Miracle-dz/UniFAR-FTUs huggingface dataset:
On Dynamic Optimization of Packet Matching in High-Speed Firewalls

Packet matching plays a critical role in the performance of many network devices and a tremendous amount of research has already been invested to come up with better optimized packet filters. However, most of the related works use deterministic techniques and do not exploit the traffic characteristics in their optimization schemes. In addition, most packet classifiers give no specific consideration for optimizing packet rejection, which is important for many filtering devices like firewalls. Our contribution in this paper is twofold. First, we present a novel algorithm for maximizing early rejection of unwanted flows with minimal impact on other flows. Second, we present a new packet filtering dynamic optimization technique that uses statistical search trees to utilize traffic characteristics and minimize the average packet matching time. The proposed techniques timely adapt to changes in the traffic conditions by performing simple calculations for optimizing the search data structure. Our techniques are practically attractive because they exhibit simple-to-implement and easy-to-deploy algorithms. Our extensive evaluation study using Internet traces shows that the proposed techniques can significantly minimize the packet filtering time with reasonable memory space requirements
An experimental study on social anchoring cege

The anchoring-and-adjustment heuristic has been studied in numerous experimental settings and is increasingly drawn upon to explain systematically biased decisions in economic areas as diverse as auctions, real estate pricing, sports betting and forecasting. In these cases, anchors result from publicly observable and aggregated decisions of other market participants. However, experimental studies have neglected this social dimension by focusing on external, experimenter-provided anchors in purely individualistic settings. We present a novel experimental design with a socially derived anchor, monetary incentives for unbiased decisions and feedback on performance to more accurately implement market conditions. Despite these factors, we find robust effects for the social anchor, an increased bias for higher cognitive load, and only weak learning effects. Finally, a comparison to a neutral, external anchor shows that the social context increases the bias, which we ascribe to conformity pressure. Our results support the assumption that anchoring remains a valid explanation for systematically biased decisions within market contexts.
SARS‐unique fold in the Rousettus bat coronavirus HKU9

The coronavirus nonstructural protein 3 (nsp3) is a multifunctional protein that comprises multiple structural domains. This protein assists viral polyprotein cleavage, host immune interference, and may play other roles in genome replication or transcription. Here, we report the solution NMR structure of a protein from the “SARS‐unique region” of the bat coronavirus HKU9. The protein contains a frataxin fold or double‐wing motif, which is an α + β fold that is associated with protein/protein interactions, DNA binding, and metal ion binding. High structural similarity to the human severe acute respiratory syndrome (SARS) coronavirus nsp3 is present. A possible functional site that is conserved among some betacoronaviruses has been identified using bioinformatics and biochemical analyses. This structure provides strong experimental support for the recent proposal advanced by us and others that the “SARS‐unique” region is not unique to the human SARS virus, but is conserved among several different phylogenetic groups of coronaviruses and provides essential functions.
Context awareness and priority control for ITS based on automatic speech recognition

Bringing rapid assistance to motorists involved in a traffic accident is an important service to be provided by Intelligent Transportation System (ITS). Existing proposals to automatic accident detection are based on the vehicle's perception point of view. This paper introduces situational awareness based on the "understandin" of conversational speech of drivers/passengers using an automatic speech recognition (ASR) system. Context-aware priority control and congestion control schemes are presented to ensure coexistence of ASR-triggered applications and cooperative awareness messages (CAM) in the IEEE 802.11p system. Finally, application risk analysis and performance evaluations of ASR and V2X communications are carried out.
Quantitative analysis of attention and detection signals during visual search.

Prior work has distinguished regions in the intraparietal sulcus (IPs) and frontal eye field (FEF) involved in the voluntary control of attention, from more ventral regions in the temporoparietal junction (TPJ) involved in target detection. The present results show that when subjects search for and detect a visual target stimulus among nontargets, these regions show sensory-, search-, and detection-related signals that both confirm and refine these functional distinctions. The different signals were isolated by an additive model that accounted for a large fraction of BOLD (blood oxygenation level-dependent) signal modulation over the brain. Both IPs and FEF were activated during search through nontargets, consistent with a role in maintaining attention-related signals during search. However, unlike FEF, IPs also showed stimulus-related activations, and may combine signals related to sensory and task-dependent components of salience. Although IPs-FEF showed search-related activations, the TPJ was deactivated during search. TPJ activations were confined to detection-related signals. These results provide a much stronger dissociation between the TPJ and IPs-FEF than previous work, while indicating functional differences between frontal and parietal regions that are often coactivated in studies of attention. Finally, continuous flow models of information processing predict that during search, signals from missed targets should be fed from sensory to associative regions rather than being gated by the decision criterion. Correspondingly, missed targets significantly activated parietal (e.g., right TPJ) and frontal (e.g., anterior insula, anterior cingulate) regions, although with a smaller magnitude than detected targets. Surprisingly, many cortical regions showed equivalent signals from detected targets and the completion of target-absent trials, reflecting a widespread signal unrelated to motor execution.
Bricolage Forever!

According to constructivism, learning takes place by constructing cognitive structures. An experiment was carried out to uncover the mental models of experienced, but not professional, users of a word processor. The study shows that the mental models are superficial, and that bricolage (trial-and-error) is extensively used even though it is not viable for solving nontrivial problems. The conclusion is that conceptual models must be explicitly taught contrary to the task-oriented approach of minimalism.
Comparative Analysis of some Low-Complexity Blind Synchronization Algorithms for IR-UWB Systems

Ultra wideband (UWB) radio has emerged as anattractive candidate for short-range wireless communicationsin recent years due to its unique features. However,implementation of UWB radios in order to utilize these features is coupled with some pronounced design challenges.To achieve precise timing synchronization is one of the most crucial task among them and is a key factor to ensure a reliable performance of such systems. In this paper,we present an overview and comparative study of three synchronization algorithms which provide efficient performance under unique signal and channel characteristics of UWB. These algorithms employ cross-correlation, PN code matching and energy detection operations respectively to achieve synchronization. The performance is then analyzed by numerical results under realistic propagation environment that takes into account the severe multipath and interframe interference (IFI). Also, hardware complexity and acquisition speeds of these algorithms are discussed.
Choosing the Metric in High-Dimensional Spaces Based on Hub Analysis

To avoid the undesired effects of distance concentration in high-dimensional spaces, previous work has already advocated the use of fractional p norms instead of the ubiquitous Euclidean norm. Closely re- lated to concentration is the emergence of hub and anti-hub objects. Hub objects have a small distance to an exceptionally large number of data points while anti-hubs lie far from all other data points. The contribution of this work is an empirical examination of concentration and hubness, re- sulting in an unsupervised approach for choosing an p norm by minimizing hubs while simultaneously maximizing nearest neighbor classification.
End-node-based congestion-aware network load balancing

Streaming services and visual communication services delivered over the Internet have become popular in recent years. In the future, broadband services using MPEG2/4 will become the dominant type. These services require transport protocols that provide high quality and high throughput from end to end of the system. We propose a new transfer method that allows the network load to be adaptively balanced according to the network's state. We also estimated the effectiveness of the proposed scheme by computer simulation.
A New Framework for Distributed Boosting Algorithm

In this paper, we propose a new framework for building boosting classifier on distributed databases. The main idea of our method is to utilize the parallelism of distributed databases. At each round of the algorithm, each site processes its own data locally, and calculates all needed information. A center site will collect information from all sites and build the global classifier, which is then a classifier in the ensemble. This global classifier is also used by each distributed site to compute required information for the next round. By repeating this process, we will have an ensemble of classifier from distributed database that is almost identical to the one built on the whole data. The experiment results show that the accuracy of our proposed method is almost equal to the accuracy when applying boosting algorithm to the whole dataset.
Understanding Behaviors that Lead to Purchasing: A Case Study of Pinterest

Online e-commerce applications are becoming a primary vehicle for people to find, compare, and ultimately purchase products. One of the fundamental questions that arises in e-commerce is to characterize, understand, and model user long-term purchasing intent, which is important as it allows for personalized and context relevant e-commerce services. In this paper we study user activity and purchasing behavior with the goal of building models of time-varying user purchasing intent. We analyze the purchasing behavior of nearly three million Pinterest users to determine short-term and long-term signals in user behavior that indicate higher purchase intent. We find that users with long-term purchasing intent tend to save and clickthrough on more content. However, as users approach the time of purchase their activity becomes more topically focused and actions shift from saves to searches. We further find that purchase signals in online behavior can exist weeks before a purchase is made and can also be traced across different purchase categories. Finally, we synthesize these insights in predictive models of user purchasing intent. Taken together, our work identifies a set of general principles and signals that can be used to model user purchasing intent across many content discovery applications.
Passwords you'll never forget, but can't recall

We identify a wide range of human memory phenomena as potential certificates of identity. These "imprinting" behaviors are characterized by vast capacity for complex experiences, which can be recognized without apparent effort and yet cannot be transferred to others. They are suitable for use in near zero-knowledge protocols, which minimize the amount of secret information exposed to prying eyes while identifying an individual. We sketch several examples of such phenomena[1-3], and apply them in secure certification protocols. This provides a novel approach to human-computer interfaces, and raises new questions in several classic areas of psychology.
Comprehensions, a Query Notation for DBPLs

This paper argues that comprehensions, a construct found in some programming languages, are a good query notation for DBPLs. It is shown that, like many other query notations, comprehensions can be smoothly integrated into DBPLs and allow queries to be expressed clearly, concisely and efficiently. More significantly, two advantages of comprehensions are demonstrated. The first advantage is that, unlike conventional notations, comprehension queries combine computational power with ease of optimisation. That is, not only can comprehension queries express both recursion and computation, but equivalent comprehension transformations exist for all of the major conventional optimisations. The second advantage is that comprehensions provide a uniform notation for expressing and performing some optimisation on queries over several bulk data types. The bulk types that comprehensions can be defined over include sets, relations, bags and lists. A DBPL can also be automatically extended to provide and partially optimise comprehension queries over new bulk types constructed by the application programmer, providing that the new type has some well-defined properties.
Using Linguistic Resource for Cross-Lingual Ontology Alignment

In the Semantic Web register, ontology alignment process can be seen as a cornerstone solution for the data heterogeneousness by allowing their interoperability. However, the most of the existing alignment methods assume that all ontologies to be aligned are described by identical languages. Indeed, very few approaches paid attention to the thriving challenge of multilingual ontology alignment. This paper introduces a new alignment method for multilingual ontologies. The proposed method implements a strategy of a direct alignment based on an external resource. Results obtained after extensive carried experiments are very encouraging and highlight many useful insights about the new proposed method.
Ignorability in Statistical and Probabilistic Inference

When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed by maintaining this proper distinction are often prohibitive, one asks for conditions under which it can be safely ignored. Such conditions are given by the missing at random (mar) and coarsened at random (car) assumptions. In this paper we provide an in-depth analysis of several questions relating to mar/car assumptions. Main purpose of our study is to provide criteria by which one may evaluate whether a car assumption is reasonable for a particular data collecting or observational process. This question is complicated by the fact that several distinct versions of mar/car assumptions exist. We therefore first provide an overview over these different versions, in which we highlight the distinction between distributional and coarsening variable induced versions. We show that distributional versions are less restrictive and sufficient for most applications. We then address from two different perspectives the question of when the mar/car assumption is warranted. First we provide a "static" analysis that characterizes the admissibility of the car assumption in terms of the support structure of the joint probability distribution of complete data and incomplete observations. Here we obtain an equivalence characterization that improves and extends a recent result by Grunwald and Halpern. We then turn to a "procedural" analysis that characterizes the admissibility of the car assumption in terms of procedural models for the actual data (or observation) generating process. The main result of this analysis is that the stronger coarsened completely at random (ccar) condition is arguably the most reasonable assumption, as it alone corresponds to data coarsening procedures that satisfy a natural robustness property.
A statistical approach to genome size evolution: Observations and explanations

Genome size evolution is a fundamental problem in molecular evolution. Statistical analysis of genome sizes brings new insight into the evolution of genome size. Although the variation of genome sizes is complicated, it is indicated that the genome size evolution can be explained more clearly at taxon level than at species level. I find that the genome size distribution for species in a taxon fits log-normal distribution. And I find a relationship between the phylogeny of life and the statistical features of genome size distributions among taxa. I observed different statistical features of genome size distributions between animal taxa and plant taxa. A log-normal stochastic process model is developed to simulate the genome size evolution. The simulation results on the log-normal distributions of genome sizes and their statistical features agree with the observations.
Do Productive Skills Improve in Content and Language Integrated Learning Contexts? The Case of Writing

This study investigates the differential effects of two learning contexts, formal instruction (FI) and content and language integrated learning (CLIL), on the written production skills of intermediate-level Catalan Spanish adolescent learners of English as a foreign language. Written samples elicited through a composition at two data collection times over one academic year were quantitatively and qualitatively assessed for complexity, accuracy, and fluency and for task fulfilment, organization, grammar, and vocabulary, respectively. Based on the findings, the superiority of CLIL cannot be confirmed: although improvement in the case of the FI + CLIL group is shown, results were only significant in the domain of accuracy.
Variation in energy and nutrient intakes among pre-school children: implications for study design.

BACKGROUND Within- and between-person variation in nutrient intakes has been characterized in adult populations, but little is known about variation in the diet of pre-school-aged children. The aim of this study was to describe dietary variations in Flemish pre-schoolers and to estimate the number of record days required for studying diet-disease associations among pre-school-aged children. METHODS Data from 3-day estimated diet records, collected in 2002-03, were used from 661 pre-school children (2.5-6.5 years) in Flanders, using parents/caregivers as a proxy. Age categories for studying differences in dietary variations between age groups were based on the age groups of the Belgian dietary recommendations (2.5-3 years and 4-6.5 years). RESULTS Overall, micronutrient intakes had smaller variance ratios than macronutrients. The largest variance ratios were found for cholesterol followed by fat, fatty acids and sodium intakes and would result in attenuated linear regression estimates of diet-disease associations in children. Within/between variance ratios were > or =1 for most nutrients in the oldest group (4-6.5 years) of pre-school children, while <1 for most micronutrients in the youngest age group (2.5-3 years), resulting in fewer days required for this youngest age group in comparison with the oldest group. No consistent differences in variance components were found between genders. Overall, 7-day dietary records were sufficient for accurately estimating 15 of the 23 nutrients in both age groups. CONCLUSION The number of record days required for reliably classifying pre-school children raises with increasing age category (from 2.5-3 years to 4-6.5 years) for most nutrients and varies from 3 or 4 days for some nutrients like carbohydrates to 2 or 3 weeks for others like dietary cholesterol or monounsaturated fatty acids.
Towards Reasoning and Coordinating Action in the Mental Space

Unlike a purely reactive system where the motor output is exclusively controlled by the actual sensory input, a cognitive system must be capable of running mental processes which virtually simulate action sequences aimed at achieving a goal. The mental process either attempts to find a feasible course of action compatible with a number of constraints (Internal, Environmental, Task Specific etc) or selects it from a repertoire of previously learned actions, according to the parameters of the task. If neither reasoning process succeeds, a typical backup strategy is to look for a tool that might allow the operator to match all the task constraints. This further necessitates having the capability to alter ones own goal structures to generate sub-goals which must be successfully accomplished in order to achieve the primary goal. In this paper, we introduce a forward/inverse motor control architecture (FMC/IMC) that relaxes an internal model of the overall kinematic chain to a virtual force field applied to the end effector, in the intended direction of movement. This is analogous to the mechanism of coordinating the motion of a wooden marionette by means of attached strings. The relaxation of the FMC/IMC pair provides a general solution for mentally simulating an action of reaching a target position taking into consideration a range of geometric constraints (range of motion in the joint space, internal and external constraints in the workspace) as well as effort-related constraints (range of torque of the actuators, etc.). In case, the forward simulation is successful, the movement is executed; otherwise the residual "error" or measure of inconsistency is taken as a starting point for breaking the action plan into a sequence of sub actions. This process is achieved using a recurrent neural network (RNN) which coordinates the overall reasoning process of framing and issuing goals to the forward inverse models, searching for alternatives tools in solution space and formation of sub-goals based on past context knowledge and present inputs. The RNN + FMC/IMC system is able to successfully reason and coordinate a diverse range of reaching and grasping sequences with/without tools. Using a simple robotic platform (5 DOF Scorbot arm + Stereo vision) we present results of reasoning and coordination of arm/tool movements (real and mental simulation) specifically directed towards solving the classical 2-stick paradigm from animal reasoning at a non linguistic level.
Animating facial expressions

Recognition and simulation of actions performable on rigidly-jointed actors such as human bodies have been the subject of our research for some time. One part of an ongoing effort towards a total human movement simulator is to develop a system to perform the actions of American Sign Language (ASL). However, one of the “channels” of ASL communication, the face, presents problems which are not well handled by a rigid model. An integrated system for an internal representation and simulation of the face is presented, along with a proposed image analysis model. Results from an implementation of the internal model and simulation modules are presented, as well as comments on the future of computer controlled recognition of facial actions. We conclude with a discussion on extensions of the system, covering relations between flexible masses and rigid (jointed) ones. Applications of this theory into constrained actions, such as across rigid nonmoving sheets of bone (forehead, eyes) are also discussed.