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This article presents an algorithm for reading both single and multiple digital video clocks by using a context-aware pixel periodicity method and a deep learning technique. Reading digital video clocks in real time is a very challenging problem. The first challenge is the clock digit localization. The existing pixel periodicity is not applicable to localizing multiple second-digit places. This article proposes a context-aware pixel periodicity method to identify the second-pixels of each clock. The second challenge is clock-digit recognition. For this task, the algorithms based a domain knowledge and deep learning technique is proposed to recognize clock digits. The proposed algorithm is better than the existing best one in two aspects. The first one is that it can read not only single digit video clock but also multiple digit video clocks. The other is that it requires a short length of a video clip. The experimental results show that the proposed algorithm can achieve 100% of accuracy in both localization and recognition for both single and multiple clocks.

Problem solving is a hot research area that develops algorithms for solving exercise problems like people. Hence, such algorithms are the crucial technology of developing digital twins such as digital teacher, digital tutor, and digital student. Solving chemistry problems is one of branch in problem solving. This paper proposes an algorithm for solving the organic chemistry problems described with text and chemical structure figure. The algorithm firstly recognizes chemical structure figure using an OSRA method, and then acquires chemical relation from problem text based on S2 method. Finally, it uses a knowledge conversion and matching procedure to solve the problem based on the knowledge obtained in first two steps. In the experiment, this work collects 373 organic chemistry problems described with text and chemical structure figure from People's Education Press (PEP) version exercise books. The experiment results show that the proposed algorithm in this paper is able to solve 86.33% of the problems.

The research of multiple negotiations considering issue interdependence across negotiations is considered as a complex research topic in agent negotiation. In the multiple negotiations scenario, an agent conducts multiple negotiations with opponents for different negotiation goals, and issues in a single negotiation might be interdependent with issues in other negotiations. Moreover, the utility functions involved in multiple negotiations might be nonlinear, e.g., the issues involved in multiple negotiations are discrete. Considering this research problem, the current work may not well handle multiple interdependent negotiations with complex utility functions, where issues involved in utility functions are discrete. Regarding utility functions involving discrete issues, an agent may not find an offer exactly satisfying its expected utility during the negotiation process. Furthermore, as sub-offers on issues in every single negotiation might be restricted by the interdependence relationships with issues in other negotiations, it is even harder for the agent to find an offer satisfying the expected utility and all involved issue interdependence at the same time, leading to a high failure rate of processing multiple negotiations as a final outcome. To resolve this challenge, this paper presents a negotiation model for multiple negotiations, where interdependence exists between discrete issues across multiple negotiations. By introducing the formal definition of “interdependence between discrete issues across negotiations”, the proposed negotiation model applies the multiple alternating offers protocol, the clustered negotiation procedure and the proposed negotiation strategy to handle multiple interdependent negotiations with discrete issues. In the proposed strategy, the “tolerance value” is introduced as an agent’s consideration to balance between the overall negotiation goal and the negotiation outcomes. The experimental results show that, 1) the proposed model well handles the multiple negotiations with interdependence between discrete issues, 2) the proposed approach is able to help agents in the decision-making process of proposing acceptable offers, 3) an agent can choose a proper “tolerance value” to balance between the success rate of multiple negotiations and its expected utility.

In the field of pedestrian dead reckoning (PDR), the zero velocity update (ZUPT) method with an inertial measurement unit (IMU) is a mature technology to calibrate dead reckoning. However, due to the complex walking modes of different individuals, it is essential and challenging to determine the ZUPT conditions, which has a direct and significant influence on the tracking accuracy. In this research, we adopted an adaptive zero velocity update (AZUPT) method based on convolution neural networks to classify the ZUPT conditions. The AZUPT model was robust regardless of the different motion types of various individuals. AZUPT was then implemented on the Zynq-7000 SoC platform to work in real time to validate its computational efficiency and performance superiority. Extensive real-world experiments were conducted by 60 different individuals in three different scenarios. It was demonstrated that the proposed system could work equally well in different environments, making it portable for PDR to be widely performed in various real-world situations.

As the smartphones are widely used in daily life, people are habituated to take photos in anywhere at anytime. The character photos, which contain both people and landscapes, are the most extensive ones in all kinds of photos. However, it is usually time-consuming and laborious for people to select and manage desired character photos manually. Therefore, automatic photo selection became extremely significant. Most of the existing methods select best character photos by assigning a absolute score or binary label, regardless of the photo’s content. The selected character photos are hence not satisfied. In this paper, we propose an effective automatic framework to select best character photos, which first automatically eliminates photos with unattractive character, then ranks photos and assists users to select the desired ones. Moreover, our framework is especially useful for mobile camera in practical application scenarios. To reduce the burden of photo selection and improve the efficiency of automatic selection from a large number of photos on mobile platform, we divide the photo selection into two stages: eliminating of unattractive photos based on designed efficient and effective features related to faces and human postures, and ranking the remaining photos using a two-stream convolution neural network. To ensure that informative features are selected and less useful ones are suppressed, we design and utilize the attention mechanism module in network. Experiments demonstrate the effectiveness of our method on the automatic selection of the satisfied photos from a large number of photos.

Robust speaker identification of Internet of Things (IoT) becomes an active research problem in the field of biometrics, and it has wide applications in commercial fields and public security. In order to improve the robustness of speaker identification of IoT, we propose a novel Stacked Sparse Denoising Auto-encoder (SSDAE) for robust speaker identification in this paper. Firstly, the i-vector features are estimated based on the universal background model and total variability space model. For the second stage, sparsity constraints and corrupting operations are fused together into the general auto-encoder, and then the single sparse denoising auto-encoder is constructed. Then, the SSDAE is built with multi-hidden sparse denoising auto-encoder layers by layer wisely stacking operation. In the final layer of the designed deep neural network, the Softmax layer is included for classification purpose. The experimental results have shown that the proposed robust speaker identification method achieves better performance in robust speaker identification task than the state-of-the-art approaches for different kinds and levels of noisy inputs.

In 2013, the Chinese government implemented Rule No. 18, which suspended the directorships of incumbent government officials and precluded those who retired within the past three years from serving as independent directors for listed firms. The surprise implementation of Rule No. 18 triggered a wave of resignations among official independent directors (OIDs). The event provided a unique opportunity to examine the impacts of the political connections of board members on firm performance. We applied a difference-in-difference technique to empirically investigate the effect of OID resignations on firm performance from the perspectives of resource dependence theory and social capital theory. The results indicate that the resignation of OIDs had a significantly negative effect on firm performance, as measured byTobin’s Qand firm leverage. This also confirmed the importance of independent directors’ political connection on firm performance, as discovered in prior research. However, this influence varied across OIDs’ heterogeneity, external environment and firm ownership. The results indicate that political connections may not be necessary channels for firms to achieve success.