Miracle-dz/UniFAR-FTUs huggingface dataset:
Video Quality Representation Classification of Encrypted HTTP Adaptive Video Streaming

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new machine learning method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. The crawler codes and the datasets are provided in [43,44,51]. An extensive empirical evaluation shows that our method is able to independently classify every video segment into one of the quality representation layers with 97% accuracy if the browser is Safari with a Flash Player and 77% accuracy if the browser is Chrome, Explorer, Firefox or Safari with an HTML5 player.
Liquid Democracy in Group-based Configuration

Group-based configuration systems support scenarios where a group of users configures a product/service. In those groupbased configuration scenarios where the knowledge of some group members regarding items is insufficient, an advice of experts is necessary in order to help members to evaluate products or services. This paper introduces a novel approach which takes advantage of the concept of liquid democracy that allows the delegation of group member votes to experts. Concerning the application of liquid democracy, we propose a new approach based on Multi-attribute Utility theory (MAUT)-based evaluation used to calculate the utility of configurable items. Compared to the traditional approach, the proposed MAUT-based evaluation focuses on the role of experts by assigning higher weights to them. Additionally, the respective expertise level of the experts is taken into account. Consequently, the main contribution of this paper consists in the improvement of group-based configuration by taking liquid democracy aspects into consideration.
Between Avoidance and Imitation: Plausible Wayfinding in Pedestrian Agent-Based Models

Extending the range of pedestrian decision making activities represented in a simulation model represents a serious challenge: different decisions are taken at distinct levels of abstraction, employing different types of information and knowledge about the environment, from path planning to the regulation of distance from other pedestrians and obstacles present in the environment. Pedestrians, moreover, are not robots: although empirical observations show that they consider congestion when planning, there are evidences that their decisions are not always optimal (even in normal situations). We present a model integrating and improving consolidated results mitigating the optimization effects of congestion aware path planning by making commonsense estimations of the effects of perceivable congestion, also embedding an imitation mechanism stimulating changes in planned decisions whenever another nearby pedestrian did the same. The model is formally described and experimented both in a validation scenario as well as in a real-world situation: an interesting counterintuitive result, in which reducing available choices and exits actually reduces overall egress time, is also presented and discussed.
Composition and distribution of lice (Insecta: Phthiraptera) on Colombian and Peruvian birds: New data on louse-host association in the Neotropics

Abstract The diversity of permanent ectoparasites is likely underestimated due to the difficulty of collecting samples. Lice (Insecta: Phthiraptera) are permanent ectoparasites of birds and mammals; there are approximately 5,000 species described and many more undescribed, particularly in the Neotropics. We document the louse genera collected from birds sampled in Peru (2006–2007) and Colombia (2009–2016), from 22 localities across a variety of ecosystems, ranging from lowland tropical forest and Llanos to high elevation cloud forest. We identified 35 louse genera from a total of 210 bird species belonging to 37 avian families and 13 orders. These genera belong to two suborders and three families of lice: Amblycera, families Menoponidae (present on 131 bird species) and Ricinidae (39 bird species); and Ischnocera, family Philopteridae (119 bird species). We compared our bird-louse associations with data in Price et al. (2003) and recently published Neotropical studies. The majority of bird-louse associations (51.9%) were new, with most of these coming from Passeriformes, the most diverse avian order, with the most poorly known louse fauna. Finally, we found geographical variation in louse infestation and prevalence rates. With this study, we report the first comprehensive documentation of bird-louse associations for Colombia and substantially increase the known associations documented for Peru.
Trust in virtual reality

The current era has seen unrestrained technological progress. New technologies are replacing common work practices and processes in several fields, such as industry, healthcare, and commerce. The main reasons for using these technologies is the reduction of time to develop products, increased quality of products and processes, and increases in security and communication. This thesis focuses on Virtual Reality (VR). VR is currently replacing old systems and modifying practices and processes in fields such as automotive, healthcare, training and psychological therapies. However, when applying technologies, it is fundamental to study the interaction between the technology and the end users. This thesis takes into consideration one aspect of human-computer interaction: trust. Trust has been seen as fundamental in technologies such as e-commerce, e-marketing, autonomous systems and social networks. This is because trust has been found to be associated with the intention to use a technology, and lack of trust could deter users from adopting the technology. This concept is particularly important for VR, since it is only recently gaining widespread adoption. However, studies on users’ trust in VR systems are limited in the literature and there is uncertainty regarding the factors which could influence end user trust. This research aimed at developing a model to investigate trust in VR. The goal was to identify the factors which have a theoretical influence on trust in VR through an analysis of the literature on trust in VR and trust in technology in general. This permitted the creation of a framework with usability, technology acceptance and presence as possible predictors of trust in VR. In order to validate this framework, six user experiments were conducted. The experiments investigated the relationships among the factors identified in the literature and their influence on trust. The first study was designed to explore possible methodological issues. The next three studies, conducted in collaboration with researchers at the University of Nottingham, analysed further the relationship between usability and trust and between technology acceptance and presence with trust. The fifth experiment was conducted to specifically explore the influence of presence on trust. The last study looked at all factors, and validated the framework, demonstrating that technology acceptance and presence are predictors of trust in VR, and usability has an indirect effect on trust, as it is a strong predictor of technology acceptance. This research generated a model which includes well-studied factors in human computer interaction and human factors and could be applied to study trust in VR for different systems. This model increases the amount of information on VR, both on an academic and industrial point of view. In addition, guidelines based on the model were generated to inform the evaluation of existing VR systems and the design of new ones.
Community Based Influence Maximization in the Independent Cascade Model

Community detection is a widely discussed topic in network science which allows us to discover detailed information about the connections between members of a given group. Communities play a critical role in the spreading of viruses or the diffusion of information. In [1], [8] Kempe et al. proposed the Independent Cascade Model, defining a simple set of rules that describe how information spreads in an arbitrary network. In the same paper the influence maximization problem is defined. In this problem we are looking for the initial vertex set which maximizes the expected number of the infected vertices. The main objective of this paper is to further improve the efficiency of influence maximization by incorporating information on the community structure of the network into the optimization process. We present different community-based improvements for the infection maximization problem, and compare the results by running the greedy maximization method.
An Agent Framework to Support Air Passengers in Departure Terminals

Airports are complex nodes performing several roles such as interchange terminal, shopping and relaxing center, meeting area for short-time business activities. Airport operators pay great attention to financial profits from their managed assets, while passengers desire spending their slack time inside the terminal in a pleasant way after wasting time in queues and controls to access the gate areas. In such a context, an agent framework is proposed to support travelers’ slack time by providing purchase suggestions potentially interesting for them. Recommendations are computed by taking into account passengers’ interests, their current position inside the departure terminal and the commercial opportunities available therein.
Identifying Hidden Influences of Traffic Incidents' Effect in Smart Cities

The road network of big cities is a complex and hardly analyzable system in which the accurate quantification of interactions between nonadjacent road segments is a serious challenge. In this paper we would like to present a novel method able to determine the effects (the time delay and the level of the correlation) of distinct road segments on each other of a smart city's road network. To reveal these relationships, we are investigating unexpected events such as traffic jams or accidents. This novel analysis can give a significant insight to improve the operation of currently widespread traffic prediction algorithms.
A low phase noise multi-band LC VCO using a switched differential inductor

This paper presents a low phase noise multi-band LC VCO using a switched differential inductor in a standard 0.18 μm CMOS technology. In the proposed LC VCO, it is achieved multi-bands of 0.9, 1.8, 2.4, and 4.5GHz, and the phase noises of −133, −135, −125, and −127 dBc/Hz at 1MHz offset, respectively. The chip size is 1.0 × 1.1mm2 including pads. The DC power consumption is 16.2mW at 1.8V supply voltage.
Studies on the intestine of Daphnidae (Crustacea, Cladocera) ultrastructure of the midgut of Daphnia magna and Daphnia obtusa

The fine structure of midgut cells in two species of Cladocera (Crustacea: Branchiopoda) has unique features when it is compared to that in the midguts of other arthropods. Features which suggest that food is absorbed in digested form are: the presence of the peritrophic membrane, the glycocalyx, the almost complete absence of pinocytotic vesicles, and the large surface area of a great number of microvilli. Digested products presumably pass into the hemolymph through the network of extracellular canals and cisterns surrounding the basal ends of intestinal epithelial cells. Cells of the midgut in Cladocera differ from those of other arthropods in the simplicity of their basal plasma membranes, which are not highly folded. The small number of membrane invaginations suggests that water reabsorption is very slight, as is the usual condition in aquatic animals.
Stereoscopy in Graphics APIs for CAVE Applications

The paper compares the advantages and disadvantages of a variety of Graphics Application Programming Interfaces (APIs) from the perspective of obtaining stereoscopy in applications written for a CAVE virtual reality environment. A number of problems have been diagnosed and an attempt has been made to solve them using OpenGL, DirectX 11 and 12, Vulkan, as well as the Unity Engine which can internally use DirectX, OpenGL and Vulkan, but has problems and limitations of its own.
Automatic Assessment of Student Understanding Level Using Virtual Reality

The improvement of the efficiency in teaching requires knowing the understanding level of each student. However, it is difficult due to limited time in a class. We propose a Virtual Reality (VR) space imposing assignments on students, to know their understanding level from their behavior which comes from cognitive loads during their answering. The VR space presents a student an assignment and a working space to answer it. In general, students solve assignments, using elements on their short term memory. When students solve same kind of assignments many times, they build generalized solution methods in their long term memory. When they engage in such assignments, their cognitive load is low enough to make them watch only the working spaces, keeping their hands working. On the other hand, when students have no solution pattern, their short term memory works hard. Their high cognitive load often stop their hands, because of confusion. They also look assignments and the working space many times, to reconsider solutions. Since answering behavior of students exposes their cognitive load, a VR space is ideal to estimate cognitive load. We conducted an experiment to evaluate the ability of the method to estimate the cognitive load. We examined the movement of the hand and the edit distance of student's answer from the correct sentence during their answering. We confirmed a fair correlation of the hands' stagnation with the confidence in students of good scores. We also found a relationship of eye movement with the change of the edit distance. The experiment result implies the possibility to estimate the cognitive load. The estimation would enable teachers to know students' understanding faults, which leads to education according to the understanding level.
Advances in compositional fitting of active appearance models

This thesis presents a detailed and complete study of compositional gradient descent (CGD) algorithms for fitting active appearance models (AAM) and advances the state-of-the-art in generative AAM fitting by incorporating: (i) novel robust texture representations; (ii) novel cost functions and compositional types; and (iii) combined fitting approaches with complementary deformable models; into the original CGD framework. In particular, a robust texture representation based on image gradient orientations is used to define a new type of generative deformable model that generalizes well to variations in identity, pose, expression, illumination and occlusions and that can be fitted to images using standard CGD algorithms. Moreover, a novel Bayesian formulation of the AAM fitting problem, which can be interpreted as a probabilistic generalization of the well-known project-out inverse compositional (PIC) algorithm, is proposed along with two new types of composition, asymmetric and bidirectional, that lead to better convergent and more robust CGD fitting algorithms. At the same time, interesting insights into existent strategies used to derive fast and exact simultaneous CGD algorithms are provided by reinterpreting them as direct applications of the Schur complement and the Wiberg method. Finally, CGD algorithms are combined with similar generative fitting techniques for constrained local models (CLM) to create a unified probabilistic fitting framework that combines the strengths of both models (AAM and CLM) and produces state-ofthe-art results on the problem of non-rigid face alignment in the wild.
Optimization design of fragment-type microstrip filter using boundary-based filtering operator

: A novel optimization design method using boundary-based weighted sum ﬁ ltering operator is proposed for microstrip ﬁ lter design. The proposed operator is combined with multiobjective evolutionary algorithm based on decomposition combined with enhanced genetic operators and two-dimensional median ﬁ ltering operator (MOEA / D-GO-II), which can maintain the population diversity and obtain wider rejection bandwidth. For veri ﬁ cation, it is used to design fragment-type microstrip band-stop ﬁ lter for rejecting one of the ﬁ fth generation (5G) bands, 3.3 GHz – 3.6 GHz. Both simulated and measured results veri ﬁ ed the expected responses of the design. Comparison result shows that more alternative designs could be obtained using the proposed method.
What Can Go Wrong in a Software Project? Have Fun Solving it

Providing stimulating and real-life experiences is a key component in teaching software project management in Computer Science or Software Engineering programs. The diversity of topics that need to be addressed and restrictions that should be considered in university courses make a challenging task of it. This paper presents a serious game, called “White Crow PM” whose objective is to make students aware of the risks they might face during software development projects. The paper describes the game design steps and provides results of its validation in Computer Science programs in two Mexican universities. The collected data showed that the participants had fun playing the game and its content is relevant for software project management courses. Although the game needs to be validated in other settings with more participants, we conclude that it fulfills the goal of motivating discussion and increasing awareness of project management concerns among students.
Is dementia incidence declining in high-income countries? A systematic review and meta-analysis

Purpose To perform a systematic review and quantitative synthesis of studies on recent trends in dementia incidence in high-income countries (HIC), considering study quality. Methods PubMed and Web of Science were searched for eligible studies, that is, population-based/community-based prospective cohort studies investigating dementia incidence with similar methods over time, published after 1990. Study selection, data extraction, and quality assessment were performed independently by two investigators. Random-effect meta-analysis and meta-regression were used to estimate incidence change (IC) and to explore associations with study attributes. PRISMA standards were followed. Results The systematic review included seven studies (42,485 individuals), and the meta-analysis included five studies of sufficient quality. Relating dementia incidence of later cohorts to earlier cohorts (reference) yielded a nonsignificant decrease across HIC (IC =0.82; 95% CI 0.51–1.33), with high heterogeneity (I2=94.9%, P<0.001) and without publication bias (Egger’s t=–1.77; P=0.18). Excluding the Japanese Hisayama study, the only study suggesting an increase, indicated borderline evidence for a decrease across Western HIC (IC =0.69; 95% CI 0.47–1.00; I2=88.1%, P<0.001; Egger’s t=–0.34, P=0.77), again with high heterogeneity. Meta-regression did not reveal an association of incidence rate with calendar year or study attributes; however, analyses were low powered. Conclusion There is evidence of favorable trends in dementia incidence in Western HIC (stabilizing/decreasing). Reverse trends may occur in HIC of other regions, as exemplified by Japan. However, study number was small and heterogeneity was high. Further cohort studies using consistent methods are needed to draw definite conclusions. Our work may inform such future studies.
Development of crowd investing on the basis of ICO crypto assets using block-options for the supply of electric generation capacity

attraction of investments into the electric power industry is complicated by a number of problems related to the long payback period and instability of the conditions on the market. Investors in the electric power industry must invest huge sums of money and hope for maintaining high demand and prices in the future in order to get a payback from the project. Decentralized investment distributes risks and allows you to raise a sufficient profit. In the paper Authors will consider the possibility of using distributed register systems to involve potential energy consumers in investing in the construction of generating facilities with a certain amount of energy at a reduced price in the future. In addition, the blockchain system allows to solve problems of the electric power market: simplify and make more flexible maintenance of transactions, automate trade settlements, reduce the risks of non-fulfillment of obligations. In turn, the improved interaction environment of market participants will make the market more stable, which will increase the investment attractiveness of the industry.
EERA-DNN: An energy-efficient reconfigurable architecture for DNNs with hybrid bit-width and logarithmic multiplier

This paper proposes an energy-efficient reconfigurable architecture for deep neural networks (EERA-DNN) with hybrid bitwidth and logarithmic multiplier. To speed up the computing and achieve high energy efficiency, we first propose an efficient network compression method with hybrid bit-width weights scheme, that saves the memory storage of network LeNet, AlexNet and EESEN by 7x-8x with negligible accuracy loss. Then, we propose an approximate unfolded logarithmic multiplier to process the multiplication operations efficiently. Comparing with state-of-the-art architectures EIE and Thinker, this work achieves over 1.8x and 2.7x better in energy efficiency respectively.
Stochastic Runway Scheduling

Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints. Different from the existing deterministic approaches in the literature, we consider a new approach to the stochastic version of this problem within the general context of machine scheduling problems. As part of our analysis, we first show that a restricted version of the stochastic runway-scheduling problem is equivalent to a machine-scheduling problem on a single machine with sequence-dependent setup times and stochastic due dates. We then extend this restricted model by considering characteristics specific to the runway-scheduling problem and present two different stochastic integer programming models. We derive some tight valid inequalities for these formulations and propose a solution methodology based on sample average approximation and Lagrangian-based scenario decomposition. Realistic data sets are then used to perform a.
Application Domains Considered in Computational Creativity

We present a review of papers presented at IJWCC and ICCC, speciﬁcally considering what applications these papers are engaged with, either directly in generative systems or indirectly in evaluation or framework proposals. The primary focus of this work was to ascertain if there are any trends in the applications considered over the years, any topics that are becoming more dominant or any that have been neglected. Our initial classiﬁcation among 16 speciﬁc categories indicated that Music was the most popular application domain; when we reconsidered seven broader categories we determined that papers involving variations of language processing were most popular. We considered the trend among application domains over the past 12 years and noted that contrary to early discussions on creativity, problems based on logic, science or mathematics do not appear often. We consider the implications of this research as to what information it may convey both to the computational creativity community and to a general computer science audience.