Miracle-dz/UniFAR-FTUs huggingface dataset:
Smartphone accelerometer data used for detecting human emotions

The paper outlines work on the classification of emotions using smartphone accelerometer data. Such classification can be used, in conjunction with other methods of emotion detection, to adapt services to the user's emotional state. The data is collected from individuals who have been carrying their phone in a pocket while walking. An Android app was developed in order to monitor the smartphone accelerometer of the individuals who participated in the study and occasionally requested them to judge and submit their emotional state. This way, data is collected from a natural environment rather than a laboratory setting. The recorded data is then processed and used to train different classifiers to be compared. The machine learning algorithms decision tree, support vector machine and multilayer perceptron are used for this purpose. Emotions are classified in two dimensions: pleasantness and arousal (activation). While the recognition rate for the arousal dimension is promising at 75%, pleasantness is harder to predict, with a recognition rate of 51%. These findings indicate that by only analyzing accelerometer data recorded from a smartphone, it is possible to make predictions of a person's state of activation.
Detection of Occluded Face Image using Mean Based Weight Matrix and Support Vector Machine

Problem statement: Face occlusion is a very challenging problem in fac e recognition. The performance of face recognition system decreases dr astically due to the presence of partial occlusion on the face. Extracting discriminative features to achieve accurate detection versus computational overhead in extracting the features, which affects the classification speed, would be a sustained problem. The objective of this study is to segment the human face into non-occluded and occluded part of the occluded human face image. In General, for f ace detection special facial features are extracted . In the proposed study a simplified algorithm to ext ract the features is developed. Approach: An algorithm which enables the automatic detection of the presence of occlusions on the face would be a useful tool to increase the performances of the sys tem. The face image was preprocessed to enhance the input face images in order to reduce the loss o f classification performance due to changes in faci al appearance. The experiment also balances both illumination and facial expression changes. Results: In this study, a Mean Based Weight Matrix (MBWM) algorithm has been proposed to enhance the performance by 4.25% than the LBP method. Conclusion: The proposed model has been tested on occluded face images with a dataset obtained from t he MIT face database.
Distributions of Birkhoff Averages with Respect to Non-invariant Measures

We consider Birkhoo averages of an observable f along orbits of a map T : X ! X with respect to a non-invariant measure m. In the simple case where the averages converge m-almost everywhere, one may discuss the distribution of values of the average in a natural way. We extend this analysis to the case where convergence does not hold m-almost everywhere. We also introduce properties of the system (X; T; m) which are related to whether or not there is an a.e. limit and in case there is not, whether a reasonable extension of the deenition of the distribution can be made.
Blood Monocyte Subsets and Selected Cardiovascular Risk Markers in Rheumatoid Arthritis of Short Duration in relation to Disease Activity

Objectives. To evaluate blood monocyte subsets and functional monocyte properties in patients with rheumatoid arthritis (RA) of short duration in the context of cardiovascular (CV) risk and disease activity. Methods. We studied conventional markers of CV risk, intima media thickness (IMT), and blood monocyte subsets in 27 patients aged 41 ± 10 years with RA of short duration (median 12 months) and 22 healthy controls. The RA subjects were divided into low (DAS28: 2.6–5.1) and high (DAS28 > 5.1) disease activity. Results. RA patients exhibited increased levels of intermediate (CD14++CD16+) monocytes with decreased CD45RA expression compared to controls, increased counts of classical (CD14++CD16−) monocytes, and decreased percentages of nonclassical (CD14+CD16++) monocytes. Patients with high disease activity had lower HLA DR expression on classical monocytes compared to low disease activity patients. There were no differences in monocyte subsets between subjects with DAS > 5.1 and DAS ≤ 5.1. There were no significant intergroup differences in IMT and the majority of classical CV risk factors. Conclusions. Patients with RA of short duration show alteration in peripheral blood monocyte subsets despite the fact that there is no evidence of subclinical atherosclerosis. Disease activity assessed with DAS28 was associated with impaired functional properties but not with a shift in monocyte subpopulations.
Efficient Management of Network Resources Using Autonomous Agents in Grid

Summary knowledge of existing grid resources and their characteristics is important for grid schedulers and resource brokers. In our work, we have proposed a network management system for heterogeneous networks using autonomous, static and mobile agents. Stationary and mobile agents co-operate to form a swarm intelligence based network management system. Stationary agents perform network measurements on the particular node of a particular cluster. Mobile agents are used to initiate measurements collect data and perform recovery mechanisms. Both, stationary and mobile agents are managed by the Mobile Agent Generator (MAG). MAG plays a key role in management related activities like creating mobile agents, estimating cost functions. The predicted and the measured values prove the accuracy of our management system.
Cryptanalysis and Security Enhancement of an Efficient and Secure Dynamic ID Based Remote User Authentication Scheme for Multi-Server Environments

Password based authentication schemes have been widely used to verify the legitimacy of a user over an insecure communication channel. A common feature among most of the published schemes is that user's identity (ID) is static in all the transaction sessions, which may leak some information about the user and can create risk of identity theft during message transaction. Therefore, to provide user anonymity, many dynamic ID based remote users authentication schemes have been proposed. Recently, Khan et al. proposed an efficient and secure dynamic ID based remote user authentication scheme and claimed that their scheme can provide strong security against various attacks. In this paper, we have demonstrated that Khan et al.'s scheme is vulnerable to server spoofing attack and insider attack, and has some flaws in login and authentication phase as well and also proposed an enhanced scheme to overcome the identified weaknesses.
A Model of a Systemic Bank Run

This paper provides a model of the view that the 2008 financial crisis is reminiscent of a bank run, focussing on six stylized key features. In particular, core financial institutions have invested their funds in asset-backed securities rather than committed to long-term projects: in distress, these can potentially be sold to a large pool of outside investors at steep discounts. I consider two different motives for outside investors and their interaction with banks trading asset-backed securities: uncertainty aversion versus adverse selection. I shall argue that the version with uncertainty averse investors is more consistent with the stylized facts than the adverse selection perspective: in the former, the crisis deepens, the larger the market share of distressed core banks, while a run becomes less likely instead as a result in the adverse selection version. Therefore, the outright purchase of troubled assets by the government at prices above current market prices may both alleviate the financial crises as well as provide tax payers with returns above those for safe securities.
Personalizing the Search for Knowledge

Recent work on building semantic search engines has given rise to large graph-based knowledge repositories and facilities for querying them and more importantly, ranking the results. While the ranking provided may prove to be acceptable in general, for a truly satisfactory search experience, it is necessary to tailor the results according to the user’s interest. In this paper, we address the issue of personalizing query results in the specific setting of graph-based knowledge bases. In particular, we address two important issues: i) construction of the user profile based on the inference of the user’s interest and ii) a formal model for personalized scoring which incorporates the user’s interest. Preliminary experimental results show that our techniques are indeed promising.
Low-level Population Exposure to Inorganic Arsenic in the United States and Diabetes Mellitus: A Reanalysis

Background: Although studies have reported associations between high concentrations of ingested inorganic arsenic and diabetes mellitus, there is no evidence of this association at low exposures. However, a well-publicized study (JAMA. 2008;300:814–822) recently produced an extraordinary finding of a more than 3-fold increase in diabetes at low concentrations of urinary arsenic. This potentially affects 40 million adults in the United States. Methods: We used the same cross-sectional data on urinary arsenic and type 2 diabetes mellitus in 795 adults from the 2003–2004 National Health and Nutrition Examination Survey to assess this evidence. Results: As in the earlier study, we found an odds ratio (OR) near 1.0 for diabetes, comparing the 80th versus 20th percentiles of urinary total arsenic (OR = 0.88 [95% confidence interval = 0.39–1.97]). This OR increased to above 3.0 when urinary arsenobetaine was added to the logistic risk model. However, this high OR was a statistical artifact because arsenobetaine, which is ingested from fish and is essentially nontoxic, is a part of measured total urinary arsenic. These 2 variables are highly correlated (correlation = 0.80). Because arsenobetaine is a part of total arsenic, it should first be subtracted from total arsenic rather than being added to the statistical model. Doing so yields an OR of 1.15 (0.53–2.50). Conclusion: These findings show no evidence of increased risk of diabetes with arsenic exposure in this dataset. This underscores the importance of valid statistical techniques and careful consideration of scientific plausibility when investigating low-concentration chemical exposures.
Defining Agile Software Quality Assurance

Agile software development methodologies have since their inception claimed to improve the quality of the software product. The agile practitioners have also claimed that use of the agile approach has greatly improved the quality of their products. However, software quality is a rather complex concept; in fact some have defined the entire discipline of software engineering as the production of quality software. Quality according to ISO 9000 is defined as "the totality of characteristics of an entity that bear on its ability to satisfy stated or implied needs. " In the existing agile literature there as not been a comprehensive definition of which characteristics of software quality are improved by the use of agile processes in developing software. In this paper an innovative technique is introduced for evaluating agile methodologies in order to determine which factors of software quality they improve. The technique uses a set of adapted software quality factors as defined by Bertrand Meyer and McCall.
Approximate capacity of a class of multi-source Gaussian relay networks

We study K-user M-hop Gaussian relay networks with K<inf>m</inf> nodes in the m-th layer, where M is even and K = K<inf>1</inf> = K<inf>M+1</inf>. We observe that the time-varying nature of wireless channels (fading) can be exploited to mitigate the inter-user interference. The proposed block Markov encoding and relaying scheme exploits such channel variations and works for any isotropically distributed channels including Rayleigh fading. We show a general achievable degrees of freedom (DoF) region of this class of Gaussian relay networks, which coincides with the cut-set outer bound if M/K<inf>min</inf> is an integer, where K<inf>min</inf> = min<inf>m</inf> {K<inf>m</inf>}. Therefore, we completely characterize the DoF region for the case where M/K<inf>min</inf> is an integer.
An ATE assisted DFD technique for Volume Diagnosis of scan chains

Volume Diagnosis is extremely important to ramp up the yield during the IC manufacturing process. Limited observability due to test response compaction negatively affects the diagnosis procedure. Hence, in a compaction environment, it is important to implement Design For Diagnosis (DFD) methodology to restore diagnostic resolution. In this paper, a novel DFD technique which makes the faulty chains to behave as good chains during loading, has been proposed. As a result, the errors introduced in the responses, must occur during unloading of the scan chains. Diagnosis can then be performed by directly comparing the actual and expected responses without any fault simulation - leading to significant reduction in time. Results on benchmark circuits show that the average number of suspected cells for single chain failure is 1.27 (ideal value being 1) and the time taken for diagnosis is in the order of milli-seconds.
Generic well-posedness in a multidimensional hyperbolic inverse problem

Let y(f) and u(p, a, h) be the solutions, respectively, to the following initial/boundary value problems in a bounded domain Ω C M (n > 1) with a smooth boundary #Ω: ^f (χ,Ο = Aj/(x,t) ρ(χ)ί/(χ,*) /(χ)λ(χ,Ο, x 6 Ω, t > Ο y ( x , t ) = 0, χ € #Ω, t > Ο ti(x,0) = a( χ 6 Ω, * > Ο χ € Ω χ € #Ω, ί > 0. For a given Γ C du and a sufficiently large T < oo, by the exact controllability method, we get stability estimates for two inverse problems: (I) Determine /(x) (x 6 Ω) from (6y(f)/dn)(x,t) (x 6 Γ, 0 < t< T) provided that p(x) and λ(χ,ί) are given functions. (II) Determine p(x) (x € Ω) from (du(p,a,/i)/dn)(x,t) (x G Γ, Ο < t < T) provided that α(χ) and /ι(χ,ί) are given functions. "Centre de Mathematiques Appliquees Unite de Recherche Associee au C.N. R.S. 756, Ecole Polytechnique, 91128 Palaiseau Cedex, France. E-mail: jppuel@cmapx.polytechnique. fr ^Department of Mathematical Sciences, The University of Tokyo, 3-8-1 Komaba Meguro 153, Tokyo, Japan. E-mail: myama@ms.u-tokyo.ac. jp This research was partially supported by the Alexander von Humboldt Foundation (Germany), Freie Universit t Berlin (Germany), Sanwa Systems Development Co., Ltd (Japan) and the Grant-in-Aid for Cooperative Researches (No. 06305005) from the Ministry of Education, Culture and Science (Japan). 56 J.-P. Pud and M. Yamamoto
Interacting process classes

Many reactive control systems consist of classes of active objects involving both intraclass interactions (i.e., objects belonging to the same class interacting with each other) and interclass interactions. Such reactive control systems appear in domains such as telecommunication, transportation and avionics. In this article, we propose a modeling and simulation technique for interacting process classes. Our modeling style uses standard notations to capture behavior. In particular, the control flow of a process class is captured by a labeled transition system, unit interactions between process objects are described as transactions, and the structural relations are captured via class diagrams. The key feature of our approach is that our execution semantics leads to an abstract simulation technique which involves (i) grouping together active objects into equivalence classes according their potential futures, and (ii) keeping track of the number of objects in an equivalence class rather than their identities. Our simulation strategy is both time and memory efficient and we demonstrate this on well-studied nontrivial examples of reactive systems. We also present a case study involving a weather-update controller from NASA to demonstrate the use of our simulator for debugging realistic designs.
GA-based feature selection for synchronous and asynchronous applause detection

Automatically extracting semantic content from audio streams can be helpful in many multimedia applications. For instance, asynchronous applause can mark the breaks between the songs in a live performance, whereas synchronous applause can indicate both the end of the performance and the appreciation of the audience. In this paper, we introduce a framework for automatic feature subspace selection from a common feature vector. The selected features build a new representation which is better suited to a given learning task and recognition. In order to solve this problem, we propose a method based on the Genetic Algorithm (GA) to improve the representativeness and robustness of the features in a generic audio recognition task. Combining only those coefficients selected by the GA increases the classification accuracy from F = 0,7961 to F = 0.9412 (an improvement about 18.2%).
Querying Timestamped Event Sequences by Exact Search or Similarity-based Search : Design and Empirical Evaluation

Specifying timestamped event sequence queries is challenging even for skilled computer professionals familiar with SQL. Most graphical user interfaces for database search use a exact search approach, which is often effective, but applies an exact match criteria. We describe a new similarity-based search interface, in which users specify a query by simply placing events on a blank timeline and retrieve a similarity-ranked list of results, and a similarity measure which the users can customize by four decision criteria, enabling them to adjust the impact of missing, extra, or swapped events or the impact of time shifts. We describe an example of use with electronic health records based on our ongoing collaboration with hospital physicians. Then we report on a controlled experiment with 18 participants that compared exact search and similarity-based search features. We report on the advantages and disadvantages of each approach and conclude with recommendations for the design of a hybrid approach combining both interfaces.
MicroRNA and gene expression profiling of response to lithium treatment for bipolar I disorder

Bipolar is a debilitating mood disorder characterized by mixed episodes of depression and mania. It is classified into bipolar I and II according to the occurrences and frequencies of depressive and manic episodes. As the sixth leading cause of disability in the world, bipolar disorder exerts a huge amount of burden on the patient's family and the society as a whole. Lithium is often the prescribed medication; however, not all patients are responsive to it. In fact, some may even develop adverse drug reactions to the drug. In an attempt to understand the mechanisms underlying the response to lithium among bipolar I patients, we profiled the gene and microRNA expression differences between the good and poor responders in peripheral blood. We identified a number of differentially expressed genes and microRNAs, determined their functions using gene ontology and pathway annotation tools, and assessed the potential interactions between these two types of molecules via target prediction algorithms, association analyses, and existing literature evidence. We found several microRNA-gene interaction biomarkers that differ between the poor and good lithium responders. With future validations, these biomarkers may provide insights into the mechanism underlying lithium response.
The Effects of Organizational Resources on Scientists' Research Production across Ranks: Does Collaboration Matter?

This study addresses two major questions: First, do resources provided by academic departments influence scientists’ production differentially across ranks? Second, does collaborative network size mediate the relationship, if any, between organizational resources and research production? Data from a national survey of academic scientists and engineers in Carnegie-designated Research I universities is used to assess the possible differential effects of resources on production by rank and to explore the possible mediating role of collaboration on production. Overall, preliminary findings suggest that certain organizational resources affect research production and these effects are different for scientists in each stage of their career. Also, the impact of certain organizational resources is in some instances partially mediated by collaborative network size. Introduction Scientific production at academic institutions not only contributes to new knowledge creation but also through knowledge transfer helps promote industrial development. In addition to these broader implications, scientific production of individual scientists is also important for their promotion in academia, since promotion in research universities in US is highly dependent on the quantity and quality of publications. In this context, resources are the means to do research and performance of scientists depend strongly on their ability to make use of resources provided by their institution (Lazega et. al., 2006). If the means are limited or removed, research production will surely suffer. Therefore, a better understanding of the relationship of resources to production is critical to an improved understanding of the conduct of science. Knowledge production by academic scientists is a collaborative process in which certain resource inputs are transformed by the work of the scientist(s) into knowledge outputs such as research publications or patents (Lee and Bozeman,2005; Bozeman and Corley, 2004). This production process is embedded in an organizational environment that shapes the preferences and actions of the scientist by providing or withholding the organizational resources required to produce knowledge as well as through norms, practices, and policies that govern the use of these resources (Fox, 1991; Fox and Mohapatra, 2007; Lambright and Teich, 1981). These resources can range from intangible resources like leadership and institutional commitment ((Bohen and Stiles, 1998) to tangible resources like graduate students, equipment, and lab space (Dundar and Lewis, 1998). Also, the resources available from organizations can vary. For example, academic departments vary in the quantity and
An Efficient Recommendation Method for Improving Business Process Modeling

In modern commerce, both frequent changes of custom demands and the specialization of the business process require the capacity of modeling business processes for enterprises effectively and efficiently. Traditional methods for improving business process modeling, such as workflow mining and process retrieval, still requires much manual work. To address this, based on the structure of a business process, a method called workflow recommendation technique is proposed in this paper to provide process designers with support for automatically constructing the new business process that is under consideration. In this paper, with the help of the minimum depth-first search (DFS) codes of business process graphs, we propose an efficient method for calculating the distance between process fragments and select candidate node sets for recommendation purpose. In addition, a recommendation system for improving the modeling efficiency and accuracy was implemented and its implementation details are discussed. At last, based on both synthetic and real-world datasets, we have conducted experiments to compare the proposed method with other methods and the experiment results proved its effectiveness for practical applications.