Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Algorithm for Controlling a Buffer with Time-Varying Arrival Rate

Due to the increase in network services including real-time video and audio applications, Quality of Service (QoS) became a crucial issue for today’s Internet users. In order to guarantee QoS to diverse Internet services, it is important to employ effective buffer management schemes at Internet routers. Effectively constraining the mean queue length to a specified level is a key QoS requirement. Controlling the size of the buffer is determined by the dynamics of TCP’s congestion control. Therefore, controlling a buffer with time-varying arrival rate can be done by controlling the mean delay or the buffer size around a specified level. The aim of this paper is to propose a new adaptive prediction algorithm for Active Queue Management (AQM) that is simple to implement and can maintain the mean queue length around its target. The algorithm has been modified in order to bind the delay at a constant value when the arrival rate varies with time. This paper presents a feedback control strategy that operates on a buffer which incorporates a moveable threshold. An algorithm is developed to control the buffer by dynamically adjusting the threshold, which In turn, controls the effective arrival rate by randomly dropping packets. The feasibility of the model is examined using both theoretical analysis and simulation.
Feedback scheme for thermal-visible video registration, sensor fusion, and people tracking

In this work, we propose a feedback scheme for simultaneous thermal-visible video registration, sensor fusion, and tracking for online video surveillance applications. The video registration is based on a RANSAC trajectory-to-trajectory matching that estimates an affine transformation matrix that maximizes the corresponding trajectory points and overlapping of foreground thermal and visible pixels. Sensor fusion uses the aligned images to compute sum-rule blobs for thermal and visible images and constructs the thermal-visible blobs. Finally, the multiple object tracking gets blobs constructed in sensor fusion as the input and outputs the trajectories of moving humans in the scene. We tested our method on long-term indoor and outdoor video sequences and demonstrate the effectiveness of our feedback design in obtaining better quality for both image registration and tracking.
Extension of Wirtinger calculus in RKH spaces and the Complex Kernel LMS

Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. However, so far, the emphasis has been on batch techniques. It is only recently, that online adaptive techniques have been considered in the context of signal processing tasks. To the best of our knowledge, no kernel-based strategy has been developed, so far, that is able to deal with complex valued signals. In this paper, we take advantage of a technique called complexification of real RKHSs to attack this problem. In order to derive gradients and subgradients of operators that need to be defined on the associated complex RKHSs, we employ the powerful tool of Wirtinger's Calculus, which has recently attracted much attention in the signal processing community. Writinger's calculus simplifies computations and offers an elegant tool for treating complex signals. To this end, in this paper, the notion of Writinger's calculus is extended, for the first time, to include complex RKHSs and use it to derive the Complex Kernel Least-Mean-Square (CKLMS) algorithm. Experiments verify that the CKLMS can be used to derive nonlinear stable algorithms, which offer significant performance improvements over the traditional complex LMS or Widely Linear complex LMS (WL-LMS) algorithms, when dealing with nonlinearities.
The Role of Attitude Functions in Persuasion and Social Judgment

During the 1950s and 1960s, a class of theories was proposed that was the first to focus attention on the motives or functions that attitudes serve for the individual. These functional theories of attitude held that attitudes serve a variety of purposes important to psychological functioning (Katz, 1960; Katz & Stotland, 1959; Kelman, 1958, 1961; Smith, Bruner, & White, 1956). Functional theories were the first to recognize attitudes as instrumental constructs designed to serve individuals’ physical, social, and emotional needs.
Emotional speech synthesis: from speech database to TTS

Modern Speech synthesisers have achieved a high degree of intelligibility, but can not be regarded as natural-sounding devices. In order to decrease the monotony of synthetic speech, the implementation of emotional effects is now being progressively considered. This paper presents a through study of emotional speech in Spanish, and its application to TTS, presenting a prototype system that simulates emotional speech using a commercial synthesiser. The design and recording of a Spanish database will be described and also the analysis of the emotional prosody (by fitting the data to a formal model). Using this collected data, a rule-based simulation of three primary emotions was implemented in the Text-to-Speech system. Finally, the assessment of the synthetic voice through perception experiments will classify the system as capable of producing quality voice with recognisable emotional effects.
Incremental behavior acquisition based on reliability of observed behavior recognition

We propose a novel approach for acquisition and development of behaviors through observation in multi-agent environment. Observed behaviors of others give fruitful hints for a learner to find a new situation, a new behavior for the situation, necessary information for the behavior acquisition. RoboCup scenario gives us a good test-bed multi-agent environment where a learner can observe behaviors of others during practices or games. It is more realistic, practical, and efficient to take advantages of observation of skilled players than to discover new skills and necessary information only through the interaction of a learner and an environment. The learner automatically detects state variables and a goal of the behavior through the observation based on mutual information. Reinforcement learning method is applied to acquire the discovered behavior suited to the robot. Experiments under RoboCup MSL scenario shows the validity of the proposed method.
Compressive Tracking based on Superpixel Segmentation

The compressive sensing trackers, which utilize a very sparse measurement matrix to capture the targets' appearance model, perform well when the tracked targets are well defined. However, such trackers often run into drifting problems due to the fact that the tracking result is a bounding box which also includes background information, especially in the case of occlusion and low contrast situations. In this paper, we propose an online compressive tracking algorithm based on superpixel segmentation (SPCT). The proposed algorithm employs a weighted multi-scale random measurement matrix along with an efficient superpixel segmentation to preserve the image structure of the targets during tracking. The superpixel segmentation is used to distinguish the target from its surrounding background, to obtain the weighted features within the bounding box. Furthermore, a feedback strategy is also proposed to update the classifier model to reduce the drifting risk. Extensive experimental results have demonstrated that our proposed algorithm outperforms several state-of-the-art tracking algorithms as well as the compressive trackers.
Overcoming Vocabulary Sparsity in MT Using Lattices

Source languages with complex word-formation rules present a challenge for statistical machine translation (SMT). In this paper, we take on three facets of this challenge: (1) common stems are fragmented into many different forms in training data, (2) rare and unknown words are frequent in test data, and (3) spelling variation creates additional sparseness problems. We present a novel, lightweight technique for dealing with this fragmentation, based on bilingual data, and we also present a combination of linguistic and statistical techniques for dealing with rare and unknown words. Taking these techniques together, we demonstrate +1.3 and +1.6 BLEU increases on top of strong baselines for Arabic-English machine translation.
The fuzzy expert system for hip motion enhancement in gait trainer robot

In this paper, an efficient gait training robot with a fuzzy expert system is presented. Subject's leg joints are tracked in each gait cycle with a few markers using a 3D camera. The inputs to the fuzzy system include the 3D kinematics of initial trajectory and the hip joint spasm. The output of the fuzzy system is the trajectory of movements in two degrees of freedom for hip joint in sagittal and frontal planes. The commands used to design the expert system are obtained from professional therapists. They are customized for patients with different ranges of motion and joint spasms for hip movement in a gait cycle. They present enhanced and safe trajectory of the hip for patient's gait with different initial spasm and range of motion of hip joint.
Cognitive Development and the Understanding of Animal Behavior

Introduction A 2-year old child at the forest edge encounters a strange object moving along a leaf. Elongated and green, the object changes shape in a fluid way, one end rising, bending and contorting in the air as it reaches the edge of the leaf, then lowering back down and continuing its undulating motion along the leaf's edge. Some things about this thing the child does not understand: what it is called, what it eats, whether it is harmful. Other things are clear to the child, even at this age: the thing is alive; it is probably a " bug " of some kind, either worm or insect; when it moves, it moves with purpose, in the direction that its sensory apparatus takes it, lifting its head when it reaches the end of the leaf to sample its surroundings before moving on. None of these things is written in the movement or features of the object itself, but rather are inferred by the child. She observes the moving object from a distance, fascinated, and cannot help but try to figure out what this new creature is up to. Parents and others who interact with young children frequently observe that they show an intense interest in animals. Children want to know what animals are called, how they are categorized, where they live, what they eat, how they stalk their prey, why they have stripes or whiskers or rattles on their tails, and so on. Most of us have encountered children whose expertise on these matters rivals or even outstrips our own: a child who knows the difference between a rainbow wrasse and a bluehead wrasse, or which of Ceratosaurus and Hadrosaurus was a predator. From a certain perspective, this is quite odd, given that the kinds of activities that our children actually engage in during their day-today lives have little to do with live animals, at least wild ones. Children's interest in the details of animal biology and behavior is often, if not usually, far out of proportion to what the actual ecological usefulness of this information will be for them. Children in industrialized countries are very unlikely to encounter wild lions, jaguars, crocodiles, or sharks, and they are unlikely indeed ever to encounter a Tyrannosaurus rex. Why, then, are children's cognitive resources so devoted to an understanding of animals and their behavior? An evolutionary developmental perspective might help to shed …
Regulation of the Expression of E-Cadherin on Human Cancer Cells by γ-Linolenic Acid (GLA)

E-cadherin is a cell to cell adhesion molecule which acts as a suppressor of metastasis. This study examined the effect of gamma-linolenic acid (GLA), a n-6 polyunsaturated fatty acid, on the expression of E-cadherin in human cancer cells. Western blotting studies demonstrated that treatment of cells with GLA for 24 h increased the expression of E-cadherin in lung, colon, breast, melanoma, and liver cancer cells, but not in endothelial cells and fibroblasts. The results were confirmed by immunocytochemistry. In contrast, two other n-6 fatty acids, linoleic acid and arachidonic acid, failed to induce these changes. The increased expression of E-cadherin was correlated with reduced in vitro invasion and increased aggregation, indicating that the increased E-cadherin expression induced by GLA was biologically active. These data add GLA to the short list of E-cadherin up-regulatory factors. The up-regulation of E-cadherin expression in human cancer cells may contribute to the anticancer properties of GLA.
Closing the Technology Gap?

This paper focuses on the dimensions shaping the dynamics of technology. We present a model where the knowledge stock of a country grows over time as a function of three main factors: innovation intensity, technological infrastructures, and human capital. The latter two variables determine the absorptive capacity of a country as well as its innovative ability. We then carry out an empirical analysis that investigates the dynamics of technology in a large sample of economies in the last two-decade period, and studies its relationships with income per capita growth. The results indicate that the cross-country distributions of technological infrastructures and human capital have experienced a process of convergence, whereas the innovative intensity is characterized by increasing polarization between rich and poor economies. Thus, while the conditions for catching up have generally improved, the increasing innovation gap represents a major factor behind the observed differences in income per capita.
Adaptive crypto-steganosystem for videos based on information content and visual perception

Steganography is the art of hiding secret data inside a carrier media. Most steganographic techniques suffer from the drawback that they are unable to retain the perceptual quality. Using saliency cues for developing an adaptive steganographic technique can help to alleviate this problem. In this work, a novel perception driven robust crypto-steganographic algorithm is proposed for embedding secure data in videos. The proposed scheme selects the payload regions based on natural scene statistics. To further strengthen the scheme and ensure intractability of secure data, the encrypted secret data is embedded in a random manner using jumbling sequence generator in the frames. We utilize perceptual hashing to evaluate the number of bit insertions that will not compromise the perceptual quality. A comprehensive performance evaluation of the proposed scheme is provided to detail the effectiveness. We demonstrate that the scheme shows a lot of promise in being robust against statistical and saliency based attacks.
Pushing the limits of one-time signatures

Computational and security advantages of one-time signatures come together with their length restrictions. In most applications, one-time signature should be accompanied with one-time public key(s) to enable multiple signing with a single certified public key. This is why most of the time decreasing the size of one-time signature at a cost of larger public key is not what we desire. In this paper, we show the most efficient one-time signature construction proposed so far in the sense that the total length of a signature and a public key is minimized. Requiring heavier offline computation while keeping the online computation cost both for signing and verification not changed, our improvement is a significant result especially for wireless sensor networks. This is because limited battery power puts stringent limitations on message sizes however heavy offline computation is acceptable and can be performed prior to the deployment of the sensor network.
Prices in experimental asset markets under uncertainty

This paper presents an experiment which investigates whether asset prices are affected in markets where state probabilities are not exactly known and traders have to form subjective probabilities of payoffs. Results show that the presence of vague probabilities leads to higher average prices with respect to assets characterised by known probabilities. However, prices under known and vague probabilities draw closer when traders get a sounder understanding of how to arbitrate between markets.
Implementation of fuzzy multiple objective decision making algorithm in a heterogeneous mobile environment

Mobile satellite networks are now being utilized to provide complementary service coverage to their terrestrial counterparts. The migration from voice-dominated service delivery to a data-centric environment implies that the choice of one particular access network over another will need to take into account many different criteria, which previously need not have been considered. For this reason, it is important that a suitable decision making algorithm is designed to ensure that all of the different characteristics of the system are taken into account when making the segment selection for a particular service delivery. In this paper, a segment selection algorithm based on the fuzzy multiple objective decision making algorithm is presented. This algorithm is analyzed to show how the overall performance of a multi-access segment network can be improved by varying the relative importance of certain service and network characteristics.
Nsort: a Parallel Sorting Program for NUMA and SMP Machines

Introduction Ordinal TM Nsort TM is a high-performance sort program for SGI IRIX, Sun Solaris and HP-UX servers. Nsort allows its users to realize the full processing potential of their multi-processor, multi-disk Unix systems for sorting data. Unlike the slow, single-threaded Unix sort utility or other third-party sort programs that achieve only minor processing parallelism, only Nsort can use tens of processors and hundreds of disks to quickly sort and merge data.
Auxiliary-carrier load-shift keying for reverse data telemetry from biomedical implants

This paper introduces a new approach for data telemetry from biomedical implantable devices to the external world (also known as reverse data telemetry). In the proposed approach, while power (and perhaps data) is transferred to the implant using a main carrier, a second carrier is also sent to the implant through the same link. The second carrier, which is of higher frequency and smaller amplitude, is envisioned for reverse data telemetry by load-shift keying. In this work, the wireless link is realized with two resonant frequencies using closely-coupled printed spiral coils and LC matching networks. The main and auxiliary carrier frequencies are 1MHz and 10MHz with 5V and 1V peak amplitudes, respectively. While the link is capable of continuously supplying unregulated power of up to 80mW at 4V(peak) to a 100-Ω resistive load, data telemetry at a rate of 100 kbps is successfully tested in the reverse direction.
Adaptive N-normalization for enhancing music similarity

The N-Normalization is an efficient method for normalizing a given similarity computed among multimedia objects. It can be considered for clustering and kernel enhancement. However, most approaches to N-Normalization parametrize the method arbitrarily in an ad-hoc manner. In this paper, we show that the optimal parameterization is tightly related to the geometry of the problem at hand. For that purpose, we propose a method for estimating an optimal parameterization given only the associated pair-wise similarities computed from any specific dataset. This allows us to normalize the similarity in a meaningful manner. More specifically, the proposed method allows us to improve retrieval performance as well as minimize unwanted phenomena such as hubs and orphans.
An Engine-Independent Framework for Business Rules Development

There is a compelling need for highly customized Domain Specific Languages and Business Vocabulary in certain industries such as insurance, mortgage, and finance to enable Knowledge Workers to articulate and to automate complex rules pertinent to their areas of function within their companies. Rule Engine vendors attempt to provide a solution to the problem by selling an integrated Rules Engine and Business Rules Management System. Usually, the BRMS's provided by vendors need to be customized and integrated into the overall Enterprise Architecture. This results in the Enterprise Architecture to be tightly coupled with the vendor's rule offering. Moreover, it poses a significant risk to the Enterprise as vendor solutions change between releases. The Enterprise Architecture needs a way to insulate itself from such impacts. This paper describes a framework that delivers the core BRMS functions of authoring and representation in a vendor neutral fashion. In addition, the paper sheds light on specific areas of the framework that can be standardized.
Towards a Planning-Based Approach to the Automated Design of Chemical Processes

The design of chemical processes is a central problem in organic chemistry. A chemical process is a sequence of chemical reactions capable of producing a target compound from some starting stage molecules. The manual specification of such processes can be time-consuming and error-prone, due to the high number of reactions involved and their complex chemical conditions. To tackle this issue, we propose a planning-based approach and a framework for the automated design of chemical processes. Specifically, we argue that this problem can be reduced to a planning problem in Artificial Intelligence. To this end, we adapt the situation calculus and PDDL to the task of modeling molecules and capturing semantics of generic chemical reactions, and we conduct experimental study including empirical assessment of a PROLOG planner and two state-ofthe-art planners on a set of benchmark problems.
VERBMOBIL: the evolution of a complex large speech-to-speech translation system

The ambitious goal of the project Verbmobil is the development of a portable speech to speech translation system dealing with face to face dialogs. It constitutes a new generation of translation systems in which spontaneously spoken language, speaker independence and speaker adaptability are among the main features. Verbmobil brings together researchers from the fields of speech processing, computational linguistics and artificial intelligence and goes beyond the state of the art in these areas. Besides the speech and language processing issues, the specific constraints of the project represent an extreme challenge on the part of project management, software engineering and test and evaluation of the system: size and complexity-150 researchers from 29 organizations at different sites on three continents are involved in the software development; integration of heterogeneous software-in order to reuse existing software, hardware and know how, only a few restrictions were given to the partners. The article describes the Verbmobil scenario, the system architecture and the system evolution.