Grammar Accuracy Evaluation (GAE): Quantifiable Quantitative Evaluation of Machine Translation Models

Natural Language Generation (NLG) refers to the operation of expressing the calculation results of a system in human language. Since the quality of generated sentences from an NLG model cannot be fully represented using only quantitative evaluation, they are evaluated using qualitative evaluation by humans in which the meaning or grammar of a sentence is scored according to a subjective criterion. Nevertheless, the existing evaluation methods have a problem as a large score deviation occurs depending on the criteria of evaluators. In this paper, we propose Grammar Accuracy Evaluation (GAE) that can provide the specific evaluating criteria. As a result of analyzing the quality of machine translation by BLEU and GAE, it was confirmed that the BLEU score does not represent the absolute performance of machine translation models and GAE compensates for the shortcomings of BLEU with flexible evaluation of alternative synonyms and changes in sentence structure.Comment: accepted in the Journal of KIISE (JOK

Smart sensor systems for wearable electronic devices

Wearable human interaction devices are technologies with various applications for improving human comfort, convenience and security and for monitoring health conditions. Healthcare monitoring includes caring for the welfare of every person, which includes early diagnosis of diseases, real-time monitoring of the effects of treatment, therapy, and the general monitoring of the conditions of people's health. As a result, wearable electronic devices are receiving greater attention because of their facile interaction with the human body, such as monitoring heart rate, wrist pulse, motion, blood pressure, intraocular pressure, and other health-related conditions. In this paper, various smart sensors and wireless systems are reviewed, the current state of research related to such systems is reported, and their detection mechanisms are compared. Our focus was limited to wearable and attachable sensors. Section 1 presents the various smart sensors. In Section 2, we describe multiplexed sensors that can monitor several physiological signals simultaneously. Section 3 provides a discussion about short-range wireless systems including bluetooth, near field communication (NFC), and resonance antenna systems for wearable electronic devices

Protective Effects of N

Objective. Since oligodendrocyte progenitor cells (OPCs) are the target cells of neonatal hypoxic-ischemic encephalopathy (HIE), the present study was aimed at investigating the protective effects of N-acetyl-L-cysteine (NAC), a well-known antioxidant and precursor of glutathione, in OPCs as well as in neonatal rats. Methods. In in vitro study, protective effects of NAC on KCN cytotoxicity in F3.Olig2 OPCs were investigated via MTT assay and apoptotic signal analysis. In in vivo study, NAC was administered to rats with HIE induced by hypoxia-ischemia surgery at postnatal day 7, and their motor functions and white matter demyelination were analyzed. Results. NAC decreased KCN cytotoxicity in F3.Olig2 cells and especially suppressed apoptosis by regulating Bcl2 and p-ERK. Administration of NAC recovered motor functions such as the using ratio of forelimb contralateral to the injured brain, locomotor activity, and rotarod performance of neonatal HIE animals. It was also confirmed that NAC attenuated demyelination in the corpus callosum, a white matter region vulnerable to HIE. Conclusion. The results indicate that NAC exerts neuroprotective effects in vitro and in vivo by preserving OPCs, via regulation of antiapoptotic signaling, and that F3.Olig2 human OPCs could be a good tool for screening of candidates for demyelinating diseases

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Korean-English Machine Translation with Multiple Tokenization Strategy

This work was conducted to find out how tokenization methods affect the training results of machine translation models. In this work, alphabet tokenization, morpheme tokenization, and BPE tokenization were applied to Korean as the source language and English as the target language respectively, and the comparison experiment was conducted by repeating 50,000 epochs of each 9 models using the Transformer neural network. As a result of measuring the BLEU scores of the experimental models, the model that applied BPE tokenization to Korean and morpheme tokenization to English recorded 35.73, showing the best performance

Rock-paper-scissors prediction experiments using muscle activations

Human motion prediction is becoming more and more important issue in the filed of wearable robots or biorobotics. This paper provides an initial experimental result for human motion prediction. In detail, the prediction method for ternary choice among rock-paper-scissors is presented using temporal patterns of muscle activations (Electromyography, in short EMG) controlling hand motion of subject. Initial burst part of EMG is prior to the onset of actual movement by dozens to hundreds milliseconds. Using this property, the proposed method makes the ternary choice prediction among rock-paper-scissors as soon as 10% motion variation of any finger is detected. It is shown experimentally that the success rate of the proposed prediction method is over 95%. © 2012 IEEE

Rock-Paper-Scissors Prediction Experiments Using Muscle Activations

Human motion prediction is becoming more and more important issue in the filed of wearable robots or biorobotics. This paper provides an initial experimental result for human motion prediction. In detail, the prediction method for ternary choice among rock-paper-scissors is presented using temporal patterns of muscle activations (Electromyography, in short EMG) controlling hand motion of subject. Initial burst part of EMG is prior to the onset of actual movement by dozens to hundreds milliseconds. Using this property, the proposed method makes the ternary choice prediction among rock-paper-scissors as soon as 10% motion variation of any finger is detected. It is shown experimentally that the success rate of the proposed prediction method is over 95%. © 2012 IEEE

Surface engineered gold nanoparticles through highly stable metal-surfactant complexes

Monodispersed Au nanoparticles were synthesized by the reduction of Au-decyltrimethylammonium bromide (Au-DTAB), which was easily prepared via the reaction of HAuCl4 and DTAB. This Au-DTAB complex is highly stable in air and moisture, and suitable for large-scale synthesis of uniform-sized Au nanoparticles. The nanoparticles were characterized by transmission electron microscopy, optical absorption spectrometry, X-ray diffraction, and Fourier Transform infrared spectroscopy. The size of Au nanoparticles was controlled in the range of 5-10 nm by changing the concentrations of reducing agent and Au precursor. The resulting Au nanoparticles were transferred to the aqueous phase after surface engineering using multidentate polymeric ligands with multiple imidazole functional groups. Polymeric imidazole ligands (PILs) demonstrated enhanced binding stability with the Au surface, and overcame the disadvantage of multidentate thiol ligand systems which have oxidative cross-linking and the formation of disulfide bonding. The colloidal stability of surface engineered Au nanoparticles with PILs was investigated by dynamic light scattering (DLS) characterization.close0

Lateral handling improvement with dynamic curvature control for an independent rear wheel drive EV

The integrated longitudinal and lateral dynamic motion control is important for four wheel independent drive (4WID) electric vehicles. Under critical driving conditions, direct yaw moment control (DYC) has been proved as effective for vehicle handling stability and maneuverability by implementing optimized torque distribution of each wheel, especially with independent wheel drive electric vehicles. The intended vehicle path upon driver steering input is heavily depending on the instantaneous vehicle speed, body side slip and yaw rate of a vehicle, which can directly affect the steering effort of driver. In this paper, we propose a dynamic curvature controller (DCC) by applying a the dynamic curvature of the path, derived from vehicle dynamic state variables; yaw rate, side slip angle, and speed of a vehicle. The proposed controller, combined with DYC and wheel longitudinal slip control, is to utilize the dynamic curvature as a target control parameter for a feedback, avoiding estimating the vehicle side-slip angle. The effectiveness of the proposed controller, in view of stability and improved handling, has been validated with numerical simulations and a series of experiments during cornering engaging a disturbance torque driven by two rear independent in-wheel motors of a 4WD micro electric vehicle. ? 2017, The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.112sciescopuskc