Miracle-dz/UniFAR-FTUs huggingface dataset:
Variation in prosodic planning among individuals and across languages

Previous research (Swets et al., 2007) found that working memory (WM) was associated with the manner in which silent readers in multiple languages package linguistic material together. Specifically, those with high WM were more likely to create larger linguistic packages than those with low WM, which in turn influenced the manner in which they interpreted syntactic ambiguities. One possibility that gained support in subsequent research on language production (Petrone et al., 2011) is that this packaging associated with WM is prosodic in nature. In the current study, we present evidence that the size of prosodic increments during language production, as measured by the occurrence of pauses, is associated with WM and speed of processing. This study adds to the increasing body of literature that seeks to better understand mechanisms of language production by investigating variation in planning tendencies among individuals. In addition, we examined whether pausing tendencies vary across languages. In our study, 31 German speakers and 32 French speakers described images presented on a computer monitor while their utterances were recorded. The images contained three objects apiece, and a speaker's task was to describe the images in such a way that a person listening to the utterance could follow the instruction to move the described objects (e.g., " The box moves below the train and the mouse moves above the train. ") To manipulate sentence complexity (and cognitive load), half of the descriptions required speakers to distinguish between similar looking objects (e.g., a cat with four legs vs. a cat with three legs). In addition to number of pauses, we also measured initiation time, and separately measured WM via a Reading Span task and processing speed via a Letter Comparison task. Analyses using linear mixed effects models revealed several key findings. First, we found significant cross-linguistic differences in initiation time and number of pauses: French speakers began their speech more quickly, but paused more often than German speakers, suggesting a greater degree of " incremental " planning. Despite these cross-linguistic differences, we nevertheless found evidence across languages that both WM and processing speed explain unique variance in our measure of pause frequency, and do so in the same direction: As WM and processing speed increase, the number of pauses speakers produce per utterance decreases. There is also evidence that both effects are stronger during the high-load, complex sentences. On the other hand, neither WM nor processing speed predicted variability in initiation time. These results lend support to the hypothesis that individual differences in WM and processing speed lead to differences in planning processes, such that higher WM and processing speed support the planning of larger prosodic " chunks " .
Sense and Avoid for Small Unmanned Aircraft Systems

The ability for small Unmanned Aircraft Systems (sUAS) to safely operate beyond line of sight is of great interest to consumers, businesses, and scientific research. In this work, we investigate Sense and Avoid (SAA) algorithms for sUAS encounters using three 4k cameras for separation distances between 200m and 2000m. Video is recorded of different sUAS platforms designed to appear similar to expected air traffic, under varying weather conditions and flight encounter scenarios. University partners and NASA both developed SAA methods presented in this report. This report is organized as follows: Introduction, Experimental Design, Description of video and GPS data, NASA Methods and Results, University Partner Results, Recommendations, and Conclusions.
An Artificial Neural Network Controller for Three-level Shunt Active Filter to Eliminate the Current Harmonics and Compensate Reactive Power

The increased use of nonlinear devices in the industry has resulted in the direct increase of harmonic distortion in power systems during these last years. Active filter systems are proposed to mitigate current harmonics generated by nonlinear loads. The conventional scheme based on a two-level voltage source inverter controlled by a hysteresis controller has several disadvantages and cannot be used for medium or high-power applications. To overcome these drawbacks and improve the APF performance, there’s a great tendency to use multilevel inverters controlled by intelligent controllers. Three level (NPC) inverter is one of the most widely used topologies in various industrial applications such as machine drives and power factor compensators. On the other hand, artificial neural networks are under study and investigation in other power electronics applications. In order to gain the advantages of the three-level inverter and artificial neural networks and to reduce the complexity of classical control schemes, a new active power filter configuration controlled by two MLPNN (Multi-Layer Perceptron Neural Network) is proposed in this paper. The first ANN is used to replace the PWM current controller, and the second one to maintain a constant dc link voltage across the capacitors and compensate the inverter power losses. The performance of the global system, including power and control circuits is evaluated by Matlab-Simulink and SimPowerSystem Toolbox simulation. The obtained results confirm the effectiveness of the proposed control scheme.
Are Individuals with Entrepreneurial Characteristics More Likely to Engage in Destruction? An Experimental Investigation Among Potential 21st Century Entrepreneurs in Tunisia

We test experimentally whether individuals high in some entrepreneurial characteristics are more likely to engage in destructive behavior than other individuals. In the joy‐of‐destruction game, participants can either preserve or costly burn the endowment of each other. In some treatments they can hide from other participants whether they are responsible for destruction. We found that participants high in some entrepreneurial characteristics are significantly more likely to adopt destructive behaviours.
Hopeful Voices: Reflective Perspectives on Ways to Advancing Higher Education Development in Post-Civil War Liberia

Liberia’s recent civil war that end in 2003 negatively impacted the higher education system of Liberia, which is now in the process of further development. In the summer of 2016, a qualitative phenomenological case study was conducted consisting of eighteen interviews with administration, faculty, and students from four Liberian universities: Liberia International Christian College (LICC) (8), University of Liberia (UL) (7), Stella Maris Polytechnic (1), and Cuttington University (1). Furthermore, a National Commission of Higher Education (NCHE) advisor was interviewed. The hope is for this research into Liberian higher education development following the civil war to inspire Liberians, nongovernmental organizations (NGOs), and higher education practitioners to understand further what happened, how development is occurring, and what still needs to be done in order to provide effective solutions in post-conflict nations. The research revealed the impact of the civil war upon faculty, students, and institutional structures. Additionally, the research explored the development of the University of Liberia and Liberian International Christian College since the war. The findings present proposed solutions and implications for higher education development within Liberia.
Access Privilege Management for Specific Vehicle in Internet of Things

IoT prevent information leakage to vehicle with the development of the Internet of Things (IoT), it must provide to the second user a vehicle data to keep track of a specific vehicle. In this paper, we propose an access rights approved scheme that used by attribute-based threshold decryption scheme (CP-ABTD) and dynamic threshold cryptography in order to solve this problem.
Robust Beamforming in GPS Arrays

Multipath frequently limits the performance of carrier phase-based relative positioning and precision navigation techniques. Multipath mitigation techniques that discriminate between the direct signal path and reflections based on differences in time-of-arrival are of little utility in combating close-in multipath. In this paper, we describe an antenna array for mitigating multipath based on angle-of-arrival differences. We also describe signal processing algorithms for combining the output of the array elements to minimize the effect of interferences and multipath. This paper introduces an extension of minimum variance beamforming that explicitly takes into account variation or uncertainty in the assumed array response. Sources of this uncertainty include imprecise knowledge of the angle of arrival and uncertainty in the array manifold. In our method, uncertainty in the array manifold is modeled explicitly via an uncertainty ellipsoid that gives the possible values of the array for a particular look direction. We choose weights that minimize the total weighted power output of the array, subject to the constraint that the gain exceeds unity for all array responses in this uncertainty ellipsoid. Hence, we can guarantee performance of the robust method in the presence of uncertainties. We show that the robust weights can be computed very e±ciently. These methods extend naturally to the case where the aggregate uncertainty arises from more than one component in the signal path, e.g., the array manifold, the radio frequency (RF) electronics, etc. These methods are shown to be directly applicable to antenna arrays with non-uniform responses, e.g., closely-spaced arrays in which the coupling between elements is significant. Measurements taken from a vertically-stacked turnstile array are presented.
Multi-Dimensional Pass-Through and Welfare Measures under Imperfect Competition

This paper provides a comprehensive analysis of welfare measures when oligopolistic firms face multiple policy interventions and external changes under general forms of market demands, production costs, and imperfect competition. We present our results in terms of two welfare measures, namely, marginal cost of public funds and incidence, in relation to multi-dimensional pass-through. Our arguments are best understood with two-dimensional taxation where homogeneous firms face unit and ad valorem taxes. The first part of the paper studies this leading case. We show, e.g., that there exists a simple and empirically relevant set of sufficient statistics for the marginal cost of public funds, namely unit tax and ad valorem pass-through and industry demand elasticity. We then specialize our general setting to the case of price or quantity competition and show how the marginal cost of public funds and the pass-through are expressed using elasticities and curvatures of regular and inverse demands. Based on the results of the leading case, the second part of the paper presents a generalization with the tax revenue function specified as a general function parameterized by a vector of multi-dimensional tax parameters. We then argue that our results are carried over to the case of heterogeneous firms and other extensions.
OPTIMAL LINEAR DETECTION OF SOQPSK

Shaped Offset QPSK (SOQPSK), as proposed and analyzed by Terrance Hill, is a family of constant envelope waveforms that is non-proprietary and exhibits excellent spectral containment and detection efficiency. Detection results using the filtering found in conventional OQPSK demodulators have been published for two variants of SOQPSK, namely SOQPSK-A and –B. This paper describes a method of synthesizing an optimal linear detection filter, with regard to bit error probability (BEP), and presents the resulting performance.
Facial Expression and Visual Speech BasedPerson Authentication Using Local BinaryPattern Histogram

This paper is an important milestone towards automatic person authentication. In this work, from a video source, video frames with pose free face is automatically sensed. Then mouth region is localized automatically. Then person identification is done using local binary pattern histogram of mouth region. Person identification performance while using neutral face, smile expression and visual speech are compared. A big custom dataset of 180 videos of 30 persons are used. The result gives the conclusion that for person identification visual speech performs well with F1 score of 0.95, followed by neutral face, 0.93 and then smile expression, 0.87. It also concludes that the mouth component of the face itself is efficient enough to identify persons.
Implementing a Negative Income Tax. Net Cost, Poverty and Inequality Effects

The main aim of this paper is to propose a financially viable alternative to the current Spanish system of social protection: A Negative Income Tax (NIT) unifying in a single mechanism the system of pub¬lic benefits and income tax. We analyse the main characteristics of the NIT and simulate several NIT proposals for Spain, using the Living Conditions Survey. These proposals are distinct in that they do not suppose an additional cost in the tax-benefit system. The results of our simulations indicate a radi¬cal improvement in the indicators of poverty and inequality, especially extreme poverty, and also a redistribution of income from the elderly to families with children
ESP Header Compression and Diet-ESP

With the use of encrypted ESP for secure IP communication, the compression of IP payload is only possible with complex frameworks, such as RObust Header Compression (ROHC). Such frameworks are too complex for numerous use cases and especially for IoT scenarios, which makes IPsec not being used here, although it offers architectural benefits. ESP Header Compression (EHC) defines a flexible framework to compress communications protected with IPsec/ESP. Compression and decompression is defined by EHC Rules orchestrated by EHC Strategies. The necessary state is hold within the IPsec Security Association and can be negotiated during key agreement, e.g. with IKEv2. The document specifies the necessary parameters of the EHC Context to allow compression of ESP and the most common included protocols, such as IPv4, IPv6, UDP and TCP and the corresponding EHC Rules. It also defines the Diet-ESP EHC Strategy which compresses up to 32 bytes per packet for traditional IPv6 VPN and up to 66 bytes for IPv6 VPN sent over a single TCP or UDP session.
Towards Deep Image Understanding: From pixels to semantics

Understand the content of the images is one of the great challenges of computer vision. Being able to recognize which are the objects in the images, what actions are doing, and finally understand why it happens, is the purpose of Image Understanding. The fact of understanding what is happening in a given time, either by taking a picture, video, or simply the image on the retina of the eye (human or robot) is a fundamental step to become part of that instant. For example, for a robot or smart car is essential to recognize what is succeeding to navigate around and interact with the environment safely. Another example can be found by interacting with the image content, so that their textual concepts can be used in modern Web searchers. This thesis seeks to discover what appears in a picture, and how to extract semantic information of higher level. In other words, the objective is to categorize and locate objects within an image. First of all, to deepen the knowledge on the formation of images, we propose a method that learns to recognize the physical properties that have created the image. By combining photometric and geometric information, we can learn to say whether a gradient is created by variations in the materials or objects, or it is caused by alterations in the scene as shadows or reflections. Entering the field of semantic recognition of objects, we focus on two approaches to describe the objects. First, we recognize which object category is hidden behind the pixels, which we call semantic segmentation. The second approach is included in the topic of object detection, which is not as important outcome in pixels, but where there is a whole object. Is represented by a frame which surrounds the object. Semantic segmentation is a problem in which the ambiguity of the pixels must be resolved by adding contextual features. We propose that the context at various scale levels should be treated differently. At low level, we learn whether the appearance of a pixel resembles the object or not, but to become confident, more information is required. We add information about the object as an entity and we enforce consistency with the rest of the scene, introducing the concept of semantic co-occurrence. As for object detection, we propose two new algorithms. The first is based on improving the representation of objects locally, with the concept of factorize appearances. Thus, an object is represented by several parts, and each of the parts can be represented by more than one appearance. Finally, the last proposed method addresses the computational problem of identifying and locating thousands of categories of objects in an image. The basic principle is to create representations of objects that are useful for any type of object, and thus reuse the computation of the performance.
Pairwise geometric histograms for object recognition : developments and analysis

One of the fundamental problems in the field of computer vision is the task of classifying objects, which are present in an image or sequence of images, based on their appearance. This task is commonly referred to as the object recognition problem. A system designed to perform this task must be able to learn visual cues such as shape, colour and texture from examples of objects presented to it. These cues are then later used to identify examples of the known objects in previously unseen scenes. The work presented in this thesis is based on a statistical representation of shape known as a pairwise geometric histogram which has been demonstrated by other researchers in 2-dimensional object recognition tasks. An analysis of the performance of recognition based on this representation has been conducted and a number of contributions to the original recognition algorithm have been made. An important property of an object recognition system is its scalability. This is the. ability of the system to continue performing as the number of known objects is increased. The analysis of the recognition algorithm presented here considers this issue by relating the classification error to the number of stored model objects. An estimate is also made of the number of objects which can be represented uniquely using geometric histograms. One of the main criticisms of the original recognition algorithm based on geometric histograms was the inability to recognise objects at different scales. An algorithm is presented here that is able to recognise objects over a range of scale using the geometric histogram representation. Finally, a novel pairwise geometric histogram representation for arbitrary surfaces has been proposed. This inherits many of the advantages of the 2-dimensional shape descriptor but enables recognition of 3-dimensional object from arbitrary viewpoints.
A Pricing Model for Data Markets

As companies and organizations explore the booming frontier of data, they are operating in data markets that are largely unregulated. One of the foremost challenges within these emerging markets is establishing an accepted methodology for assessing the value of datasets. Current data pricing strategies are often driven by the seller, with little visibility into the cost of collection, cleansing, and packaging to the buyer. This asymmetry of information results in a lack of pricing transparency, hurting the seller, who is unable to price optimally in the market, and hurting the buyer, who cannot strategically assess pricing options across data service providers. A more structured data market with a standardized pricing model would improve the transaction experience for all parties. In this paper, we describe a potential dataset valuation model and the impact such a model could have on data markets. We also explore how the model would influence certain practices, such as by adding proprietary datasets as assets on corporate balance sheets, as well as how it could contribute to the formation of a futures market for data.
PRIME—Toward Process-Integrated Modeling Environments

Research in process-centered environments (PCEs) has focused on project management support and has neglected method guidance for the engineers performing the (software) engineering process. It has been dominated by the search for suitable process-modeling languages and enactment mechanisms. The consequences of process orientation on the computer-based engineering environments, i.e., the interactive tools used during process performance, have been studied much less. In this article, we present the PRIME (ProcessIntegrated Modeling Environments) framework which empowers method guidance through process-integrated tools. In contrast to the tools of PCEs, the process-integrated tools of PRIME adjust their behavior according to the current process situation and the method definitions. Process integration of PRIME tools is achieved through (1) the definition of tool models; (2) the integration of the tool models and the method definitions; (3) the interpretation of the integrated environment model by the tools, the process-aware control integration mechanism, and the enactment mechanism; and (4) the synchronization of the tools and the enactment mechanism based on a comprehensive interaction protocol. We sketch the implementation of PRIME as a reusable implementation framework which facilitates the realization of process-integrated tools as well as the process integration of external tools. We define a six-step procedure for building a PRIME-based process-integrated environment (PIE) and illustrate how PRIME facilitates change integration on an easy-to-adapt modeling level. The PRIME framework is a result of the Project Ja 445/5 and PO 607/1 “Prozesintegration von Modellierungsarbeitsplatzen” funded by the Deutsche Forschungsgemeinschaft. The PRIME framework has been used to implement two prototypical process-integrated environments within the ESPRIT Reactive Long Term Research 21.903 Project CREWS (funded by the European Community) and the Collaborative Research Center (SFB) 476 IMPROVE (funded by the Deutsche Forschungsgemeinschaft).
An object tracking algorithm with embedded gyro information

The high speed attitude maneuver of Unmanned Aerial Vehicle (UAV) always causes large motion between adjacent frames of the video stream produced from the camera fixed on the UAV body, which will severely disrupt the performance of image object tracking process. To solve this problem, this paper proposes a method that using a gyroscope fixed on the camera to measure the angular velocity of camera, and then the object position’s substantial change in the video stream is predicted. We accomplished the object tracking based on template matching. Experimental result shows that the object tracking algorithm’s performance is improved in its efficiency and robustness with embedded gyroscope information.
Fast KNN Algorithm for Text Classification Based on Rough Set

The traditional K Nearest Neighbor(KNN) has a fatal defect that time of similarity computing is huge.For text classification task with high dimension and huge samples,it has extremely complexity.This is not practicable for real applications.In this paper,rough set theory is introduced into classification process.The distribution of training samples is described with the concepts of upper approximation and lower approximation and also the range of upper approximation space and lower approximation space of each class are computed in the training process.According to the position of the documents in the sample space,this algorithm can label some documents directly.It reduces the searching range of KNN of some documents in the classification process.The results of experiments show that this algorithm can save largely the classification time and has almost the same classification performance as that of the traditional KNN classification algorithm.