Miracle-dz/UniFAR-FTUs huggingface dataset:
CLASSIFICATION OF TRAFFIC EVENTS BASED ON THE SPATIO-TEMPORAL MRF MODEL AND THE BAYESIAN NETWORK

In order to support safe and efficient driving, it is important to classify the behaviors of vehicles and to understand what is going on with the traffic situations. For that purpose, the system employed a vision sensor rather than spot sensors because of its rich information. A dedicated vehicle-tracking algorithm based on the Spatio-Temporal MRF model, which is robust against heavy occlusions even in low-angle images, was developed. This algorithm outputs maps that represent distributions of vehicle regions and motions by every image frames. By analyzing time-series variations of these distribution maps, the system extracts observation sequences for traffic events classification. A classification network based on the Bayesian Network that consists of a fusion of deterministic sub-networks and probabilistic sub-networks was constructed. Since vehicle behaviors in the traffic should be restricted by traffic rules, such behaviors would be classified by deterministic network based on explicitly defined traffic rules. However, it is difficult to classify and to understand behaviors such as accidents, near misses, and some other ambiguous behaviors by deterministic classification networks. In order to classify such ambiguous behavior sequences, it is effective to employ probabilistic sub-networks such as Hidden Markov Model with its learning methods. Consequently, by integrating those deterministic networks and probabilistic networks into the Bayesian Network, the system was able to successfully classify behaviors of rule violations, accidents, near-miss situations distinguishing from ordinary behaviors in traffic images.
DIGITAL MARKETING PERFORMANCE EVALUATION METHODS

The share of digital marketing from total marketing spending is increasing fast year by year. Still the majority of companies admit that they are missing clear methods on how to evaluate (positive/negative) effects obtained using digital marketing. As this field of digital marketing is relatively new, there are no firm and widely recognized measurement methods resulting in marketing specialists being confused by an overwhelming amount and variety of different digital marketing metrics and large amounts of data. Therefore, the aim of this paper is to list the existing methods which could help marketers to evaluate their digital marketing campaign effectiveness and to discuss these methods’ effectiveness with experts. To achieve this aim, the author defined the following objectives for the paper: 1) to explore which digital marketing campaign effectiveness evaluation methods can be found in realized scientific research so far; 2) to assess the strengths and weaknesses of each method by conducting expert interviews; 3) to determine the practical application opportunities for each identified method; and 4) to draw conclusions and make recommendations for the practical usage of the studied methods. Research methods used were secondary data analysis and expert interviews. The secondary data analysis contained reviews of scientific research results in the field mentioned above. The expert interviews covered in-depth interviews with the main stakeholders – leading online retailers in the Baltics and digital marketing experts. The results show that the conversion evaluation method is most frequently used in theory and in practice as it can be best connected with direct business goals to increase sales. It also highlights that each method has some benefits to be used in specific situations, with specific aims.
Comparison of performance using Daubechies Wavelet family for facial expression recognition

This paper investigates the performance of a Daubechies Wavelet family in recognizing facial expressions. A set of luminance stickers were fixed on subject's face and the subject is instructed to perform required facial expressions. At the same time, subject's expressions are recorded in video. A set of 2D coordinate values are obtained by tracking the movements of the stickers in video using tracking software. Daubechies wavelet transform with different orders (db1 to db20) performed on obtained data. Standard deviation is derived from wavelet approximation coefficients for each daubechies wavelet orders. This standard deviation is used as an input to the neural network for classifying 8 facial expressions.
Improving Computer Network Performance by Using Knowledge Discovery

During the past decade, the computer world has been changing a dramatic change in many aspects. In the past, personal computer was known as a desktop machine that functioned alone. All the resources, data, and computing power were within the same machine. However, in the network world today, more and more computers are connected together through the media to share data and resources or parallel the computation. The necessity to evaluate the performance of these computer networks has been recognized for a long time. Performance measurement has been significantly important to computer networks designers, administrators, and analysts to justify the impact of a new design or change compared to existing system. On the other hand, this information is also important to the users or customers to evaluate different systems from different vendors to determine whether their needs can be fulfilled, and to measure the performance of the running network Overall network performance is integration between two categories networkrelated like packet size, throughput, delay time and workstationrelated category like CPU utilization, memory utilization, resource response time, resource utilization. By recording all the events triggered in the network workstations using network monitoring tools we have a very large number of records that describe in details the network characteristics. By analyzing and mining this large amount of data we will get valuable information and extract a hidden predictive knowledge that will guide network administrators how to adjust different network parameters and adjust hardware and software resources attributes.
In vivo hematopoietic Myc activation directs a transcriptional signature in endothelial cells within the bone marrow microenvironment

Cancer pathogenesis involves tumor-intrinsic genomic aberrations and tumor-cell extrinsic mechanisms such as failure of immunosurveillance and structural and functional changes in the microenvironment. Using Myc as a model oncogene we established a conditional mouse bone marrow transduction/transplantation model where the conditional activation of the oncoprotein Myc expressed in the hematopoietic system could be assessed for influencing the host microenvironment. Constitutive ectopic expression of Myc resulted in rapid onset of a lethal myeloproliferative disorder with a median survival of 21 days. In contrast, brief 4-day Myc activation by means of the estrogen receptor (ER) agonist tamoxifen did not result in gross changes in the percentage/frequency of hematopoietic lineages or hematopoietic stem/progenitor cell (HSPC) subsets, nor did Myc activation significantly change the composition of the non-hematopoietic microenvironment defined by phenotyping for CD31, ALCAM, and Sca-1 expression. Transcriptome analysis of endothelial CD45- Ter119- cells from tamoxifen-treated MycER bone marrow graft recipients revealed a gene expression signature characterized by specific changes in the Rho subfamily pathway members, in the transcription-translation-machinery and in angiogenesis. In conclusion, intra-hematopoietic Myc activation results in significant transcriptome alterations that can be attributed to oncogene-induced signals from hematopoietic cells towards the microenvironment, e. g. endothelial cells, supporting the idea that even pre-leukemic HSPC highjack components of the niche which then could protect and support the cancer-initiating population.
Self-Organization and Natural Selection in the Evolution of Complex Despotic Societies

Differences between related species are usually explained as separate adaptations produced by individual selection. I discuss in this paper how related species, which differ in many respects, may evolve by a combination of individual selection, self-organization, and group-selection, requiring an evolutionary adaptation of only a single trait. In line with the supposed evolution of despotic species of macaques, we take as a starting point an ancestral species that is egalitarian and mildly aggressive. We suppose it to live in an environment with abundant food and we put the case that, if food becomes scarce and more clumped, natural selection at the level of the individual will favor individuals with a more intense aggression (implying, for instance, biting and fierce fighting). Using an individual-centered model, called DomWorld, I show what happens when the intensity of aggression increases. In DomWorld, group life is represented by artificial individuals that live in a homogeneous world. Individuals are extremely simple: all they do is flock together and, upon meeting one another, they may perform dominance interactions in which the effects of winning and losing are self-reinforcing. When the intensity of aggression in the model is increased, a complex feedback between the hierarchy and spatial structure results; via self-organization, this feedback causes the egalitarian society to change into a despotic one. The many differences between the two types of artificial society closely correspond to those between despotic and egalitarian macaques in the real world. Given that, in the model, the organization changes as a side effect of the change of one single trait proper to an egalitarian society, in the real world a despotic society may also have arisen as a side effect of the mutation of a single trait of an egalitarian species. If groups with different intensities of aggression evolve in this way, they will also have different gradients of hierarchy. When food is scarce, groups with the steepest hierarchy may have the best chance to survive, because at least a small number of individuals in such a group may succeed in producing offspring, whereas in egalitarian societies every individual is at risk of being insufficiently fed to reproduce. Therefore, intrademic group selection (selection within an interbreeding group) may have contributed to the evolution of despotic societies.
Plant Defense and Density Dependence in the Population Growth of Herbivores

Long‐standing theory has predicted that plant defensive and nutritional traits contribute to the population dynamics of insect herbivores. To examine the role of plant variation in density dependence, I took a comparative approach by conducting density manipulation experiments with the specialist aphid, Aphis nerii, on 18 species of milkweed (Asclepias spp.). The strength of density dependence varied on the plant species. Variation in plant secondary compounds (cardenolides), trichomes, leaf carbon and nitrogen concentrations, and seed mass of the milkweed species predicted the Rmax of aphid populations, while specific leaf weight, carbon concentration, latex, water content, and trichome density were significant predictors of the strength of density dependence. Thus, plant traits that probably evolved for primary and defensive functions contribute to the ecological dynamics of herbivore populations.
Towards Avatars with Artificial Minds: Role of Semantic Memory

he first step towards creating avatars with human-like artificial minds is to give them human-like memory structures with an access to general knowledge about the world. This type of knowledge is stored in semantic memory. Although many approaches to modeling of semantic memories have been proposed they are not very useful in real life applications because they lack knowledge comparable to the common sense that humans have, and they cannot be implemented in a computationally efficient way. The most drastic simplification of semantic memory leading to the simplest knowledge representation that is sufficient for many applications is based on the Concept Description Vectors (CDVs) that store, for each concept, an information whether a given property is applicable to this concept or not. Unfortunately even such simple information about real objects or concepts is not available. Experiments with automatic creation of concept description vectors from various sources, including ontologies, dictionaries, encyclopedias and unstructured text sources are described. Haptek-based talking head that has an access to this memory has been created as an example of a humanized interface (HIT) that can interact with web pages and exchange information in a natural way. A few examples of applications of an avatar with semantic memory are given, including the twenty questions game and automatic creation of word puzzles.
Automatic Suggestion for PubMed Query Reformulation

Query reformulation is an interactive process of revising user queries according to the query results. To assist biomedical researchers in this process, we present novel methods for automatically generating query reformulation suggestions. While previous work on query reformulation focused on addition of words to user queries, our method can deal with three types of query reformulation (i.e., addition, removal and replacement). The accuracy of the method for the addition type is ten times better than PubMed’s “Also try”, while the execution time is short enough for practical use.
Overlapped fountain coding for delay-constrained priority-based broadcast applications

In this paper, we propose a new rateless code based on overlapped generations. This proposed scheme works very well especially in a broadcast scenario, where channels with different characteristics are involved in the system. In the proposed scheme, we allow the adjacent generations to overlap. Our main focus is on the design of rateless codes to make any receiver able to recover all the source file with reduction in the overhead and the delay time. Indeed, we show that for several channels characteristics, our proposed scheme outperforms the regular rateless codes such as Luby transform (LT) code. The good advantage of our proposed scheme that it can be used with any kind of degree distributions in the literature and the same improvements are guaranteed.
Existence and uniqueness of a solutionto a three-dimensional axially symmetric Biot problem arising inmodeling blood flow

We prove the existence of a unique weak solution to a problem associated with studying blood flow in compliant, viscoelastic arteries. The model problem is a linearization of the leading-order approximation of a viscous, incompressible, Newtonian fluid flow in a long and slender viscoelastic tube with small aspect ratio. The resulting model is of Biot type. The linearized model equations form a hyperbolic-parabolic system of partial differential equations with degenerate diffusion. The degenerate diffusion is a consequence of the fact that the effects of the fluid viscosity in the axial direction of a long and slender tube are small in comparison with the effects of the fluid viscosity in the radial direction. Degenerate fluid diffusion and hyperbolicity of the hyperbolic-parabolic system cause lower regularity of a weak solution and are a source of the main difficulties associated with the existence proof. Crucial for the existence proof is the viscoelasticity of vessel walls which provides the main smoothing mechanisms in the energy estimates which, via the compactness arguments, leads to the proof of the existence of a solution of this problem. This has interesting consequences for the understanding of the underlying hemodynamics application. Our analysis shows that the viscoelasticity of the vessel walls is crucial in smoothing sharp wave fronts that might be generated by the steep pressure pulses emanating from the heart, which are known to occur in, for example, patients with aortic insufficiency.
Model Estimation and Selection towardsUnconstrained Real-Time Tracking and Mapping

We present an approach and prototype implementation to initialization-free real-time tracking and mapping that supports any type of camera motion in 3D environments, that is, parallax-inducing as well as rotation-only motions. Our approach effectively behaves like a keyframe-based Simultaneous Localization and Mapping system or a panorama tracking and mapping system, depending on the camera movement. It seamlessly switches between the two modes and is thus able to track and map through arbitrary sequences of parallax-inducing and rotation-only camera movements. The system integrates both model-based and model-free tracking, automatically choosing between the two depending on the situation, and subsequently uses the “Geometric Robust Information Criterion” to decide whether the current camera motion can best be represented as a parallax-inducing motion or a rotation-only motion. It continues to collect and map data after tracking failure by creating separate tracks which are later merged if they are found to overlap. This is in contrast to most existing tracking and mapping systems, which suspend tracking and mapping and thus discard valuable data until relocalization with respect to the initial map is successful. We tested our prototype implementation on a variety of video sequences, successfully tracking through different camera motions and fully automatically building combinations of panoramas and 3D structure.
The development of the Indonesian college student social anxiety scale

Social anxiety among Indonesian college students is still high. Therefore a screening tool is needed for prevention to be done optimally. Indonesia has a very wide cultural diversity, so it needs to construct social anxiety instrument in accordance with the culture of Indonesian society. The respondents were 664 students from several universities in Malang, East Java, Indonesia. A measuring instrument is considered good if it has high validity and reliability. The results showed that Social Anxiety Scale has content validity with Aiken V value from 0.838 - 0.975. The construct validity was tested by model fit test with parameter value GFI = 0,916; AGFI = 0.880; CFI = 0.938; RMSEA = 0.064. Reliability of the instruments is 0.931. Property-based testing shows that the Social Anxiety Scale has been empirically tested.
Waterfall and agile requirements-based model for automated test cases generation

Requirements-based testing is a testing approach in which test cases are derived from requirements. Requirements represent the initial phase in software developments life cycle. Requirements are considered the basis of any software project. Therefore, any ambiguity in natural language requirements leads to major errors in the coming phases. Moreover, poorly defined requirements may cause software project failure. There exist many software development models as waterfall model, agile model, etc. In this paper, we propose a novel automated approach to generate test cases from requirements. Requirements can be gathered from different models either waterfall model (functional and non-functional) or agile model. SRS documents, non-functional requirements and user stories are parsed and used by the proposed approach to generate test cases in which requirements with different types are covered. The proposed approach uses text mining and symbolic execution methodology for test data generation and validation, where a knowledge base is developed for multi-disciplinary domains.
New evidence for the cerebellar involvement in personality traits

Following the recognition of its role in sensory-motor coordination and learning, the cerebellum has been involved in cognitive, emotional, and even personality domains. This study investigated the relationships between cerebellar macro- and micro-structural variations and temperamental traits measured by Temperament and Character Inventory (TCI). High resolution T1-weighted, and Diffusion Tensor Images of 100 healthy subjects aged 18–59 years were acquired by 3 Tesla Magnetic Resonance scanner. In multiple regression analyses, cerebellar Gray Matter (GM) or White Matter (WM) volumes, GM Mean Diffusivity (MD), and WM Fractional Anisotropy (FA) were used as dependent variables, TCI scores as regressors, gender, age, and education years as covariates. Novelty Seeking scores were associated positively with the cerebellar GM volumes and FA, and negatively with MD. No significant association between Harm Avoidance, Reward Dependence or Persistence scores and cerebellar structural measures was found. The present data put toward a cerebellar involvement in the management of novelty.
Modular placement for interposer based multi-FPGA systems

Novel device with multiple FPGAs on-chip based on interposer interconnection has emerged to resolve the IOs limit and improve the inter-FPGA communication delay. However, new challenges arise for the placement on such architecture. Firstly, existing work does not consider the detailed models for the path wirelength and delay estimation for interposer, which may significantly affect the placement quality. Secondly, previous work is mostly based on traditional tile-based placement which is slow for the placement of large design on multiple FPGAs. In this paper, we propose a new fast two-stage modular placement flow for interposer based multiple FPGAs aiming for delay optimization with the incorporation of a detailed interposer routing model for wirelength and delay estimation. Firstly, we adopt the force-directed method for its global property to get an efficient solution as a start point of the placement. Secondly, we adopt the simulated annealing (SA) for its efficiency and effectiveness in searching the refinement solution. In order to speed up the refinement, the hierarchical B*-tree (HB*-tree) is employed to enable a fast search and convergence. The experiments demonstrate that our flow can achieve an efficient solution in a comparable time. The proposed approach is scalable to different design size.
Statistical Analyses of E-space Communication AB Database

The use of internet has increased dramatically in the last ten years thanks to broadband connections and relatively cheap prices. At the same time, the attention towards the ”web” world not only from a technical point of view has grown . The combination of surveys and world wide web has made the origin of a ”new” business based on customer relationship management possible. This project is intended to focus on statistical analyses about a dataset provided by E-space Communication AB. The demo of the survey can be found by the following link: survey (reported also at the end of the abstract). A first wide analysis has been carried out to give an overall picture of the dataset and discover preliminary findings. Multidimensional scaling and bootstrapping techniques have also been used to graphically represent the data and give further insight. http://www.sitester. com/sform/start.asp? PK_Id=271&Language=se&SwapLanguage=en
Novel Programmable FIR Filter Based on Higher Radix Recoding for Low-Power and High-Performance Applications

This paper proposes the novel programmable digital finite impulse response (FIR) filters for low-power and high-performance applications. The architecture is based on the higher radix recoding scheme which specifically targets the reduction of power consumption by decreasing partial products and precomputation sharing in each partial product. We extend higher radix recoding scheme with secondary radix recoding to further improve performance by reducing the propagation delay of precomputing odd multiples of the multiplicand. A 10-tap programmable FIR filter based on proposed schemes was design in TSMC 0.18-μm technology. The performance and power consumption of the proposed schemes can improve about 40.9-65.5% and 28.3-50.2% over existing designs.