Miracle-dz/UniFAR-FTUs huggingface dataset:
Anterior Prefrontal Cortex Contributes to Action Selection through Tracking of Recent Reward Trends

The functions of prefrontal cortex remain enigmatic, especially for its anterior sectors, putatively ranging from planning to self-initiated behavior, social cognition, task switching, and memory. A predominant current theory regarding the most anterior sector, the frontopolar cortex (FPC), is that it is involved in exploring alternative courses of action, but the detailed causal mechanisms remain unknown. Here we investigated this issue using the lesion method, together with a novel model-based analysis. Eight patients with anterior prefrontal brain lesions including the FPC performed a four-armed bandit task known from neuroimaging studies to activate the FPC. Model-based analyses of learning demonstrated a selective deficit in the ability to extrapolate the most recent trend, despite an intact general ability to learn from past rewards. Whereas both brain-damaged and healthy controls used comparisons between the two most recent choice outcomes to infer trends that influenced their decision about the next choice, the group with anterior prefrontal lesions showed a complete absence of this component and instead based their choice entirely on the cumulative reward history. Given that the FPC is thought to be the most evolutionarily recent expansion of primate prefrontal cortex, we suggest that its function may reflect uniquely human adaptations to select and update models of reward contingency in dynamic environments.
Author's Personal Copy Ecological Indicators Monitoring Animal Diversity Using Acoustic Indices: Implementation in a Temperate Woodland

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t Biodiversity assessment is one of the major challenges for ecology and conservation. With current increase of biodiversity loss during the last decades, there is an urgent need to quickly estimate biodiversity levels. This study aims at testing the validity of new biodiversity indices based on an acoustic analysis of choruses produced by animal communities. The new Acoustic Richness index (AR) and the former dissimilarity index (D) aim at assessing ␣ and ␤ diversity respectively. Both indices were tested in three woodland habitats: a mature forest, a young forest and a forest-cropland ecotone within the Parc Naturel Régional of Haute-Vallée de Chevreuse (France). Three recorders running for 74 days generated 5328 files of 150 s for a total of 222 h of recording. All files were treated with frequency and amplitude filters to try to remove anthropogenic and environmental background noise. The AR index was in agreement with traditional aural identification of bird species. The AR index revealed an expected gradient of diversity with higher diversity values in the young forest that potentially provides a higher number of microhabitats. The D index also indicated expected differences in the acoustic environment across sites with distinct habitat structure. Both indices reveal significant peak during dawn chorus. These results suggest that diversity could be estimated through acoustics at both ␣ and ␤ scales. Our analyses reveal that, even if background noise needs to be considered with great care, the use of acoustic indices has the potential to facilitate animal diversity assessments over seasons or years and landscape scales.
Integration of multi-level postural balancing on humanoid robots

This paper discusses an integration issue of multi-level postural balancing on humanoid robot. We give a unified viewpoint of postural balancing, which covers Ankle Strategy to Hip Strategy. Two kinds of distributor of desired ground reaction force to whole-body joint torque are presented. The one distributor leads to a dynamic balancer which covers Hip strategy, with the under-actuated situation. A simple angular momentum regulator is also proposed to stabilize the internal motions due to the joint redundancy. The other distributor leads to a static balancer which lies between Ankle and Hip strategy. Furthermore, this paper demonstrates that replacement of the center of mass feedback with the local joint stiffness makes the robot much stabler for some fast motions. Motivated by the practicability of the static balancer and the strong push-recovery performance of the dynamic balancer, this paper presents a simple integration by superposition of the both balancers on a compliant human-sized biped robot. The simulation and experimental videos are supplemented.
Licensed under Creative Commons Attribution Cc by Text Clustering and Classification on the Use of Side Information

Side-information is present with the text document in many text mining applications. An user-access behavior from web logs, or other non-textual attributes embedded into the text document, the links in the document, document provenance information etc are nothing but side information. These attributes contains a vast amount of information for clustering purposes. But it is difficult to estimate the relative importance when some information is noisy. In that case, it will be risky to incorporate side-information into mining process as there is possibility that it will increase the quality of the representation for the mining process or may add a noise to process. Thus a proper way to carry out the mining process is needed such that it will maximize the advantages form using side information. So in this project, an algorithm is designed, in order to give an effective clustering algorithm. This algorithm combines classical partitioning algorithms with probabilistic models. We then show how to extend the approach to the classification problem.
Mechanism design for abstract argumentation

Since their introduction by Dung over a decade ago, abstract argumentation frameworks have received increasing interest in artificial intelligence as a convenient model for reasoning about general characteristics of argument. Such a framework consists of a set of arguments and a binary defeat relation among them. Various semantic and computational approaches have been developed to characterise the acceptability of individual arguments in a given argumentation framework. However, little work exists on understanding the strategic aspects of abstract argumentation among self-interested agents. In this paper, we introduce (game-theoretic) argumentation mechanism design (ArgMD), which enables the design and analysis of argumentation mechanisms for self-interested agents. We define the notion of a direct-revelation argumentation mechanism, in which agents must decide which arguments to reveal simultaneously. We then design a particular direct argumentation mechanism and prove that it is strategy proof under specific conditions; that is, the strategy profile in which each agent reveals its arguments truthfully is a dominant strategy equilibrium.
Layers of user expectations of future technologies: an early framework

User's expectations are identified as a factor affecting the actual user experience in human-computer interaction. Considering the context of emerging and future technologies, users' expectations can become increasingly diverse, especially in terms of where they stem from. This paper presents an early framework for understanding different layers of expectations that people might have of technologies in the near future: for example, "desires" and "social and societal norms". The framework provides understanding of the spectrum of user expectations and what different aspects of them could be identified in user inquiries and evaluations. For concretization and credibility of this work-in-progress framework, examples from recent research on user expectations of mobile augmented reality are provided.
Evidence Games: Truth and Commitment

An evidence game is a strategic disclosure game in which an informed agent who has some pieces of verifiable evidence decides which ones to disclose to an uninformed principal who chooses a reward. The agent, regardless of his information, prefers the reward to be as high as possible. We compare the setup where the principal chooses the reward after the evidence is disclosed to the mechanism-design setup where he can commit in advance to a reward policy. The main result is that under natural conditions on the truth structure of the evidence, the two setups yield the same equilibrium outcome.
The CUED HiFST System for the WMT10 Translation Shared Task

This paper describes the Cambridge University Engineering Department submission to the Fifth Workshop on Statistical Machine Translation. We report results for the French-English and Spanish-English shared translation tasks in both directions. The CUED system is based on HiFST, a hierarchical phrase-based decoder implemented using weighted finite-state transducers. In the French-English task, we investigate the use of context-dependent alignment models. We also show that lattice minimum Bayes-risk decoding is an effective framework for multi-source translation, leading to large gains in BLEU score.
The effect of region segmentation on object categorization

The success of object categorization is heavily dependent on the extracted image descriptors. In general, image or region segmentation is usually performed to segment an image into several regions or objects, and then some level-level features, such as color and texture, are extracted from each region. As a result, the region descriptor or the combination of multiple region descriptors can be used to represent a specific object or the whole image for categorization. Since there are many well-known region segmentation algorithms proposed in literature, and using different region segmentation algorithms can produce different region descriptors for the same images, no study examines the effect of region segmentation on object categorization. In this paper, we apply three well-known region segmentation algorithms for image feature extraction and representation, which are graph cuts, mean-shift segmentation, and normalized cuts. Then, the support vector machine (SVM) is used as the classifier for object categorization. Our experimental results based on Caltech 5, Caltech 8, and Corel 10 datasets show that the normalized cuts algorithm performs best. In addition, the image feature representation based on multiple region descriptors can provide more discriminative power than using center region descriptors.
Transmitted Reference-Based Spectrum Sensing in IEEE 802.22 WRAN Networks

Cognitive radio (CR) is an effective technology for flexible utility of radio spectrum and has received increasing attention so far. To ensure reliable spectrum sensing, quiet periods (QPs) are allocated during which all secondary users cease transmission and perform spectrum sensing. Such that, regular transmission will be interrupted periodically and the system quality of service (QoS) could be decreased to some extent. In this paper, a spectrum sensing scheme which is based on the transmitted reference (TR) technique is proposed where a secondary signal is transmitted in the first half frame and is reversed to retransmit in the second half one. For the sensing CR user, the first half received signals are delayed and added to the second half ones. By doing so, the strong interference from the other CR user can be alleviated without affecting the spectrum detection performance. Simulation results show that the proposed scheme is very effective and a nearly same detection performance can be obtained comparing with the method when a long enough QP is used.
Adaptive digital audio effects (a-DAFx): a new class of sound transformations

After covering the basics of sound perception and giving an overview of commonly used audio effects (using a perceptual categorization), we propose a new concept called adaptive digital audio effects (A-DAFx). This consists of combining a sound transformation with an adaptive control. To create A-DAFx, low-level and perceptual features are extracted from the input signal, in order to derive the control values according to specific mapping functions. We detail the implementation of various new adaptive effects and give examples of their musical use
Massive symmetric tensor field on AdS

The two-point Green function of a local operator in CFT corresponding to a massive symmetric tensor field on the AdS background is computed in the framework of the AdS/CFT correspondence. The obtained two-point function is shown to coincide with the two-point function of the graviton in the limit when the AdS mass vanishes.
Miler - A Tool Infrastructure to Analyze Mailing Lists

The information that can be used to analyze software systems is not limited to the raw source code, but also to any other artifact produced during its evolution. In our recent work we have focused on how archives of e-mails that concern a system can be exploited to enhance program comprehension. In this paper we present Miler, a tool we have built for that purpose. We describe its architecture and infrastructure, and the FAMIX meta-model extension we have devised to model mailing list archives.
Mining Behavioral Groups based on Usage Data in Large Wireless LANs

Wireless networks and personalized mobile devices are deeply integrated and embedded in our lives. Such wide adoptions of new technologies will impact user behavior and in turn will affect network performance. It is imperative to characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks. One major challenge in characterizing user behavior stems from the significant size and complexity of user behavioral data. Without summarization and dimension reduction, the sheer amount of data does not provide much useful information. The key contribution of the paper is a novel similarity metric based on a matrix representation of mobility preferences and its decomposition. This method provides an efficient way to reduce important spatiotemporal dynamics in user mobility into a few eigen-behavior vectors. This also facilitates nodes to exchange their mobility summaries and determine their mutual similarity locally. Without any assumption on the properties of user population, we use unsupervised learning (clustering) techniques to classify WLAN users. Such a user grouping scheme based on learned user behavior is crucial for applications relying on the usage context of each mobile device (e.g., participatory sensing, social-relationship-aware message forwarding). In this study, using our systematic TRACE approach, we analyze wireless users’ behavioral patterns by extensively mining wireless network logs from two major university campuses to showcase its efficacy. While our findings partly validate intuitive repetitive behavioral trends and user grouping, it is surprising to find the qualitative commonalities and striking consistency of user behavior from the two universities. We discover multi-modal user behavior for more than 60% of the users, and there are hundreds of distinct groups with unique behavioral patterns in both campuses. The sizes of the major groups follow a power-law distribution.
Coupling Effects of Aperture Coupled Microstrip Antenna

The coupling mechanism mainly depends on the shape of aperture, patch and the feed line; it also affects the radiation properties. The paper analyses some of the different shapes of these elements on the aperture coupled microstrip antenna. One effective way to increase the impedance bandwidth is to change the patch shape, feed shape and the slot shape. The shape of coupling aperture has a great effect on the strength of coupling between the feed line and the patch. Mainly thin rectangular shape of the slot is used in an aperture coupled microstrip antenna. If a suitable combination of the shape of feed and slot is chosen and tuned perfectly, it gives an optimum impedance bandwidth with an improved radiation pattern. Keywords—:-microstrip antennas,aperture,coupling
Articulating a space for critical learning with a social justice orientation in an adult education programme

This article reflects on work as a radical-feminist adult educator and as part of a group of academics from 10 universities who have developed new national qualifications in the Adult Education Community and Training Sector (ACET) for Higher Education in South Africa. The qualification will respond to the training needs of Adult Education Community lecturers and practitioners, and thereby indirectly contribute to the education and training needs of the communities, unemployed adults and youth who require skills to find employment. In the design of this qualification we sought to ensure the inclusion in the qualification of the new policy requirements, critical transformative educational practices, and perspectives from community educators, as well as recent demands from students for a decolonised curriculum. Sometimes these frameworks are in contradiction to one another, particularly in a neo-liberal context in which education has a strong focus on the workplace. I provide evidence from a qualitative study on student motivations to study further that shows that whilst there is a concern with education for skills development to grow the economy, there are still present strong political motivations to learn on the part of students and therefore it is imperative to teach from all these standpoints. Keywords: context; critical learning; education policy; social justice orientation; student motivations
Ontological Stream Reasoning via Syntactic Approximation

Traditionally Description Logic (DL)-based ontologies are only used to capture static knowledge. Recently the fast development of the Semantic Web and its evolving data raises the requirements of reasoning services on dynamic knowledge streams. In this paper, we present an efficient ontology stream reasoning approach which combines the delete and re-derive algorithm and the syntactic approximation to guarantee soundness and tractability. Compared with existing works, it allows stream reasoning for ontologies in very expressive language profiles and requires no prior knowledge about the streams.
Realistic Model for Temporal Reasoning in Real-Time Patient Monitoring

Time is a central factor in patient monitoring. Introduction of domain-dependent knowledge is essential to ensure efficiency of time managers, especially when embedded into systems that interact with the real world. We present a realistic temporal reasoning model based on two basic cognitive mechanisms: aggregation of similar observed situations and forgetting of non-relevant information. We describe in detail how we represented the proposed model and how, by refinement of domain-independent temporal entities and inferences, we added domain specific knowledge to manage a clinical therapy. The model allows clinical observations to be incrementally interpreted as they are acquired by an intelligent system, mainly reactive in its reasoning, for the management of patients receiving respiratory support.
Hybrid Modulation for Dual Active Bridge Bi-Directional Converter With Extended Power Range For Ultracapacitor Application

In order to actively control the power flow between the load and ultracapacitor, a dual active bridge (DAB) converter can be utilized. Conventional phase shift modulation has difficulties in working with a wide range of source voltage levels and load power levels. In addition, traditional PI controller does not provide satisfactory behavior as the relation between the control variable and output voltage is nonlinear. In this paper, a hybrid modulation scheme is proposed to extend the power range of operation to 100% to 16.67% instead of 100% to 58.33% that is achieved by conventional phase shift modulation. A feedback linearized controller is also designed to achieve a better dynamic response under various load. The detail modulation principle and controller design is given and the experimental results verify the proposed modulation and control scheme.
Language Richness of the Web

We have built a corpus containing texts in 106 languages from texts available on the Internet and on Wikipedia. The W2C Web Corpus contains 54.7~GB of text and the W2C Wiki Corpus contains 8.5~GB of text. The W2C Web Corpus contains more than 100~MB of text available for 75 languages. At least 10~MB of text is available for 100 languages. These corpora are a unique data source for linguists, since they outclass all published works both in the size of the material collected and the number of languages covered. This language data resource can be of use particularly to researchers specialized in multilingual technologies development. We also developed software that greatly simplifies the creation of a new text corpus for a given language, using text materials freely available on the Internet. Special attention was given to components for filtering and de-duplication that allow to keep the material quality very high.
CPSec manager: Secure content protection management technology with time and space constraint for E-document

In this paper, a secure Content Protection Secure Management Scheme about electronic documents (named CPSec EDMS) will be proposed, in which the architecture, key technology and authorization protocol, security analysis of CPSec EDMS will be discussed. To prevent document leakage, the client of CPSec EDMS adopts transparent encryption technology based on filter driver, in which the client encrypts files automatically. Considering the off-line mode exiting in the application, the client of the CPSec EDMS will call function of offline decryption interface, which is running in application layer. In view of different receiver, the CPSec EDMS will adopt different strategy, in which the client will encapsulate the permission and the ciphertext for the receiver internal while the receiver will apply for decryption from the server. In contrast, the client will encapsulate the permission and the plaintext for the receiver external.
Exploring self-regulated learning during middle school: views of parents and students on parents’ educational support at home

ABSTRACT The transition period from elementary to middle school is challenging. Students are expected to be more autonomous. To cope with these increased levels of autonomy, self-regulated learning skills might help. Previous research has shown that parents play a crucial role in students’ self-regulated learning development. However, research focusing on parental involvement during middle school and the ways in which parents support self-regulated learning at home is limited. Therefore, the present study explores the practices that parents use at home to support middle school students’ autonomy and self-regulated learning. It draws upon semi-structured interviews with eighteen families. The results show that parents mainly guide students’ learning behaviours and motivation. Additionally, parents and students can be divided into three groups based on differences in autonomy granted during learning; these groups reported varying parental practices in fostering self-regulated learning. Overall, the present study highlights the importance of parents’ educational involvement during middle school.
A Modified Differential Algorithm with Pitch Adjustment for Numerical Optimization

In this paper, A Modified Differential Algorithm (MDE) developed to solve unconstrained problems. The MDE algorithm use pitch adjustment replaces the traditional mutation operation and design a dynamic method to adjust the crossover rate (CR). The former can enhance the convergence of the MDE, and the latter can effectively prevent the MDE from being trapped into the local optimum. Based on several benchmark experiments, the MDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and its two improved algorithms (JADE and SaDE).