Miracle-dz/UniFAR-FTUs huggingface dataset:
DEAL: dialogue management in SCXML for believable game characters

In order for game characters to be believable, they must appear to possess qualities such as emotions, the ability to learn and adapt as well as being able to communicate in natural language. With this paper we aim to contribute to the development of believable non-player characters (NPCs) in games, by presenting a method for managing NPC dialogues. We have selected the trade scenario as an example setting since it offers a well-known and limited domain common in games that support ownership, such as role-playing games. We have developed a dialogue manager in State Chart XML, a newly introduced W3C standard, as part of DEAL --- a research platform for exploring the challenges and potential benefits of combining elements from computer games, dialogue systems and language learning.
Propagation of back‐arc extension into the arc lithosphere in the southern New Hebrides volcanic arc

New geophysical data acquired during three expeditions of the R/V Southern Surveyor in the southern part of the North Fiji Basin allow us to characterize the deformation of the upper plate at the southern termination of the New Hebrides subduction zone, where it bends eastward along the Hunter Ridge. Unlike the northern end of the Tonga subduction zone, on the other side of the North Fiji Basin, the 90° bend does not correspond to the transition from a subduction zone to a transform fault, but it is due to the progressive retreat of the New Hebrides trench. The subduction trench retreat is accommodated in the upper plate by the migration toward the southwest of the New Hebrides arc and toward the south of the Hunter Ridge, so that the direction of convergence remains everywhere orthogonal to the trench. In the back‐arc domain, the active deformation is characterized by propagation of the back‐arc spreading ridge into the Hunter volcanic arc. The N‐S spreading axis propagates southward and penetrates in the arc, where it connects to a sinistral strike‐slip zone via an oblique rift. The collision of the Loyalty Ridge with the New Hebrides arc, less than two million years ago, likely initiated this deformation pattern and the fragmentation of the upper plate. In this particular geodynamic setting, with an oceanic lithosphere subducting beneath a highly sheared volcanic arc, a wide range of primitive subduction‐related magmas has been produced including adakites, island arc tholeiites, back‐arc basin basalts, and medium‐K subduction‐related lavas.
Movement-Based Location Management for General Cell Residence Times in Wireless Networks

Location management of mobile users is of primary importance in personal communication networks (PCNs), and it is mainly aimed to facilitate the delivery of incoming calls to destination users and to make a more efficient use of network resources. In practical PCNs, the location management algorithms need to keep track of mobile users in a dynamic manner, and it is a design concern to minimize the generated signaling traffic. In this paper, we present the cost analysis for the dynamic movement-based location update scheme. Our model is based on renewal theory arguments, and it is general enough to include a variety of probability distributions for modeling cell residence times (CRTs) in hyperexponentially distributed location area residence times and exponentially distributed intercall times. We compare the location update cost for the dynamic and static schemes. We present numerical results regarding the exponential and circular CRT distributions and discuss the optimal threshold distance for location update that minimizes the cost of the dynamic movement-based scheme.
Density clustering based on border-expanding

DBSCAN is a clustering algorithm based on density. It can divide regions which have a high density for clusters, shield the noise effectively and discover clusters of arbitrary shape and any size from dataset. However, DBSCAN algorithm needs to traverse dataset to find core objects, so it results in large amount of I/O cost when processing large-scale datasets. A fast algorithm (BEDBSCAN) is developed which expands the cluster by employing border objects as seeds. Experimental results show that BEDBSCAN performs obvious efficiency improvement than DBSCAN algorithm especially when processing large datasets.
Probing insulin bioactivity in oral nanoparticles produced by ultrasonication-assisted emulsification/internal gelation

Alginate–dextran sulfate-based particles obtained by emulsification/internal gelation technology can be considered suitable carriers for oral insulin delivery. A rational study focused on the emulsification and particle recovery steps was developed in order to reduce particles to the nanosize range while keeping insulin bioactivity. There was a decrease in size when ultrasonication was used during emulsification, which was more pronounced when a cosurfactant was added. Ultrasonication add-on after particle recovery decreased aggregation and led to a narrower nanoscale particle-size distribution. Insulin encapsulation efficiency was 99.3%±0.5%, attributed to the strong pH-stabilizing electrostatic effect between insulin and nanoparticle matrix polymers. Interactions between these polymers and insulin were predicted using molecular modeling studies through quantum mechanics calculations that allowed for prediction of the interaction model. In vitro release studies indicated well-preserved integrity of nanoparticles in simulated gastric fluid. Circular dichroism spectroscopy proved conformational stability of insulin and Fourier transform infrared spectroscopy technique showed rearrangements of insulin structure during processing. Moreover, in vivo biological activity in diabetic rats revealed no statistical difference when compared to nonencapsulated insulin, demonstrating retention of insulin activity. Our results demonstrate that alginate–dextran sulfate-based nanoparticles efficiently stabilize the loaded protein structure, presenting good physical properties for oral delivery of insulin.
Inhibition of β‐TrCP Function Potentiates UVB‐induced Apoptosis in hTERT‐immortalized Normal Human Keratinocytes †

Chronic skin exposure to UV radiation manifests in a score of biochemical events, DNA damage and mutations which can potentially cause skin cancer. The ubiquitin proteasome pathway controls the degradation of a majority of regulatory eukaryotic proteins including those which play a key role in tumorigenesis. SCFβTrCP E3 ubiquitin ligases mediate ubiquitination and proteasomal degradation of phosphorylated substrates that play a key role in signal transduction. Activation of several signaling pathways involved in tumorigenesis was shown to elevate expression and activity of β‐TrCP1/2. In this study, we established and characterized human neonatal foreskin keratinocytes, rendered immortal by retroviral introduction of human telomerase reverse transcriptase (hTERT). These skin hTERT immortalized normal keratinocytes (STINKs) maintain characteristic traits of keratinocytes, such as expression of keratins, cytoplasmic localization of basonuclin and susceptibility to high concentration of calcium. We analyzed the response of STINKs to UVB radiation and its classical markers, such as p53 and nuclear factor (NF)‐κB. We also demonstrate that inhibition of β‐TrCP2 function, by induction of dominant negative β‐TrCP2 (β‐TrCP2ΔN), accentuates UVB induced apoptosis, and this phenomenon is independent of NF‐κB and p53 pathways.
Fast INC-XOR codec for low-power address buses

A very fast and low-power address bus encoder, whose critical path delay and area are only weakly dependent on the address bus width, is presented. Although the encoding algorithm of the proposed structure is the same as the INC-XOR encoding, its encoder and decoder architectures, called DX, are much faster. The DX architecture implements the INC-XOR encoding partially (partial DX architecture) or fully (registered DX architecture). The partial implementation, which is faster and consumes less power and silicon area, is appropriate for cases where the size of the basic block (sequential addresses without branches or jumps) is bounded, for example, by 256. The registered DX architecture uses a multi-stage pipelined structure with pseudo-incrementers to reduce the combinational delay of each pipeline stage. The two DX implementations (partial and registered) are compared with three conventional implementations of INC-XOR realised using the ripple carry, the carry look-ahead and Sklansky prefix incrementers. The results of the critical path delay, gate count, power-delay product and energy-delay product show considerable improvements over the conventional implementations.
Provision of quality of service for active services

6 A novel approach to quality of service control in an active service network (application layer active network) is 7 described. The approach makes use of a distributed genetic algorithm based on the unique methods that bacteria use to 8 transfer and share genetic material. We have used this algorithm in the design of a robust adaptive control system for 9 the active nodes in an active service network. The system has been simulated and results show that it can oer clear 10 dierentiation of active services. The algorithm places the right software, at the right place, in the right proportions; 11 allows dierent time dependencies to be satis®ed and simple payment related increases in performance. Ó 2001 Elsevier 12 Science B.V. All rights reserved.
PRAM: privacy preserving access management scheme in cloud services

Identity privacy and access control pose a significant challenge for cloud services security. This is because a cloud service may have different owner and users, which necessitates privacy preserving access control. Although most existing identity management and access control schemes solve these problems to a certain extent, they also have some limitations. In this paper, we propose a new approach, called Privacy pReserving Access Management scheme (PRAM), which can satisfy all the desirable security requirements in cloud services. Specifically, there are two main contributions in this paper. First, we use two cryptographic primitives: Blind signature and Hash chain to protect identity privacy and secure authentication. Second, we combine on-demand access control with Service-Level Agreements (SLA) to provide flexible fine-grained access management. As a result, our PRAM scheme is applicable in cloud services due to its simplicity, low overhead, and efficiency.
Machine Learning in the Real World

Machine Learning (ML) has become a mature technology that is being applied to a wide range of business problems such as web search, online advertising, product recommendations, object recognition, and so on. As a result, it has become imperative for researchers and practitioners to have a fundamental understanding of ML concepts and practical knowledge of end-to-end modeling. This tutorial takes a hands-on approach to introducing the audience to machine learning. The first part of the tutorial gives a broad overview and discusses some of the key concepts within machine learning. The second part of the tutorial takes the audience through the end-to-end modeling pipeline for a real-world income prediction problem.
Memristive devices fabricated with silicon nanowire schottky barrier transistors

This paper reports on the memory and memristive effects of Schottky barrier field effect transistors (SBFET) with gate-all-around (GAA) configuration and Si nanowire (SiNW) channel. Similar behavior has also been investigated for SBFETs with poly-Si nanowire (poly-SiNW) channel in back-gate configuration. The memristive devices presented here have the potential of a very high integration density, and they are suitable for hybrid CMOS co-fabrication with a CMOS-compatible process. We show that 2 different regimes are possible, making these devices suitable either for volatile ambipolar memory or resistive random access memory (RRAM) applications. In addition, frequency- and amplitude- dependence of the memristive behavior are reported.
A path selection algorithm for sparse massive MIMO channels

mm-Wave communications and massive MIMO are two promising technologies to increase the capacity and coverage of future cellular networks. Channel sparsity, where there exist only few propagation paths enabling signal transmission to and reception from mobile users, is a key property of mm-Wave channels. In this paper, we consider the downlink transmission over a sparse massive MIMO channel and propose a novel path selection algorithm to maximize sum rate. We introduce a bipartite graph, which connects angle of departure (AoD) to users in the beam-space domain. The simulation results demonstrate that the proposed path selection algorithm outperforms the conventional user selection algorithms.
A graphical query language supporting recursion

We define a language G for querying data represented as a labeled graph G. By considering G as a relation, this graphical query language can be viewed as a relational query language, and its expressive power can be compared to that of other relational query languages. We do not propose G as an alternative to general purpose relational query languages, but rather as a complementary language in which recursive queries are simple to formulate. The user is aided in this formulation by means of a graphical interface. The provision of regular expressions in G allows recursive queries more general than transitive closure to be posed, although the language is not as powerful as those based on function-free Horn clauses. However, we hope to be able to exploit well-known graph algorithms in evaluating recursive queries efficiently, a topic which has received widespread attention recently.
A generalized scattering matrix method using the method of moments for electromagnetic analysis of multilayered structures in waveguide

The method of moments (MoM) in conjunction with the generalized scattering matrix (GSM) approach is proposed to analyze transverse multilayered structures in a metal waveguide. The formulation incorporates ports as an integral part of the GSM formulation, thus, the resulting model can be integrated with circuit analysis. The proposed technique permits the modeling of interactive discontinuities due to the consideration of a large number of modes in the cascade. The GSM-MoM method can be successfully applied to the investigation of a variety of shielded multilayered structures, iris coupled filters, determining the input impedance of probe excited waveguides, and of waveguide-based spatial power combiners.
Nanomolar-Potency Small Molecule Inhibitor of STAT5 Protein

We herein report the design and synthesis of the first nanomolar binding inhibitor of STAT5 protein. Lead compound 13a, possessing a phosphotyrosyl-mimicking salicylic acid group, potently and selectively binds to STAT5 over STAT3, inhibits STAT5–SH2 domain complexation events in vitro, silences activated STAT5 in leukemic cells, as well as STAT5′s downstream transcriptional targets, including MYC and MCL1, and, as a result, leads to apoptosis. We believe 13a represents a useful probe for interrogating STAT5 function in cells as well as being a potential candidate for advanced preclinical trials.
State Dynamics of the Epileptic Brain

Communication between specialized regions of the brain is a dynamic process allowing for different connections to accomplish different tasks. While the content of interregional communication is complex, the pattern of connectivity (i.e., which regions communicate) may lie in a lower dimensional state-space. In epilepsy, seizures elicit changes in connectivity, whose patterns shed insight into the nature of seizures and the seizure focus. We investigated connectivity in 3 patients by applying network-based analysis on multi-day subdural electrocorticographic recordings (ECoG). We found that (i) the network connectivity defines a finite set of brain states, (ii) seizures are characterized by a consistent progression of states, and (iii) the focus is isolated from surrounding regions at the seizure onset and becomes most connected in the network towards seizure termination. Our results suggest that a finite-dimensional state-space model may characterize the dynamics of the epileptic brain, and may ultimately be used to localize seizure foci. © 2013 ASME
Microcirculatory Marker for the Prediction of Renal End Points: A Prospective Cohort Study in Patients With Chronic Kidney Disease Stage 2 to 4

Retinal arteriolar narrowing reflects aging, hypertension, chronic kidney disease (CKD), and other vascular processes. We examined the predictive value of retinal arteriolar narrowing alone and in combination with albuminuria on renal disease progression in CKD. A white CKD stage 2 to 4 cohort of 164 men and women (60.8±13.8 years) underwent retinal photography and determination of albuminuria. The calibers of all retinal arterioles were measured after digital conversion of the photographs. Cases of incident renal end points defined as 50% renal function loss and start of renal replacement therapy were identified and validated by case record reviews. Over an average period of 1410 (range, 106–1606) days, 25 patients with CKD had incident renal end points. Kaplan–Meier analysis revealed that patients with CKD within the tertile of narrowest arterioles had more renal end points (log-rank P<0.001). Cox regression analysis confirmed this before (&bgr;=1.183±0.411) and after adjusting for age and baseline renal function (&bgr;=1.204±0.416). With respect to renal end points, a significant interaction was present between narrow arterioles and albuminuria. The relative risk for renal end points of narrow arterioles was 3.7 (1.7–8.4), of albuminuria was 5.4 (2.5–12.0), and of combined narrow arterioles and albuminuria was 16.2 (4.6–57.2). Hence retinal arteriolar narrowing is related to incident renal end points. Narrow arterioles and albuminuria reveal a synergistic predictive value. The findings support a leading role of the microvasculature in the pathogenesis of renal disease progression. They also suggest that retinal photography in combination with albuminuria determination may be useful for risk stratification with respect to renal disease progression in patients with CKD stage 2 to 4.
Toward high-throughput algorithms on many-core architectures

Advanced many-core CPU chips already have a few hundreds of processing cores (e.g., 160 cores in an IBM Cyclops-64 chip) and more and more processing cores become available as computer architecture progresses. The underlying runtime systems of such architectures need to efficiently serve hundreds of processors at the same time, requiring all basic data structures within the runtime to maintain unprecedented throughput. In this paper, we analyze the throughput requirements that must be met by algorithms in runtime systems to be able to handle hundreds of simultaneous operations in real time. We reach a surprising conclusion: Many traditional algorithm techniques are poorly suited for highly parallel computing environments because of their low throughput. We reach the conclusion that the intrinsic throughput of a parallel program depends on both its algorithm and the processor architecture where the program runs. We provide theory to quantify the intrinsic throughput of algorithms, and we provide a few examples, where we describe the intrinsic throughput of existing, common algorithms. Then, we go on to explain how to follow a throughput-oriented approach to develop algorithms that have very high intrinsic throughput in many core architectures. We compare our throughput-oriented algorithms with other well known algorithms that provide the same functionality and we show that a throughput-oriented design produces algorithms with equal or faster performance in highly concurrent environments. We provide both theoretical and experimental evidence showing that our algorithms are excellent choices over other state of the art algorithms. The major contributions of this paper are (1) motivating examples that show the importance of throughput in concurrent algorithms; (2) a mathematical framework that uses queueing theory to describe the intrinsic throughput of algorithms; (3) two highly concurrent algorithms with very high intrinsic throughput that are useful for task management in runtime systems; and (4) extensive experimental and theoretical results that show that for highly parallel systems, our proposed algorithms allow greater or at least equal scalability and performance than other well-known similar state-of-the-art algorithms.
Predicting fault-prone software modules using feature selection and classification through data mining algorithms

Software defect detection has been an important topic of research in the field of software engineering for more than a decade. This research work aims to evaluate the performance of supervised machine learning techniques on predicting defective software through data mining algorithms. This paper places emphasis on the performance of classification algorithms in categorizing seven datasets (CM1, JM1, MW1, KC3, PC1, PC2, PC3 and PC4) under two classes namely Defective and Normal. In this study, publicly available data sets from different organizations are used. This permitted us to explore the impact of data from different sources on different processes for finding appropriate classification models. We propose a computational framework using data mining techniques to detect the existence of defects in software components. The framework comprises of data pre-processing, data classification and classifier evaluation. In this paper; we report the performance of twenty classification algorithms on seven publicly available datasets from the NASA MDP Repository. Random Tree Classification algorithm produced 100 percent accuracy in classifying the datasets and hence the features selected by this technique were considered to be the most significant features. The results were validated with suitable test data.
Coevolutionary arms races: increased host immune defense promotes specialization by avian fleas

We investigated the relationship between host defense and specialization by parasites in comparative analyses of bird fleas and T‐cell mediated immune response of their avian hosts, showing that fleas with few main host species exploited hosts with weak or strong immune defenses, whereas flea species that parasitized a large number of host species only exploited hosts with weak immune responses. Hosts with strong immune responses were exploited by a larger number of flea species than hosts with weak responses. A path analysis model with an effect of T‐cell response on the number of host species, or a model with host coloniality directly affecting host T‐cell response, which in turn affected the number of host species used by fleas, best explained the data. Therefore, parasite specialization may have evolved in response to strong host defenses.
Predicting Forest Stand Properties from Satellite Images with Different Data Mining Techniques

This paper work is focused on the comparison of different data mining techniques and their performances by building predictive models of forest stand properties from satellite images. We used the WEKA data mining environment to implement our numeric prediction experiments, applying linear regression, model (regression) trees, and bagging. The best results (with regard to correlation) we obtained by bagging model trees for considered target attributes.