人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

Communication without words

Optical techniques have boosted a new class of cryptographic systems with some remarkable advantages, and optical encryption not only has spurred practical developments but also has brought a new insight into cryptography. However, this does not mean that it is elusive for the opponents to attack optical encryption systems. In this paper, for the first time to our knowledge, we experimentally demonstrate the machine-learning attacks on interference-based optical encryption. Using machine-learning models that are trained by a series of ciphertext-plaintext pairs, an unauthorized person is capable to retrieve the unknown plaintexts from the given ciphertexts without the usage of various different optical encryption keys existing in interference-based optical encryption. In comparison with conventional cryptanalytic methods, the proposed machine-learning-based attacking method can estimate transfer function or point spread function of interference-based optical encryption systems without subsidiary conditions. Simulations and optical experiments demonstrate feasibility and effectiveness of the proposed method, and the proposed machine-learning-based attacking method provides a versatile approach to analyzing the vulnerability of interference-based optical encryption.

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Machine-learning attacks on interference-based optical encryption: experimental demonstration

Most Wanted investigates the nature of perception, recognition, and representation while examining construction of the image of the new enemy. Interpretations of a series of faceless portrayals of the most-wanted terrorists as identified by the United States government contemplate the ways in which stereotypical perceptions of people are created while pondering the relationship between image and identity.

Most wanted

This paper describes the development of neural network models for noise reduction. The networks used to enhance the performance of modeling captured signals by reducing the effect of noise. Both recurrent and multi&layer Backpropagation neural networks models are examined and compared with different training algorithms. The paper presented is to illustrate the effect of training algorithms and network architecture on neural network performance for a given application.

Development of Neural Networks for Noise Reduction

We propose a method for synthesizing body sway to give human-like movement to image-based avatars. This method is based on an analysis of body sway in real people. Existing methods mainly handle the action states of avatars without sufficiently considering the wait states that exist between them. The wait state is essential for filling the periods before and after interaction. Users require both wait and action states to naturally communicate with avatars in interactive systems. Our method measures temporal changes in the body sway motion of each body part of a standing subject using a single-camera video sequence. We are able to synthesize a new video sequence with body sway over an arbitrary length of time by randomly transitioning between points in the sequence when the motion is close to zero. The results of a subjective assessment show that avatars with body sway synthesized by our method appeared more alive to users than those using baseline methods.

Synthesizing Realistic Image-based Avatars by Body Sway Analysis

Extracting discriminative features using task-oriented gaze maps measured from observers for personal attribute classification

We propose a method for generating facial expressions emphasized with cartoon techniques using a cellular-phone-type teleoperated android with a mobile projector. Elfoid is designed to transmit the speaker’s presence to their communication partner using a camera and microphone, and has a soft exterior that provides the look and feel of human skin. To transmit the speaker’s presence, Elfoid sends not only the voice of the speaker but also emotional information captured by the camera and microphone. Elfoid cannot, however, display facial expressions because of its compactness and a lack of sufficiently small actuator motors. In this research, facial expressions are generated using Elfoid’s head-mounted mobile projector to overcome the problem. Additionally, facial expressions are emphasized using cartoon techniques: movements around the mouth and eyes are emphasized, the silhouette of the face and shapes of the eyes are varied by projection effects, and color stimuli that induce a particular emotion are added. In an experiment, representative face expressions are generated with Elfoid and emotions conveyed to users are investigated by subjective evaluation.

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Generation of Facial Expression Emphasized with Cartoon Techniques Using a Cellular-Phone-Type Teleoperated Robot with a Mobile Projector

We propose a novel method for identifying individuals by resampling images using resolution inference to increase the performance of person recognition. The resolution of images dynamically changes the appearances of faces or whole bodies and low resolution decreases the performance of person recognition. To overcome the problem of resolution, we need to adequately normalize the sizes of images using resampling techniques before identification. In our preliminary experiment, we observe that downsampling high-resolution images to adjust to low-resolution images increases the identification performance while upsampling low-resolution images to adjust to high-resolution images decreases performance. The proposed method thus applies a downsampling technique to images of higher resolution by comparing the resolutions inferred for query and target images. We demonstrate that our method substantially improves the performance of person recognition on the publicly available datasets Multi-PIE and CUHK01 artificially degraded to low resolution.

Low-resolution person recognition using image downsampling

We propose a novel method for extracting features from images of people using co-occurrence attributes, which are then used for person re-identification. Existing methods extract features based on simple attributes such as gender, age, hair style, or clothing. Our method instead extracts more informative features using co-occurrence attributes, which are combinations of physical and adhered human characteristics (e.g., a man wearing a suit, 20-something woman, or long hair and wearing a skirt). Our co-occurrence attributes were designed using prior knowledge of methods used by public websites that search for people. Our method first trains co-occurrence attribute classifiers. Given an input image of a person, we generate a feature by vectorizing confidences estimated using the classifiers and compute a distance between input and reference vectors with a metric learning technique. Our experiments using a number of publicly available datasets show that our method substantially improved the matching performance of the person re-identification results, when compared with existing methods. We also demonstrated how to analyze the most important co-occurrence attributes.

Hiroki Yoshimura

Papers

Synthesizing Realistic Image-based Avatars by Body Sway Analysis

Extracting discriminative features using task-oriented gaze maps measured from observers for personal attribute classification

Generation of Facial Expression Emphasized with Cartoon Techniques Using a Cellular-Phone-Type Teleoperated Robot with a Mobile Projector

Low-resolution person recognition using image downsampling

Person re-identification using co-occurrence attributes of physical and adhered human characteristics