Miracle-dz/UniFAR-FTUs huggingface dataset:
Shaping Opinion Dynamics in Social Networks

A networked opinion diffusion process that usually involves ex- tensive spontaneous discussions between connected users, is of- ten propelled by external sources of news or feeds recommended to them. In many applications like marketing design, or product launch, etc., corporations often post curated news or feeds on social media in order to steer the users' opinions in a desired way. We call such scenarios as opinion shaping or opinion control whereby a few select users called control users post opinionated messages to drive the others' opinions to reach a given state. In this paper, we propose SmartShape, an opinion control package that jointly selects the control users, as well as computes the optimum rate of control messages, thereby driving the networked opinion dynamics to the desired direction. Furthermore, our proposal also includes a robust shaping suit which makes our control framework resilient to stochastic fluctuations of opinion dynamics, originating from several sources of randomness. Experiments on several synthetic and real datasets gathered from Twitter, show that SmartShape can accurately determine the quality of a set of control users as well as shape the opinion dynamics more effectively than several baselines.
Visual rhythm and beat

We present a visual analogue for musical rhythm derived from an analysis of motion in video, and show that alignment of visual rhythm with its musical counterpart results in the appearance of dance. Central to our work is the concept of visual beats --- patterns of motion that can be shifted in time to control visual rhythm. By warping visual beats into alignment with musical beats, we can create or manipulate the appearance of dance in video. Using this approach we demonstrate a variety of retargeting applications that control musical synchronization of audio and video: we can change what song performers are dancing to, warp irregular motion into alignment with music so that it appears to be dancing, or search collections of video for moments of accidentally dance-like motion that can be used to synthesize musical performances.
Point Cloud Based Autonomous Area Exploration Algorithm

Autonomous navigation is highly important in robotics, especially when it comes to the robotic applications in disaster management etc. There are many algorithms to implement autonomous navigation and most of them are dependent on prior knowledge of the environment and apriori maps. Although they are effective in some scenarios, these algorithms fail to perform when the environment has been subjected to changes that might invalidate the prior map. This paper presents a point cloud based algorithm which can be used in a situation where the prior knowledge of the environment is highly inaccurate. The proposed algorithm uses depth images to get a local map, which it expands by searching for uncharted areas picking the next best location to explore using a breadth first approach given a set of constraints. The proposed algorithm exploits the maps in the 3D space allowing the navigation system to perform effectively in uneven terrains and use inclined planes for its advantage.
Bike flow prediction with multi-graph convolutional networks

One fundamental issue in managing bike sharing systems is bike flow prediction. Due to the hardness of predicting flow for a single station, recent research often predicts flow at cluster-level. However, they cannot directly guide fine-grained system management issues at station-level. In this paper, we revisit the problem of the station-level bike flow prediction, aiming to boost the prediction accuracy using the breakthroughs of deep learning techniques. We propose a multi-graph convolutional neural network model to predict flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective. More specifically, we construct multiple graphs for a bike sharing system to reflect heterogeneous inter-station relationships. Afterward, we fuse multiple graphs and apply the convolutional layers to predict station-level future bike flow. The results on realistic bike flow datasets verify that our multi-graph model can outperform state-of-the-art prediction models by reducing up to 25.1% prediction error.
On Ambient Backscatter Multiple-Access Systems

In this paper, we propose an ambient backscatter multiple-access system, in which a receiver (Rx) simultaneously detects the information sent from an active transmitter (Tx) and a passive Tag. Specifically, the information-carrying signal sent by the Tx arrives at the Rx through two wireless channels: one is the direct Tx-Rx channel, and the other is the backscatter channel, i.e., the Tx- Tag-Rx channel, which further carries the Tag's information due to the multiplicative backscatter operation at the Tag. The proposed multiple-access scheme introduces a new channel model named as the multiplicative multiple-access channel (M-MAC), which has not been addressed before. We study the achievable rate region and the capacity region of the M-MAC, and prove that the achievable rate region of the M-MAC is strictly larger than that of the conventional time-sharing one (i.e., the M- MAC capacity region is strictly convex) in many cases, including the high SNR case and the typical case that the direct channel is much stronger than the backscatter channel. Moreover, the numerical results have also validated this phenomenon under a practical range of SNR and channel conditions. The proposed multiple-access scheme is an attractive technique to improve the throughput of ambient backscatter communication systems.
Cross-lingual implicit discourse relation recognition with co-training

A lack of labeled corpora obstructs the research progress on implicit discourse relation recognition (DRR) for Chinese, while there are some available discourse corpora in other languages, such as English. In this paper, we propose a cross-lingual implicit DRR framework that exploits an available English corpus for the Chinese DRR task. We use machine translation to generate Chinese instances from a labeled English discourse corpus. In this way, each instance has two independent views: Chinese and English views. Then we train two classifiers in Chinese and English in a co-training way, which exploits unlabeled Chinese data to implement better implicit DRR for Chinese. Experimental results demonstrate the effectiveness of our method.
Learning Simpliﬁcations for Speciﬁc Target Audiences

Text simpliﬁcation (TS) is a monolingual text-to-text transformation task where an original (complex) text is transformed into a target (simpler) text. Most recent work is based on sequence-to-sequence neural models similar to those used for machine translation (MT). Different from MT, TS data comprises more elaborate transformations, such as sentence splitting. It can also contain multiple simpliﬁcations of the same original text targeting different audiences, such as school grade levels. We explore these two features of TS to build models tailored for speciﬁc grade levels. Our approach uses a standard sequence-to-sequence architecture where the original sequence is annotated with information about the target audience and/or the (predicted) type of simpliﬁcation operation. We show that it outperforms state-of-the-art TS approaches (up to 3 and 12 BLEU and SARI points, respectively), including when training data for the speciﬁc complex-simple combination of grade levels is not available, i.e. zero-shot learning.
Assessing Performance of Silicon Photonic Modulators for Pulse Amplitude Modulation

Silicon photonic (SiP) electro-optic modulators are a key component in cost-efficient and integrated optical transmitters. Modulator design traditionally uses figure of merits (FOMs) that characterize modulation efficiency and propagation loss of light, which underestimate the modulator-induced power penalty due to intersymbol interference, as they do not consider the electro-optic bandwidth limitation. We show that in the presence of limited electro-optic bandwidth of the SiP modulator, the conventional FOMs, such as VπL and V παL, are unable to predict the minimum transmitter power penalty (TPP). Normalized optical modulation amplitude (OMAN) is proved through simulation to be a reliable tool to predict the minimal TPP point. Then, we introduce a new FOM that includes not only the efficiency of the modulator, but also the bandwidth limitation from the SiP electro-optic modulator. The new FOM that is derived from OMAN translates the system-level requirements of a PAM-M optical link to the device-level design parameters. This FOM can be hired to optimize driving voltage swing, bias voltage, and phase-shifter length or to simply choose a SiP modulator with minimal imposed TPP.
Threat, Coping, and Cost: Protection Motivation in the Context of Consumer Complaining

Despite doing their best to keep customers satisfied, organizations can upset their patrons when expectations go unmet. When these organizational failures occur, consumers sometimes complain directly to companies. This type of behavior may be considered adaptive insofar as it helps individuals rectify their problems and allows organizations to fix issues for others as well. However, not all consumers complain to companies when they experience dissatisfaction; instead, many people choose to engage in less adaptive behaviors such as spreading negative word of mouth or withholding their patronage. This study was conducted to examine how variables related to protection motivation theory (i.e., threat, coping, and cost) relate to consumers’ choices regarding complaint behaviors. Four hundred fifty-four participants were solicited online to respond to hypothetical scenarios regarding organizational failures. The data indicated participants’ perceptions of threat and cost interacted to predict complaining behavior. Results are discussed as they pertain to organizational practices.
Who Am I? Personality Detection Based on Deep Learning for Texts

Recently, personality detection based on texts from online social networks has attracted more and more attentions. However, most related models are based on letter, word or phrase, which is not sufficient to get good results. In this paper, we present our preliminary but interesting and useful research results to show that the structure of texts can be also an important feature in the study of personality detection from texts. We propose a model named 2CLSTM, which is a bidirectional LSTMs (Long Short Term Memory networks) concatenated with CNN (Convolutional Neural Network), to detect user's personality using structures of texts. Besides, a concept, Latent Sentence Group (LSG), is put forward to express the abstract feature combination based on closely connected sentences and we use our model to capture it. To the best of our knowledge, most related works only conducted their experiments on one data set, which may not well explain the versatility of their models. We implement our evaluations on two different kinds of datasets, containing long texts and short texts. Evaluations on both datasets have achieved better results, which demonstrate that our model can efficiently learn valid text structure features to accomplish the task.
Applying Hardware Transactional Memory for Concurrency-Bug Failure Recovery in Production Runs

Concurrency bugs widely exist and severely threaten system availability. Techniques that help recover from concurrency-bug failures during production runs are highly desired. This paper proposes BugTM, an approach that leverages Hardware Transactional Memory (HTM) on commodity machines for productionrun concurrency-bug recovery. Requiring no knowledge about where are concurrency bugs, BugTM uses static analysis and code transformation to insert HTM instructions into multi-threaded programs. These BugTMtransformed programs will then be able to recover from a concurrency-bug failure by rolling back and re-executing the recent history of a failure thread. BugTM greatly improves the recovery capability of state-of-the-art techniques with low run-time overhead and no changes to OS or hardware, while guarantees not to introduce new bugs.
A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

We propose a novel discriminative learning approach for Bayesian pattern classification, called ‘constrained maximum margin (CMM)’. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the constraint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential unconstrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach.
Rheotaxis-based separation of sperm with progressive motility using a microfluidic corral system

Significance The separation of motile sperm from semen samples is required for medical infertility treatments and clinical studies. Conventional methods are time- and labor-intensive and could be potentially hazardous to morphology and paternal content of the sperm. Using a microfluidic corral system and the ability of viable sperm to swim against flow, we were able to passively isolate motile sperm from the semen sample inside a corral. This device can separate sperm with velocities higher than a cutoff, which is tunable with the injection rate. The unprecedented efficiency of our device in comparison to previous studies and its benign passive nature make it favorable for sperm separation. The separation of motile sperm from semen samples is sought after for medical infertility treatments. In this work, we demonstrate a high-throughput microfluidic device that can passively isolate motile sperm within corrals inside a fluid channel, separating them from the rest of the diluted sample. Using finite element method simulations and proposing a model for sperm motion, we investigated how flow rate can provide a rheotaxis zone in front of the corral for sperm to move upstream/downstream depending on their motility. Using three different flow rates that provided shear rates above the minimum value within the rheotaxis zone, we experimentally tested the device with human and bovine semen. By taking advantage of the rheotactic behavior of sperm, this microfluidic device is able to corral motile sperm with progressive velocities in the range of 48–93 μm⋅s−1 and 51–82 μm⋅s−1 for bovine and human samples, respectively. More importantly, we demonstrate that the separated fractions of both human and bovine samples feature 100% normal progressive motility. Furthermore, by extracting the sperm swimming distribution within the rheotaxis zone and sperm velocity distribution inside the corral, we show that the minimum velocity of the corralled sperm can be adjusted by changing the flow rate; that is, we are able to control the motility of the separated sample. This microfluidic device is simple to use, is robust, and has a high throughput compared with traditional methods of motile sperm separation, fulfilling the needs for sperm sample preparation for medical treatments, clinical applications, and fundamental studies.
Simple and Effective Text Simplification Using Semantic and Neural Methods

Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.
Exploring Pupil Dilation in Emotional Virtual Reality Environments

Previous investigations have shown that pupil dilation can be affected by emotive pictures, audio clips, and videos. In this paper, we explore how emotive Virtual Reality (VR) content can also cause pupil dilation. VR has been shown to be able to evoke negative and positive arousal in users when they are immersed in different virtual scenes. In our research, VR scenes were used as emotional triggers. Five emotional VR scenes were designed in our study and each scene had ﬁve emotion segments; happiness, fear, anxiety, sadness, and disgust. When participants experienced the VR scenes, their pupil dilation and the brightness in the headset were captured. We found that both the negative and positive emotion segments produced pupil dilation in the VR environments. We also explored the effect of showing heart beat cues to the users, and if this could cause difference in pupil dilation. In our study, three different heart beat cues were shown to users using a combination of three channels; haptic, audio, and visual. The results showed that the haptic-visual cue caused the most signiﬁcant pupil dilation change from the baseline.
A Data Analysis Application of Formal Independence Analysis

In this paper we present a new technique for the analysis of data tables by means of Formal Independence Analysis (FIA). This is an analogue of Formal Concept Analysis for the study of independence relations in data, instead of hierarchical relations. A FIA of a context produces, when possible, its block diagonalization by detecting pairs of sets of objects and attributes that are not mutually incident, or tomoi, that partition the context. In this paper we combine this technique with the exploration of contexts with entries in a semifield to find independent sets in contingency matrices. Specifically, we apply it to a number of confusion matrices issued from cognitive experiments to find evidences for the hypothesis of perceptual channels.
Tearing of Indian mantle lithosphere from high-resolution seismic images and its implications for lithosphere coupling in southern Tibet

Significance We show high-resolution P and S images of the subducted Indian mantle lithosphere, which has been torn into four main pieces. The tearing provides a unified mechanism for (i) Late Miocene and Quaternary rifting, (ii) mantle earthquakes in southern and central Tibet, and (iii) patterns of current crustal deformation (seismicity and GPS strain rate). It suggests (iv) that the deformations of the crust and the mantle lithosphere in southern Tibet are strongly coupled and thus we need to take a “deeper view” to understand the Himalayan–Tibetan continental deformation and evolution. The geometry of the Indian mantle lithosphere and the above issues are of fundamental importance and have been debated for decades. The proposed model has implications across geoscience disciplines. What happened to the Indian mantle lithosphere (IML) during the Indian–Eurasian collision and what role it has played on the plateau growth are fundamental questions that remain unanswered. Here, we show clear images of the IML from high-resolution P and S tomography, which suggest that the subducted IML is torn into at least four pieces with different angles and northern limits, shallower and extending further in the west and east sides while steeper in the middle. Intermediate-depth earthquakes in the lower crust and mantle are located almost exclusively in the high-velocity (and presumably strong) part of the Indian lithosphere. The tearing of the IML provides a unified mechanism for Late Miocene and Quaternary rifting, current crustal deformation, and intermediate-depth earthquakes in the southern and central Tibetan Plateau and suggests that the deformations of the crust and the mantle lithosphere are strongly coupled.
Stochastic Proximal Algorithms for AUC Maximization

Stochastic optimization algorithms such as stochastic gradient descent (SGD) update the model sequentially with cheap per-iteration costs, making them amenable for large-scale data analysis. Most of the existing studies focus on the classiﬁcation accuracy. However, these can not be directly applied to the important problems of maximizing the Area under the ROC curve (AUC) in imbalanced classiﬁcation and bipartite ranking. In this paper, we develop a novel stochastic proximal algorithm for AUC maximization which is referred to as SPAM. Compared with the previous literature, our algorithm SPAM applies to a non-smooth penalty function, and achieves a convergence rate of O ( log t t ) for strongly convex functions while both space and per-iteration costs are of one datum.
A Preterm Birth Risk Prediction System for Mobile Health Applications Based on the Support Vector Machine Algorithm

The process of knowledge discovery in health databases has steadily been developing. Data mining (DM) techniques integrate several knowledge fields for extraction of reliable, understandable, and useful patterns, such as statistics and artificial intelligence (AI). In this sense, this paper proposes the application of a machine learning (ML) technique, named support vector machine (SVM), for the recognition of patterns in a pregnancy database. This approach has outperformed other ML methods, representing a valuable tool for smart decision support systems (DSSs) and mobile health (m-health) applications. For the performance assessment of the proposed model, this work uses the 10-fold cross-validation method. This MLbased technique obtained encouraging results with an accuracy of 0.821, a true positive (TP) rate of 0.839, a false positive (FP) rate of 0.268, and receiver operating characteristic (ROC) area of 0.785. These indicators show that this approach is an excellent pattern recognizer for pregnancy care. This research provides a comprehensive inference mechanism for mobile DSSs capable of enhancing the care provided to women who are at a risk of developing pregnancy-related problems. Thus, this work can contribute to improve the maternal and fetal health conditions, predicting preterm birth risk early.