Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence

In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome. By evidence transfer we define the process by which the categorical outcome of an external, auxiliary task is exploited to improve a primary task, in this case representation learning for clustering. Our proposed method makes no assumptions regarding the categorical evidence presented, nor the structure of the latent space. We compare our method, against the baseline solution by performing k-means clustering before and after its deployment. Experiments with three different kinds of evidence show that our method effectively manipulates the latent representations when introduced with real corresponding evidence, while remaining robust when presented with low quality evidence

Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process

Less-structured time in children's daily lives predicts self-directed executive functioning.

Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6-7 year-old children's daily, annual, and typical schedules. We categorized children's activities as "structured" or "less-structured" based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain–body– environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour

Organisational Change in Europe: National Models or the Diffusion of a New "One Best Way"?

Drawing on the results of the third European Survey on Working Conditions undertaken in the 15 member nations of the European Union in 2000, this paper offers one of the first systematic comparisons of the adoption of new organisation forms across Europe. The paper is divided into five sections. The first describe the variables used to characterise work organisation in the 15 countries of the European Union and presents the results of the factor analysis and hierarchical clustering used to construct a 4-way typology of organisational forms, labelled the 'learning , 'lean , 'taylorist and 'traditional forms. The second section examines how the relative importance of the different organisational forms varies according to sector, firm size, occupational category, and certain demographic characteristics of the survey population. The third section makes use of multinomial logit analysis to assess the importance of national effects in the adoption of the different organisational forms. The results demonstrate significant international differences in the adoption of organisational forms characterised by strong learning dynamics and high problem-solving activity. The fourth section takes up the issue of HRM complementarities by examining the relation between organisation forms and the use of particular pay and training policies. The concluding section explores the relation between national differences in the use of the four organisational forms and differences in the way labour markets are regulated and in such research and technology measures as patenting and R&D expenditures. The results show that the relative importance of the learning form of organisation is both positively correlated with the extent of labour market regulation, as measured by the OECD's overall employment protection legislation index, and with innovative performance, as measured by the number of EPO patent application per million inhabitants.Firm organisation; learning; Europe

The Effects of Verbal Fluency Interventions: Phonemic versus Semantic Fluency Outcomes in Parkinson\u27s Disease

Verbal fluency (VF) tasks are well-established and widely used tools in clinical assessment and research settings to evaluate executive functioning skills. They consist of verbally generating as many different items as possible that either begin with a specified letter (i.e., phonemic) or belong to a category (i.e., semantic) within 60 seconds. Due to deficits in executive functioning, individuals with Parkinson’s disease (PD) have increased difficulty with phonemic compared to semantic fluency. Although VF tasks are commonly used as intervention tools within speech-language pathology clinical practice, there is limited research investigating their therapeutic benefit. The purpose of this study was to investigate the effectiveness of a VF task intervention program at rehabilitating VF performances of an individual with PD. Additionally, this study investigated any effects of intervention on other measures of executive functioning. A quasi-experimental, pretest/posttest design was used. The 10-session intervention period focused on teaching and practicing the clustering and switching approach to VF tasks. Results revealed no significant changes in VF performances after intervention. Significant changes to other executive functioning measures validate the need for further investigation into VF tasks as therapeutic tools

The Organization of Work and Innovative Performance A comparison of the EU-15

It is widely recognised that while expenditures on research and development are important inputs to successful innovation, these are not the only inputs. Further, rather than viewing innovation as a linear process, recent work on innovation in business and economics literatures characterises it as a complex and interactive process involving multiple feedbacks. These considerations imply that relevant indicators for innovation need to do more than capture material inputs such as R&D expenditures and human capital inputs. The main contribution of this paper is to develop EU-wide aggregate measures that are used to explore at the level of national innovation systems the relation between innovation and the organisation of work. In order to construct these aggregate measures we make use of micro data from two European surveys: the third European survey of Working Conditions and the third Community Innovation Survey (CIS-3). Although our data can only show correlations rather than causality they support the view that how firms innovate is linked to the way work is organised to promote learning and problem-solving.National innovation systems, measuring, methodology

Learning to Associate Words and Images Using a Large-scale Graph

We develop an approach for unsupervised learning of associations between co-occurring perceptual events using a large graph. We applied this approach to successfully solve the image captcha of China's railroad system. The approach is based on the principle of suspicious coincidence. In this particular problem, a user is presented with a deformed picture of a Chinese phrase and eight low-resolution images. They must quickly select the relevant images in order to purchase their train tickets. This problem presents several challenges: (1) the teaching labels for both the Chinese phrases and the images were not available for supervised learning, (2) no pre-trained deep convolutional neural networks are available for recognizing these Chinese phrases or the presented images, and (3) each captcha must be solved within a few seconds. We collected 2.6 million captchas, with 2.6 million deformed Chinese phrases and over 21 million images. From these data, we constructed an association graph, composed of over 6 million vertices, and linked these vertices based on co-occurrence information and feature similarity between pairs of images. We then trained a deep convolutional neural network to learn a projection of the Chinese phrases onto a 230-dimensional latent space. Using label propagation, we computed the likelihood of each of the eight images conditioned on the latent space projection of the deformed phrase for each captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on average. Our work, in answering this practical challenge, illustrates the power of this class of unsupervised association learning techniques, which may be related to the brain's general strategy for associating language stimuli with visual objects on the principle of suspicious coincidence.Comment: 8 pages, 7 figures, 14th Conference on Computer and Robot Vision 201