Miracle-dz/UniFAR-FTUs huggingface dataset:
Flexible decision-making in recurrent neural networks trained with a biologically plausible rule

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Recurrent neural networks operating in the near-chaotic regime, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide the learning process. The lack of a biological learning method currently restricts the plausibility of recurrent networks as models of cortical computation. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial, for nontrivial tasks. We use this method to learn various tasks from the experimental literature, showing that this learning rule can successfully implement flexible associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. We show that the resulting networks exhibit complex dynamics previously observed in animal cortex, such as dynamic encoding and maintenance of task features, switching from stimulus-specific to response-specific representations, and selective integration of relevant input streams. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.
Noise Reduction for Iris Images

In many institutions and organization the attendance is very important factor for various purposes and its one of the important criteria that is to follow for students and organization employees. The previous approach in which manually taking and maintains the attendance records was very inconvenient task. In this paper, noise detection techniques have been used to develop the accurate and noise free Iris technique. In previous research there are so many techniques has been introduced to detect the accurate iris detection. But the existing techniques are not fulfilling the exact requirements. The proposed system provides accuracy by making experiments on 10 sample images. The proposed method is highly competitive compared with other noise detection approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.
Identification and Analysis of Multi-tasking Product Information Search Sessions with Query Logs

Abstract Purpose This research aims to identify product search tasks in online shopping and analyze the characteristics of consumer multi-tasking search sessions. Design/methodology/approach The experimental dataset contains 8,949 queries of 582 users from 3,483 search sessions. A sequential comparison of the Jaccard similarity coefficient between two adjacent search queries and hierarchical clustering of queries is used to identify search tasks. Findings (1) Users issued a similar number of queries (1.43 to 1.47) with similar lengths (7.3–7.6 characters) per task in mono-tasking and multi-tasking sessions, and (2) Users spent more time on average in sessions with more tasks, but spent less time for each task when the number of tasks increased in a session. Research limitations The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior. Practical implications These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction. Originality/value The originality of this research is its use of query clustering with online shopping task identification and analysis, and the analysis of product search session characteristics.
Novel experimental techniques for granular flow

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Provisioning end-to-end quality of service for real-time interactive video over software-defined networking

Owens II, Harold. Ph.D., Purdue University, December 2016. Provisioning EndTo-End Quality of Service for Real-Time Interactive Video over Software-Defined Networking. Major Professor: Arjan Durresi. This thesis contains four interrelated research areas. Before presenting the four research areas, this thesis presents literature review on Software-Defined Networking (SDN), a network architecture that allows network operator to manage the network using high level abstractions. This thesis presents taxonomy for classifying SDN research. In research first area, this thesis presents Video over Software-Defined Networking (VSDN), a network architecture that selects feasible paths using the network-wide view. This thesis describes the VSDN protocol which is used for requesting service from the network. This thesis presents the results of implementing VSDN prototype and evaluates behavior of VSDN. Requesting service from the network requires developer to provide three input parameters to application programmable interface. The message complexity of VSDN is linear. In research second area, this thesis presents Explicit Routing in Software-Defined Networking (ERSDN), a routing scheme that selects transit routers at the edge of network. This thesis presents the design and implementation of ERSDN. This thesis evaluates the effect of ERSDN on the scalability of controller by measuring the control plane network events—packets. ERSDN reduces the network events in the control plane by 430%. In research third area, this thesis presents Reliable Video over Software-Defined Networking (RVSDN) which builds upon previous work of Video over SoftwareDefined Networking (VSDN) to address the issue of finding most reliable path. This
A Systematic Review of Instructional Comparisons in Single-Case Research

Comparison studies conducted to determine which instructional interventions are most efficient for teaching discrete behaviors to individuals with disabilities are potentially valuable, although some threats to internal validity may be more likely in these studies. Studies included in this review typically met common internal validity standards, such as reliability measurement, but often did not include controls specific to comparison designs. Comparisons often included young children with autism and were frequently conducted by researchers in self-contained classroom settings. Systematic instruction was effective in nearly all comparisons, although many included undifferentiated data (i.e., both interventions were equally effective), and within-participant replications were often inconsistent (i.e., outcomes varied across comparisons for a single participant). Results suggest implementers should conduct high-fidelity instruction with corrective and instructive feedback and should choose intervention variations based on participant preference. We recommend researchers include control sets or time-lagged introductions, counterbalance behavior sets, and measure differential acquisition over time.
Performance Analysis of Various Recommendation Algorithms Using Apache Hadoop and Mahout

- Recommendations are becoming personnel assistance to customers to find out the best item out of most used ones or the best item which has maximum popularity. Most of the recommendations are based on the machine learning algorithms which will perform learning using the datasets and standard set of programs which uses the underlined dataset for further prediction. It also utilizes knowledge discovery techniques to the problem of making personalized product recommendations during a live customer interaction. The tremendous growth of customers and products in recent years poses some key challenges for recommender systems. They include producing high quality recommendations and performing many recommendations per second for millions of customers and products. For building recommender systems many of them use Collaborative Filtering technique. It predicts the utility of an item for a particular user based on the user’s previous interests and takes the opinions from other users. When the data set becomes larger, the processing time and recommendations will have latency. It is a greatest challenge to provide recommendation for large-scale problems to produce high quality recommendations. There are various libraries have been released for the development of the recommender system. This paper focuses on comparing the various similarity measurement algorithms and classification accuracy metrics on Hadoop and non-Hadoop environment using Apache Mahout and item-based collaborative filtering. The whole work is done on the Movielens dataset.
Virtual Mobile Networks in Clouds

The ubiquity of the cloud computing allows the mobile devices to connect and use the traditional cloud computing services [1]. However, unlike the normal computing machines, the mobile devices are resource constrained. The precincts of low processing power, less storage capacity, limited energy, and the capricious internet connectivity does not allow the compute and storage mandating applications to run on mobile devices [2]. The aforementioned limitations served as the motivation for a new computing paradigm called Mobile Cloud Computing (MCC) that enhances the abilities of mobile devices by offloading resource intensive applications to the cloud. Mobile devices can now execute heavy compute and storage intensive applications by using the computation and storage services of the cloud [3]. The MCC paradigm enables the users to access and manage their applications and data through mobile devices without the need to move to traditional computing machines.
Activity-centric computing and context awareness in mobile personal information managemen

Activity-Centric Computing and Context Awareness in Mobile Personal Information Management by Torstein Thune Information and tools on modern smartphones are tightly tied to applications. It is hard to move information between applications, and it is often near impossible to collect and compare information from several sources. In this thesis I will explore how we through applying activity-centric computing and context awareness to the current generation of smartphone operating systems can advance the state of mobile personal information management. The thesis presents two prototypes, Activity Ant 1 and Activity Ant 2, showing how activity-centric computing and context awareness can be implemented from an Human-Computer Interaction perspective. User reactions and suggestions are also presented and discussed.
Keeping threat at arm’s length: counter-revolutionary interventions by third-party states in support of governments

ABSTRACT During the 1970s the military juntas in South America engaged in a cross-national campaign of repression, code-named Operation Condor, targeted against leftist militant groups inspired to action by the Cuban Revolution. This case illustrates an understudied motivation for third-party intervention in domestic conflict: counter-revolution. We therefore formulate a theory in which revolutions shock the international system by empowering new revolutionary regimes that, in turn, inspire dissidents abroad to take up arms. Status quo elites in foreign states seek to staunch this diffusion of revolution by engaging in international repressive campaigns, manifested as third party intervention in civil conflict. We test this expectation on a global sample of intervention opportunities for the period 1975–2004, and assess the threat that revolutionary regimes pose to status quo governments in two ways: (1) the geographic proximity of a revolutionary state to pairs of status quo states; and (2) the geographic proximity of internal-armed conflicts featuring rebels that are supported by a revolutionary states. We find evidence that status quo states respond to the proximity of a revolutionary state, but not to the proximity of support for rebels.
Study on the adaptive algorithm of the force reflection robotic master under large stiffness of the environment

The stability and transparency of the robotic master with force reflection will be degraded under large stiffness of the environment.An adaptive algorithm of haptic rendering was proposed to solve this problem by modifying the model of the virtual wall and introducing the concept of force space gradient.The methods of haptic rendering of the impedance display master under large stiffness were automatically selected according to the change rate of the force with respect to the end position of the robotic master.Simulation results showed that the proposed algorithm of haptic rendering can not only eliminate the force oscillation to keep stability,but also can recover the real output stiffness to improve the transparency of robotic master.
Development of Cognitive Control across Childhood and Adolescence

This chapter describes recent progress in understanding the development of cognitive control in relation to brain development. Across childhood and adolescence, individuals learn to control thought and actions for the purpose of obtaining future goals. These changes in cognitive control have been linked to structural and functional changes of the prefrontal and parietal cortex. In this chapter, we describe the implications of these changes in three domains. The first domain concerns cognitive and adaptive control of thought and actions, with a focus on working memory, inhibition and performance monitoring. The second domain describes the role of cognitive control in affective decision-making, with a focus on delay of gratification. The third domain focuses on social decisionmaking, specifically how the development of control allows children and adolescents to consider fairness and reciprocity. Together, the findings are summarized in the context of current models of child and adolescent brain development. Key Terms: cognitive control, brain development, adolescence, performance monitoring, response inhibition, working memory, fMRI, ERP
Mathematical Modeling of Heart Rate Deflection Point in Relation to Respiratory Compensation and Treadmill Running Performance

Heart rate deflection point (HRDP), identified as the second breakpoint in the “intensity / heart rate” relationship, is indicative of the anaerobic threshold (AT). This point can be determined via bi-segmental linear regression (2SEG) or through use of the maximal distance model (D-max); however, the relationship between these methods has yet to be investigated 2. Purpose: To compare the use of 2SEG and D-max methods to determine HRDP and to examine the relationship between these values with a metabolic threshold, respiratory compensation point (RCP), as well as running performance [5,000 meter treadmill time trial (5Ktime)]. Methods: Nineteen recreationally active men (n=9, 25.56±3.17 y, 1.77±0.05 m, 83.52±6.77 kg, 48.98±7.37 ml·kg·min-1) and women (n=10, 22.78±2.11 y, 1.64±0.07 m, 62.28±6.20 kg, 42.32±4.13 ml·kg·min-1) were recruited for this study. Participants completed two experimental trials, consisting of a graded exercise test to exhaustion (GXT) and 5,000m time trial on the treadmill. Estimates of HRDP and RCP were calculated from data collected during the GXT. One-way repeated measures analysis of variance was used to compare HRDP found through 2SEG (HRDP2SEG), HRDP found through D-max (HRDPD-max), and RCP. Pearson product moment correlations were used to examine the relationship between variables (HRDP2SEG, HRDPD-max, RCP, and 5Ktime). Results: No differences were found between HRDP2SEG (176.70±9.40 bpm), HRDPD-max (178.18±6.85 bpm), and RCP (176.92±6.63 bpm) (p = 0.533). Strong correlations were found between HRDP2SEG and HRDPD-max (r = 0.831, p < 0.0001), RCP and HRDP2SEG (r = 0.650, p = 0.003), and RCP and HRDPD-max (r = 0.619, p = 0.005). No relationship was found between 5Ktime and HRDP2SEG (r = 0.419, p = 0.074), HRDPD-max (r = 0.241, p = 0.321), or RCP (r = 0.193, p = 0.429). Similar limits of agreement were found for all comparisons (HRDP2SEG and RCP, p = 0.070; HRDPD-max and RCP, p = 0.868; HRDP2SEG and HRDPD-max, p = 0.029),
Trojan Rapid Detection Method Based on Heartbeat Behavior Analysis

Trojan detection algorithm based on behavior analysis of communication has high computational complexity.Addressing the problem,this paper proposes a Trojan rapid detection based on heartbeat behavior analysis.The method selects two session attributes to describe the difference between Trojan communication flow and normal communication flow on the basis of description of heartbeat behavior in the Trojan communication large numbers of analysis on Trojan samples.And then Trojan Rapid Detection System(TRDS) is built based on the method.Experimental results show that TRDS can detect the Trojan communication in the 100 Mbit/s network rapidly and efficiently.
A critical analysis of Contiki's network stack for integrating new MAC protocols

Recent Wireless Sensor Network (WSN) MAC protocols focus on both very low idle trafic duty-cycle and high throughput when bursts of trafic occur. It is highly desirable to be able to integrate them into a common and open platform, not only for easing their performance comparison, but also for their effective interaction with existing higher-layer protocols. As part of our work on WSN, we have implemented a new Media Access Control (MAC) protocol into the Contiki OS. When doing so, we stumbled into various limitations and quirks, relative to this system—and especially its network stack. The present report summarizes the critical issues we have faced, and gives some ideas to fix them or work around them. Considering the widespread use of Contiki and its netstack, we believe that knowing those issues will be helpful for other researchers and developers.
The Antecedent Impact of Culture and Economic Growth on Nations` Creativity and Innovation Capability

Even though the literature has widely examined culture’s effect on nations’ creativity and innovation level, it has done little to address the system theory’s perspective. However, as proponents of the system theory have argued, nations’ creativity and innovation is the cumulative effort of the various stakeholders who operate as a system, so any assessment of a nation’s creativity and innovation must consider these stakeholders’ role. This study examines the overlooked effect of culture on the broader system aspect of eighty nations’ creativity and innovationto ensure representation by both developed and developing countries. Structural equation modeling using partial least squares software is applied to analyze the data. The study found out that nations with individualist and low power distance cultural values have the highest creativity and innovation, and that a nation’s economic growth level moderates the impact of the individualist cultural value. Policymakers who seek to develop a strategy that promotes creativity and innovation at the national level benefit from these findings. Future research directions are suggested.
Predicting Students’ Intention and Actual Use of E-Learning Using the Technology Acceptance Model: A Case from Zimbabwe

This study investigates the extent to which the Technology Acceptance Model (TAM) constructs predict intention to accept and use technology in learning. Data were obtained from 337 students from Bindura University of Science Education (BUSE) in Zimbabwe. The findings made three revelations. First, usefulness appears as an important driver for intention to use e-technology in education. Second, Ease-of-Use was contrary to the hypothesis, but was statistically insignificant. Third, Behavioural Intention has a positive and a strong association with Actual Use. The findings suggest several implications for theory and policy. In theoretical terms, the study provides evidence for the predominance of Usefulness over Ease-of-Use in predicting intention to adopt e-learning among students. In practical terms, the study shows that to ensure that students use particular technologies for study, a functionally useful system must be put in place. As such, technologies, which do not meet this condition, may simply be ignored. https://doi.org/10.26803/ijlter.18. 6.7
Pola Pengalaman Depresi Perempuan yang Mengalami Kekerasan dalam Berpacaran: Kajian Perspektif Cognitive-Behavioural

This review aimed to investigate the suitability of therapy derived from Cognitive-Behavioural perspective to be applied as an intervention to treat depressive symptoms in women with dating violence experiences. As this will become a preliminary study to be conducted in Indonesia, a review literature method was employed to gather relevant and up-to-date materials on the topic. Firstly, the issue of dating violence and its urgency in Indonesia were described in the introduction section. In the discussion section, Cognitive-Behavioural perspective and the therapy derived from it, Cognitive Behavioural Therapy (CBT) was described and elaborated in detail. In general, CBT approach was effective in reducing depressive symptoms in women with intimate partner violence (IPV) experiences. As dating violence also belonged to IPV cluster, it is expected that CBT would also become an effective treatment to treat depression in women with dating violence experiences.
Building Trust: The Effect of US Troop Deployments on Public Opinion in Peru

Since the 1950s, US military personnel have taken on an increasingly diverse set of responsibilities, including less traditional roles delivering disaster aid and engaging in public diplomacy. Focusing on a particular subset of deployments, humanitarian and civic-assistance deployments to Latin America, we examine the effect that a US military presence can have on public opinion in the host country. We focus on the microfoundations of popular support and use survey data and newly collected subnational data on deployments to examine the effect of these deployments on mass attitudes toward the US military and government in Peru. We find that these deployments do improve perceptions of the US military and government, and correlate with assessments of US influence that are more positive. Our findings bolster the conclusions of previous research that shows how aid can both improve public attitudes toward the donor country and address the foreign aid attribution problem.
From online strangers to offline friends: a qualitative study of video game players in Hong Kong

This exploratory study adopts a dynamic approach to the understanding of video-gaming and social relationships. It examines the development of friendship ties initiated by video-gaming over a 10-year period of time. Based on qualitative data from 22 Nintendo DS players in Hong Kong, we find that the game play platform functions as a social media platform enabling individuals to make new friends. Four stages of friendship tie development are identified, from total strangers, online acquaintances, online friends, to offline friends. While the digital technology overcomes the structural constraints for relationship building and fosters perceived trust and intimacy among people, face-to-face interactions are important for authenticating these perceptions and sustaining the relationships. Our study also underscores the interconnection of the game world, virtual world, and physical world. Directions for future research are suggested.