Miracle-dz/UniFAR-FTUs huggingface dataset:
SARIMA based network bandwidth anomaly detection

Network bandwidth is considered a valuable resource in most computer systems. To precisely detect network anomalies (with a few false alarms), an intrusion detection system requires reliable methods. A potential solution in predicting network bandwidth usage is using a time-series model with a threshold. This paper proposes a network anomaly detection technique based on SARIMA, a time-series model, to capture seasonal behavior of bandwidth usage of most networks. Our proposed SARIMA based anomaly detection is capable of detecting network bandwidth anomalies effectively when a threshold equals to 8.5 percents of maximum bandwidth in a day. Our result yields 3.57 percents of false alarms. We concluded that SARIMA is a better instrumental tool for intrusion detection comparing to ARIMA.
Social Signal Detection by Probabilistic Sampling DNN Training

When our task is to detect social signals such as laughter and filler events in an audio recording, the most straightforward way is to apply a Hidden Markov Model–or a Hidden Markov Model/Deep Neural Network (HMM/DNN) hybrid, which is considered state-of-the-art nowadays. In this hybrid model, the DNN component is trained on frame-level samples of the classes we are looking for. In such event detection tasks, however, the training labels are seriously imbalanced, as typically only a small fraction of the training data corresponds to these social signals, while the bulk of the utterances consists of speech segments or silence. A strong imbalance of the training classes is known to cause difficulties during DNN training. To alleviate these problems, here we apply the technique called probabilistic sampling, which seeks to balance the class distribution. Probabilistic sampling is a mathematically well-founded combination of upsampling and downsampling, which was found to outperform both of these simple resampling approaches. With this strategy, we managed to achieve a 7–8 percent relative error reduction both at the segment level and frame level, and we efficiently reduced the DNN training times as well.
BEHAVIORAL GENETICS FOUNDATIONS OF RELATIONS BETWEEN PERSONALITY TRAITS AND SATISFACTION WITH LIFE

The long-term stability of subjective wellbeing has directed an attention to stable dispositions as the probable source of individual differences in the satisfaction with life (SWL). The main objective of this study was to examine the extent of genetic overlap between SWL and personality traits of the five-factor model (FFM). The sample consisted of 121 monozygotic and 61 dizygotic twin pairs (the average age was 24.59, SD = 7.11). Satisfaction with Life Scale and The Revised NEO Personality Inventory (NEO-PIR) were applied. Multivariate genetic modeling was performed. The results show the most appropriate fit indices for Independent AE model(χ²/df = 1.41, CFI = .92, TLI = .91, RMSEA = .07, AIC = 17400.81, BIC = 17558.68, SRMR = .10). SWL and all NEOPI- R personality traits have a moderate to strong genetic bases, while the common genetic influences for SWL are 40%. The results show that unique environmental contributions are moderate to strong (from 61% for Neuroticism, 41% for SWL, to 23% for Conscientiousness). Genetically driven tendency common to Neuroticism, Extraversion, and Conscientiousness, underlines individual differences in SWL, and therefore a cognitive evaluation of SWL seems to be substantially based on emotional tendencies encompassed by the FFM. Also, SWL appears to be uniquely environmentally influenced, which implies benefits of wellbeing interventions through the process of learning or adopting a different life philosophy.
Complete and Partial Lesions of the Pyramidal Tract in the Rat Affect Qualitative Measures of Skilled Movements: Impairment in Fixations as a Model for Clumsy Behavior

Little is known about prenatal and perinatal brain injury resulting in subsequent clumsy behavior in children. One candidate motor system is the pyramidal tract. The tract traverses the entire central nervous system and, through direct and indirect connections to the brainstem and spinal cord sensory and motor nuclei, is involved in the learning and execution of skilled movements. Here, rats, either naive or pretrained on a number of motor tasks, were assessed for acute and chronic impairments following complete or incomplete pyramidal tract lesions. Postsurgery rats with complete lesions were impaired on the qualitative measures of limb aiming, supination, and posture. Impaired movements require fixations, complementary movements in different body segments. The impairment in fixations was manifest acutely and underwent no improvement with subsequent training/testing. The finding that complete and partial pyramidal tract lesions produce chronic impairment in fixations provides insight for understanding clumsy behavior in humans and its potential remediation via specific training in making fixations.
Learning and Inference in Tractable Probabilistic Knowledge Bases

Building efficient large-scale knowledge bases (KBs) is a longstanding goal of AI. KBs need to be first-order to be sufficiently expressive, and probabilistic to handle uncertainty, but these lead to intractable inference. Recently, tractable Markov logic (TML) was proposed as a non-trivial tractable first-order probabilistic representation. This paper describes the first inference and learning algorithms for TML, and its application to real-world problems. Inference takes time per query sublinear in the size of the KB, and supports very large KBs via parallelization and a disk-based implementation using a relational database engine. Query answering is fast enough for interactive and real-time use. We show that, despite the data being non-i.i. d. in general, maximum likelihood parameters for TML knowledge bases can be computed in closed form. We use our algorithms to build a very large tractable probabilistic KB from numerous heterogeneous data sets. The KB includes millions of objects and billions of parameters. Our experiments show that the learned KB outperforms existing specialized approaches on challenging tasks in information extraction and integration.
The Prevalence of Depressive and Anxiety Symptoms in Student-Athletes and the Relationship With Resilience and Help-Seeking Behavior

The purpose of this study was to investigate the prevalence of depressive and anxiety symptoms within student-athletes and to examine protective factors which may act as a buffer against mental ill-health. A cross-sectional design was employed. A sample of 185 student-athletes (M = 20.77; SD = .50; 35% female) agreed to take part. Participants completed measures of depression, anxiety, psychological resilience and formal and informal help-seeking behavior. Thirty one percent of student-athletes reported moderate to severe symptoms of depression and/or anxiety. Student-athletes who reported requiring professional help for problems were more likely to record moderate to severe symptoms of depression and anxiety. Student-athletes who did not seek informal support were more likely to report symptom levels for depression outside the normal range. Higher resilience scores were associated with lower symptom reporting for both depression and anxiety. Practical implications for supporting student-athletes’ mental health across institutional, interpersonal and intrapersonal levels are discussed.
Frontiers in Synaptic Neuroscience Synaptic Neuroscience Synapse and Dendrite Structural Abnormalities

There are many signaling pathways implicated in the underlying causes of MR but the majority have direct interactions with Rho, Rac and Ras small GTPases (Ramakers, 2002; Newey et al., 2005). These proteins are all key regulators of the actin and microtu-bule cytoskeleton and affect both the structure and function of dendrites and synapses. A morphological characteristic common to many different forms of syndromic and non-syndromic MR is an altered filopodia-to-spine ratio and abnormal distribution of dendritic protrusions in both humans and mouse models for MR (Ramakers, 2002). The majority of excitatory synapses in the brain are made on to these dendritic protrusions. Over the course of normal development, long, thin filopodia commonly found in immature networks are replaced in later stages by shorter, more stubby spines (Harris, 1999). Spine abnormalities in MR are seen in many different brain regions and during both early and late stages of postnatal development (Purpura, 1974). For example, in the Fmr1-KO mouse model of Fragile X syndrome, more immature filopodial protrusions are observed along with an increased protrusion density along the dendrite (Irwin et al., 2000) and are seen from the end of the first postnatal week (Nimchinsky et al., 2001). In Rett syndrome, differing spine distribution patterns are reported in mouse models but the overall consensus is introduction: mental retardation, SynapSe pathology and plaSticity
Smart Redundancy for Distributed Computation

Many distributed software systems allow participation by large numbers of untrusted, potentially faulty components on an open network. As faults are inevitable in this setting, these systems utilize redundancy and replication to achieve fault tolerance. In this paper, we present a novel "smart" redundancy technique called iterative redundancy, which ensures efficient replication of computation and data given finite processing and storage resources, even when facing Byzantine faults. Iterative redundancy is more efficient and more adaptive than comparable state-of-the-art techniques that operate in environments with unknown system resource reliability. We show how systems that solve computational problems using a network of independent nodes can benefit from iterative redundancy. We present a formal analytical analysis and an empirical analysis, demonstrate iterative redundancy on a real-world volunteer-computing system, and compare it to existing methods.
Cloud-scale SNP detection from RNA-Seq data

Next generation sequencing (NGS) is a powerful method increasing in popularity for the study of human variation and disease. This paper proposes a cloud-computing algorithm that detects SNPs from high-throughput RNA sequence data. By executing in parallel using Hadoop, it identifies SNPs by carefully considering the alignment method and sequencing errors inherent in real data. The analysis results containing the RNA reads and SNP calls can be imported directly into Sequence_Analyzer for the visualization and validation of results.
Task-at-hand interface for change detection in stock market data

Companies trading stocks need to store information on stock prices over specific time intervals, which results in very large databases. Large quantities of numerical data (thousands of records) are virtually impossible to understand quickly and require the use of a visual model, since that is the fastest way for a human brain to absorb those enormous collections of data. However, little work has been done on verifying which visualizations are more suitable to represent these data sets. Such work is of crucial importance, since it enables us to identify those useful visual models and, in addition, opens our minds to new research possibilities. This paper presents an empirical study of different visualizations, that have been employed for stock market data, by comparing the results obtained by all studied techniques in typical exploratory data analysis tasks. This work provides several research contributions to the design of advanced visual data exploration interfaces.
Analysis and design of curved support structures

Curved beams along freeform skins pose many challenges, not least on the level of basic geometry. A prototypical instance of this is presented by the glass facades of the Eiffel tower pavilions, and the interrelation between the differential-geometric properties of the glass surface on the one hand, and the layout of beams on the other hand. This paper discusses how curved beams are represented by developable surfaces, and studies geometric facts relevant to beam placement along guiding surfaces. Surprisingly, many of the curves which are interesting from the viewpoint of pure geometry (geodesics, principal curves, etc.) occur in this context too. We discuss recent advances in the modeling of developable surfaces, and show how they permit the interactive design of arrangements of curved beams, in particular the design of so-called geometric support structures.
Asynchronous Congestion Control in Multi-Hop Wireless Networks With Maximal Matching-Based Scheduling

We consider a multi-hop wireless network shared by many users. For an interference model that constrains a node to either transmit to or receive from only one other node at a time, and not to do both, we propose an architecture for fair resource allocation that consists of a distributed scheduling algorithm operating in conjunction with an asynchronous congestion control algorithm. We show that the proposed joint congestion control and scheduling algorithm supports at least one-third of the throughput supportable by any other algorithm, including centralized algorithms.
Methicillin-Susceptible and Methicillin-Resistant Staphylococcus aureus from the Retail Meat Shops and Customers

Antibiotic resistance and its exchange to different microorganisms are turning into a rising and serious pattern in developing countries like India. Community related sources are one such source that are imperative in harboring and dissemination of drug resistant microorganisms like S. aureus. S. aureus is universal in nature and ordinarily present on the skin and mucous membrane of animal and human, in soil and water (Irlinger 2008). It is likewise an imperative food-borne pathogen (Morgan 2008). In spite of the fact International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 04 (2019) Journal homepage: http://www.ijcmas. com
The aging episodic memory and factors which influence it : an electrophysiological investigation

XVII I AGING AND EPISODIC MEMORY CHANGES . 1 1.1 Declarative memory across the lifespan . 1 1.2 Effects of aging on episodic memory . 4 1.2.1 Deficient self-initiated processing in older adults . 5 1.2. 2 Associative memory deficit in older adults . 6 1.2. 3 Two-component framework of episodic memory across the life-span . 8 1.3 The aging episodic memory from a dual-process perspective: familiarity and recollection . 9 1.3. 1 Behavioral assessment of familiarity and recollection . 9 1.3. 2 Age-associated effects on behavioral estimates of familiarity and recollection . 11 1.3. 3 The neural network of episodic memory . 14 1.3. 4 Structural changes of the aging brain. 18 1.3. 5 Functional changes of the aging brain . 19 1.3. 6 Electrophysiological estimates of familiarity and recollection . 22 1.3. 7 Dissociating familiarity from conceptual fluency . 24 1.3. 8 Absolute and relative familiarity . 27 1.3. 9 Age-related changes in the ERP correlates of episodic memory . 30 II THE CONTRIBUTION OF FAMILIARITY TO ASSOCIATIVE RECOGNITION . 33 2.1 The unitization versus domain-dichotomy view . 34 2.1.1 Manipulations of unitization and its effects on measures of familiarity . 36 2.1.2 Unitization and semantic memory . 40
Properties of Cold Dark Matter Halos at z>6

We compute the properties of dark matter halos with mass $10^{6.5}-10^9\msun$ at redshift $z=6-11$ in the standard cold dark matter cosmological model, utilizing a very high resolution N-body simulation. We find that dark matter halos in these mass and redshift ranges are significantly biased over matter with a bias factor in the range 2-6. The dark matter halo mass function displays a slope of $2.05\pm 0.15$ at the small mass end. We do not find a universal dark matter density profile. Instead, we find a significant dependence of the central density profile of dark matter halos on halo mass and epoch with $\alpha_0=0.4-1.0$; the high-mass ($M\ge 10^8\msun$) low-redshift ($z\sim 6$) halos occupy the high end of the range and low-mass ($M\sim 10^{7}\msun$) high-redshift ($z\sim 11$) halos occupy the low end. Additionally, for fixed mass and epoch there is a significant dispersion in $\alpha_0$ due to the stochastic assembly of halos. Our results fit a relationship of the form $\alpha_0=0.75((1+z)/7.0)^{-1.25}(M/10^7\msun)^{0.11(1+z)/7.0}$ with a dispersion about this fit of $\pm 0.5$ and no systematic dependence of variance correlated with environment. The median spin parameter of dark matter halos is $0.03-0.04$ but with a large lognormal dispersion of $\sim 0.4$. Various quantities are tabulated or fitted with empirical formulae.
of the 2 nd BCS-FACS Northern Formal Methods Workshop , Ilkley , 14-15 July 1997 Relaxing Property Preservation in the Refinement of Concurrent Systems

One of the major development strategies for concurrent systems suggests to start the system development from a socalled functional design of the envisaged system and to distribute/parallelize this design in subsequent development steps towards a concurrent system. In this paper we argue that this strategy is not supported by the standard state-based refinement approaches. This phenomenon is traced back to the fact that these approaches are constructed such that necessarily all temporal properties of the refined system are preserved during refinement. We explain that the key feature of a suitable refinement notion for the above strategy has to relax this strict preservation of properties. Rather than preserving all temporal properties of the refined system the required refinement notion has to support the exclusive preservation of specific properties. We present such a refinement approach and prove that the standard state-based refinement relations are particular instances of the advocated notion.
An Algorithm for Detection of DVB-T Signals Based on Their Second-Order Statistics

We propose in this paper a detection algorithm based on a cost function that jointly tests the correlation induced by the cyclic prefix and the fact that this correlation is time-periodic. In the first part of the paper, the cost function is introduced and some analytical results are given. In particular, the noise and multipath channel impacts on its values are theoretically analysed. In a second part of the paper, some asymptotic results are derived. A first exploitation of these results is used to build a detection test based on the false alarm probability. These results are also used to evaluate the impact of the number of cycle frequencies taken into account in the cost function on the detection performances. Thanks to numerical estimations, we have been able to estimate that the proposed algorithm detects DVB-T signals with an SNR of dB. As a comparison, and in the same context, the detection algorithm proposed by the 802.22 WG in 2006 is able to detect these signals with an SNR of dB.
ADAPTIVE CONTROL OF BILATERAL TELEOPERATION WITH UNSYMMETRICAL TIME-VARYING DELAYS

In this paper, adaptive control is proposed for master-slave teleoperation systems with dynamical uncertainties and unsymmetric time-varying delays in communication channels. With the technique of force-motion transformation based on impedance model, we can convert the objective of force reflection into motion synchronization. Using partial feedback linearization, the whole teleoperation dynamics including both master and slave robots are transformed into two subsystems. Then, a novel adaptive control is proposed to deal with the unsymmetric time-varying delays and the dynamical uncertainties. The stability of two subsystems is proved with LMIs (linear matrix inequalities) based on Lyapunov stability synthesis. Extensive simulations and experiments are conducted to validate the effectiveness of the proposed control and illustrate the performance under this control law.
Simulation of IP Packet Classification on Network Simulator ( NS 2 )

Packet classification means categorizing the packet into different flows based on one or more fields in its header. It is widely used in the intermediate network devices, such as router, to perform the admission control, flow queueing and appropriate networking services. This technique is widely used for firewall packet filtering and can provide required service differentiation for virtual private networks (VPNs) and quality of service (QoS) guarantees. As more and more advanced services are required in the future networks, the performance of packet classification is quite critical to the performance of the networks. There are several packet classification algorithms invented in the research area, such as linear search, HiCut, HyperCut , BDD, FDD, TCAM based methods. They all present the evaluation of their algorithms based on the real networks. However, very limited work has been done to provide the network simulation based on these algorithms.
A framework for enhanced QoS support in IEEE802.11e networks

IEEE 802.11 based WLANs have became popular, but they can only provide best effort services and so they are poorly suitable for multimedia applications. Recently IEEE 802.11e standard has been proposed to support quality of service. The new standard introduces a so-called Hybrid Coordination Function containing two medium access mechanisms: contention-based channel access and controlled channel access. In this paper we propose a novel framework to better support QoS guarantees for multimedia applications. It comprises QoS Manager, Admission Control, Enhanced Scheduler, Predictor and Feedback System. The scheduler adopted supports real-time applications, variable packet sizes and variable bit rate traffic streams. We show that this framework is suitable to be used by applications requesting Application Level Contracts which will be translated in Resource Level Contracts to the scheduler subsystem. The QoS manager component is able to dynamically manage available resources under different load conditions.
The Maturing Concept of E-Democracy: From E-Voting and Online Consultations to Democratic Value Out of Jumbled Online Chatter

ABSTRACT Early literature on e-democracy was dominated by euphoric claims about the benefits of e-voting (digital direct democracy) or continuous online citizen consultations (digital representative democracy). High expectations have gradually been replaced with more genuine approaches that aim to break with the dichotomy of traditional notions of direct and representative democracy. The ensuing question relates to the adequate design of information and communication technology (ICT) applications to foster such visions. This article contributes to this search and discusses issues concerning the adequate institutional framework. Recently, so-called Web 2.0 applications, such as social networking and Wikipedia, have proven that it is possible for millions of users to collectively create meaningful content online. While these recent developments are not necessarily labeled e-democracy in the literature, this article argues that they and related applications have the potential to fulfill the promise of breaking with the longstanding democratic trade-off between group size (direct mass voting on predefined issues) and depth of argument (deliberation and discourse in a small group). Complementary information-structuring techniques are at hand to facilitate large-scale deliberations and the negotiation of interests between members of a group. This article presents three of these techniques in more depth: weighted preference voting, argument visualization, and the Semantic Web initiative. Notwithstanding these developments, the maturing concept of e-democracy still faces serious challenges. Questions remain in political and computer science disciplines that ask about adequate institutional frameworks, the omnipresent democratic challenges of equal access and free participation, and the appropriate technological design.