The Effect of Information Design on Cognitive Processing of Website Navigation

The information displayed on a website is a critical factor that affects the cognition of internet users. This study adopted a between‐subjects design to examine the effect of the information design of a website and the involvement of internet users on purchasing cognition. The results indicated significant differences between high‐ and low‐quality diagnostic information on the purchasing cognition of subjects. The subjects had better purchasing cognition when navigating a high‐quality diagnostic information website than when navigating a low‐quality diagnostic information website. In addition, there was a significant interactive effect of different qualities of nondiagnostic information and subject involvement on purchasing cognition. Namely, the subjects who had low involvement had better purchasing cognition when navigating a website with high‐quality nondiagnostic information than when browsing one with low‐quality nondiagnostic information, but there was no significant difference between navigating websites with high‐ and low‐quality nondiagnostic information if the subjects had high involvement.     

Complex cognitive skill retention: The roles of general mental ability and refresher interventions in a simulated vocational setting

In organizations, the assessment of mental abilities is a common way to predict learning success. This paper analyses the effect of general mental ability on skill retention and examines the relationship between refresher interventions and general mental ability in terms of skill retention. Two hundred sixty‐six adult participants were trained to perform a complex cognitive skill in a simulated chemical processing plant (Week 1). After 1 week, 158 participants received intermediate computer‐assisted refresher intervention training in which the skill was applied (Week 2; 108 received no intervention). Finally, participants had to recall (perform) the learned skill after 2 weeks (Week 3). Results show that general mental ability alone affects skill retention with small effect sizes and that refresher interventions affect skill retention positively for low and high general mental ability levels. The findings suggest that persons with low and high general mental abilities benefit equally from refresher interventions in order to maintain complex cognitive skills.     

Making instructions “visible” on the interface: an approach to learning fault diagnosis skills through guided discovery

Operator training in fault diagnosis of complex systems has taken several forms including heuristics, decision flow charts and qualitative plant modelling. Having to comply to a specific learning strategy, however, may increase workload in remembering instructions, constrain people in accommodating their own styles and deny opportunities for exploiting other strategies. A means for increasing learning flexibility and adaptability would be to manipulate the design of the interface in ways that prompt operators to recall past instructions or develop their own strategies. In this study, instructions are madevisible on the interface by presenting trainees with a set of tell-tale signs derived from diagnostic heuristics.A group of subjects T (new) was trained in using this interface while verbal instructions (e.g. plant theory) were provided to guide discovery of diagnostic rules; a second group T (old) received the same plant theory but practised on a conventional interface. Two other groups used the conventional interface and were trained to apply a set of heuristics with or without the support of a plant theory (H+T and H groups, respectively). Making instructions visible helped the T (new) group to achieve higher accuracy scores than the T (old) group on a subset of fault scenarios. On a near-transfer task, both the T (new) and T (old) groups were superior to the heuristics (H) group. On transfer to another plant, the T (new) group maintained superiority to the heuristics (H) group and exceeded the T (old) group only in a subset of fault scenarios; the differences between theT (new) and the H+T groups were not significant. The results may indicate that making instructionsvisible could enhance acquisition and also the transfer of complex skills while allowing for flexibility and adaptability in the learning environment.     

Learning Physically Based Humanoid Climbing Movements

We propose a novel learning‐based solution for motion planning of physically‐based humanoid climbing that allows for fast and robust planning of complex climbing strategies and movements, including extreme movements such as jumping. Similar to recent previous work, we combine a high‐level graph‐based path planner with low‐level sampling‐based optimization of climbing moves. We contribute through showing that neural network models of move success probability, effortfulness, and control policy can make both the high‐level and low‐level components more efficient and robust. The models can be trained through random simulation practice without any data. The models also eliminate the need for laboriously hand‐tuned heuristics for graph search. As a result, we are able to efficiently synthesize climbing sequences involving dynamic leaps and one‐hand swings, i.e. there are no limits to the movement complexity or the number of limbs allowed to move simultaneously. Our supplemental video also provides some comparisons between our AI climber and a real human climber.     

基于深度学习特征字典的单帧图像超分辨率重建

在基于字典的单帧图像超分辨率重建算法中,依赖人工浅层特征设计的字典表达图像特征能力有限。为此,提出基于深度学习特征字典的超分辨重建方法。该算法首先利用深度网络进行高、低分辨率训练样本图像深层次特征学习;然后,在稀疏字典超分辨框架下联合训练特征字典;最后,输入单帧低分辨率图像并利用该字典实现超分辨率重建。理论分析表明,引入深度网络提取图像深层次特征并用于字典训练,对低分辨率图像的高频信息补充更加有利。实验证明,与双三次插值以及基于一般人工特征字典的超分辨重建算法相比,本文算法的主观视觉和客观评价指标均高于对比算法。     

Using pop-up windows to improve multimedia learning

Abstract The aim of the present study is to evaluate the effects on learning of the spatial integration of textual information incorporated into illustrations in the form of pop‐up windows that are opened by the user. Three groups of students viewed illustrated texts depicting the functioning of the heart and the replication of the AIDS virus either with textual information presented below the picture, with textual information integrated within the picture, or with textual information integrated within the picture with pop‐up windows. The results showed that the integrated formats were more effective than the separated format when we tested the retention of textual information, comprehension and the matching of textual elements to the appropriate illustrated elements. Furthermore, they indicated that the group working with pop‐up windows performed better than the integrated groups on the retention test for illustrated information as well as when asked to find correct solutions to problems in a comprehension test. Consequently, these findings support the use of pop‐up windows in learning with this kind of illustrated explanatory text.     

Hybridizations of GRASP with path relinking for the far from most string problem

Among the sequence selection and comparison problems, the far from most string problem (FFMSP) is one of the computationally hardest with applications in several fields, including molecular biology where one is interested in creating diagnostic probes for bacterial infections or in discovering potential drug targets. In this paper, we describe several heuristics that hybridize GRASP with different path‐relinking strategies, such as forward, backward, mixed, greedy randomized adaptive forward, and evolutionary path relinking. Experiments on a large set of both real‐world and randomly generated test instances indicate that these hybrid heuristics are both effective and efficient. In particular, the hybrid GRASP with evolutionary path relinking finds slightly better quality solutions compared to the other variants when running for the same number of iterations, while the hybrid with backward path relinking finds better quality solution within a fixed running time.     

Physical fitness and mental performance during and after work

Two groups of subjects who differed in physical fitness (24 well-trained and 24 less well-trained male students) participated in an experiment concerned with mental performance during and after physical work. The physical work was matched between the groups in terms of the percentage of maximal work capacity. Three mental tasks were used: task I involved high information load, placing great demands on continuous concentration and switching of attention as well as on short-term memory; task 2 involved paired associate learning with recall following short and long retention delays; task 3 was a multiplication task with great demands on concentration and on short-term memory. Tasks 1 and 2 were performed during the four different work-load conditions and task 3 after the physical work. As expected, heart rate during physical work was about the same in both groups. No intergroup difference was found in mental performance during physical work. However, recovery rate in terms of heart rate was faster, and mental performance after physical work was significantly better in the fit group than in the unfit group. The results indicate that, even though physical work loads were matched between the groups to make subjective effort equal, the fit subjects were more able to resist the negative after-effects of physical effort.     

Individual profiles and team classes of the climate for creativity: A multilevel latent profile analysis

Understanding the team climate to enhance creativity is important in academia and industry given the need for organizations to respond to the changing environment. Research on team creativity is a relatively recent trend, but most studies have been conducted from a variable‐centered perspective. Despite the contributions of this type of research, there are limitations in understanding subgroups based on individuals' perceptions. To address the limitations, the purposes of this person‐centered study are firstly to identify individual‐level latent profiles and team‐level latent classes based on the climate for creativity, and secondly to examine the differences in individual‐ and team‐level outcomes between the identified profiles and classes. This study used multilevel latent profile analysis (MLPA) with 238 individuals in 26 project teams. We identified two individual profiles, high impediments and high stimulants, and two team classes, low impediments teams and mixed impediments teams. Low impediments teams included mostly high stimulants, and mixed impediments teams included half high stimulants and half low impediments. We further determined that high stimulants and low impediments teams showed higher individual‐ and team‐level outcomes than the other profile and class. This study theoretically and practically contributes to team creativity management from a person‐centered perspective.     

Teaching genetics with multimedia results in better acquisition of knowledge and improvement in comprehension

The main goal of this study was to explore whether the use of multimedia in genetics instruction contributes more to students' knowledge and comprehension than other instructional modes. We were also concerned with the influence of different instructional modes on the retention of knowledge and comprehension. In a quasi‐experimental design, four comparable groups of 3rd and 4th grade high school students were taught the process of protein synthesis: group 1 was taught in the traditional lecture format (n = 112 students), group 2 only by reading text (n = 124 students), group 3 through multimedia that integrated two short computer animations (n = 115 students) and group 4 by text supplemented with illustrations (n = 117 students). All students received one pre‐test in order to estimate their prior knowledge, and two post‐tests in order to assess knowledge and comprehension immediately after learning and again after 5 weeks. Results showed that students comprising groups 3 and 4 acquired better knowledge and improved comprehension skills than the other two groups. Similar results were observed for retention of acquired knowledge and improved comprehension. These findings lead to the conclusion that better learning outcomes can be obtained by the use of animations or at least illustrations when learning genetics.     

Motivational and cognitive benefits of training in immersive virtual reality based on multiple assessments

The main objective of this study was to examine the effectiveness of immersive virtual reality (VR) as a medium for delivering laboratory safety training. We specifically compare an immersive VR simulation, a desktop VR simulation, and a conventional safety manual. The sample included 105 first year undergraduate engineering students (56 females).  We include five types of learning outcomes including post‐test enjoyment ratings; pre‐ to post‐test changes in intrinsic motivation and self‐efficacy; a post‐test multiple choice retention test; and two behavioral transfer tests. Results indicated that the groups did not differ on the immediate retention test, suggesting that all three media were equivalent in conveying the basic knowledge. However, significant differences were observed favoring the immersive VR group compared to the text group on the two transfer tests involving the solving problems in a physical lab setting (d = 0.54, d = 0.57), as well as enjoyment (d = 1.44) and increases in intrinsic motivation (d = 0.69) and self‐efficacy (d = 0.60). The desktop VR group scored significantly higher than the text group on one transfer test (d = 0.63) but not the other (d= 0.11), as well as enjoyment (d =1.11) and intrinsic motivation (d =0.83).     

A dual‐polarized low‐profile microstrip patch antenna with U‐ or M‐shaped feed network

In this article, a dual‐polarized low‐profile microstrip patch antenna with U‐ or M‐shaped feed network is presented. The U‐ or M‐shaped feed network is printed on the same layer, which can achieve dual bands (5.3 and 5.8 GHz) and low profile (0.06 λ_g). Dual polarizations and high isolation are realized by making use of a quasi‐cross‐shaped slot feeding. Moreover, the port isolation is better than 25 dB, and the antenna gain is above 8.4 dBi for the two ports. And the cross‐polarization levels in both E and H planes are better than ‐30 dB for the two polarization ports, respectively. The design is suitable for array application in MIMO system. Details of the proposed design and experimental results are presented and well agreed.     

基于SVR和PCA的超分辨率图像恢复算法应用研究

利用单幅低分辨率图像重构超分辨率图像的算法中,通常基于样本库进行图像重构,而这类算法效率较低。提出了一种利用SVR和PCA进行特征压缩的图像重构算法,其基本思路是将训练图像分解成若干个基本小块作为样本库;然后利用PCA对低分辨率图像基本小块进行降维处理,并将得到的主成分系数作为特征加以训练,在识别和重构过程中,将待恢复图像进行回归分析,找到相应的超分辨率图像块,然后进行重构。实验结果表明,本文方法较其他算法有更优的恢复结果,并能同时保证较好的实时处理特性,很好地逼近了原始的真实图像。     

Gratifications and Seeding Behavior of Online Adolescents

Several studies indicate that only a small minority of Web 2.0 users actively participates, while the minority do not contribute at all. This article investigates whether a similar division applies for adolescents' Internet behavior. Using Szuprowicz’ (1995) typology of interactivity, we distinguish different types of user‐generated content (UGC): media, narrative, and metadata UGC. Our results show a 20%–80% division between high‐ and low‐frequency seeders. Furthermore, we utilize the uses‐and‐gratifications paradigm to investigate how these high‐ and low‐frequency seeders differ in their overall gratifications obtained by WWW use. Although the gratifications' rank orders are identical for all groups, their magnitudes differ significantly. Finally, this article focuses on how these WWW gratifications can predict seeding, while controlling for socio‐demographics and usage frequency.     

基于深度可分离卷积和宽残差网络的医学影像超分辨率重建

为提高医学影像超分辨率的重建质量,提出了一种基于深度可分离卷积的宽残差超分辨率神经网络算法。首先,利用深度可分离卷积改进网络的残差块,扩宽残差块中卷积层的通道,将更多的特征信息传入了激活函数,使得网络中浅层低级图像特征更容易地传播到高层,提高了医学影像超分辨率的重建质量;然后,采用组归一化的方法训练网络,将卷积层的通道维度划分为组,在每个组内计算归一化的均值和方差,使得网络训练过程更快地收敛,解决了深度可分离卷积扩宽通道数导致网络训练难度增加的问题,同时网络表现出更好的性能。实验结果表明,对比传统的最近邻插值、双三次插值超分辨率算法,以及基于稀疏表达的超分辨率算法,所提算法重建出的医学影像纹理细节更加丰富、视觉效果更加逼真。对比基于卷积神经网络的超分辨率算法,基于宽残差超分辨率神经网络算法和生成对抗网络超分辨率算法,所提算法在峰值信噪比（PSNR）和结构相似性（SSIM）上有显著的提升。     

ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content

High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end‐to‐end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.     

A comparison study of 12 paradigms for developing embodied agents

We report on a study in which 12 different paradigms were used to implement agents acting in an environment which borrows elements from artificial life and multi‐player strategy games. In choosing the paradigms we strived to maintain a balance between high‐level, logic‐based approaches and low‐level, physics‐oriented models; between imperative programming, declarative approaches and ‘learning from basics’; between anthropomorphic or biologically inspired models on one hand and pragmatic, performance‐oriented approaches on the other. We have found that the choice of the paradigm determines the software development process and requires a different set of skills from the developers. In terms of raw performance, we found that the best performing paradigms were those which (a) allowed the knowledge of human experts to be explicitly transferred to the agent and (b) allowed the integration of well‐known, high‐performance algorithms. We have found that maintaining a commitment to the chosen paradigm can be difficult; there is a strong temptation to offer shallow fixes to perceived performance problems through a ‘flight into heuristics’. Our experience is that a development process without the discipline enforced by a central paradigm leads to agents which are a random collection of heuristics whose interactions are not clearly understood. Although far from providing a definitive verdict on the benefits of the different paradigms, our study provided a good insight into what kind of conceptual, technical or organizational problems would a development team face depending on their choice of agent paradigm. Copyright © 2007 John Wiley & Sons, Ltd.     

Production scheduling for blocking flowshop in distributed environment using effective heuristics and iterated greedy algorithm

Production scheduling plays an important role in the intelligent decision support system and intelligent optimization decision technology. In the context of the globalization trend, the current production and management may extend from a single factory to a distributed production network. In this paper, we study the distributed blocking flowshop scheduling problem (DBFSP) that is an important generalization of the traditional blocking flowshop scheduling problem in the distributed environment. Six constructive heuristics and an iterated greedy (IG) algorithm are proposed to minimize the makespan, which provides procedures for obtaining efficient and effective solutions to make decision-making sounder. The first five heuristics are developed based on the well-known NEH2 heuristic [B. Naderi, R. Ruiz, The distributed permutation flowshop scheduling problem, Computers & Operations Research, 37 (4) (2010) 754–768.] and the last heuristic is presented by extending the PW heuristic [Q.K. Pan, L. Wang, Effective heuristics for the blocking flowshop scheduling problem with makespan minimization, Omega, 40 (2) (2012) 218–229.] to DBFSP in an effective way. The composite heuristics that combining constructive heuristics and local searches are also studied. The proposed composite heuristics are chosen to generate an initial solution with a high level of quality. Keeping the simplicity of the IG algorithm, three local search procedures, two destruction procedures, an improved reconstruction procedure, and a simulated annealing-like acceptance criterion are well designed based on the problem-specific knowledge to enhance the IG algorithm. The computational experiments are carried out based on the 720 benchmark instances from the literature. The results show that the proposed heuristics are very effective for solving the problem under consideration and the presented IG algorithm performs significantly better than the other state-of-the-art metaheuristics from the literature.     

Android malware detection based on system call sequences and LSTM

As Android-based mobile devices become increasingly popular, malware detection on Android is very crucial nowadays. In this paper, a novel detection method based on deep learning is proposed to distinguish malware from trusted applications. Considering there is some semantic information in system call sequences as the natural language, we treat one system call sequence as a sentence in the language and construct a classifier based on the Long Short-Term Memory (LSTM) language model. In the classifier, at first two LSTM models are trained respectively by the system call sequences from malware and those from benign applications. Then according to these models, two similarity scores are computed. Finally, the classifier determines whether the application under analysis is malicious or trusted by the greater score. Thorough experiments show that our approach can achieve high efficiency and reach high recall of 96.6% with low false positive rate of 9.3%, which is better than the other methods.

     

Massed versus distributed wordprocessor training

Two groups of subjects were trained on 12 basic word processing (WP) functions. One group was trained in a 'massed' 60-minute session and the other on a 'distributed' schedule of 60 min in two sessions separated by a ten-minute break. Subjects were tested on speed and accuracy immediately after training, and one week later to measure retention. It was found that the distributed training group performed significantly faster and more accurately than the massed group. This result was obtained at the end of the training period and also on the retention test. The results strongly indicate that a session of 60 min is too long for efficient WP training.