Miracle-dz/UniFAR-FTUs huggingface dataset:
Team Description Paper : Darmstadt Dribblers & Hajime Team ( KidSize ) and Darmstadt Dribblers ( TeenSize )

This paper describes the hardware and software design of the three humanoid robot systems of the Darmstadt Dribblers and Hajime Team in KidSize and TeenSize. The robots are used as a vehicle for research in control of locomotion and behavior of humanoid robots and robot teams with many degrees of freedom and many actuated joints. The Humanoid League of RoboCup provides an ideal testbed for such aspects of dynamics in motion and autonomous behavior as the problem of generating and maintaining statically or dynamically stable bipedal locomotion is predominant for all types of vision guided motions during a soccer game. A new modular software architecture has been developed for efficient and effective implementation and test of modules for sensing, planning, behavior, and actions.
e-Inclusion and e-Social work: new technologies at the service of social intervention

ABSTRACT This paper defines the concept of e-inclusion in reference to programmes and projects that promote social inclusion through the use of new technologies. This concept is related to e-social work, defined as the use of ICTs in the field of social work and social services. To illustrate the implications of e-inclusion and e-social work, a case study is presented on a community involvement project using new technologies as a means and as an end. The SAREGUNE project for community use of new technologies was set up in Vitoria (Spain) in 2004. Ten years later, it gained recognition as a European e-inclusion scheme within the ‘Leonardo Da Vinci Multilateral Projects, Transfer of Innovation’ lifelong learning programme. This article explores the origins of the idea and its significance in the fight against the digital divide and in the processes of intercultural and social inclusion within the city's historic central district. A process of deconstruction, construction and reconstruction of the scheme is used to identify and describe the movements of rotation and revolution within the process of social inclusion, the levels of integration of e-social work at individual, group and community level, and the impact of the project in terms of e-inclusion.
Speaker phone mode classification using Gaussian mixture models

This study focuses on the mode classification of phones speaker modes using GMM1. In this regard, speech data in both enabled and disabled speaker modes of cell phones and telephones were collected, processed and classified into two different categories. The different mixture numbers (1 to 4) of GMM and wave files sizes of 10, 20, 40 and 80 kb were tested in order to obtain an optimal condition for classification. The GMM method attained 87.99% correct classification rate on test data. This classification is important for speech enabled IVR2 systems [1], dialog systems and many systems in speech processing in the sense that it could help to load an optimum model for increasing system accuracy.
The Measurement of Diversity

The notion of diversity is an issue that is of relevance in several contexts. For example, the biodiversity of a given ecological environment and the diversity of the options available to a decision maker have attracted some attention in recent research. This paper provides an axiomatic approach to the measurement of diversity. We characterize two nested classes of ordinal measures of diversity and an important member of these classes. We prove that the latter special case is equivalent to a diversity ordering proposed by Weitzman.
WikiNEXT: a wiki for exploiting the web of data

This paper presents WikiNEXT, a semantic application wiki. WikiNEXT lies on the border between application wikis and modern web based IDEs (Integrated Development Environments) like jsbin.com, jsfiddle.net, cloud9ide.com, etc. It has been initially created for writing documents that integrate data from external data sources of the web of data, such as DBPedia.org or FreeBase.com, or for writing interactive tutorials (e.g. an HTML5 tutorial, a semantic web programming tutorial) that mix text and interactive examples in the same page. The system combines some powerful aspects from (i) wikis, such as ease of use, collaboration and openness, (ii) semantic web/wikis such as making information processable by machines and (iii) web-based IDEs such as instant development and code testing in a web browser. WikiNEXT is for writing documents/pages as well as for writing web applications that manipulate semantic data, either locally or coming from the web of data. These applications can be created, edited or cloned in the browser and can be used for integrating data visualizations in wiki pages, for annotating content with metadata, or for any kind of processing. WikiNEXT is particularly suited for teaching web technologies or for writing documents that integrate data from the web of data.
Creating a Knowledge Base for Interventions Countering (Violent) Extremism: Intervention Goals and Mechanisms for Different Ideologies

Many interventions aim to tackle violent radicalization. Monitoring the implementation of interventions results in a better identification of effective interventions and in a more appropriate selection of applicable interventions for practitioners. Using meta-analytic and network analytic methods, we present a method to store and retrieve information about countering (violent) extremism (CVE) interventions using a knowledge base that allows for different searches for relevant information. We describe the construal of this knowledge base using data on 99 European CVE interventions. Subsequently, we present inferences that can be drawn from this sample. Key determinants to identify whether radicalizing people are eligible for participating in CVE interventions were found to be friendship relations, group affiliation and available intelligence. Dependent on the ideology targeted by the CVE intervention different goals and mechanisms were identified. Information on financial costs of CVE interventions was often not available in open sources. Implications of representing the information on CVE interventions into a knowledge base are discussed.
Can We Insure Against Political Uncertainty? Evidence from the U.S. Stock Market

I show that shares currently traded on U.S. stock markets can be used to hedge political uncertainty. Focusing on the 2000 U.S. presidential election, I construct two “presidential portfolios” composed of selected stocks anticipated to fare differently under a Bush versus a Gore presidency. To construct these portfolios I use data on campaign contributions by publicly traded corporations and identify the major contributors on each side. Using daily observations for the six months before the election took place, I show that the excess returns of these portfolios with respect to overall market movements are significantly related to changes in electoral polls.
Students Perceptions of School-based HIV/AIDS Education Programs in Western Kenya

Education on HIV infection, prevention, and treatment has the potential to improve young people’s knowledge on sexual health. Utilizing an ethnographic research approach, this study explored students’ perceptions of school-based HIV/AIDS education programs in three high schools in Western Kenya. Using a rights-based theoretical framework the authors sought to understand how such programs empower students to understand HIV vulnerability and prevention. This study contributes in two ways: first, it informs education-based policies and guidelines on the design and implementation of HIV/AIDS education programs in K-12 schools. Second, it underscores the influence of social norms, attitudes and behaviors within the broader societal context on the youth and the power of this population in participating in the design, implementation, evaluation, and sustainability of school-based HIV/AIDS prevention programs. Keywords : HIV/AIDS, School-Based HIV/AIDS Education programs, Students perceptions, Human Rights, Kenya.
(Modular) Effect Algebras are Equivalent to (Frobenius) Antispecial Algebras

Effect algebras are one of the generalizations of Boolean algebras proposed in the quest for a quantum logic. Frobenius algebras are a tool of categorical quantum mechanics, used to present various families of observables in abstract, often nonstandard frameworks. Both effect algebras and Frobenius algebras capture their respective fragments of quantum mechanics by elegant and succinct axioms; and both come with their conceptual mysteries. A particularly elegant and mysterious constraint, imposed on Frobenius algebras to characterize a class of tripartite entangled states, is the antispecial law. A particularly contentious issue on the quantum logic side is the modularity law, proposed by von Neumann to mitigate the failure of distributivity of quantum logical connectives. We show that, if quantum logic and categorical quantum mechanics are formalized in the same framework, then the antispecial law of categorical quantum mechanics corresponds to the natural requirement of effect algebras that the units are each other's unique complements; and that the modularity law corresponds to the Frobenius condition. These correspondences lead to the equivalence announced in the title. Aligning the two formalisms, at the very least, sheds new light on the concepts that are more clearly displayed on one side than on the other (such as e.g. the orthogonality). Beyond that, it may also open up new approaches to deep and important problems of quantum mechanics (such as the classification of complementary observables).
Digito: A Fine-Grain Gesturally Controlled Virtual Musical Instrument

This paper presents Digito, a gesturally controlled virtual musical instrument. Digito is controlled through a number of intricate hand gestures, providing both discrete and continuous control of Digito’s sound engine; with the finegrain hand gestures captured by a 3D depth sensor and recognized using computer vision and machine learning algorithms. We describe the design and initial iterative development of Digito, the hand and finger tracking algorithms and gesture recognition algorithms that drive the system, and report the insights gained during the initial development cycles and user testing of this gesturally controlled virtual musical instrument.
The SrkA Kinase Is Part of the SakA Mitogen-Activated Protein Kinase Interactome and Regulates Stress Responses and Development in Aspergillus nidulans

ABSTRACT Fungi and many other eukaryotes use specialized mitogen-activated protein kinases (MAPK) of the Hog1/p38 family to transduce environmental stress signals. In Aspergillus nidulans, the MAPK SakA and the transcription factor AtfA are components of a central multiple stress-signaling pathway that also regulates development. Here we characterize SrkA, a putative MAPK-activated protein kinase, as a novel component of this pathway. ΔsrkA and ΔsakA mutants share a derepressed sexual development phenotype. However, ΔsrkA mutants are not sensitive to oxidative stress, and in fact, srkA inactivation partially suppresses the sensitivity of ΔsakA mutant conidia to H2O2, tert-butyl-hydroperoxide (t-BOOH), and menadione. In the absence of stress, SrkA shows physical interaction with nonphosphorylated SakA in the cytosol. We show that H2O2 induces a drastic change in mitochondrial morphology consistent with a fission process and the relocalization of SrkA to nuclei and mitochondria, depending on the presence of SakA. SakA-SrkA nuclear interaction is also observed during normal asexual development in dormant spores. Using SakA and SrkA S-tag pulldown and purification studies coupled to mass spectrometry, we found that SakA interacts with SrkA, the stress MAPK MpkC, the PPT1-type phosphatase AN6892, and other proteins involved in cell cycle regulation, DNA damage response, mRNA stability and protein synthesis, mitochondrial function, and other stress-related responses. We propose that oxidative stress induces DNA damage and mitochondrial fission and that SakA and SrkA mediate cell cycle arrest and regulate mitochondrial function during stress. Our results provide new insights into the mechanisms by which SakA and SrkA regulate the remodelling of cell physiology during oxidative stress and development.
The Plant TPX2 Protein Regulates Prospindle Assembly before Nuclear Envelope Breakdown[W]

The Targeting Protein for Xklp2 (TPX2) is a central regulator of spindle assembly in vertebrate cells. The absence or excess of TPX2 inhibits spindle formation. We have defined a TPX2 signature motif that is present once in vertebrate sequences but twice in plants. Plant TPX2 is predominantly nuclear during interphase and is actively exported before nuclear envelope breakdown to initiate prospindle assembly. It localizes to the spindle microtubules but not to the interdigitating polar microtubules during anaphase or to the phragmoplast as it is rapidly degraded during telophase. We characterized the Arabidopsis thaliana TPX2-targeting domains and show that the protein is able to rescue microtubule assembly in TPX2-depleted Xenopus laevis egg extracts. Injection of antibodies to TPX2 into living plant cells inhibits the onset of mitosis. These results demonstrate that plant TPX2 already functions before nuclear envelope breakdown. Thus, plants have adapted nuclear–cytoplasmic shuttling of TPX2 to maintain proper spindle assembly without centrosomes.
HIERARCHICAL CLASSIFICATION TREE MODELING OF NONSTATIONARY NOISE FOR ROBUST SPEECH RECOGNITION

Noise robustness is a key issue in successful deployment of automatic speech recognition systems in demanding environments such as hospital operating rooms. Perhaps the most successful way to overcome the additive noise obstacle is to employ a model adaptation scheme built around a set of dedicated clean speech and noise-only statistical models. Existing recognizer designs generally rely on relatively simple noise models, as more detailed ones would increase computational demands significantly. Simple models are, however, unable to provide accurate characterization of highly nonstationary noise present in real-world noisy facilities and thereby provide only limited reduction in error rate of the recognizer. The present article describes a novel approach to nonstationary acoustical noise modeling via a set of hierarchically tied hidden Markov models in a classification tree structure. Proposed statistical structure allows detailed description of nonstationary ambient acoustical noise while maintaining low computational costs during recognition. Modeling performance of the proposed construction is verified on a real background noise recorded during a neurosurgery in a hospital operating room.
Progress in the Development of Intrinsically Conducting Polymer Composites as Biosensors

Abstract Biosensors are analytical devices which find extensive applications in fields such as the food industry, defense sector, environmental monitoring, and in clinical diagnosis. Similarly, intrinsically conducting polymers (ICPs) and their composites have lured immense interest in bio‐sensing due to their various attributes like compatibility with biological molecules, efficient electron transfer upon biochemical reactions, loading of bio‐reagent, and immobilization of biomolecules. Further, they are proficient in sensing diverse biological species and compounds like glucose (detection limit ≈0.18 nm), DNA (≈10 pm), cholesterol (≈1 µm), aptamer (≈0.8 pm), and also cancer cells (≈5 pm mL−1) making them a potential candidate for biological sensing functions. ICPs and their composites have been extensively exploited by researchers in the field of biosensors owing to these peculiarities; however, no consolidated literature on the usage of conducting polymer composites for biosensing functions is available. This review extensively elucidates on ICP composites and doped conjugated polymers for biosensing functions of copious biological species. In addition, a brief overview is provided on various forms of biosensors, their sensing mechanisms, and various methods of immobilizing biological species along with the life cycle assessment of biosensors for various biosensing applications, and their cost analysis.
Web Page Classification Based on SVM

This paper studies several key aspects of support vector machine (SVM) for Web page classification. Developed from statistical learning theory, SVM is widely investigated and used for text categorization because of its high generalization performance and tolerant ability of processing high dimension classification. Firstly, some methods for Web page presentation are studied. Secondly the Web page classification based on SVM is implementation on data set, and NB classifier is used for study the performance of the SVM classifier processing high dimension space. Finally, the comparison on the polynomial kernel function and the radius basis function (RBF) kernel function is studied. It is proved that if a kernel has a perfect alignment with the classification task, the SVM classifier has better performances
A Static Priority Assignment Algorithm with the Least Number of Priority Levels

With the increased penetration of real time systems into rapidly evolving systems, especially, in on-chip systems such as PDA (personal digital assistant) and PSP (play station portable) etc., the performance/price ratio is becoming a major concern for the system designers. At present, to maximize the performance/price ratio, these systems provide a limited number of priority levels to reduce price and the same priority level is assigned to multiple tasks. Such a trade off makes the most widely used static priority assignment algorithms such as RM (rate monotonic) and DM (deadline monotonic), impractical. As an alternative approach, static limited priority assignment assigns priority to tasks in such a way that the system feasibility is maintained by employing only a few priority levels. To date, static limited priority assignments can be classified into two categories, fixed-number priority and least-number priority assignment. This paper proposes a necessary and sufficient condition for analyzing task set feasibility under static limited priority assignment to make it suitable for a wide rage of applications. In addition, the superiority of low-to-high priority assignment strategy and the concept of saturated task group/assignment are highlighted along with several important properties for transformation among the limited priority assignments. A formal proof is drawn in favor of the least-number priority assignment when tasks are staticpriority schedulable. Finally, a least-number priority assignment algorithm with low time complexity is presented and its efficiency is verified by the experimental results.
Superspreader detection system on NetFPGA platform

We propose a system to detect superspreaders based on combinations of FM sketch, Bloom filter and hash table. It first eliminates sources which are not potential superspreaders, then counts the number of connections to distinct destinations (fan-out) for remaining sources. The proposed system is implemented on NetFPGA platform. Our experiment results show that the system can detect superspreaders with higher fan-out and estimate their fan-outs accurately using small amount of memory.
On Abnormality Detection in Spuriously Populated Data Streams

In recent years, advances in hardware technology have made it increasingly easy to collect large amounts of multidimensional data in an automated way. Such databases continuously grow over time, and are referred to as data streams. The behavior of such streams is sensitive to the underlying events which create the stream. In many applications, it is useful to predict abnormal events in the stream in a fast and online fashion. This is often a difficult goal in a fast data stream because of the time criticality of the detection process. Furthermore, the rare events may often be embedded with other spurious abnormalities, which affect the stream in similar ways. Therefore, it is necessary to be able to distinguish between different kinds of events in order to create a credible detection system. This paper discusses a method for supervised abnormality detection from multi-dimensional data streams, so that high specificity of abnormality detection is achieved. We present empirical results illustrating the effectiveness of our method.
Resource Efficient Mobile Computing Using Cloudlet Infrastructure

Mobile Cloud Computing (MCC) has been introduced as a viable solution to the inherited limitations of mobile computing. These limitations include battery lifetime, processing power, and storage capacity. By using MCC, the processing and the storage of intensive mobile device jobs will take place in the cloud system and the results will be returned to the mobile device. This will reduce the required power and time for completing such intensive jobs. However, connecting mobile devices with the cloud suffers from the high network latency and the huge transmission power consumption especially when using 3G/LTE connections. In this paper, we introduce a Cloudlet based MCC system aiming to reduce the power consumption and the network delay while using MCC. We merged the MCC concepts with the proposed Cloudlet framework and propose a new framework for the MCC model. Our practical experimental results showed that using the proposed model reduces the power consumption from the mobile device, besides reducing the communication latency when the mobile device requests a job to take place remotely while keeping high quality of service stander.
The Use of Interviews and Focus Groups in Teacher Research

ABSTRACT Despite posing a number of benefits, teacher research might be something classroom practitioners avoid engaging in due to inadequate research literacy. Part of this literacy consists of the knowledge and skills required to use popular research methods. Given that teachers might find it useful to read about how their peers have attempted to use such methods in a systematic fashion, this article describes my experience of using interviews and focus groups to better understand students' attitudes, beliefs and practices.
The Influence of Transformational Leadership on Organizational Commitment and Job Satisfaction with Procedural Justice as Mediating Variable

The purpose of this research is to test the influence of transformational leadership on organizational commitment and job satisfaction with procedural justice as mediating variable. The role of the leader is very important given the nature of the leadership taken by these leaders can exert influence on employee commitment and job satisfaction, building on the principles of justice. This study uses a quantitative approach using a survey method and questionnaire instruments. Sampling of this research using purposive sampling that employees who have worked at least two years and subordinate superintendent in Coal and Processing Handling Division (CPHD) PT KPC. Hypothesis testing is conducted using hierarchical regression methods to test mediation in accordance with the rules of Baron and Kenny (1986). Questionnaire data processing taken by SPSS Statistics 21. The results of this study indicate that procedural justice is partially mediates the influence of transformational leadership on organizational commitment and job satisfaction.