Miracle-dz/UniFAR-FTUs huggingface dataset:
Phonological Parsing for Bi-directional Letter-to-Sound/Sound-to-Letter Generation

In this paper, we describe a reversible letter-to-sound/sound-to-letter generation system based on an approach which combines a rule-based formalism with data-driven techniques. We adopt a probabilistic parsing strategy to provide a hierarchical lexical analysis of a word, including information such as morphology, stress, syllabification, phonemics and graphemics. Long-distance constraints are propagated by enforcing local constraints throughout the hierarchy. Our training and testing corpora are derived from the high-frequency portion of the Brown Corpus (10,000 words), augmented with markers indicating stress and word morphology. We evaluated our performance based on an unseen test set. The percentage of nonparsable words for letter-to-sound and sound-to-letter generation were 6% and 5% respectively. Of the remaining words our system achieved a word accuracy of 71.8% and a phoneme accuracy of 92.5% for letter-to-sound generation, and a word accuracy of 55.8% and letter accuracy of 89.4% for sound-to-letter generation. We also compared our hierarchical approach with an alternative, single-layer approach to demonstrate how the hierarchy provides a parsimonious description for English orthographic-phonological regularities, while simultaneously attaining competitive generation accuracy.
Robustness-guided temporal logic testing and verification for Stochastic Cyber-Physical Systems

We present a framework for automatic specification-guided testing for Stochastic Cyber-Physical Systems (SCPS). The framework utilizes the theory of robustness of Metric Temporal Logic (MTL) specifications to quantify how robustly an SCPS satisfies a specification in MTL. The goal of the testing framework is to detect system operating conditions that cause the system to exhibit the worst expected specification robustness. The resulting expected robustness minimization problem is solved using Markov chain Monte Carlo algorithms. This also allows us to use finite-time guarantees, which quantify the quality of the solution after a finite number of simulations. In a Model-Based Design (MBD) process, our framework can be combined with Statistical Model Checking (SMC). Finally, we present a case study on a high fidelity engine model where the goal is to verify the air-to-fuel ratio problem.
Medical equipment library design: revealing issues and best practice using DiCoT

Medical Equipment Libraries (MELs) are a relatively new function for hospitals in the UK, which aim to save money and make medical practice safer. They centralize the management, maintenance and purchasing of medical equipment. They are being embraced and developed by some hospitals, and considered by others. Hence, there is a growing need to understand MEL practice and design. This paper compares three MELs through interviews and observations of everyday practice using DiCoT (Distributed Cognition for Teamwork) as a method for multisite comparison. This is a novel use of the method that reveals general issues and best practices across contexts. Our results complement the little formal information that is available on MELs, and explores the workings of the library as a socio-technical system. As far as we are aware no empirical studies have been published in this area. We conclude with design opportunities and requirements for MELs, and propose DiCoT as an effective way to compare socio-technical systems, including revealing issues and best practices in other clinical contexts more broadly.
Automated design of algorithms and genetic improvement: contrast and commonalities

Automated Design of Algorithms (ADA) and Genetic Improvement (GI) are two relatively young fields of research that have been receiving more attention in recent years. Both methodologies can improve programs using evolutionary search methods and successfully produce human competitive programs. ADA and GI are used for improving functional properties such as quality of solution and non-functional properties, e.g. speed, memory and, energy consumption. Only GI of the two has been used to fix bugs, probably because it is applied globally on the whole source code while ADA typically replaces a function or a method locally. While GI is applied directly to the source code ADA works ex-situ, i.e. as a separate process from the program it is improving. Although the methodologies overlap in many ways they differ on some fundamentals and for further progress to be made researchers from both disciplines should be aware of each other's work.
Drowsiness Recognition Using the Least Correlated LBPH

In recent years, the drowsiness recognition is widely applied to the driver alerting or distance learning. The drowsiness recognition system is constructed on the basis of the recognition of eye states. The conventional methods for recognizing the eye states are often influenced by the illumination variations or hair/glasses occlusion. In this paper, we propose a new image feature called "least correlated LBP histogram (LC-LBPH)" to generate a high discriminate image features for recognizing the eye states robustly. Then, the method of independent component analysis (ICA) is applied to derive the low-dimensional and statistical independent feature vectors. Finally, support vector machines (SVM) are trained to recognize the eye states. Furthermore, we design four rules to classify three eye transition patterns which define the normal (consciousness), drowsiness, and sleeping situations. Experimental results show that the eye-state recognition rate is about 0.08 seconds per frame and the drowsiness recognition accuracy approaches 98%.
Heat Shock Protein 70 and 90 Genes in the Harmful Dinoflagellate Cochlodinium polykrikoides: Genomic Structures and Transcriptional Responses to Environmental Stresses

The marine dinoflagellate Cochlodinium polykrikoides is responsible for harmful algal blooms in aquatic environments and has spread into the world's oceans. As a microeukaryote, it seems to have distinct genomic characteristics, like gene structure and regulation. In the present study, we characterized heat shock protein (HSP) 70/90 of C. polykrikoides and evaluated their transcriptional responses to environmental stresses. Both HSPs contained the conserved motif patterns, showing the highest homology with those of other dinoflagellates. Genomic analysis showed that the CpHSP70 had no intron but was encoded by tandem arrangement manner with separation of intergenic spacers. However, CpHSP90 had one intron in the coding genomic regions, and no intergenic region was found. Phylogenetic analyses of separate HSPs showed that CpHSP70 was closely related with the dinoflagellate Crypthecodinium cohnii and CpHSP90 with other Gymnodiniales in dinoflagellates. Gene expression analyses showed that both HSP genes were upregulated by the treatments of separate algicides CuSO4 and NaOCl; however, they displayed downregulation pattern with PCB treatment. The transcription of CpHSP90 and CpHSP70 showed similar expression patterns under the same toxicant treatment, suggesting that both genes might have cooperative functions for the toxicant induced gene regulation in the dinoflagellate.
A social agent dynamic honeynet for attack modeling

In our paper we present initial findings related to an effort to develop an agent based dynamic honeynet that simulates user interactions with social networks for the purposes of developing attack models. Our solution allows security professionals to create networks simulating user activity for companies and government entities through the provision of a set of parameters. Our research pointed to the importance of instantiating a social dimension to our virtual agents, providing the agent with the ability to interact with a variety of social networks. For this purpose, we developed influence models to learn patterns from actual users' activity on social networks to improve the effectiveness of the social agents. We discuss our responses to the technical challenges and analytically investigate the initial results of our honeynet's ability to attract adversary activity.
A new selective harmonic elimination pulse-width and amplitude modulation (SHE-PWAM) for drive applications

Compared to the conventional selective harmonic elimination-pulse width modulation (SHE-PWM), the selective harmonic elimination-pulse width and amplitude modulation (SHE-PWAM) control strategy results in significant improvements in the performance of CHB inverters. This fact is due to considering the optimization of the CHB dc sources' values along with the optimized switching angles. This paper proposes a new SHE-PWAM control strategy and its realization in a drive application. Analysis and simulations are carried out on a five-level CHB inverter. Experimental verifications also validate the simulation results. The results demonstrate that the new SHE-PWAM technique improves the performance of the drive compared to the conventional SHE-PWM.
An event-driven system for distributed multimedia applications

We propose an architecture for distributed multimedia applications based on an event-driven programming model. To avoid the synchronization problems inherent to multi-threaded programming, the proposed architecture is based on a single-threaded structure. Instead of multi-threading, we opted for the event-oriented approach allied to multiple communication channels with user-defined handling procedures to allow the application to deal concurrently with control and data streams. We discuss this programming model, present the system we have implemented based on this model, and describe the experience we have had with this system.
A Fast Circuit Topology for Finding the Maximum of N k-bit Numbers

Finding the value and/or address (position) of the maximum element of a set of binary numbers is a fundamental arithmetic operation. Numerous systems, which are used in different application areas, require fast (low-latency) circuits to carry out this operation. We propose a fast circuit topology called Array-Based maximum finder (AB) to determine both value and address of the maximum element within an n-element set of k-bit binary numbers. AB is based on carrying out all of the required comparisons in parallel and then simultaneously computing the address as well as the value of the maximum element. This approach ends up with only one comparator on the critical path, followed by some selection logic. The time complexity of the proposed architecture is O(log2n + log2k) whereas the area complexity is O(n2k). We developed RTL code generators for AB as well as its competitors. These generators are scalable to any value of n and k. We applied a standard-cell based iterative synthesis flow that finds the optimum time constraint through binary search. The synthesis results showed that AB is 1.2-2.1 times (1.6 times on the average) faster than the state-of-the-art.
A Cost-Efficient and Reliable Resource Allocation Model Based on Cellular Automaton Entropy for Clou

Resource allocation optimization is a typical cloud project scheduling problem: a problem that limits a cloud system’s ability to execute and deliver a project as originally planned. The entropy, as a measure of the degree of disorder in a system, is an indicator of a system’s tendency to progress out of order and into a chaotic condition, and it can thus serve to measure a cloud system’s reliability for project scheduling. In this paper, cellular automaton is used for modeling the complex cloud project scheduling system. Additionally, a method is presented to analysis the reliability of cloud scheduling system by measuring the average resource entropy (ARE). Furthermore, a new cost-efficient and reliable resource allocation (CERRA) model is proposed based on cellular automaton entropy to aid decision maker for planning projects on the cloud. At last, the proposed model is designed using Matlab toolbox and simulated with three basic cloud scheduling algorithm, First Come First Served Algorithm (FCFS), Min-Min Algorithm and Max-Min Algorithm. The simulation results show that the proposed model can lead to achieve a cost-efficient and reliable resource allocation strategy for running projects on the cloud environment.
Neural Basis of Individual Differences in Synesthetic Experiences

Little is known about how the properties of our private mental world relate to the physical and functional properties of our brain. Studying synesthesia, where a particular experience evokes a separate additional sensory experience, offers the unique opportunity to study phenomenological experiences as a stable trait in healthy subjects. A common form of synesthesia is grapheme–color synesthesia, where a particular letter or number evokes a particular color experience. We studied the neural basis of qualitative different properties of the synesthetic experience by using individual differences in grapheme–color synesthesia. Specifically, the synesthetic color can be experienced “in the mind” (associator synesthetes) or “in the outside world” (projector synesthetes). Gray matter structure and functioning (imaged using voxel-based morphometry and functional magnetic resonance imaging, respectively) were examined in grapheme–color synesthetes (N = 42, 16 projectors and 26 associators) and nonsynesthetes. Results indicated partly shared mechanisms for all grapheme–color synesthetes, particularly in posterior superior parietal lobe, which is involved in the integration of sensory information. In addition, the nature of synesthetic experience was found to be mediated by distinct neural mechanisms. The outside-world experience is related to brain areas involved in perceiving and acting in the outside world (visual cortex, auditory cortex, motor cortex) as well as frontal brain areas. In contrast, the in-the-mind experience is related to the hippocampus and parahippocampal gyrus, known for their role in memory. Thus, the different subjective experiences are related to distinct neural mechanisms. Moreover, the properties of subjective experiences are in accordance with functional properties of the mediating brain mechanisms.
How Do Low-Income Chilean Parents Support Their Preschoolers’ Writing and Math Skills in a Grocery Game?

ABSTRACT This study examined how parents supported children’s writing and math and whether parents’ support predicted children’s academic skills from the beginning of pre-kindergarten to the end of kindergarten. Two hundred ten Chilean parents from low-income households were observed playing a grocery game with their preschoolers (M = 53 months). The grocery game (a game where dyads make a grocery list together and shop at a pretend store) could be used to foster writing and mathematics. Coders noted how often parents engaged in writing support, provided math support, and displayed directiveness (i.e., taking over the child’s activity). Children’s literacy and math skills were assessed at the beginning of pre-kindergarten and the end of kindergarten. Research Findings. Parents engaged in little writing support but displayed moderate levels of math support and high levels of directiveness in the grocery game. Parents’ math support uniquely predicted gains in children’s math skills, while parents’ directiveness negatively predicted gains in children’s literacy skills. Practice or Policy. Results from this study have the potential to inform parent-focused interventions. Findings highlight the need to provide parents with specific strategies to support their children’s literacy and math development and guidance about embedding these strategies into family routines.
SOAR as a Mediator of the Relationship Between Emotional Intelligence and Collaboration Among Professionals Working in Teams: Implications for Entrepreneurial Teams

A majority of ventures are started by entrepreneurial teams rather than individuals. More than half of team-based new venture failures are believed to be due to problems with the entrepreneurial team. Collaboration among entrepreneurial team members is important for increasing the likelihood of successful venture outcomes. We investigated collaboration in professionals working in teams using survey methodology. Results found a framework for collaborative dialogue, SOAR (strengths, opportunities, aspirations, and results), mediates the relationship between emotional intelligence and team collaboration. We discuss practical recommendations to increase the strength of the relationship between emotional intelligence and collaboration to increase entrepreneurial team performance. For example, behavioral coaches are recommended to use dialogical exercises to help team members develop emotional intelligence competencies of awareness and regulation of emtions in self and others. Entrepreneurs are recommended to create teams that build on member strengths and aspirations, and identify opportunities for achieving measurable positive results.
Functional-repair-by-transfer regenerating codes

In a distributed storage system, a data file is distributed to several storage nodes, such that the original file can be decoded from any subset of the storage nodes of size larger than or equal to a certain threshold. Upon the failure of a storage node, we would like to regenerate it with minimal amount of data transmissions from the surviving nodes to the new node. This performance metric is called the repair-bandwidth. Another performance metric is the disk input/output (I/O) cost, which measures the number of bits a storage node needs to read out from its memory in order to repair the failed node. In this paper, we give examples of linear regenerating codes with minimal disk I/O cost and repair-bandwidth, without any linear mixing in the helping storage nodes.
Efficiently supporting procedures in relational database systems

We examine an extended relational database system which supports database procedures as full fledged objects. In particular, we focus on the problems of query processing and efficient support for database procedures. First, a variation to the original INGRES decomposition algorithm is presented. Then, we examine the idea of storing results of previously processed procedures in secondary storage (caching). Using a cache, the cost of processing a query can be reduced by preventing multiple evaluations of the same procedure. Problems associated with cache organizations, such as replacement policies and validation schemes are examined. Another means for reducing the execution cost of queries is indexing. A new indexing scheme for cached results, Partial Indexing, is proposed and analyzed.
Valuable innovations out of nonsense? Expansive organizational learning and transformative agency in the Mann Gulch disaster and in the Finnish homelessness strategy

The study explores the role nonsense may play when innovations are conceived and how innovations out of nonsense possibly inform processes of organizational learning. It argues that an expansive learning and transformative agency framework offers an alternative to primarily retrospective views of organizational sensemaking. The argument in favour of a forward-oriented and materially grounded dialectical perspective builds on two examples of innovations initially conceived as nonsensical and which nevertheless proved to be meaningful and generative: the Mann Gulch disaster and the Finnish national strategy for the eradication of homelessness.
Tighter Value Function Bounds for Bayesian Reinforcement Learning

Bayesian reinforcement learning (BRL) provides a principled framework for optimal exploration-exploitation tradeoff in reinforcement learning. We focus on model based BRL, which involves a compact formulation of the optimal tradeoff from the Bayesian perspective. However, it still remains a computational challenge to compute the Bayes-optimal policy. In this paper, we propose a novel approach to compute tighter value function bounds of the Bayes-optimal value function, which is crucial for improving the performance of many model-based BRL algorithms. We then present how our bounds can be integrated into real-time AO* heuristic search, and provide a theoretical analysis on the impact of improved bounds on the search efficiency. We also provide empirical results on standard BRL domains that demonstrate the effectiveness of our approach.
ON DETECTION AND VISIBILITY OF A COMPLEX OBSTACLE USING MULTIPOLES SOURCES FROM FAR FIELD DATA

We deal with the acoustic inverse scattering problem for detecting an obstacle with mixed boundary conditions from the far field map. We show how the geometric properties and the material parameter distributed on the surface are involved in the obstacle reconstruction numerically. The main advance of this research on our recently work (SIAM J. Applied Math, 67(4), 2007) is the higher-order asymptotic expansion of the indicator function and the introduction of complex valued surface impedance, which makes the reconstruction more (or less) accurate depending on how we choose this surface impedance. Precisely, using the relation between the surface impedance and the obstacle curvature contained in the higher-order expansion of the indicators, we reveal how the obstacle curvature and the surface impedance on the coated part influence the blowing-up behavior. Using this theoretical result, we can specify the complex surface impedance in terms of the obstacle curvature to make the obstacle more (or less) visible. By establishing the property of the minimum norm solution for approximating the singular sources, efficient realizations for approximating the multipoles by Herglotz wave function and therefore the implementations of the probing methods are developed with an error estimate. Such an estimate gives a thorough explanation on the reasons why these probing methods can reach only finite accuracy of reconstruction. We finally show extensive numerical tests explaining how and to what extent the coupling relation between the curvature and the surface impedance changes the visibility of the obstacles.
Extremity Bias in User-Generated Content Creation and Consumption in Social Media

Abstract The present research finds support for an extremity bias in social media. User-generated content in social media is found to portray more extreme behavior than what is observed in offline and nonmedia contexts. This bias is observed in the context of makeup-related consumer behavior. Study 1 finds that consumers with higher (lower) self-reported extremity in their makeup style are more likely to create (consume) makeup-related content in social media. Likewise, Study 2 finds that makeup styles portrayed in social media are perceived as more extreme than those portrayed offline. Study 3 finds that the extremity bias, observed in the previous studies, can shift other perception; brief exposure to makeup styles on Instagram was found to lessen the perceived extremity of offline makeup styles. The results suggest that, like traditional media, content in social media may be biased toward extremity. This bias may be the result of motivational differences between creators versus consumers of social media content and may result in exposure to social media shifting social norms to more extreme levels.
An efficient and reconfigurable architecture for RC5

RC5 is a widely used symmetric block cipher which has a variable word size, number of rounds, and length of secret key. In this paper, we propose an efficient and reconfigurable hardware architecture for the RC5 block cipher implementation. The design can be reconfigured according to the different application requirements with variable parameters. It is simulated in Verilog HDL and implemented on Xilinx FPGA. Comparison shows that it has high throughput and low hardware complexity