Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.
Dynamic instability of cooperation due to diverse activity patterns in evolutionary social dilemmas

Individuals might abstain from participating in an instance of an evolutionary game for various reasons, ranging from lack of interest to risk aversion. In order to understand the consequences of such diverse activity patterns on the evolution of cooperation, we study a weak prisoner's dilemma where each player's participation is probabilistic rather than certain. Players that do not participate get a null payoff and are unable to replicate. We show that inactivity introduces cascading failures of cooperation, which are particularly severe on scale-free networks with frequently inactive hubs. The drops in the fraction of cooperators are sudden, while the spatiotemporal reorganization of compact cooperative clusters, and thus the recovery, takes time. Nevertheless, if the activity of players is directly proportional to their degree, or if the interaction network is not strongly heterogeneous, the overall evolution of cooperation is not impaired. This is because inactivity negatively affects the potency of low-degree defectors, who are hence unable to utilize on their inherent evolutionary advantage. Between cascading failures, the fraction of cooperators is therefore higher than usual, which lastly balances out the asymmetric dynamic instabilities that emerge due to intermittent blackouts of cooperative hubs.
Climate sensitivity of global terrestrial net primary production (NPP) calculated using the reduced-form model NNN

In order to assess the potential impact of climate change on terrestrial equilibrium net pri- mary production (NPP), information about the sensitivity of terrestrial NPP to climate change is needed. A novel approach to the definition of climate sensitivity is introduced; it does not depend on specific (and uncertain) scenarios, but rather describes the worst-case climate sensitivity of NPP, as measured by the magnitude of the gradient of NPP, as a function of the climate variables. The metric in climate space necessary for the determination of the gradient is calculated using the existing spatial variation of the climate variables as a measure of the potential for climate change, taking into account the unchanging determinants of the climate— latitude and altitude. The current correlations between the climate variables are preserved using principal component analysis. The resulting map of global NPP sensitivity shows especially high values, e.g. in the US Midwest, southern Africa, Australia, west- ern Kazakhstan, the Maghreb and Spain. The sensitivity is aggregated to the country level, excluding the effects of the very insensitive deserts, in order to make these results applicable for policy analysis. The reduced-form model 'NNN' predicts annual terrestrial NPP of potential natural vegetation in equilibrium on the basis of a climatology including monthly values for temperature, precipitation and light. The very short computing time of this model is a prerequisite for the above-described multidi- mensional sensitivity study. To construct NNN, the average of the global NPP results of 7 advanced cli- matology-driven functional vegetation models was used to obtain a 'best guess' NPP field at a 0.5° × 0.5° spatial resolution. With the underlying climatology (36 values for monthly mean tempera- ture, precipitation and light intensity per grid element), 62 483 points of an � 36 → � 1 mapping are de- fined. A subset of these was used to train a neural network yielding a good reproduction of the spatial pattern with a mean absolute error of 0.026 kgC m -2 yr -1 , which is significantly less than the mean un- certainty of the NPP average (mean absolute deviation: 0.097 kgC m -2 yr -1 ). Furthermore, it is shown that the simple model calculates moisture-limited regions correctly, indicating that functional proper- ties of the original models are reproduced. We hope with the NNN to make a contribution to other re- search which needs a very fast reduced-form NPP model. The NNN model is accessible in FORTRAN or as a C-subprogram available at: http://www.pik-potsdam.de/~oliver/NPP.html.
A Tool for Supporting Requirements Formalization Based on Specification Pattern Knowledge

Despite the effectiveness of requirements formalization in producing accurate requirements documentation, this technique can hardly be accepted by software industry mainly due to the difficulty in manipulating formal notations by practitioners. To handle the challenge, this paper describes an interactive tool for supporting requirements formalization based on specification pattern knowledge comprising a set of inter-related specification patterns. Each pattern defines the knowledge for formalizing one kind of function, including derivation knowledge for guiding the clarification of the function and transformation knowledge for formally representing the clarified function. The tool derives necessary function details of the intended requirement through interactions by applying the derivation knowledge and transforms these details into formal specifications by applying the transformation knowledge.
The Audit of Business Intelligence Solutions

Although in this period humanity passes through a relative economic crisis, we all agree that our environment is that of a society of information and knowledge, based on communication and teleactivity, one that is also called information society. Every new form of activity in the information society has an associated informational component consisting in a software program, an application, a system, etc. It is a certainty that in the new economic environment it is necessary to adjust quickly to the opportunities of the market, through Business Process Reengineering, adoption of Business Intelligence solutions, implementation of complex automation applications like Enterprise Resource Planning. But, more than this, in, the digital economy the stress is put on the “label”, the “image”, the “brand”, and these features that are associated to organizations may be obtained by the information audit processes. The present study is focusing on the problem of information audit developed in one of the upper forms of manifestation of the information society in the field of changing the ways of doing business: Business Intelligence.
A comparative study of some greedy pursuit algorithms for sparse approximation

Solving an under-determined system of equations for the sparsest solution has attracted considerable attention in recent years. Among the two well known approaches, the greedy algorithms like matching pursuits (MP) are simpler to implement and can produce satisfactory results under certain conditions. In this paper, we compare several greedy algorithms in terms of the sparsity of the solution vector and the approximation accuracy. We present two new greedy algorithms based on the recently proposed complementary matching pursuit (CMP) and the sensing dictionary framework, and compare them with the classical MP, CMP, and the sensing dictionary approach. It is shown that in the noise-free case, the complementary matching pursuit algorithm performs the best among these algorithms.
Video Based Human Emotion Estimation

This paper addresses the problem of human emotion estimation. Human emotion understanding will play a very important role in future humancomputer interaction systems. Human emotion is a complicated temporal behavior. The technical difficulty in human emotion estimation lies in that the inherent emotional states are not measured directly. The only way to estimate the emotional states is from observable signals, such as facial expressions, voice, and gestures, which are closely related to the emotional states. In this paper, we show how to make use of facial expressions to infer inherent emotional states. Hidden Markov Models are used to model the dynamic facial events which characterize human emotions. Facial expression parameters, action units, are extracted from video sequences containing faces and used as observations for the underlying Hidden MarkovModels. This methodology has been used to detect fatigue, which can be treated as an emotional state with associated typical facial events. Promising results are reported.
Gaze-Following and Awareness of Visual Perspective in Chimpanzees

Abstract : Recent research suggests that chimpanzees are capable of level 1 perspective taking (Flavell, 1992), but that its expression is limited to situations of increased competition (Brauer, Call, & Tomasello, 2007). We present a model utilizing gaze-following that learns in response to the behavior of a competitor. The model not only learns the proper application of the perspective taking strategy but also the critical spatial characteristics that influence the competitive pressure.
Applying Spiking Neural Nets to Noise Shaping

In recent years, there has been an increased focus on the mechanics of information transmission in spiking neural networks. Especially the Noise Shaping properties of these networks and their similarity to Delta-Sigma Modulators has received a lot of attention. However, very little of the research done in this area has focused on the effect the weights in these networks have on the Noise Shaping properties and on post-processing of the network output signal. This paper concerns itself with the various modes of network operation and beneficial as well as detrimental effects which the systematic generation of network weights can effect. Also, a method for post-processing of the spiking output signal is introduced, bringing the output signal more in line with conventional Delta-Sigma Modulators. Relevancy of this research to industrial application of neural nets as building blocks of oversampled A/D converters is shown. Also, further points of contention are listed, which must be thoroughly researched to add to the above mentioned applicability of spiking neural nets.
Improving diffusion practices in a software organization

Methods and tools that support systems development (SD) are usually motivated by a promise of better quality or increased productivity. In large software organizations, specialised methods departments often manage these methods and tools. However, these methods departments often focus more on the purchasing, tailoring and maintenance of technologies, rather than on systematically diffusing methods and tools into current practices. This paper reports on a systematic improvement initiative in a large software organization. The aim of this effort was to diagnose diffusion practices, to evaluate and adapt a service that can improve those diffusion practices, and to diffuse this new service. Insights from this initiative, together with theoretical reflections on the diffusion of SD technologies, are provided. It is suggested that the provision of services that help project personnel to understand and plan the diffusion of new SD technologies can play a key role in developing the focus and competencies needed to improve diffusion practices in large software organizations.
Simulation of clustered multi-state survival data based on a copula model

Generating survival data with a clustered and multi-state structure is useful to study multi-state models, competing risks models and frailty models. Simulations should allow to introduce dependence between times of different transitions and between those of grouped subjects. At the same time they should allow to control the probability of each competing event, the median time to each transition, the effect of covariates and the type and magnitude of heterogeneity. We propose a simulation procedure based on a copula model for each competing events block, allowing to specify the marginal distributions of time variables. The effect of simulated frailties and covariates can be added in a proportional hazards way. The tuning of parameters is done by numerical minimization of a criterion function based on the ratios of target and observed values of median times and of probabilities of competing events. An example is provided of simulation of data mimicking those from a multicenter study on head and neck cancer, where the interest is in studying both time to local relapses and to distant metastases before death. We show that our proposed method reaches very good convergence to the target values.
A method of detecting human body falling action in a complex background

Humans have various complex postures and movements. Considerable attention is given to the problem of recognizing a human fall. However, the recognition rates must be further improved, for practical applications, from that obtained in the previous research. In this paper, a new recognition method, based on the analysis of a human fall, is provided. Furthermore, five eigenvectors that describe a fall are defined i.e. the aspect ratio, effective area ratio, human point margin, body axis angle, and centrifugal rate of the body contour. Then, a support vector machine based on the Gauss radial basis function is trained to obtain a better identification result. The simulation results show that the model, though the combination of the five eigenvectors, has a recognition rate of 94.5%, which is a significant improvement as compared to the previous research.
Zoolander: efficient latency management in NoSQL stores

NoSQL stores expose narrow APIs for data access, e.g., get(key) or put(key, val). While these APIs often give up strong consistency and transactions, they can scale throughput under intense workloads. Widely used stores, e.g., Apache Zookeeper, Cassandra, and Memcached, have been shown to achieve 1010 accesses per day in the face of workload shifts, software faults, and performance bugs. However providing low latency for every access remains challenging. Latency, unlike throughput, quickly yields diminishing returns under scale out approaches, making it important to choose the most efficient approach. Further, DNS timeouts, GC, and other rare system events can hold resources from time to time [3]. These events hardly impact throughput, but they can increase latency for some accesses a lot. Internet services that access a lot of data under tight response time demands need NoSQL stores that provide low latency all the time. Figure 1 depicts such services and their demands. We describe them below. 1. Old-school services, such as e-commerce websites, are increasingly using NoSQL instead of databases. In these embarrassingly parallel services, end-user requests access data independently but each request must complete quickly. Slow accesses translate to unhappy end users. 2. Map reduce services spawn parallel worker nodes that compute local results and forward them to reducers to produce the final output. Here, the term service reflects a growing trend where jobs are expected to complete within response time targets. We note two reasons for this trend. First, jobs that run on pay-as-you-go clouds cost less if they finish within 1-hour leasing intervals. Second, jobs that finish quickly offer qualitative business advantages, e.g., real-time Twitter analysis. 3. Scientific computing as a service is an emerging workload on public clouds [2]. In the past, these workloads ran only on private, custom hardware but public clouds can offer performance-to-cost efficiency. The challenge is matching the absolute performance of private hardware. These workloads use barriers and synchronization heavily. One slow data access can delay the completion of a barrier and ultimately delay the entire workload.
Performance of switched-beam antennas for Wireless Mesh Networks using Synchronous Collision Resolution protocol

The use of switched-beam antennas for Wireless Mesh Networks (WMNs) has gained a lot of attention recently. However, it has not been concluded yet which protocol is suitable for this approach. One interesting protocol named as Synchronous Collision Resolution (SCR) has been proposed to manage the packet schedule between mesh routers. The SCR concept is of advantage over other protocols such as CSMA/CA and TDMA. Nevertheless, the evaluation of SCR protocol has never been performed in literatures so far. Therefore, in this paper, the study of switched-beam antennas in cooperating with SCR protocol is proposed by concerning both desired and interference mesh routers. The simulation results confirm the success of using switched-beam antennas for WMN using SCR protocol.
A Greedy Randomized Adaptive Search Procedure to the Clustering Problem

The aim of this paper is to present a new proposal for Cluster Analysis based on a Greedy Randomized Adaptive Search Procedure (GRASP), with the objective of overcoming the convergence to a local solution. It uses a probabili stic greedy Kaufman initialization method for getting initial solutions and the K-Means algorithm as a local search algorithm. The new proposal wil l become a new initialization approach to K-Means. Hence, we have compared some initialization methods for the K-Means algorithm: Random, Forgy, Macqueen and Kaufman, with the GRASP. Our results suggest that the Kaufman initialization method performs better than the other three methods and the hybrid GRASP – K-Means with Kaufman inicialization improves these results even more. The proposed method obtains high qualit y solutions for the benchmark problems considered.
Label Configuration Priors for Continuous Multi-Label Optimization

We propose a general class of label configuration priors for continuous multi-label optimization problems. In contrast to MRF-based approaches, the proposed framework unifies label configuration energies such as minimum description length priors, co-occurrence priors and hierarchical label cost priors. Moreover, it does not require any preprocessing in terms of super-pixel estimation. All problems are solved using efficient primal-dual algorithms which scale better with the number of labels than the alpha-expansion method commonly used in the MRF setting. Experimental results confirm that label configuration priors lead to drastic improvements in segmentation. In particular, the hierarchical prior allows to jointly compute a semantic segmentation and a scene classification.
CORPUS-BASED GENERATION OF F0 CONTOURS USING GENERATION PROCESS MODEL FOR EMOTIONAL SPEECH SYNTHESIS

A corpus-based method was developed for generating fundamental frequency contours in emotional speech synthesis. The method assumes the generation process model and predicts its command parameters (positions and amplitudes) using binary regression trees with the input of linguistic information of the sentence to be synthesized. Because of the model constraint, a certain quality is still kept in synthesized speech even if the prediction is done incorrectly. The speech corpus includes three types of emotional speech (anger, joy, sadness) and calm speech uttered by a female narrator. The command parameters necessary for the training and testing of the method were automatically extracted from speech using a program developed by the authors. Since the accuracy of the extraction largely affects the prediction performance, a constraint is applied on the position of phrase commands during the extraction. The method first predicts the phrase command parameters, which are then used for the prediction of accent command parameters. The mismatches between the predicted and target contours for angry speech were similar to those for calm speech. Synthesis of emotional speech was conducted with text inputs. The segmental features were handled by the HMM synthesis method and the phoneme durations are predicted in a similar corpus-based method. Perceptual experiment was conducted using the synthesized speech, and the result indicated that the anger could be well conveyed by the developed method. The result came worse for joy and sadness.
Managing Personality Influences in Dialogical Agents

We present in this article an architecture implementing personality traits from the FFM/NEO PI-R taxonomy as influence operators upon the rational decision making process of dialogical agents. The objective is to separate designer-dependent resources (traits taxonomies, influence operators, behaviors/operators links) from the core part of the computational implementation (the personality engine). Through a case study, we show how our approach makes it easier to combine various resources and to observe various scenarios within a single framework.
The Effects of Customer Relationship Programmes on Customer Loyalty in Insurance

The rapid development of information technology enables businesses to promptly assimilate customer information, understand their needs and create values for them. Through the improvement of the operation process, channel integration, enhancement of employees’ capability, prompt and flexible responses, customer segmentation, value analysis and customised service, customer relationship management maintains the current customers, attracts the potential customers and enhances customer value. Based on the literature reviewed and the theories generated, this study builds a model to examine the impact of the process/channel and customisation functions of the relationship programme on relationship commitment and customer loyalty. To gain insight into the relationship, a questionnaire was distributed to life insurance policyholders of five largest Taiwanese life insurers in terms of market share. The empirical analysis was carried out using a structural equation model. We find that the customisation function poses a greater influence on customer loyalty than the channel function and that the mediating role of affective commitment is supported.