms are provided. It was verified that the obtained results complied with the expected ones. The developed algorithms are recommended for implementation on the 5,000 mm mill.
A Junction Temperature-based PSpice Short-circuit Model of SiC MOSFET Considering Leakage Current

In order to estimate the short-circuit capability of SiC MOSFET-based power converter, this paper proposes a behavioral model of SiC MOSFET that can be used for short-circuit simulation in PSpice, and the leakage current generated during short circuit is considered, which is an early warning of device failure. The short-circuit characteristic of SiC MOSFET is discussed and analyzed, and a thermal model of case-to-junction is built to describe the junction temperature of SiC MOSFET during short-circuit condition. Based on the junction temperature information, the channel current model and leakage current model are established respectively. The simulation results show that the short-circuit model can correctly reflect the static characteristic and short-circuit characteristic of SiC MOSFET. This circuit model can provide guidance for the design of short circuit protection circuit.
Closed-loop DC-link Voltage Balancing Algorithm for a Four-level π-type Converter

A four-level π-type converter is a Neutral Point Clamped (NPC) multilevel converter topology for low/medium voltage applications. As the inherent issue with this topology, the DC-link capacitor voltage balancing is challenging, especially when it operates as a single-end inverter/rectifier with unity power factor. This paper proposes a closed-loop DC-link voltage balancing algorithm of a π-type converter that is effective and simple to implement. In principle, this approach is based on Redundant Level Modulation (RLM). The RLM utilizes additional voltage levels in each switching window to gain extra controllability of the DC-link capacitor voltages without affecting the average output voltage. An algorithm based on mathematical and logical operations is developed to utilize RLM to achieve the closed-loop voltage balancing. The proposed control method is effective over the full modulation index ($M=0\sim$ 1.15) and full power factor range $(cos\varphi=0\sim 1)$. The algorithm is implemented in a test rig, and the experiment confirms its effectiveness.
Drift-free FBAR oscillator using an atomic-resonance-stabilization technique

We developed a thin-film bulk acoustic resonator oscillator (FBAR-OSC) with drift-free and excellent frequency stability. The FBAR-OSC outputted the 3.5 GHz band oscillation and successfully stabilized it employing the coherent population trapping (CPT) resonance of clock transition in Rb alkali metal atoms. As a result of the stabilization, the temperature drift was canceled, and the frequency fluctuation was suppressed to 0.5 ppb or less in the measurement time of 104 s.
Social Community Detection Scheme Based on Social-Aware in Mobile Social Networks

Currently many factors can influence community detection in mobile social networks, where node mobility is a key factor to influence the stability of community structure. In this paper, we propose a social community detection scheme for mobile social networks based on social-ware, including social attribute similarity, node interest similarity and node mobility. Compared with other community detection schemes, our proposed scheme can accurately detect the communities based on community attribute and node mobility. The experiments show the numbers of detected communities and members in the maximal size community generated by our scheme are both smaller than those of the GN and NM schemes. Additionally, since the nodes (users) both have higher mobility in mobile social networks, the efficiency of our proposed scheme relatively becomes higher. The experiments show when the values of mobility in the test data sets increase, the running time of our proposed scheme decreases when the number of edges is fixed. For example, the running time of our proposed scheme is about 17s when the maximum value of mobility is set as 0.5 and the number of edges is about 16000, and further the running time is only about 13s when the maximum value of mobility is set as 1. Therefore, our proposed scheme can more accurately and efficiently make community detection to increase the stability of mobile community structure.
Adaptive Denoising Approach for High-Rate GNSS Seismic Waveform Preservation: Application to the 2010 EI Mayor-Cucapah Earthquake and 2012 Brawley Seismic Swarm

The high noise levels of high-rate Global Navigation Satellite System (GNSS) solutions limit their seismological applications, including capturing earthquake-induced coseismic displacements. In this study, we developed a new adaptive denoising approach for high-rate GNSS observations to improve the precision of seismic displacements and preserve seismic waveforms in GNSS coseismic signals. The performance of the proposed method was evaluated using high-rate (5 Hz) GNSS data acquired from the moderate EI Mayor–Cucapah earthquake (Mw 7.2) on 4 April 2010 and the small Brawley seismic swarm (Mw 4.6–5.5) on 26 August 2012. The performance of the proposed method was compared with those of modified sidereal filtering, Stacking filtering, and MSF plus Stacking. The comparison showed that the proposed method is more precise than other widely used method. It can significantly remove high-frequency white noise and low-frequency colored noise caused by CME, multipath errors, and/or other unmodeled systematic errors in high-rate GNSS displacements. The results were also compared with collocated strong motion data (50 and 200 Hz). The high precision of the proposed method was mainly afforded by the high performance of complete ensemble empirical mode decomposition, which was used to decompose the GNSS signal into different frequency modes. However, the normalized autocorrelation function and correlation coefficients used to determine the noise-dominated high-frequency modes and the “wavelet-like” soft-threshold used for direct denoising of the noise-dominated high-frequency modes also contributed. Despite the high noise levels of GNSS solutions, especially regarding the vertical displacement components, some small-amplitude details, which are usually only detectable by seismic instruments, could be observed in the denoised displacements in this study. The results reported herein indicate that the proposed method significantly improves the precision and reliability of GNSS displacements and the effectiveness of seismic signal detection, which is particularly critical for the measurement of earthquake-induced coseismic displacements.
Nutritional profile and in situ digestion kinetics of some irrigated grasses at pre-bloom stage

Institute of Animal Nutrition and Feed Technology, University of Agriculture, Faisalabad – 38040, Pakistan Abstract. The study was aimed to examine the nutritional value, mineral profile, gross energy and in situ digestion kinetics of 10 abundantly available grasses of irrigated areas of Pakistan. Malai grass/Blue Panic, Barru/Johnson grass, Mott grass, Khas Khas grass/Vetiver, Lemon grass, Dhaman grass/Bulled grass, Kallar grass, Rhodes grass, Khabal grass/Bermuda grass and Swank grasses with botanical names Panicum antidotale, Sorghum halepense, Pennisetum purpureum, Vetiveria zizanioides, Cymbopogon citrates, Cenchrus ciliaris, Leptochloa fusca, Chloris gayana, Cynodon dactylon and Panicum colunum were included in the study, respectively. The data collected was analyzed by using analysis of variance technique in a completely randomized design and means were compared by least significant difference (SPSS, 1999). All grasses were cut at pre-bloom stage and were subjected to drying for analysis of dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), gross energy and minerals content by using their respective standard methods. For in situ digestion kinetics, data on each parameter (lag time, rate and extent of digestion of DM and NDF) was analyzed according to completely randomized design using the GLM procedure of SAS (1988). The findings revealed highest (14.28%) and lowest (7.90%) CP contents for P. antidotale and P. colunum, respectively. Gross energy content of grasses ranged from 3654 to 2799 Kcal/kg, maximum for P. antidotale and minimum for C. citrates. The C. gayana contained maximum Ca (4 g/kg DM), whereas, minimum Ca (1.6 g/kg DM) was noticed in C. ciliaris. The Mg concentration varied from 0.84 g/kg in L. fusca to 0.264 g/kg in C. ciliaris. Maximum (10.5 g/kg DM) and minimum (2.5 g/kg DM) Na contents were found in V. zizanioides and C. citrates, respectively. Likewise, K concentration varied from 27.2 g/kg to 11.6 g/kg with highest and lowest contents in P. colunum and L. fusca, respectively. Maximum (0.59 g/kg DM) and minimum (0.14 g/kg DM) P concentrations were observed in P. purpureum and C. gayana, respectively. The In Situ digestion kinetics indicated highest (74.2%) and lowest (29.5%) DM digestibility of P. antidotale and V. zizanioides grasses, respectively. The highest (67.96%) NDF digestibility was observed for P. antidotale while the lowest (27.37%) for L. fusca. Nutritional profile, shorter lag time, faster rate of digestion and high extent of digestion reflected these grasses as valuable ruminant feed.
ENHANCED CONVOLUTIONAL NEURAL KERNELS

Recent research shows that for training with `2 loss, convolutional neural networks (CNNs) whose width (number of channels in convolutional layers) goes to infinity correspond to regression with respect to the CNN Gaussian Process kernel (CNN-GP) (Novak et al., 2019) if only the last layer is trained, and correspond to regression with respect to the Convolutional Neural Tangent Kernel (CNTK) if all layers are trained. An exact algorithm to compute CNTK (Arora et al., 2019) yielded the finding that classification accuracy of CNTK on CIFAR-10 is within 6-7% of that of the corresponding CNN architecture (best figure being around 78%) which is interesting performance for a fixed kernel. Here we show how to significantly enhance the performance of these kernels using two ideas. (1) Modifying the kernel using a new operation called Local Average Pooling (LAP) which preserves efficient computability of the kernel and inherits the spirit of standard data augmentation using pixel shifts. Earlier papers were unable to incorporate naive data augmentation because of the quadratic training cost of kernel regression. This idea is inspired by Global Average Pooling (GAP), which we show for CNN-GP and CNTK is equivalent to full translation data augmentation. (2) Representing the input image using a pre-processing technique proposed by Coates et al. (2011), which uses a single convolutional layer composed of random image patches. On CIFAR-10, the resulting kernel, CNN-GP with LAP and horizontal flip data augmentation, achieves 89% accuracy, matching the performance of AlexNet (Krizhevsky et al., 2012) , and outperforms the best previous classifier that is not a trained neural network (Mairal, 2016). Similar improvements are obtained for Fashion-MNIST.
ANALYSIS OF SOFTWARE-BASED RECOVERY SCHEMES FOR SMT PROCESSORS

Today’s microprocessors are prone to transient hardware faults caused by e.g. ionizing particles. The usual method to detect and correct such faults is to use duplex systems in software. Fault detection and correction can be accelerated by taking advantage of logical processors available since the introduction of commercial SMT systems, e.g. by performing a simultaneous retry and roll-forward on different logical processors. We derive four different recovery schemes ({probabilistic, deterministic} × {pessimistic, optimistic}), each of which can be applied after an error has been detected. The recovery software is modular and requires only minor extensions to existing code to provide protection. The schemes are tailored to be executed on an SMT processor. Their execution times are measured under the influence of transient faults, injected at rates of 10−5, 14 · 10 −5 and 10−6. Depending on fault rate, checkpoint distance and the probability to correctly guess correct versions, we make recommendations about which variant to choose. An important insight is that a high rate of successful guesses p is needed for the probabilistic schemes to provide significant advantage over the deterministic ones. When randomly choosing the version to roll-forward (p = 0.5), the optimistic deterministic variant is faster than the optimistic probabilistic one. With p = 0.7, the optimistic probabilistic variant begins to perform better than its deterministic counterpart. The comparison of pessimistic schemes yields similar results.
One-Pump Phase Sensitive Fiber Optical Parametric Amplifier With Dual-Core Fiber

The gain performance of one pump phase sensitive fiber optical parametric amplifier (PS-FOPA) in the dual-core fiber is numerically investigated without pump depleting. The effects of the core coupling coefficient(C), the pump wavelength and the initial signal phase on the gain of PS-FOPA are analyzed in detail. It shows that, the signal gain is greatly affected by different core coupling coefficients, and a flatter gain is achieved with suitable C value. Also, when the pump wavelength is greater than the zero-dispersion wavelength (ZDW), both the peak gain and gain bandwidth decrease with the increase of C, but the trend is opposite in case of the pump wavelength less than the ZDW. Additionally, the flatter and broader gain spectra are obtained by adjusting the initial signal phase in core 2. This simulation provides a detail analysis for the dual-core PS-FOPA, which is significant for the all optical fiber amplifier systems.
Detecting Overlapping Community Structure With Node Influence

Discovering the underlying overlapping community divisions can guide us in better exploring and predicting the structure and properties of the network. However, a large number of existing methods assume that nodes belong only to a single community. In this paper, we designed a posterior probabilistic prediction model under the Mixed-Membership Stochastic Blockmodel framework to accurately detect the overlapping community structure that exists in the network. In order to capture the degree of nodes that exhibit heterogeneous characteristics in the network, the model takes into account the influence of the nodes. In addition, we developed a non-conjugated stochastic variational inference to deduce the link probability prediction model with node influence. The key strategy is to use the mean-domain variational family with variable distribution to approximate the posterior community strengthen and node influence distribution in the prediction model. We compared the performance of this model with the previous algorithm models on computer-generated and real-world networks and found that it gives better results, especially when the heterogeneity of the network is very serious. In general, the combination of node influence and link probabilistic predictive model provides a new idea for us to use a statistical model to explore large-scale overlapping networks.