Multilevel metric rank match for person re-identification

Metric learning is one of the important ways to improve the person re-identification (ReID) accurate, of which triplet loss is the most effect metric learning method. However, triplet loss only ranks the extracted feature at the end of the network, in this paper, we propose a multilevel metric rank match (MMRM) method, which ranks the extracted feature on multilevel of the network. At each rank level, the extracted features are ranked to find the hard sample pairs and the backward dissemination triplet loss. Each rank level has different penalize value to adjust the network, in which the value is bigger with the deeper level of the whole network. Experiment results on CUHK03, Market1501 and DukeMTMC datasets indicate that The MMRM algorithm can outperform the previous state-of-the-arts.     

De l’estime de soi et du sentiment d’auto-efficacité à la motivation au travail : le rôle médiateur du sens du travail chez les alternants en France

The purpose of this study was to test the links between self-esteem, the feeling of self-efficacy and the different forms of motivation. It also aimed to understand the mediating effect of the meaning of work between the variables. French work-integrated learning students (n = 471) completed a self-administered online questionnaire. The results showed significant links between self-esteem, perceived self-efficacy, work meaning, and different dimensions of motivation (extrinsic, intrinsic, and amotivation). In addition, the meaning of work played a mediating role in the relationship between self-esteem, perceived self-efficacy, and dimensions of motivation. This study showed the central mechanism of work meaning in work motivation.     

Detecting anonymous attacks in wireless communication medium using adaptive grasshopper optimization algorithm

Intrusion Detection Systems (IDSs) is a system that monitors network traffic for suspicious activity and issues alert when such activity is revealed. Moreover, the existing IDSs-based methods are based on outdated attacks that unable to identify modern attacks or malicious trends. For this reason, in this study we developed a new multi-swarm adaptive grasshopper optimization algorithm to utilize adaptation mechanism in a group of swarms based on fuzzy logic to protect against sophisticated attacks. The proposed (MSAGOA) technique has the capability of global optimization and rapid convergence that are used to attain optimal feature subsets to identify attack types on IDS datasets. In the MSAGOA technique, learning engine as Extreme learning Machine, Naive Bayes, Random Forest and Decision Tree is applied as a fitness function to select the highly discriminating features and to maximize classification performance. Afterward, select the best classifier which works as a fitness function in our approach to measure the performance in terms of accuracy, detection rate, and false alarm rate. The simulations are performed on three IDS datasets such as NSL-KDD, AWID-ATK-R, and NGIDS-DS. The experimental results demonstrated that MSAGOA method has performed better and obtained high detection rate of 99.86%, accuracy of 99.89% in NSL-KDD and high detection rate of 98.73%, accuracy of 99.67% in AWID-ATK-R and detection rate of 89.50%, accuracy of 90.23% in NGIDS-DS. In addition, the performance is compared with several other existing techniques to show the efficacy of the proposed approach.     

双眼视差的神经机制与知觉学习效应

双眼瞳距使得空间某物体在左右眼视网膜的成像存在微小位置差异, 这种差异被称为双眼视差(binocular disparity), 是立体视知觉的重要信息来源。对双眼视差的心理物理学研究始于18世纪初, 迄今已有接近两百年的历史。近年来, 双眼视差研究主要集中在两方面。其一是用电生理、脑成像技术考察双眼视差在视觉背、腹侧通路的模块化表征, 其脑区表征反映出视觉系统的层级式、平行式加工规律。其二是应用知觉学习范式研究双眼视差的可塑性。未来研究应综合脑成像和神经调控技术考察双眼视差的神经机制及其学习效应, 包括双眼视差与多种深度线索间的信息整合和交互作用。应用方向上, 可结合虚拟现实等技术优化训练范式, 实现立体视力的康复和增强。     

象似性和手语学习经验对中国手语-汉语跨模态语义启动效应的影响

采用启动条件下的词汇判断任务,考察熟练手语使用者和无手语经验成年听人的跨模态语义启动效应。结果发现：1)在象似词条件下,两组被试判断汉语语义相关词的反应时均快于语义无关词,说明手语象似词和汉语词之间存在跨模态语义启动效应。2)在非象似词条件下,仅手语熟练被试判断汉语语义相关词的反应时快于语义无关词,无手语经验被试判断汉语语义相关词和无关词的速度没有差异。这是由于前者心理词库中的手语词和口语词共享语义表征,而后者主要依赖手语象似词的视觉模拟性。整个研究表明,中国手语和汉语间存在跨模态语义启动效应,但该效应受到手语词象似性和手语学习经历的调节。     

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client’s needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.     

The Natural Selection of Prediction Heuristics: Anchoring and Adjustment versus Representativeness

There are several heuristics which people use in making numerical predictions and these heuristics compete for the determination of prediction output. Some of them (e.g. representativeness) lead to excessively extreme predictions while others (e.g. anchoring and adjustment) lead to regressive (and even over-regressive) predictions. In this paper we study the competition between these two heuristics by varying the representation of predictor and outcome. The results indicate that factors which facilitate reliance on representativeness (e.g. compatibility between predictor and outcome) indeed lead to an increase in extremity, while factors that facilitate reliance on anchoring and adjustment (e.g. increased salience of a potential anchor) lead to a decrease in extremity.     

A comparison of accumulated and distributed reinforcement periods with children exhibiting escape-maintained problem behavior

Differential reinforcement is a common treatment for escape-maintained problem behavior in which compliance is reinforced on a fixed-ratio (FR) 1 schedule with brief access to positive and/or negative reinforcement. Recent research suggests some individuals prefer to complete longer work requirements culminating in prolonged (i.e. accumulated) reinforcement periods relative to brief (i.e. distributed) periods, but prolonged work exposure may evoke problem behavior and prevent compliance from contacting reinforcement when treating escape-maintained problem behavior. We exposed 3 children with escape-maintained problem behavior to both distributed (FR 1 resulting in 30 s of reinforcement) and accumulated (FR 15 resulting in 7.5 min of reinforcement) arrangements to compare their efficacy in maintaining low levels of problem behavior. We then assessed participants' preferences for these conditions in a concurrent-chains arrangement. Accumulated-reinforcement arrangements did not occasion additional problem behavior, but rather resulted in consistently lower levels of problem behavior for 2 of 3 participants. Participants demonstrated idiosyncratic preferences.     

Comparing skill acquisition under varying onsets of differential reinforcement: A preliminary analysis

The purpose of the current study was to evaluate the effect of implementing differential reinforcement at different times relative to the onset of teaching new skills to learners with autism spectrum disorder. Specifically, we first determined the most efficient differential reinforcement arrangement for each participant. Using the most efficient arrangement, we evaluated if differential reinforcement from the immediate onset, early onset, or late onset is the most efficient for learners to acquire a new skill. Three children diagnosed with autism spectrum disorder who have a history of receiving intervention based on the principles of applied behavior analysis participated in this study. The immediate onset of differential reinforcement resulted in the most efficient instruction in 6 of 7 comparisons. The results are discussed in light of previous studies and suggestions for future research are provided.