Challenging Institutional Denial: Psychological Discourse,Therapeutic Culture and Public Inquiries

ABSTRACT

The damaging effects of abuse in childhood were repeatedly emphasised in public hearings and in media coverage of the Royal Commission into Institutional Responses to Child Sexual Abuse. Testimony from earlier Australian inquiries, which documented widespread experiences of child maltreatment, particularly in institutions, also underscored the ongoing and often intergenerational impact of abuse. Taking institutional child abuse inquiries as a case study, this article examines how psychological and therapeutic concepts have been mobilised politically. It argues that therapeutically oriented and psychologically informed cultural narratives of childhood trauma and its ongoing effects have provided a framework for making sense of long-term experiences of adversity and suffering and have enriched attention to “the question of justice” for survivors of historical institutional child abuse.     

浅析心理咨询技能在高校思想政治教育中的应用

心理咨询与思想政治教育是高校大学生工作中两个重要方面,两者既有区别,又有联系。将心理咨询的技能融入思想政治教育工作,既培养了大学生健康的心理和健全的人格,又解决大学生的思想问题,适应了高校全面提高人才素质的要求。     

从人格特质及心理健康水平预测护士职业适应

本文运用了人格检查法及心理健康量表调查了168名护士,并与有关资料对比,旨在了解护士人格特质及心理健康水平与职业适应的预测关系。研究发现,敏感性、世故性、忧虑性、紧张性人格特质与躯体化、人际关系敏感、抑郁、焦虑、敌对、恐怖、精神病性症状呈现显著正相关;自律性人格与躯体化呈现显著负相关。结论认为,人格特质与心理健康相关,据此可预测职业适应情况。     

论当代大学生管理工作的着力点——基于大学生心理发展的思考

本文通过对大学生心理发展的一般特点、不同发展阶段、心理发展过程中的矛盾与冲突的分析,提出大学生管理工作的着力点--制度管理与人本管理的和谐统一,旨在为当代大学生管理工作提供一些借鉴.     

监狱警察职业心理健康现状调查

监狱警察是指在监狱工作机关从事刑罚执行、实施惩罚和改造罪犯的人民警察,他们既是执法者,又是管理者,也是教育者,正是由于这种特殊的社会角色,他们不得不面对单调、枯燥的工作内容,偏远、闭塞的工作场所,压抑、沉闷的工作环境,不近人情的工作制度等等。长期在这种状态下生活、工作,他们中的很多人都产生了各种各样的职业心理健康危机,严重影响了他们的工作绩效、身心健康、生活满意指数。不能以社会认可的行为进行克服,致使紧张、焦虑、烦躁甚至抑郁等消极情绪体验,未得到及时有效的心理输导和宣泄,最终引发严重的心理问题。     

公安政法院校心理测试技术教学的思考

文章对当前阻碍公安政法院校心理测试技术教学发展的主要问题进行了总结,详细分析了产生问题的各种原因,并从改善心理测试技术人才的培养目标、教学内容、教学方法、教学条件等角度,探讨了从根本上促进心理测试技术教学发展的思路和方法,以顺应新时期培养新型侦查专门人才和"科技强警"的要求。     

Selection of an adhesive tape suitable for forensic fiber sampling

Five commercial adhesive tapes were tested for fiber uptake and saturation, for recovery and for ease of analysis. On the basis of the results, a high tack adhesive tape has been selected to be used for forensic fiber sampling. This adhesive tape is used as sampling material in two different micro trace kits. The first tape lifting kit is used mainly for the sampling of cars and the second is a 1:1 tape lifting kit for the collection of fibers on a corpse.     

Evaluation of the NITV CVSA

The purpose of this study was to evaluate a commonly used voice stress analyzer, the National Institute of Truth Verification's (NITV) Computer Voice Stress Analyzer (CVSA), using a speech database containing materials recorded (i) in the laboratory, while highly controlled deceptive and shock-induced stress levels were systematically varied, and (ii) during a field procedure. Subjects were 24 each males/females (age range 18-63 years) drawn from a representative population. All held strong views on an issue and were required to make sharply derogatory statements about it. The CVSA system was then evaluated in a double-blind study using three sets of examiners: (i) two UF scientists trained/certified by NITV in CVSA operation, (ii) three experienced NITV operators provided by the manufacturer and (iii) five experimental phoneticians. The results showed that the "true positive" (or hit) rates for all examiners ranged from chance to somewhat higher levels (c. 50-65%) for all conditions and types of materials (e.g., stress vs. unstressed, truth vs. deception). However, the false-positive rate was just as high - often higher. Sensitivity statistics demonstrated that the CVSA system operated at about chance level.     

Stress and Deception in Speech: Evaluating Layered Voice Analysis*

Abstract: This study was designed to evaluate commonly used voice stress analyzers—in this case the layered voice analysis (LVA) system. The research protocol involved the use of a speech database containing materials recorded while highly controlled deception and stress levels were systematically varied. Subjects were 24 each males/females (age range 18–63 years) drawn from a diverse population. All held strong views about some issue; they were required to make intense contradictory statements while believing that they would be heard/seen by peers. The LVA system was then evaluated by means of a double blind study using two types of examiners: a pair of scientists trained and certified by the manufacturer in the proper use of the system and two highly experienced LVA instructors provided by this same firm. The results showed that the “true positive” (or hit) rates for all examiners averaged near chance (42–56%) for all conditions, types of materials (e.g., stress vs. unstressed, truth vs. deception), and examiners (scientists vs. manufacturers). Most importantly, the false positive rate was very high, ranging from 40% to 65%. Sensitivity statistics confirmed that the LVA system operated at about chance levels in the detection of truth, deception, and the presence of high and low vocal stress states.     

Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor

The Virtual Machine Introspection (VMI) has emerged as a fine-grained, out-of-VM security solution that detects malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS). Specifically, it functions by the Virtual Machine Monitor (VMM), or hypervisor. The reconstructed semantic details obtained by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, the existing out-of-VM security solutions require extensive manual analysis. In this paper, we propose an advanced VMM-based, guest-assisted Automated Internal-and-External (A-IntExt) introspection system by leveraging VMI, Memory Forensics Analysis (MFA), and machine learning techniques at the hypervisor. Further, we use the VMI-based technique to introspect digital artifacts of the live guest OS to obtain a semantic view of the processes details. We implemented an Intelligent Cross View Analyzer (ICVA) and implanted it into our proposed A-IntExt system, which examines the data supplied by the VMI to detect hidden, dead, and dubious processes, while also predicting early symptoms of malware execution on the introspected guest OS in a timely manner. Machine learning techniques are used to analyze the executables that are mined and extracted using MFA-based techniques and ascertain the malicious executables. The practicality of the A-IntExt system is evaluated by executing large real-world malware and benign executables onto the live guest OSs. The evaluation results achieved 99.55% accuracy and 0.004 False Positive Rate (FPR) on the 10-fold cross-validation to detect unknown malware on the generated dataset. Additionally, the proposed system was validated against other benchmarked malware datasets and the A-IntExt system outperforms the detection of real-world malware at the VMM with performance exceeding 6.3%.