Achieving Hate Speech Detection in a Low Resource Setting

Online social networks provide people with convenient platforms to communicate and share life moments. However, because of the anonymous property of these social media platforms, the cases of online hate speeches are increasing. Hate speech is defined by the Cambridge Dictionary as “public speech that expresses hate or encourages violence towards a person or group based on something such as race, religion, sex, or sexual orientation”. Online hate speech has caused serious negative effects to legitimate users, including mental or emotional stress, reputational damage, and fear for one’s safety. To protect legitimate online users, automatically hate speech detection techniques are deployed on various social media. However, most of the existing hate speech detection models require a large amount of labeled data for training. In the thesis, we focus on achieving hate speech detection without using many labeled samples. In particular, we focus on three scenarios of hate speech detection and propose three corresponding approaches. (i) When we only have limited labeled data for one social media platform, we fine-tune a per-trained language model to conduct hate speech detection on the specific platform. (ii) When we have data from several social media platforms, each of which only has a small size of labeled data, we develop a multitask learning model to detect hate speech on several platforms in parallel. (iii) When we aim to conduct hate speech on a new social media platform, where we do not have any labeled data for this platform, we propose to use domain adaptation to transfer knowledge from some other related social media platforms to conduct hate speech detection on the new platform. Empirical studies show that our proposed approaches can achieve good performance on hate speech detection in a low resource setting

Institutional Investors, Earnings Management And Mispricing Of Accruals: Evidence From China

This study examines the role of institutional investors in the pricing of normal accruals and discretionary accruals using the firms listed in the Chinese A-share Market. The results show that significant overpricing of discretionary accruals exists for individual investors and institutional investors, suggesting that they are both misled by the earnings management, while institutional investors are associated with significantly less overpricing. With respect to normal accruals, we find there is no evidence that institutional investors misprice normal accruals, while the individual investors overprice normal accruals. Our results suggest that institutional investors’ superiority in mitigating the mispricing of total accruals is mainly due to their accurate pricing of normal accruals, and the reason why institutional investors cannot fully eliminate mispricing of accruals is that they are partly misled by earnings management

Baicalein administration protects against pentylenetetrazole-induced chronic epilepsy in rats

Purpose: To investigate the protective effect of baicalein against chronic seizures in pentylenetetrazole induced epilepsy in a rat model.Methods: A rat model of chronic epilepsy was prepared by administration of pentylenetetrazole at a dose of 35 mg/kg to Sprague-Dawley rats. The animals were divided into 6 groups (5 rats/group): normal control, model (untreated epilepsy) and four treatment groups that received separately, intraperitoneal injection of 20, 30, 40 and 50 mg/kg baicalein, respectively, on alternate days for 30 days. On each day following baicalein treatment, behavioural alterations in the  rats were assessed.Results: Analyses of behavioural changes revealed significant (p < 0.05) decrease in pentylenetetrazole-induced convulsions by baicalein treatment at a dose of 50 mg/kg. Immunohistochemical studies revealed that treatment with baicalein caused significant (p < 0.05) dosedependent reductions in the levels of inducible nitric oxide synthase (iNOS). Baicalein treatment inhibited alterations in cell morphology, and also inhibited pentylenetetrazole-induced increase in the proportion of glial fibrillary acidic protein (GFAP)-positive cells in a dose-dependent manner (p < 0.05). Real-time polymerase chain reaction (RT-PCR) analysis showed that baicalein significantly inhibited the expression of mRNA of NR1 subunit N methyl D aspartic acid (NMDA) receptor, without any effect on the expression of the NR2b (N-methyl D-aspartate receptor subtype 2B ) subunit mRNA (p < 0.05).Conclusion: These results indicate that baicalein inhibits pentylenetetrazole-induced chronic seizures in rats via reduction in astrocytes, inhibition of neuronal death and reduction of NR1 mRNA expression. Thus, baicalein has a potential for development into a new drug for the treatment of chronic epilepsy.Keywords: Pentylenetetrazole, Epilepsy, Baicalein, Convulsion, Inhibition, behavioural changes, Hippocampu

Brain Injury Differences in Frontal Impact Crash Using Different Simulation Strategies

In the real world crashes, brain injury is one of the leading causes of deaths. Using isolated human head finite element (FE) model to study the brain injury patterns and metrics has been a simplified methodology widely adopted, since it costs significantly lower computation resources than a whole human body model does. However, the degree of precision of this simplification remains questionable. This study compared these two kinds of methods: (1) using a whole human body model carried on the sled model and (2) using an isolated head model with prescribed head motions, to study the brain injury. The distribution of the von Mises stress (VMS), maximum principal strain (MPS), and cumulative strain damage measure (CSDM) was used to compare the two methods. The results showed that the VMS of brain mainly concentrated at the lower cerebrum and occipitotemporal region close to the cerebellum. The isolated head modelling strategy predicted higher levels of MPS and CSDM 5%, while the difference is small in CSDM 10% comparison. It suggests that isolated head model may not equivalently reflect the strain levels below the 10% compared to the whole human body model

ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization

With the spread of tampered images, locating the tampered regions in digital images has drawn increasing attention. The existing image tampering localization methods, however, suffer from severe performance degradation when the tampered images are subjected to some post-processing, as the tampering traces would be distorted by the post-processing operations. The poor robustness against post-processing has become a bottleneck for the practical applications of image tampering localization techniques. In order to address this issue, this paper proposes a novel restoration-assisted framework for image tampering localization (ReLoc). The ReLoc framework mainly consists of an image restoration module and a tampering localization module. The key idea of ReLoc is to use the restoration module to recover a high-quality counterpart of the distorted tampered image, such that the distorted tampering traces can be re-enhanced, facilitating the tampering localization module to identify the tampered regions. To achieve this, the restoration module is optimized not only with the conventional constraints on image visual quality but also with a forensics-oriented objective function. Furthermore, the restoration module and the localization module are trained alternately, which can stabilize the training process and is beneficial for improving the performance. The proposed framework is evaluated by fighting against JPEG compression, the most commonly used post-processing. Extensive experimental results show that ReLoc can significantly improve the robustness against JPEG compression. The restoration module in a well-trained ReLoc model is transferable. Namely, it is still effective when being directly deployed with another tampering localization module.Comment: 12 pages, 5 figure

Laser facilitates vaccination

Development of novel vaccine deliveries and vaccine adjuvants is of great importance to address the dilemma that the vaccine field faces: to improve vaccine efficacy without compromising safety. Harnessing the specific effects of laser on biological systems, a number of novel concepts have been proposed and proved in recent years to facilitate vaccination in a safer and more efficient way. The key advantage of using laser technology in vaccine delivery and adjuvantation is that all processes are initiated by physical effects with no foreign chemicals administered into the body. Here, we review the recent advances in using laser technology to facilitate vaccine delivery and augment vaccine efficacy as well as the underlying mechanisms

The ILGDB database of realistic pen-based gestural commands

International audienceIn this paper, we introduce the Intuidoc-Loustic Gestures DataBase (ILGDB), a new publicly available database of realistic pen-based gestures for evaluation of recognition systems in pen-enabled interfaces. ILGDB was collected in a real world context and in an immersive environment. As it contains a large number of unconstrained user-defined gestures, ILGDB offers a unique diversity of content that is likely to serve as a precious tool for benchmarking of gesture recognition systems. We report first baseline experimental results on the task of Writer-Dependent gesture recognition

Evaluation of Continuous Marking Menus for Learning Cursive Pen-based Commands

International audienceWe present here the Continuous Marking Menus, which help users learning a set of handwritten commands on a pen-based interface. The aim of this paper is to experimentally attest the interest of this new type of menu by evaluating its ability to help the learning of a set of gestures. We describe an experimental comparison on the task of learning a set of gestures with or without the help of Continuous Marking Menus, and we conclude that with the help of Continuous Marking Menus, people learn more easily the gestures

Variation of Korotkoff stethoscope sounds during blood pressure measurement: Analysis using a convolutional neural network

Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during linear cuff deflation. Three repeat BP measurements were taken from 140 healthy subjects (age 21 to 73 years; 62 female and 78 male) by a trained observer, giving 420 measurements. During the BP measurements, the cuff pressure and stethoscope signals were simultaneously recorded digitally to a computer for subsequent analysis. Heart beats were identified from the oscillometric cuff pressure pulses. The presence of each beat was used to create a time window (1s, 2000 samples) centered on the oscillometric pulse peak for extracting beat-by-beat stethoscope sounds. A time-frequency two-dimensional matrix was obtained for the stethoscope sounds associated with each beat, and all beats between the manually determined SBPs and DBPs were labeled as ‘Korotkoff’. A convolutional neural network was then used to analyze consistency in sound patterns that were associated with Korotkoff sounds. A 10-fold cross-validation strategy was applied to the stethoscope sounds from all 140 subjects, with the data from ten groups of 14 subjects being analysed separately, allowing consistency to be evaluated between groups. Next, within-subject variation of the Korotkoff sounds analysed from the three repeats was quantified, separately for each stethoscope sound beat. There was consistency between folds with no significant differences between groups of 14 subjects (P = 0.09 to P = 0.62). Our results showed that 80.7% beats at SBP and 69.5% at DBP were analysed as Korotkoff sounds, with significant differences between adjacent beats at systole (13.1%, P = 0.001) and diastole (17.4%, P < 0.001). Results reached stability for SBP (97.8%, at 6th beats below SBP) and DBP (98.1%, at 6th beat above DBP) with no significant differences between adjacent beats (SBP P = 0.74; DBP P = 0.88). There were no significant differences at high cuff pressures, but at low pressures close to diastole there was a small difference (3.3%, P = 0.02). In addition, greater within subject variability was observed at SBP (21.4%) and DBP (28.9%), with a significant difference between both (P < 0.02). In conclusion, this study has demonstrated that Korotkoff sounds can be consistently identified during the period below SBP and above DBP, but that at systole and diastole there can be substantial variations that are associated with high variation in the three repeat measurements in each subject