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 Moreover, the existing IDSs-based methods are based on outdated attacks that unable to identify modern attacks or malicious trends.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。     

语法的进化连续性及进化起源解释

语法是人类交流系统有别于其他动物的关键特征之一。行为比较研究表明, 除人类外的其他灵长目动物也能理解并掌握抽象的顺序排序规则; 神经生物学比较研究表明, 支持排序处理的神经机制来自人类与其他灵长目动物共同具有的脑区。因此语法所依赖的序列学习能力在人类与其他灵长目动物间具有进化连续性。词汇限制假说、事件感知假说与自我驯化假说分别从不同角度对人类语法的进化起源进行了解释。未来研究需要探讨人工语法任务中所发现的脑神经机制是否是层级结构加工的通用处理器, 并进一步澄清语义加工与语法加工的关系。     

教师的存在能否促进视频学习?

随着网络教学和视频学习的普及, 近期大量研究关注视频学习过程中教师角色的作用。真人教师对学习的作用存在不同的理论解释：准社会交往理论、社会存在理论和社会代理理论支持真人教师能够促进学习; 而多媒体学习认知理论、认知负荷理论等则认为教师的存在会阻碍视频学习。汇总以往实证研究发现：第一, 教师对视频学习效果有比较弱的促进效应, 这主要体现在保持测验(d = 0.23)和迁移测验(d = 0.1)中; 第二, 教师对视频学习主观体验的影响主要体现在学习感知(d = 0.49), 学习兴趣(d = 0.5)和学习满意度(d = 2.21)上, 显示中等及大的效应; 第三, 教师的存在会降低学习者对视频学习材料的注视时间(d = -2.02)。未来研究仍需要从真人教师呈现方式、学习者特征、学习材料特征以及测量方式等方面加以探讨。     

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。     

心理因素与学业表现：机器学习分类预测模型

随着高等教育规模的扩大,学业表现不良逐渐成为一个不容忽视的现象,对学业表现不良的学生进行预测并提早给予干预可降低退学率并减少教育资源的损失。由于导致学业表现不良的因素众多且关系复杂,传统的基于相关分析的研究方法很难建立早期预测模型并进行应用。本研究旨在利用机器学习算法,对数据进行挖掘,并建立学业表现预测模型。研究对653名大一新生的心理健康状况、应对方式、人格、内外控倾向和相关人口统计学信息进行了收集,并在一年后采集了其学业成绩,利用随机森林(RF)、K邻近（KNN）、支持向量机（SVM）、决策树（DT）、朴素贝叶斯（NB）等机器学习算法建立了分类模型。结果显示,随机森林算法在识别学业表现不良学生时有最好的表现,其中准确率95.86%, 召回率91.83%,f1分数为93.80%。特征重要性分析显示,前10个对模型有最高贡献度的特征包括：年龄、性别、是否为独生子、内外控倾向、神经质倾向、积极应对倾向、宜人性倾向、一般症状指数、开放性倾向和焦虑水平。为避免过度拟合问题,本研究在一年后收集的166名新生样本中进行了模型验证,结果显示模型在新数据集上有较好的泛化表现,其中f1分数90.90％,准确率92.60％,召回率89.26％。研究提示基于人口统计学和心理测评信息,机器学习算法有助于及早识别学业表现不良学生并为开展早期干预提供启示。     

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.