Miracle-dz/UniFAR-FTUs huggingface dataset:
Seems Fair to Me: Dyadic Leader Consensus Mediates Fairness and Group Performance

Drawing on both relational and shared leadership theory and utilizing social consensus, we examine the relationship between percieved leader fairness, leader consensus (LC), and group performance. We do so by conceptualizing LC as a new way of hypothesizing and examining shared leadership. LC derives from mutual dyadic perceptions of all members in a team. First, we examine perceptions of leader fairness as a possible antecedent of LC. Second, we investigate the mediational effect of dyadic perceptions of leadership (i.e., LC predicts group performance). In two multisource studies using a round-robin design, we demonstrate that when team members reach a clear consensus about their team leader, perceived leader fairness was positively associated with LC. Furthermore, teams who perceived their leaders as fair exhibited higher group performance.
Combining prestige and relevance ranking for personalized recommendation

In this paper, we present an adaptive graph-based personalized recommendation method based on combining prestige and relevance ranking. By utilizing the unique network structure of n-partite heterogeneous graph, we attempt to address the problem of personalized recommendation in a two-layer ranking process with the help of reasonable measure of high and low order relationships by analyzing the representation of user's preference in the graph. With different initialization and surfing strategies, this graph-based ranking model can take different type of data into account to capture personal interests from multiple perspectives. The experiments show that this algorithm can achieve better performance than the traditional CF methods and some graph-based recommendation methods.
Graph clustering based on Structural Attribute Neighborhood Similarity (SANS)

Graph Clustering techniques are widely used in detecting densely connected graphs from a graph network. Traditional Algorithms focus only on topological structure but mostly ignore heterogeneous vertex properties. In this paper we propose a novel graph clustering algorithm, SANS (Structural Attribute Neighborhood Similarity) algorithm, provides an efficient trade-off between both topological and attribute similarities. First, the algorithm partitions the graph based on structural similarity, secondly the degree of contribution of vertex attributes with the vertex in the partition is evaluated and clustered. An extensive experimental result proves the effectiveness of SANS cluster with the other conventional algorithms.
Digital watermarking using multiresolution wavelet decomposition

We present a novel technique for the digital watermarking of still images based on the concept of multiresolution wavelet fusion. The algorithm is robust to a variety of signal distortions. The original unmarked image is not required for watermark extraction. We provide analysis to describe the behaviour of the method for varying system parameter values. We compare our approach with another transform domain watermarking method. Simulation results show the superior performance of the technique and demonstrate its potential for the robust watermarking of photographic imagery.
Expanding the Recall of Relation Extraction by Bootstrapping

Abstract : Most works on relation extraction assume considerable human effort for making an annotated corpus or for knowledge engineering. Generic patterns employed in KnowItAll achieve unsupervised, high-precision extraction, but often result in low recall. This paper compares two bootstrapping methods to expand recall that start with automatically extracted seeds by KnowItAll. The first method is string pattern learning, which learns string contexts adjacent to a seed tuple. The second method learns less restrictive patterns that include bags of words and relation-specific named entity tags. Both methods improve the recall of the generic pattern method. In particular, the less restrictive pattern learning method can achieve a 250% increase in recall at 0.87 precision, compared to the generic pattern method.
Asymptotic analysis of Bayesian quickest change detection procedures

In the sixties, Shiryaev obtained the structure of the optimal quickest change detection procedure for detecting changes in i.i. d. sequences in a Bayesian setting. However, the analysis of the performance of this procedure in terms of average detection delay versus false alarm probability has been an open problem. In this paper, we investigate the performance of the optimal Bayesian quickest change detection procedure in an asymptotic setting where the false alarm probability goes to zero. The results of this study are shown to be especially important in deriving asymptotically optimal solutions to decentralized quickest change detection problems.
SoftAlloc: A Work Allocation Language with Soft Constraints

Today's business process orchestration languages such as WS-BPEL and BPML have high-level constructs for specifying flow of control and data, but facilities for allocating tasks to humans are largely missing. This paper presents SoftAlloc, a work allocation language with soft constraints, and explains the requirements and trade-offs that led to its design, in particular, what soft constraints are, and how they enable business process definitions to capture allocation rules, best practices, and organizational goals without rendering the business processes too strict. SoftAlloc combines with virtually any business process language and any conceivable legacy system, while guaranteeing polynomial performance. We present the design, the formal definition, and an evaluation of SoftAlloc.
Fault creep along the southern San Andreas from interferometric synthetic aperture radar, permanent scatterers, and stacking

[1] Interferometric synthetic aperture radar (InSAR) provides a practical means of mapping creep along major strike-slip faults. The small amplitude of the creep signal (<10 mm/yr), combined with its short wavelength, makes it difficult to extract from long time span interferograms, especially in agricultural or heavily vegetated areas. We utilize two approaches to extract the fault creep signal from 37 ERS SAR images along the southern San Andreas Fault. First, amplitude stacking is utilized to identify permanent scatterers, which are then used to weight the interferogram prior to spatial filtering. This weighting improves correlation and also provides a mask for poorly correlated areas. Second, the unwrapped phase is stacked to reduce tropospheric and other short-wavelength noise. This combined processing enables us to recover the near-field (∼200 m) slip signal across the fault due to shallow creep. Displacement maps from 60 interferograms reveal a diffuse secular strain buildup, punctuated by localized interseismic creep of 4–6 mm/yr line of sight (LOS, 12–18 mm/yr horizontal). With the exception of Durmid Hill, this entire segment of the southern San Andreas experienced right-lateral triggered slip of up to 10 cm during the 3.5-year period spanning the 1992 Landers earthquake. The deformation change following the 1999 Hector Mine earthquake was much smaller (<1 cm) and broader than for the Landers event. Profiles across the fault during the interseismic phase show peak-to-trough amplitude ranging from 15 to 25 mm/yr (horizontal component) and the minimum misfit models show a range of creeping/locking depth values that fit the data.
Lumbar posture and trunk muscle activation during a typing task when sitting on a novel dynamic ergonomic chair

Low back pain (LBP) is a common musculoskeletal disorder and prolonged sitting often aggravates LBP. A novel dynamic ergonomic chair (‘Back App’), which facilitates less hip flexion while sitting on an unstable base has been developed. This study compared lumbar posture and trunk muscle activation on this novel chair with a standard backless office chair. Twelve painfree participants completed a typing task on both chairs. Lumbar posture and trunk muscle activation were collected simultaneously and were analysed using paired t-tests. Sitting on the novel dynamic chair significantly (p < 0.05) reduced both lumbar flexion and the activation of one back muscle (Iliocostalis Lumborum pars Thoracis). The discomfort experienced was mild and was similar (p > 0.05) between chairs. Maintaining lordosis with less muscle activation during prolonged sitting could reduce the fatigue associated with upright sitting postures. Studies with longer sitting durations, and in people with LBP, are required. Practitioner Summary: Sitting on a novel dynamic chair resulted in less lumbar flexion and less back muscle activation than sitting on a standard backless office chair during a typing task among pain-free participants. Facilitating lordotic sitting with less muscle activation may reduce the fatigue and discomfort often associated with lordotic sitting postures.
Self-Coordinated and Self-Traced Composite Services with Dynamic Provider Selection

The growth of Internet technologies has unleashed a wave of innovations that are having tremendous impact on the way organisations interact with their partners and customers. It has undoubtedly opened new ways of automating Business-to-Business (B2B) collaboration. Unfortunately, as electronic commerce applications are most likely autonomous and heterogeneous, connecting and coordinating them in order to build inter-organisational services is a difﬁcult task. To date, the development of integrated B2B services is largely ad-hoc, time-consuming and requires an enormous effort of low-level programming. This approach is not only tedious, but also hardly scalable because of the volatility of the Internet, and the dynamic nature of business alliances. In this paper, we consider the efﬁcient composition and execution of B2B services. Speciﬁcally, we present a framework through which services can be declaratively composed, and the resulting composite services can be executed in a decentralised way within a dynamic environment. The underlying execution model supports the incremental collection of the execution trace of each composite service instance. These traces are particularly useful for customer feedback, and for detecting malfunc-tionings in the constitution of a composite service.
Haar like and LBP based features for face, head and people detection in video sequences

Actual computer vision algorithms cannot extract semantic information of people activity coming from the large and increasing amount of surveillance cameras installed around the world. Algorithms need to analyse video content at real time frame rate and with a false alarm detection rate as small as possible. Such algorithms can be dedicated and specifically parameterised in certain applications and restrained environment. To make algorithms as useful as possible, they need to tackle many challenging issues in order to correctly analyse human activties. For instance, people are rarely entirely seen in a video because of static (contextual object or they are partly seen by the camera field of view) and dynamic occlusion (e.g. person in front of another). We here present a novel people, head and face detection algorithm using Local Binary Pattern based features and Haar like features which we refer to as couple cell features. An Adaboost training scheme is adopted to train object features. During detection, integral images are used to speed up the process which can reach several frames per second in surveillance videos.
GPS lens: GPS based controlling of pointers on large-scale urban displays using mobile devices

The increasing number of media façades in urban spaces offers great potential for interaction. Due to their size and physical properties interacting with them directly by touching them is not possible. In this paper, we present a sensor-based approach, which relies on the GPS, compass and accelerometer data of a mobile device to control a pointer on large-scale urban displays, such as media façades. We calculate the pointing direction based on the location and orientation of the mobile device and display a preview of the content around the current cursor position to overcome the limitations of current GPS sensors. We further report on an initial user evaluation, revealing the presented approach as accurate and easy-to-use.
Retinal image enhancement by index of fuzziness in multi-mode histogram

Evaluation of a retinal image is widely employed to help doctors diagnose many diseases, such as diabetes or hypertension. From acquisition process, retinal images often have low grey level contrast and dynamic range. This paper proposes histogram analysis for solving the problems of retinal image enhancement. The proposed method uses fuzzy set to enhance the images by partitioning the image histogram to multimodes with derivative equation. Each mode of the histogram is contrasted by finding the optimal crossover point of S-function with an index of fuzziness, which is designed for contrasting a field of view in the images. Our algorithm can achieve a number of properties of an importance for contrast stretching, such as compressed the noise in background and produced a high contrast in the field of view that provide feasibility in diagnosis from a retinal image.
Related Work 2.1 Recommender Systems

In this chapter, we introduce the general related work for context-aware ranking with factorization models. Related work on specific issues like tag recommenders, Mar-kov chains, etc. is discussed in detail in the corresponding chapters. Here, we discuss three general topics. The first one is recommender systems because the standard task of personalized item recommendation (a two mode problem) can be seen as context-aware ranking where the context is the user. Nevertheless in recommender systems, the term 'context' is usually used only for cases with at least three modes and furthermore the first mode is typically assumed to be the user. Thus, in the discussion about recommender systems we stick to the definition within the rec-ommender community and use the term context-aware recommender system only for ranking problems with at least three modes. In contrast to this, in this book we use the term context-aware ranking for any number of modes. Secondly, we investigate factorization models on which our proposed approach is based. Finally, we discuss the literature about ranking in general and context-aware ranking in particular. Traditionally, recommender systems are designed for two-mode problems. The task is to predict how much a user likes an item. In context-aware recommender systems, additional modes are available such as time, mood, etc. Recommender settings can be distinguished into rating prediction (regression) and item recommendation (ranking). Both settings differ in the type of observations that are available. For rating prediction the observations are explicitly given real values. Item recommendation problems usually have only implicit binary feedback – where only positive feedback is observed. The later problem is the more challenging one as the interpretation of the observation is difficult. Furthermore, it appears more often in practice because implicit feedback is easy to gather – e.g. almost every web server records implicit behavior in log files by default. This book focus on the recommendation problem from implicit feedback.
Replacement policies for shared caches on symmetric multicores: a programmer-centric point of view

The presence of shared caches in current multicore processors may generate a lot of performance variability in multi-programmed environments. For applications with quality-of-service requirements, this performance variability may lead the programmer to be overly pessimistic about performance and reduce the application features and/or spend a lot of effort optimizing the algorithms. To solve this problem, there must be a way for the programmer to define a reasonable performance target and a guarantee that the actual performance is very unlikely to be below the targeted performance. We propose that the performance target be defined as the performance measured when each core runs a copy of the application, which we call self-performance. This study characterizes self-performance and explains how the shared-cache replacement policy can be modified for self-performance to be meaningful.
Wacky Pirate Hijinks : causing and resolving conflictbetween autonomous agents in a virtual storyworld

The Virtual Storyteller is a story generation system based on emergent narrative. Emergent narrative is a method in story generation in which the characters all have their own goals, desires and motivations upon which they base their actions within the story. This way, the story is formed around the characters, instead of the characters adapting themselves to the story. While this does increase the believability, it makes it harder to generate an interesting story; the characters choose their actions based on what is best for them, instead of what makes a story interesting. In this thesis, it is tried to remedy this, and make the stories generated by the Virtual Storyteller more interesting. This is done by following Freytag's dramatic structure, which describes how the conflicts in a story develop. Therefore, this thesis focuses on introducing and resolving these conflicts within the Virtual Storyteller, while trying not to impact the characters' autonomy significantly. The conflicts are described by so called plot threads, that describe requirements the story world must meet in order to place two characters into conflict with each other. By making sure the world meets these requirements, the environment of the characters can be changed to instigate the conflict between them. This conflict can then be resolved by giving the story's protagonist access to slightly better abilities than his antagonist, that appear to make him just a bit better or luckier, thus giving him enough of an advantage in the conflict to allow him to win it. By controlling when new conflicts are introduced, and when the protagonist gains access to his better abilities, it becomes possible to direct enough to allow the action to rise, reach a climax, and then fall, and thus follow a dramatic structure.
An Integrated Smooth T ansmission Control and Temporal Scaling Scheme for MPEG-4 Streaming Video r

Limited bandwidth and variable network conditions compromise throughput performance and smoothness, and eventually impair the playback quality of video streams. Scalable video coding provides enhanced flexibility in stream manipulation, adapting video to the network dynamics. The efficiency of a scalability technique is dependent on the underlying congestion control and particularly on its ability to maintain a smooth transmission rate. In this context, we combine a smooth transmission control, namely Scalable Streaming Video Protocol (SSVP), with a temporally coding algorithm in order to optimize the delivery and playback of MPEG-4 streaming video. Simulation experiments indicate that this combined approach can provide significant performance gains over a wide range of network and session dynamics.
Border surveillance: A dynamic deployment scheme for WSN-based solutions

Wireless Sensor Networks (WSNs) are based on elementary sensors that detect the occurrence of particular events in a monitored area. Among the recent critical WSN applications one can find the border surveillance applications. The first aim of this class of applications is to monitor a country border and detect the presence of intruders near the border line. In this paper, we investigate theoretically the effects of natural factors on dynamic deployment scheme of a hierarchical WSN-based solution providing two lines of surveillance. Parameters such as the wind effect, the altitude and velocity of the airplane from which the sensors are thrown are put into equation to optimize the area coverage and WSN connectivity. Then, we propose mathematical models that evaluate the quality of connectivity and coverage of the deployed network and allow planning and dimensioning of a border solution.
The Correlated Knowledge Gradient for Simulation Optimization of Continuous Parameters using Gaussian Process Regression

We extend the concept of the correlated knowledge-gradient policy for the ranking and selection of a finite set of alternatives to the case of continuous decision variables. We propose an approximate knowledge gradient for problems with continuous decision variables in the context of a Gaussian process regression model in a Bayesian setting, along with an algorithm to maximize the approximate knowledge gradient. In the problem class considered, we use the knowledge gradient for continuous parameters to sequentially choose where to sample an expensive noisy function in order to find the maximum quickly. We show that the knowledge gradient for continuous decisions is a generalization of the efficient global optimization algorithm proposed in [D. R. Jones, M. Schonlau and W. J. Welch, J. Global Optim. , 13 (1998), pp. 455–492].
Relational Reinforcement Learning in Infinite Mario

Relational representations in reinforcement learning allow for the use of structural information like the presence of objects and relationships between them in the description of value functions. Through this paper, we show that such representations allow for the inclusion of background knowledge that qualitatively describes a state and can be used to design agents that demonstrate learning behavior in domains with large state and actions spaces such as computer games.
The Effect on Selenium Concentrations of a Randomized Intervention with Fish and Mussels in a Population with Relatively Low Habitual Dietary Selenium Intake

Selenium status of the Danish population is below that assumed optimal for the suggested protective effects against chronic diseases, including certain cancers. Fish and shellfish are important dietary sources of selenium in Denmark. We investigated the effect of increased fish and mussel intake on selenium blood concentrations in a population with relatively low habitual dietary selenium intake. We randomly assigned 102 healthy men and women (all non-smokers) aged 48–76 years to an intervention group (n = 51) or a control group (n = 51). Intervention participants received 1000 g fish and mussels/week for 26 weeks (~50 μg selenium/day). Controls received no intervention. Non-fasting blood samples were taken and whole blood selenium was determined using inductively coupled plasma-mass spectrometry (ICP-MS), and plasma selenoprotein P (SelP) was determined by high performance liquid chromatography coupled to ICP-MS. All available observations were included in linear multiple regression analysis to evaluate the effect of the intervention. The difference in mean change for intervention compared with control persons was 14.9 ng/mL (95% CI: 10.2, 19.7) for whole blood selenium, and 7.0 ng/mL (95% CI: 3.1, 10.9) for plasma SelP (Weeks 0–26). Selenium concentrations were significantly increased after 26 weeks of intervention, albeit to a lower degree than expected.
Personalized Services Research Based on Web Data Mining Technology

This paper use the source of web log data and through AprioriAll algorithm among sequential pattern mining to identify the user pattern in the data pre-processing stage and based on pattern to cluster users’ path pattern and make the similarity users cluster one group in order to reduce pattern. Through testing, the method has been proven to be effective for users in personalized service.