Emotion inference in multi-turn conversations aims to predict the participant's emotion in the next upcoming turn without knowing the participant's response yet, and is a necessary step for applications such as dialogue planning. However, it is a sev
ere challenge to perceive and reason about the future feelings of participants, due to the lack of utterance information from the future. Moreover, it is crucial for emotion inference to capture the characteristics of emotional propagation in conversations, such as persistence and contagiousness. In this study, we focus on investigating the task of emotion inference in multi-turn conversations by modeling the propagation of emotional states among participants in the conversation history, and propose an addressee-aware module to automatically learn whether the participant keeps the historical emotional state or is affected by others in the next upcoming turn. In addition, we propose an ensemble strategy to further enhance the model performance. Empirical studies on three different benchmark conversation datasets demonstrate the effectiveness of the proposed model over several strong baselines.
Despite the success of neural dialogue systems in achieving high performance on the leader-board, they cannot meet users' requirements in practice, due to their poor reasoning skills. The underlying reason is that most neural dialogue models only cap
ture the syntactic and semantic information, but fail to model the logical consistency between the dialogue history and the generated response. Recently, a new multi-turn dialogue reasoning task has been proposed, to facilitate dialogue reasoning research. However, this task is challenging, because there are only slight differences between the illogical response and the dialogue history. How to effectively solve this challenge is still worth exploring. This paper proposes a Fine-grained Comparison Model (FCM) to tackle this problem. Inspired by human's behavior in reading comprehension, a comparison mechanism is proposed to focus on the fine-grained differences in the representation of each response candidate. Specifically, each candidate representation is compared with the whole history to obtain a history consistency representation. Furthermore, the consistency signals between each candidate and the speaker's own history are considered to drive a model prefer a candidate that is logically consistent with the speaker's history logic. Finally, the above consistency representations are employed to output a ranking list of the candidate responses for multi-turn dialogue reasoning. Experimental results on two public dialogue datasets show that our method obtains higher ranking scores than the baseline models.
Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications. To perform ABSA, the trained model is required to have a good understanding
of the contextual information, especially the particular patterns that suggest the sentiment polarity. However, these patterns typically vary in different sentences, especially when the sentences come from different sources (domains), which makes ABSA still very challenging. Although combining labeled data across different sources (domains) is a promising solution to address the challenge, in practical applications, these labeled data are usually stored at different locations and might be inaccessible to each other due to privacy or legal concerns (e.g., the data are owned by different companies). To address this issue and make the best use of all labeled data, we propose a novel ABSA model with federated learning (FL) adopted to overcome the data isolation limitations and incorporate topic memory (TM) proposed to take the cases of data from diverse sources (domains) into consideration. Particularly, TM aims to identify different isolated data sources due to data inaccessibility by providing useful categorical information for localized predictions. Experimental results on a simulated environment for FL with three nodes demonstrate the effectiveness of our approach, where TM-FL outperforms different baselines including some well-designed FL frameworks.
Multi-turn response selection models have recently shown comparable performance to humans in several benchmark datasets. However, in the real environment, these models often have weaknesses, such as making incorrect predictions based heavily on super
ficial patterns without a comprehensive understanding of the context. For example, these models often give a high score to the wrong response candidate containing several keywords related to the context but using the inconsistent tense. In this study, we analyze the weaknesses of the open-domain Korean Multi-turn response selection models and publish an adversarial dataset to evaluate these weaknesses. We also suggest a strategy to build a robust model in this adversarial environment.
Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence. Previous methods treat it either as a boundary regression task or a span extraction task. This paper will formulate temporal language groun
ding into video reading comprehension and propose a Relation-aware Network (RaNet) to address it. This framework aims to select a video moment choice from the predefined answer set with the aid of coarse-and-fine choice-query interaction and choice-choice relation construction. A choice-query interactor is proposed to match the visual and textual information simultaneously in sentence-moment and token-moment levels, leading to a coarse-and-fine cross-modal interaction. Moreover, a novel multi-choice relation constructor is introduced by leveraging graph convolution to capture the dependencies among video moment choices for the best choice selection. Extensive experiments on ActivityNet-Captions, TACoS, and Charades-STA demonstrate the effectiveness of our solution. Codes will be available at https://github.com/Huntersxsx/RaNet.
In this study, we have developed a mathematical model, which formed the basis for
the development of a digital diagnostic model, for the question of the relation between the
engine torque (and therefore power), and the clearances in the basic and s
econdary
bearings of the crankshaft, to diagnose clearance values in these bearings, /which will
inevitably affect on the torque and the vibrations of the engine/, where multiple and
various grand-scale case experiments were carried out through simulation and arithmetic
experimentation on a diagnostic model, which constituted the first and main phase (the
instruction phase) of the full diagnosis. And at the end we discussed the results and put
some conclusions and recommendations to complete the work in the future.
The purpose of this research is to design and realization of an electronic circuitthat is able to control the water level in a tank. The circuit shows a tank water level in decimal numbers and controls the volume of water in the tank. So that when th
e level of water drops below a specific value that is preselected using a probe, the pump water works. When the water level reaches to another limit value, the pump stops working.We used for water level control a container which is made by transparent glass with volume of 15 L. the container is divided in nine levels. In addition to this purpose, because of large applications for realized electronic board, The circuit measures the speed of rotation of DC motorsin a wide range (0001 to 9999) cycles during a chosen period of time that ranges from 1 sec to 110 sec and display the speed of rotation in decimal numbers that appears on four displays. Moreover, the circuit contains a switch reset/start, for display clear and restart of measurement.
This study was carried out on samples of tap water, for several values of distance between the probes, and power supply. We found a quick response in showing levels and high efficiency in performance. We measured the number of special motor cycles for several values of the power supply during 30 s and 60 s, and the relation between the number of cycles and applied voltagewas drawingwe found it a linear relation.
Induction motors are the most widely used electrical motors due to their reliability,
low cost and robustness. However, induction motors do not inherently have the capability
of variable speed operation. Due to this reason, earlier dc motors were a
pplied in most of
the electrical drives. But the recent developments in speed control methods of the induction
motor have led to their large scale use in almost all electrical drives.
Out of the several methods of speed control of an induction such as pole changing,
frequency variation, variable rotor resistance, variable stator voltage, constant V/f control,
slip recovery method etc, the closed loop constant V/f speed control method is most widely
used. In this method, the V/f ratio is kept constant which in turn maintains the magnetizing
flux constant so that the maximum torque remains unchanged. Thus, the motor is
completely utilized in this method.
This research deals with analytical study of exterior Beam-Column
connections behavior under seismic load.
Tow parameters were considered: axial load on the column and
confinement of joint region with stirrups.
Ansys program was used to analytica
l study of three types of exterior
Beam-Column connections under cyclic load.
The results were shown a good agreement with general behavior of
three types.
The analytical results indicate that the tow parameters will improve
the behavior of the connections delay cracking at joint core and
increasing connection stiffness in rotation and displacement with
decreasing shear value at joint.
The aime of this study was to determinethe effect of gender on the soft tissue of the
chin at each type of rotation patterns of the lower jaw (Anterior - normal - Posterior)
The sample consisted of 100 X-ray Lateral cephalometric radiographs of (69
female _ 31
male) adults, has been selected according to the following conditions: Age between 18-26
years old, , exclusion of all cases of Intenseocclusal abnormalities ,
Permanentocclusionand there is noformaldentalabnormalitieswithout takinginto accountthe
thirdmolars, The absence ofa previousorthodontic treatment, the patient free fromsystemic
diseasesorcongenital malformationsanddevelopmentaldisordersandsyndromesand there is
no story of a previous surgery in the head and neck area
The sample was divided into groups according tosex andmandibularrotation(Anterior -
normal - Posterior). Then we studied variablesrepresentative the lower jaw rotation
andanother ones representativethemeasurementsof the chinsoft tissue.
The results showed a difference theaveragesof the soft tissue thickness and the
indicators of mandible rotation between the sexes In favor of males, And theaveragesof
thevariables showed Correlationbetween chinsofttissuethicknessin femaleswiththree
typesoflower jawrotation Compared with males.
