Miracle-dz/UniFAR-FTUs huggingface dataset:
No-Limit Texas Hold'em Poker agents created with evolutionary neural networks

In order for computer Poker agents to play the game well, they must analyse their current quality despite imperfect information, predict the likelihood of future game states dependent upon random outcomes, model opponents who are deliberately trying to mislead them, and manage finances to improve their current condition. This leads to a game space that is large compared to other classic games such as Chess and Backgammon. Evolutionary methods have been shown to find relatively good results in large state spaces, and neural networks have been shown to be able to find solutions to non-linear search problems such as Poker. In this paper, we develop No-Limit Texas Hold'em Poker agents using a hybrid method known as evolving neural networks. We also investigate the appropriateness of evolving these agents using evolutionary heuristics such as co-evolution and halls of fame. Our agents were experimentally evaluated against several benchmark agents as well as agents previously developed in other work. Experimental results show the overall best performance was obtained by an agent evolved from a single population (i.e., no co-evolution) using a large hall of fame. These results demonstrate an effective use of evolving neural networks to create competitive No-Limit Texas Hold'em Poker agents.
NUMERICAL SUPPRESSION OF LINEAR EFFECTS IN AN OPTICAL CDMA TRANSMISSION

Fiber characteristics play a critical role in the propagation of short optical pulses. To implement a temporal Optical Code-Division-Multiple-Access (OCDMA) solution, it is necessary to take into account the linear and nonlinear effects resulting from the propagation inside the fiber. In this paper, we study the importance of these phenomena along with the performance of the network. This study is intended to improve the OCDMA technique by numerically suppressing the linear effects in a Single-Mode Fiber (SMF). For this goal, Fractional Step Method (FSM) is adapted to predict the deformation of Gaussian signals introduced by the support and to reconstitute them after propagation inside the SMF. To evaluate our study, these phenomena are analyzed starting from the case of a single Gaussian pulse, going through the case of perfectly-synchronized Gaussian signals for OCDMA transmission and ending with the case of asynchronous Gaussian signals.
Predictive Value of Sympathetic Skin Response in Diagnosing Complex Regional Pain Syndrome: A Case-Control Study

Objective To investigate the predictive value of the sympathetic skin response (SSR) in diagnosing complex regional pain syndrome (CRPS) by comparing three diagnostic modalities-SSR, three-phasic bone scans (TPBS), and thermography. Methods Thirteen patients with severe limb pain were recruited. Among them, 6 were diagnosed with CRPS according to the proposed revised CRPS clinical diagnostic criteria described by the International Association for the Study of Pain. SSR was measured in either the hands or feet bilaterally and was considered abnormal when the latency was prolonged. A positive TPBS finding was defined as diffuse increased tracer uptake on the delayed image. Thermographic findings were considered positive if a temperature asymmetry greater than 1.00℃ was detected between the extremities. Results Five of 6 CRPS patients showed prolonged latency on SSR (83% sensitivity). TPBS was positive in the 5 CRPS patients who underwent TPBS (100% sensitivity). Thermography was positive in 4 of 5 CRPS patients who underwent the procedure (80% sensitivity). The remaining 7 non-CRPS patients differed on examination. SSR latencies within normal limit were noted in 4 of 7 non-CRPS patients (57% specificity). Results were negative in 4 of 5 non-CRPS patients who underwent TPBS (80% specificity), and negative in 3 of 5 non-CRPS patients who underwent thermography (60% specificity). Conclusion SSR may be helpful in detecting CRPS.
MOSGRAD-a tool for simulating the effects of systematic and random channel parameter variations

A CAD tool, MOSGRAD, that can be used to simulate the effects of distributed two-dimensional systematic and random variations in device parameters on the performance of matching-critical circuits has been developed. In addition to applications for layouts with conventional rectangular transistors, this tool can predict the performance of nonconventional circuit structures in which multiple drain and/or source regions share a common channel region as well as predict the performance of nonconventional layouts that may incorporate nonrectangular transistors or segmented transistors.
Genome Sequence of Citrobacter sp. CtB7.12, Isolated from the Gut of the Desert Subterranean Termite Heterotermes aureus

ABSTRACT The draft genome of Citrobacter sp. CtB7.12, isolated from termite gut, is presented here. This organism has been reported as a cellulolytic bacterium, which is biotechnologically important because it can be used as a gene donor for the ethanol and biofuel industries.
Impact of Neu-botulinumtoxinA on the Severity and Quality of Life of Cervical Dystonia Patients

Background Cervical dystonia (CD) is a debilitating neurological disorder that may gravely affect a patient’s quality of life (QoL). Botulinum toxin treatment has been approved as a first-line treatment for this condition. This study aims to look at the efficacy and impact on the QoL of neu-botulinumtoxinA, a newer and cheaper botulinum toxin type A, in patients with CD. Methods This is a prospective, open-label, single-arm study. CD patients were recruited and evaluated for severity of CD using the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), and for QoL using the Craniocervical Dystonia Questionnaire (CDQ-24), and the 36-item Short Form Health Survey questionnaire (SF-36) at baseline and 6 weeks after injection. Results Twenty patients were recruited. Significant improvement was shown in part 1 and total TWSTRS score and total CDQ-24 scores. Analysis of individual items of the TWSTRS scale showed significant improvement in rotation, duration of CD, and work ability. Significant improvements in the QoL were also seen in some items of the stigma, emotional wellbeing, and energy/fatigue domains of the CDQ-24 and SF-36 questionnaires. Discussion Neu-botulinumtoxinA is efficacious in treating CD symptoms and improving QoL of patients with CD. A larger, double-blinded study is needed to study the extent of improvements.
Input-Error-Limiting Automata

Some properties of automata are investigated, which are capable of limiting the e f fec t of input errors on their behavior. First necessary and sufficient conditions arc de r ived for an automaton to be capable of always being resynchronized wi th in a bo~mded xmmber of input letters after an error has occurred, and then the results are specialized to finit e s t a t e completely specified automata. Automata are investigated, which are capable of be ing resynchronized with probability one and it is shown that a finite-state completely specified automaton possesses this property if and only if there exists a finite sequence which is a un ive r sa l synchronizer for the automaton. Some connections with similar problems for var i~b leAength codes are indicated.
RECONFIGURABLE ANTENNA ARRAY ARCHITECTURE FOR OFDM RECEIVERS

In this paper, we deal with the problem of the multi-antenna array receiver optimization for OFDM-based wireless LANs. In fact, it has been stated that the best algorithm and even its key parameters depend deeply on the working environment. For these choices, two solutions are here proposed: the classical bayesian channel classification and a fuzzy inference-based selection for a single antenna. After that, we exploit the receiver spatial diversity by using a Fuzzy Logic System (FLS). Conducted simulations show that the performance of the fuzzy inference-based classifiers can reach the optimum bayesian results at good SINR, while the proposed FLS exploits successfully the array at reception when the errors of the single antenna classifier are over a certain threshold.
New active contours approach and phase wavelet maxima to improve iris recognition system

Due to the randomness of iris patterns, iris recognition systems are the most accurate, reliable and efficient way to recognize and identify people. However, the complex structure of an iris image results in the difficulty of iris representation especially for iris images of insufficient quality. In this paper we propose a new active contours models applied to segment iris images based on active contours without edges to localize the pupil and GVF snake combined with Wavelet maxima multiscale edge detection to localize the outer iris boundary, after normalization step using Daugman method (homogeneous rubber sheet model) a new technique for feature extraction based on phase wavelet maxima is used to extract iris features, evaluation tests with Casia v3 database confirmed the efficiency of the proposed methods for iris segmentation and feature extraction, The experimental results have shown that the proposed system could be used for personal identification in an efficient and effective manner (accuracy over 99%) and comparable to the best existing iris recognition systems.
Virtual-channel flow control

Network throughput can be increased by dividing the buffer storage associated with each network channel into several virtual channels. Each physical channel is associated with several small queues, virtual channels, rather than a single deep queue. The virtual channels associated with one physical channel are allocated independently but compete with each other for physical bandwidth. Virtual channels decouple buffer resources from transmission resources. This decoupling allows active messages to pass blocked messages using network bandwidth that would otherwise be left idle. Simulation studies show that, given a fixed amount of buffer storage per link, virtual-channel flow control increases throughput by a factor of 3.5, approaching the capacity of the network. <<ETX>>
Automatic Tamil Content Generation

Automatic content generation aims on developing an intelligent tutoring system in tamil language. This system focuses on delivering personalized content in Tamil language to an individual user needs based on their learning abilities and interests. This paper deals with automatic classification of Tamil documents and also the information extraction from those documents to construct the knowledge base. Documents are repositories of knowledge. There are numerous documents available and effective search in documents is time consuming. To make document search a simpler task, we need to perform document categorization. Document category can be found out using various techniques. In this paper, Naive Bayes (NB) algorithm is used to classify Tamil documents to one of pre-defined categories. Experiments are used to evaluate the Naive Bayes categorizer. The experimental results show that the Naive Bayes classifier performs well and its effectiveness is achieved with 89.8% accuracy. Informational words or sentences of the documents are then extracted using heuristic rules to fill up the predefined templates. An individual user's interests are identified and recorded to create a user profile. A user profile is specific to a user and is subjected to change over time. The Topic categorizer is used to categorize the topic based on user's query. The topic analyzer is used to analyze the user's profile and evaluate the user's knowledge using intelligent evaluator system. Based on the user's knowledge, intelligent evaluator system makes a decision to suggest the Location or to suggest a new topic retrieved from the knowledge base. Then the personalized content will be generated based on the knowledge level of the user. The experimental results show that the Content Generator performs well and its effectiveness is achieved with 82.35% accuracy.
AST Based JAVA Software Evolution Analysis

In the process of software evolution, software change information can help software developers and system analysts to get a better understanding of differential part of software. In this paper, we construct different versions of source code into abstract syntax trees, and use an improved tree matching algorithm which combines multiple string matching algorithms, bilateral best match strategy and predicate information of program to improve the match result. Also a detailed change type list is defined and all the tree editing operations will be mapped to refined program change classification. We evaluated our tree match algorithm on a medium-size project of different versions. Ten typical classes were picked as a benchmark and 456 changes were specified manually. Our method show a good performance on this benchmark with the average change number bias controlled in 1.75, and the average change number error percentage is controlled fewer than 5.38%.
The Analogical Thesaurus An Emerging Application at the Juncture of Information Retrieval and Analogical Reasoning

Innovative applications often occur at the juncture of radically different domains of research. This paper describes an emerging application , called the analogical thesaurus, that arises at the boundary of two very different domains, the highly applied domain of information retrieval, and the esoteric domain of lexical metaphor interpretation. This application has the potential to not just to improve the utility of conventional electronic thesauri, but to serve as an intelligent mapping component in any system that uses analogical reasoning or case-based reasoning.
Junction-Explorer Help File

Overview RNA junctions are important structural elements of three or more helices in the organization of the global structure of RNA molecules [3, 4, 6]. A common motif among junctions is the coaxial stacking of helices [2, 3, 4, 9]. This motif occurs when two separate helical elements stack to form coaxial helices as a pseudo-continuous helix. In addition, analysis from solved crystal structures indicates that RNA junctions can be classified into families according to their 3D shape or topology [4, 6]. The information obtained from coaxial stacking and topology (family) prediction can help predict RNA three-dimensional structures and gain a better understanding of RNA tertiary interactions.
Hamming network circuits based on CMOS/memristor hybrid design

Memristor, as an innovative technology, has been proposed in the application of neural networks since its physical implementation was reported by HP lab. In this paper, we proposed a Hamming network circuits based on CMOS/memristor hybrid design which is compact in device size and circuit structure. Through HSPICE simulation, pattern recognition and classification functions of hamming network circuits are demonstrated using a 16× 16 nanocrossbar memory.
New broadcast encryption scheme using tree-based circle

Since broadcast encryption was first introduced in 1993 by Fiat and Naor, many broadcast encryption schemes have been developed. Among these, schemes based on tree structure and linear structure are notable. The subset difference (SD) scheme and layered subset difference (LSD) scheme based on tree structure have small user-key size and small transmission overhead when the number r of revoked users is very small. The punctured interval (PI) scheme based on linear (or circular) structure has better transmission overhead when r is not too small.In this paper, we propose a new broadcast encryption scheme, called the tree-based circle (TC) scheme, combining tree structure and circular structure. In this scheme, the transmission overhead is proportional to r like in the SD scheme for small r and becomes asymptotically same as that of the PI scheme when r grows, keeping the computation cost and the storage size small. The TC scheme also inherits the flexibility of the PI scheme. We further improve the transmission overhead of the TC scheme, when r is very small, by adopting the notion of cascade arc.
Information-theoretic software clustering

The majority of the algorithms in the software clustering literature utilize structural information to decompose large software systems. Approaches using other attributes, such as file names or ownership information, have also demonstrated merit. At the same time, existing algorithms commonly deem all attributes of the software artifacts being clustered as equally important, a rather simplistic assumption. Moreover, no method that can assess the usefulness of a particular attribute for clustering purposes has been presented in the literature. In this paper, we present an approach that applies information theoretic techniques in the context of software clustering. Our approach allows for weighting schemes that reflect the importance of various attributes to be applied. We introduce LIMBO, a scalable hierarchical clustering algorithm based on the minimization of information loss when clustering a software system. We also present a method that can assess the usefulness of any nonstructural attribute in a software clustering context. We applied LIMBO to three large software systems in a number of experiments. The results indicate that this approach produces clusterings that come close to decompositions prepared by system experts. Experimental results were also used to validate our usefulness assessment method. Finally, we experimented with well-established weighting schemes from information retrieval, Web search, and data clustering. We report results as to which weighting schemes show merit in the decomposition of software systems.
Pathway Correlation Profile of Gene-Gene Co-Expression for Identifying Pathway Perturbation

Identifying perturbed or dysregulated pathways is critical to understanding the biological processes that change within an experiment. Previous methods identified important pathways that are significantly enriched among differentially expressed genes; however, these methods cannot account for small, coordinated changes in gene expression that amass across a whole pathway. In order to overcome this limitation, we use microarray gene expression data to identify pathway perturbation based on pathway correlation profiles. By identifying the distribution of gene-gene pair correlations within a pathway, we can rank the pathways based on the level of perturbation and dysregulation. We have shown this successfully for differences between two experimental conditions in Escherichia coli and changes within time series data in Saccharomyces cerevisiae, as well as two estrogen receptor response classes of breast cancer. Overall, our method made significant predictions as to the pathway perturbations that are involved in the experimental conditions.
A CAN-based protocol for reliability testing of embedded software

Master/slave method is often adopted as a common approach in reliability testing of embedded software, which needs real-time and reliable communication between master and slave. To meet that request, this paper designed a reliable, real-time and flexible CAN-based communication protocol. In order to facilitate the test case designing with this protocol, a formula for calculating the bus load is also provided.
Characterization of Non-Specific Cytotoxic Cell Receptor Protein 1: A New Member of the Lectin-Type Subfamily of F-Box Proteins

Our previous microarray study showed that the non-specific cytotoxic cell receptor protein 1 (Nccrp1) transcript is significantly upregulated in the gastric mucosa of carbonic anhydrase IX (CA IX)-deficient (Car9−/−) mice. In this paper, we aimed to characterize human NCCRP1 and to elucidate its relationship to CA IX. Recombinant NCCRP1 protein was expressed in Escherichia coli, and a novel polyclonal antiserum was raised against the purified full-length protein. Immunocytochemistry showed that NCCRP1 is expressed intracellularly, even though it has previously been described as a transmembrane protein. Using bioinformatic analyses, we identified orthologs of NCCRP1 in 35 vertebrate genomes, and up to five paralogs per genome. These paralogs are FBXO genes whose protein products are components of the E3 ubiquitin ligase complexes. NCCRP1 proteins have no signal peptides or transmembrane domains. NCCRP1 has mainly been studied in fish and was thought to be responsible for the cytolytic function of nonspecific cytotoxic cells (NCCs). Our analyses showed that in humans, NCCRP1 mRNA is expressed in tissues containing squamous epithelium, whereas it shows a more ubiquitous tissue expression pattern in mice. Neither human nor mouse NCCRP1 expression is specific to immune tissues. Silencing CA9 using siRNAs did not affect NCCRP1 levels, indicating that its expression is not directly regulated by CA9. Interestingly, silencing NCCRP1 caused a statistically significant decrease in the growth of HeLa cells. These studies provide ample evidence that the current name, “non-specific cytotoxic cell receptor protein 1,” is not appropriate. We therefore propose that the gene name be changed to FBXO50.
Applying QR code in augmented reality applications

In this paper we present an augmented reality (AR) application based on the QR Code. The system can extract the information embedded in a QR Code and show the information in an extended 3D form with the QR Code being the traditional AR marker. Traditional AR systems often use a particularly designed pattern (the marker) to recover the 3D scene structure and identify the object to be displayed on the scene. In these systems, the marker is used only for tracking and identification. They do not convey any other information. Consequently, the applications of these systems are limited and often a registration procedure is required for a new marker. QR Code has the advantage of large information capacity and is similar to an AR marker in appearance. Thus, more interesting and useful applications can be developed by combining the QR Code with the traditional AR system. In this paper, we combine these two techniques to develop a product demo system. A QR Code is pasted on the package of a product and then a 3D virtual object is displayed on the QR Code. This system allows the customer to visualize the product via a more direct and interactive way. Our system demonstrates the success of using QR Code as the AR marker to a particular application and we believe it can bring more convenient to our life in the future.
Estimating user's engagement from eye-gaze behaviors in human-agent conversations

In face-to-face conversations, speakers are continuously checking whether the listener is engaged in the conversation and change the conversational strategy if the listener is not fully engaged in the conversation. With the goal of building a conversational agent that can adaptively control conversations with the user, this study analyzes the user's gaze behaviors and proposes a method for estimating whether the user is engaged in the conversation based on gaze transition 3-gram patterns. First, we conduct a Wizard-of-Oz experiment to collect the user's gaze behaviors. Based on the analysis of the gaze data, we propose an engagement estimation method that detects the user's disengagement gaze patterns. The algorithm is implemented as a real-time engagement-judgment mechanism and is incorporated into a multimodal dialogue manager in a conversational agent. The agent estimates the user's conversational engagement and generates probing questions when the user is distracted from the conversation. Finally, we conduct an evaluation experiment using the proposed engagement-sensitive agent and demonstrate that the engagement estimation function improves the user's impression of the agent and the interaction with the agent. In addition, probing performed with proper timing was also found to have a positive effect on user's verbal/nonverbal behaviors in communication with the conversational agent.