Miracle-dz/UniFAR-FTUs huggingface dataset:
Framing the Progress of Collaborative Teacher Education.

Each of the 10 postulates in this article describes what we believe to be true about collaboration in special education. Each program description offers an important example of serious advancement toward collaborative programs in teacher education. Within this collective movement some programs can be placed at one end of a hypothetical continuum of development, and others at the other end. Despite these evolutionary differences, each program and each group of faculty has made significant steps toward programmatic reform in teacher education. After considering what these programs have accomplished already, we thought about how they might be improved. We asked ourselves questions such as: Have these programs gone far enough? What areas haven 't they addressed? Would differences in the nature of the process used to arrive at the new program have changed the outcome in any way? As we questioned certain features of the existing programs, we also thought about programs yet to be developed. We then generated sets of questions, starting on page 8, which we hope will serve all of our readers as they work to strengthen existing collaborative programs and promote the development of new ones.
Policy Gradient Based Semi-Markov Decision Problems: Approximation and Estimation Errors

In [1] and [2] we have presented a simulation-based algorithm for optimizing the average reward in a parameterized continuous-time, finite-state semi-Markov Decision Process (SMDP). We approximated the gradient of the average reward. Then, a simulation-based algorithm was proposed to estimate the approximate gradient of the average reward (called GSMDP), using only a single sample path of the underlying Markov chain. GSMDP was proved to converge with probability 1. In this paper, we give bounds on the approximation and estimation errors for GSMDP algorithm. The approximation error of that approximation is the size of the difference between the true gradient and the approximate gradient. The estimation error, the size of the difference between the output of the algorithm and its asymptotic output, arises because the algorithm sees only a finite data sequence.
BotProfiler: Profiling Variability of Substrings in HTTP Requests to Detect Malware-Infected Hosts

Malware is constantly evolving, which makes it difficult to prevent it from infecting hosts. Many countermeasures against malware infection, such as generating network-based signatures or templates, have been investigated. Such templates are designed to introduce regular expressions to detect polymorphic attacks conducted by attackers. A potential problem with such templates, however, is that they sometimes falsely regard benign communications as malicious, resulting in false positives, due to an inherent aspect of regular expressions. Since the cost of responding to malware infection is quite high, the number of false positives should be kept to a minimum. Therefore, we propose a system to generate templates that cause fewer false positives than a conventional system. We focused on the key idea that malicious infrastructures, such as command and control, tend to be reused instead of created from scratch. The results of implementing our system and validating it using real traffic data indicate that it reduced false positives by up to two-thirds compared to the conventional system and even increased the detection rate of infected hosts.
Extraction of arc length from voltage and current feedback

The method of controlling a welding system comprises determining the arc length based, at least in part, on the arc voltage, a controlled welding current and changes as controlling the welding current supplied to the electrode at a current ramp rate. Arc length and a distance, and the arc voltage between the electrode and the workpiece includes a voltage between the electrode and the workpiece.
A review of biological, biomimetic and miniature force sensing for microflight

Insect sensors are fascinating for their simplicity in design, efficiency in function, and robustness to variable environments. Many engineering applications would benefit from these features, therefore, much research, especially recently in biorobotics, has focused on biomimetic sensors. However, human engineering ingenuity still falls short in comparison to nature's capabilities. This research area is inherently multidisciplinary in nature, calling for collaboration from biologists, neurologists and engineers. This paper is intended to provide an insight into force sensor design for microflight based on current biological, biomimetic and miniature sensors available to compare existing attempts and to make a framework to identify issues that need further attention. This review covers recent experiments concerning the sensor design, as well as different key works in this area. The limitations and scaling issues with some of these methods are explored.
Learning Complex Swarm Behaviors by Exploiting Local Communication Protocols with Deep Reinforcement Learning

Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. Although there have been recent advances of deep RL algorithms applied to multi-agent systems, learning communication protocols while simultaneously learning the behavior of the agents is still beyond the reach of deep RL algorithms. However, while it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building, building a communication link, and pushing an intruder. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols.
Deep Learning with Energy-efficient Binary Gradient Cameras

Power consumption is a critical factor for the deployment of embedded computer vision systems. We explore the use of computational cameras that directly output binary gradient images to reduce the portion of the power consumption allocated to image sensing. We survey the accuracy of binary gradient cameras on a number of computer vision tasks using deep learning. These include object recognition, head pose regression, face detection, and gesture recognition. We show that, for certain applications, accuracy can be on par or even better than what can be achieved on traditional images. We are also the first to recover intensity information from binary spatial gradient images--useful for applications with a human observer in the loop, such as surveillance. Our results, which we validate with a prototype binary gradient camera, point to the potential of gradient-based computer vision systems.
Reading a Neural Code

Traditional approaches to neural coding characterize the encoding of known stimuli in average neural responses. Organisms face nearly the opposite task--extracting information about an unknown time-dependent stimulus from short segments of a spike train. Here the neural code was characterized from the point of view of the organism, culminating in algorithms for real-time stimulus estimation based on a single example of the spike train. These methods were applied to an identified movement-sensitive neuron in the fly visual system. Such decoding experiments determined the effective noise level and fault tolerance of neural computation, and the structure of the decoding algorithms suggested a simple model for real-time analog signal processing with spiking neurons.
Simple voice security protocol

The public telephone network has been evolving from manually switching wires carrying analog encoded voice to an automatically switched grid of copper-wired, fiber optical and wireless mobile connectivity carrying digitally encoded voice, image and data. The evolution of the information technology yields to a converged data and voice network based on IP technology. VoIP emerged and it is taking a large part of the telephony market nowadays. However, the IP network presents security threats to both data and voice packets. To ensure a secure voice network, the security services and mechanisms are defined and the security protocols are created. But, the existing security solutions are related to each network infrastructure. Also, the existing public telephone network does not provide such a service. This paper describes the Simple Voice Security Protocol that provides a secure end-to-end voice communication with an abstraction of the underlying infrastructure.
Effect of Hypothermia on Amplitude-Integrated Electroencephalogram in Infants With Asphyxia

OBJECTIVES: Amplitude-integrated electroencephalogram (aEEG) at <6 hours is the best single outcome predictor in term infants with perinatal asphyxia at normothermia. Hypothermia has been used to treat those infants and proved to improve their outcome. The objectives of this study were to compare the predictive value of aEEG at <6 hours on outcomes in normothermia- and hypothermia-treated infants and to investigate the best outcome predictor (time to normal trace or sleep–wake cycling [SWC]) in normothermia- and hypothermia-treated infants. METHODS: Seventy-four infants were recruited by using the CoolCap entry criteria, and their outcomes were assessed by using the Bayley Scales of Infant Development II at 18 months. The aEEG was recorded for 72 hours. Patterns and voltages of aEEG backgrounds were assessed. RESULTS: The positive predictive value of an abnormal aEEG pattern at the age of 3 to 6 hours was 84% for normothermia and 59% for hypothermia. Moderate abnormal voltage background at 3 to 6 hours of age did not predict outcome. The recovery time to normal background pattern was the best predictor of poor outcome (96.2% in hypothermia, 90.9% in normothermia). Never developing SWC always predicted poor outcome. Time to SWC was a better outcome predictor for infants who were treated with hypothermia (88.5%) than with normothermia (63.6%). CONCLUSIONS: Early aEEG patterns can be used to predict outcome for infants treated with normothermia but not hypothermia. Infants with good outcome had normalized background pattern by 24 hours when treated with normothermia and by 48 hours when treated with hypothermia.
Level of interest sensing in spoken dialog using multi-level fusion of acoustic and lexical evidence

Accurately sensing a user’s interest in spoken dialog plays a signiﬁcant role in many applications, such as tutoring systems and customer service systems. In addition to the widely used acoustic evidence, we introduce different lexical features for interest level prediction and evaluate the impact of automatic speech recognition (ASR) on the effectiveness of lexical information. In order to capture contextual information, we combine the system’s hypothesis for the previous turn with the current one. Our ﬁnal system uses a multi-level fusion method for this task. Each fusion step uses different information such as acoustic and lexical cues, contextual information, or hypotheses from different classiﬁers. Our experiments show that various combinations improve system performance. In particular, we found that even though the word error rate is quite high, there is still performance gain by incorporating lexical information obtained from ASR output.
Crafting a public for geoengineering

In a short period of time, climate ‘geoengineering’ has been added to the list of technoscientific issues subject to deliberative public engagement. Here, we analyse this rapid trajectory of publicization and explore the particular manner in which the possibility of intentionally altering the Earth’s climate system to curb global warming has been incorporated into the field of ‘public engagement with science’. We describe the initial framing of geoengineering as a singular object of debate and subsequent attempts to ‘unframe’ the issue by placing it within broader discursive fields. The tension implicit in these processes of structured debate – how to turn geoengineering into a workable object of deliberation without implying a commitment to its reality as a policy option – raises significant questions about the role of ‘public engagement with science’ scholars and methods in facilitating public debate on speculative technological futures.
The Quarterly Journal of Experimental Psychology Self-directed Speech Affects Visual Search Performance

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Information Systems Management

With the globalization of trade and economics, large multinational enterprises are turning to enterprise resource planning (ERP) systems to meet their international information management needs. However, misalignments between the firm’s ERP system and its international strategy can often result in unsuccessful ERP implementations and sub-optimal business performance. Building on prior research, this article presents a systematic logic that can be used by business executives to address the alignment challenges. In particular, it provides details on three ERP systems issues that should be addressed: (1) systems configuration, (2) information architecture, and (3) systems rollout to reap the benefits of ERP system alignment.
Synthesis for Full Testability of Partitioned Combinational Circuits Using Boolean Diierential Calculus

| Synthesis for testability has been taken as an important topic of research and application. Tools for synthesis of fully testable circuits have been developed. These design tools are faced with an ever increasing complexity. Methods to partition large logics into parts and synthesis of each sublogic independently have been provided from different points of view recently. However, a large circuit may be no longer testable even if all sublogics are designed for full testability. To make large circuits fully testable, we propose a method of synthesis for full testability of two-level-partitioned circuits , in which full testability is obtained by changing the behavior descriptions of each module using some information about the other module without changing the global behavior. We also prove the termination of our algorithm. Some experimental results have been included in this paper.
Shape detector for generic ball detection

This paper describes a novel approach for generating object proposals for ball detection. Our method, called shape detector, captures the possible contours of balls and then transfers them into proposal bounding boxes which may contain the target object. These proposal bounding boxes can be further used in class-specific object detection task. Our experiment results on part of ILSVRC dataset show that shape detector can achieve 71.13% recall and a mean average best overlap of 0.648 using less than 300 proposals. It also shows strong performance in object detection, in which we get a mean average precision of 34.33%.
Spatial Updating in the Lateral Intraparietal Cortex

Recent experiments in neurophysiology have begun to examine the active nature of our perceptual experience. One area of research focuses on the impact of eye movements on visual perception. With each eye movement, a new image is presented to the brain, yet our perception is that the world remains stable. This phenomenon, termed spatial constancy, depends on a convergence of information about our eye movements with sensory information from the visual system. Neurons in the lateral intraparietal cortex (LIP) contribute to the construction of an internal representation of space that is updated or "remapped" with each eye movement. Although the basic phenomenon of remapping has been described, many questions remain unanswered. Here we describe two experiments designed to gain a greater understanding of spatial updating in the primate brain. First, we hypothesized that spatial updating would be equally robust throughout the visual field. We tested this by monitoring the activity of neurons in LIP while varying the direction over which a stimulus trace must be updated. We found that individual neurons remap stimulus traces in multiple directions, though the strength of the remapped response is variable. Across the population of LIP neurons, remapping is effectively independent of saccade direction. These findings indicate that the activity of LIP neurons can contribute to the maintenance of spatial constancy throughout the visual field. Second, to begin to understand the circuitry underlying remapping, we studied a special case: when a stimulus must be updated from one visual hemifield to the other. We hypothesized that the forebrain commissures provide the primary route for this across-hemifield remapping. We tested this by comparing the signal related to within- and across-hemifield remapping. We predicted that in split-brain monkeys, across-hemifield remapping would be abolished while within-hemifield remapping would remain robust. Surprisingly, we found that in split-brain monkeys, LIP neurons can remap stimulus traces across hemifields, though this signal is weaker than that associated with within-hemifield remapping. This finding implies that while the forebrain commissures are likely to be the primary route for the interhemispheric transfer of visual information, they are not the only route available. This indicates that a distributed network of brain regions supports spatial updating.
Effect of distance and delay in access to care on outcome of snakebite in rural north-eastern Nigeria.

INTRODUCTION Snakebite envenoming is a major cause of morbidity and mortality in rural areas of the tropics. Timely administration of effective antivenom remains the mainstay of management. METHODS The study was a quantitative descriptive study aimed at exploring the causes and effects of delay, distance and time taken to access care on snakebite outcomes in Nigeria. All prospective snakebite victims reporting to Kaltungo General Hospital were enrolled. Data on demography, date and time bitten, date and time admitted, site of bite, circumstances of snakebite, responsible snake, clinical features, 20-minute whole-blood clotting test, antivenom administered and outcome were recorded. Delay arising from use of traditional first aid (TFA), time elapsed from snakebite to presentation and the shortest distance from bite location to the hospital was calculated or obtained using a global positioning system. RESULTS The association between delay before hospital presentation and poor outcome was not statistically significant, even though there was a 2% higher likelihood of poor outcome among those with a 1-hour delay compared to those without delay (odds ratio 1.02, 95% confidence interval 1.00-1.03). There was no difference in distance from bite location to hospital between those with a poor outcome (74) compared to those with a good outcome (325). Those with a poor outcome had more severe envenomation requiring more antivenoms and longer hospital stays. Given poor access to antivenom therapy at distant locations ≥100 km, victims were more likely to use TFA such as black 'snake' stone, with consequent prolonged delays. Antivenoms should be more readily available at distant places. CONCLUSIONS Community education on avoiding potentially harmful TFA and prompt access to care is recommended. There is a need to provide snakebite care to multiple peripheral, relatively more rural inaccessible areas.
Three Essays on Social Learning

Social learning broadly refers to learning through the acquisition of information from social sources. In the three essays of my dissertation, I investigate the various underlying drivers of social learning and how such learning can impact purchase decisions. In Essay 1, I investigate the link between social learning and sales of experiential products. In particular, I focus on how social capital (i.e., the propensity for people to trust and communicate with each other) moderates the level of social learning for experiential products and thus impacts aggregate sales. In Essay 2, I study how social learning operates differently across the various stages of physician prescription trial and repeat of a new prescription drug. Given that the mechanisms of social influence varies across trial and repeat stages, the second essay further assesses who is most influential and who is most influenceable across stages. In Essay 3, I examine how consumers make purchases of experiential products and link it to their active search for information from interdependent social sources. Essay 3 assesses the impact of the pattern of similarity of preferences in individual-level social networks (homophily, i.e., the tendency of individuals to associate with similar others, and structural balance, i.e., the congruency of preference in a social network) on consumer search, learning, and purchase. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Marketing First Advisor Raghuram Iyengar
Ecosystems perspective on financial networks: Diagnostic tools

The world economy consists of highly interconnected and interdependent commercial and financial networks. Here, we develop temporal and structural network tools to analyze the state of the economy and the financial markets. Our analysis indicates that a strong clustering can be a warning sign. Reduction in diversity, which was an essential aspect of the dynamics surrounding the financial markets crisis of 2008, is seen as a key emergent feature arising naturally from the evolutionary and adaptive dynamics inherent to the financial markets. Similarly, collusion amongst construction firms in a number of regions in Japan in the 2000s can be identified with the formation of clusters of anomalous highly connected companies. © 2013 Wiley Periodicals, Inc. Complexity 19: 22–36, 2013