Miracle-dz/UniFAR-FTUs huggingface dataset:
Biota and Biomolecules in Extreme Environments on Earth: Implications for Life Detection on Mars

The three main requirements for life as we know it are the presence of organic compounds, liquid water, and free energy. Several groups of organic compounds (e.g., amino acids, nucleobases, lipids) occur in all life forms on Earth and are used as diagnostic molecules, i.e., biomarkers, for the characterization of extant or extinct life. Due to their indispensability for life on Earth, these biomarkers are also prime targets in the search for life on Mars. Biomarkers degrade over time; in situ environmental conditions influence the preservation of those molecules. Nonetheless, upon shielding (e.g., by mineral surfaces), particular biomarkers can persist for billions of years, making them of vital importance in answering questions about the origins and limits of life on early Earth and Mars. The search for organic material and biosignatures on Mars is particularly challenging due to the hostile environment and its effect on organic compounds near the surface. In support of life detection on Mars, it is crucial to investigate analogue environments on Earth that resemble best past and present Mars conditions. Terrestrial extreme environments offer a rich source of information allowing us to determine how extreme conditions affect life and molecules associated with it. Extremophilic organisms have adapted to the most stunning conditions on Earth in environments with often unique geological and chemical features. One challenge in detecting biomarkers is to optimize extraction, since organic molecules can be low in abundance and can strongly adsorb to mineral surfaces. Methods and analytical tools in the field of life science are continuously improving. Amplification methods are very useful for the detection of low concentrations of genomic material but most other organic molecules are not prone to amplification methods. Therefore, a great deal depends on the extraction efficiency. The questions “what to look for”, “where to look”, and “how to look for it” require more of our attention to ensure the success of future life detection missions on Mars.
Pisces: Anonymous Communication Using Social Networks

The architectures of deployed anonymity systems such as Tor suffer from two key problems that limit user's trust in these systems. First, paths for anonymous communication are built without considering trust relationships between users and relays in the system. Second, the network architecture relies on a set of centralized servers. In this paper, we propose Pisces, a decentralized protocol for anonymous communications that leverages users' social links to build circuits for onion routing. We argue that such an approach greatly improves the system's resilience to attackers. A fundamental challenge in this setting is the design of a secure process to discover peers for use in a user's circuit. All existing solutions for secure peer discovery leverage structured topologies and cannot be applied to unstructured social network topologies. In Pisces, we discover peers by using random walks in the social network graph with a bias away from highly connected nodes to prevent a few nodes from dominating the circuit creation process. To secure the random walks, we leverage the reciprocal neighbor policy: if malicious nodes try to exclude honest nodes during peer discovery so as to improve the chance of being selected, then honest nodes can use a tit-for-tat approach and reciprocally exclude the malicious nodes from their routing tables. We describe a fully decentralized protocol for enforcing this policy, and use it to build the Pisces anonymity system. Using theoretical modeling and experiments on real-world social network topologies, we show that (a) the reciprocal neighbor policy mitigates active attacks that an adversary can perform, (b) our decentralized protocol to enforce this policy is secure and has low overhead, and (c) the overall anonymity provided by our system significantly outperforms existing approaches.
Exploratory study on perceived loneliness, personality traits and need for peer support among youth

The present research study entitled, “Exploratory study on perceived loneliness, personality traits and need for peer support among youth” was conducted to study perceived loneliness, personality traits, need for psychological counselling and peer support. The study was based on a sample of 400 undergraduates in the age range of 19-24 years. The sample was purposively selected from four colleges; College of Home Science, College of agriculture, College of Basic Sciences and Humanities and College of Agricultural Engineering and Technology of PAU, Ludhiana. Standardized scales like Perceived Loneliness Scale ( Jha 1971), Kundu Introversion Extraversion Inventory (Kundu 1976), Psychological Counselling Need Scale (Chouhan & Arora 2009) and Self-structured tool on Peer Support Need was used to assess the above mention variables. Results of the study indicated that females in comparison to males perceived high level of loneliness and percentage of females in comparison to males was also high. Females in first year perceived an average-high level of loneliness when compared to females in 2 nd , 3 rd and 4 th year. Loneliness is significantly associated with gender. Majority of respondents were ambiverts and felt low need of psychological counselling. Females from COA, and COBS&H expressed most need of peer support in comparison to their male counterparts. Whereas in COHSc and COAE&T large proportion of males expressed the most need of peer support in comparison to females. As per study, peer support system in the university is strongly recommended.
Studying CSSR Algorithm Applicability on NLP Tasks

CSSR algorithm learns automata representing the patterns of a process from sequential data. This paper studies the applicability of CSSR to some Noun Phrase detection. The ability of the algorithm to capture the patterns behind this tasks and the conditions under which it performs better are studied. Also, an ap- proach to use the acquired models to annotate new sentences is pointed out and, at the sight of all results, the applicability of CSSR to NLP tasks is discussed. Keywords: NLP sequential tasks, automata acquisition, Noun Phrase detection
A Learning-Based Approach for Lane Departure Warning Systems With a Personalized Driver Model

Misunderstanding of driver correction behaviors is the primary reason for false warnings of lane-departure-prediction systems. We proposed a learning-based approach to predict unintended lane-departure behaviors and chances of drivers to bring vehicles back to the lane. First, a personalized driver model for lane-departure and lane-keeping behavior is established by combining the Gaussian mixture model and the hidden Markov model. Second, based on this model, we developed an online model-based prediction algorithm to predict the forthcoming vehicle trajectory and judge whether the driver will act a lane departure behavior or correction behavior. We also develop a warning strategy based on the model-based prediction algorithm that allows the lane-departure warning system to be acceptable for drivers according to the predicted trajectory. In addition, the naturalistic driving data of ten drivers were collected to train the personalized driver model and validate this approach. We compared the proposed method with a basic time-to-lane-crossing (TLC) method and a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. Experimental results show that the proposed approach can reduce the false-warning rate to 3.13% on average at 1-s prediction time.
Privacy Theft Malware Detection with Privacy Petri Net

Privacy theft malware has become serious and challenging problem to cyber security. Previous works are based on two categories of road map, the one focuses on the outbound network traffic, the other one dives into the inside information flow. We incorporate dynamic behavior analysis with network traffic analysis and present abstract model called Privacy Petri Net (PPN) which is more applicable to various kinds of malware and more meaningful to users. We apply our approach on real world malware and the experiment result shows that our approach can effectively find categories, content, source and destination of the privacy theft behavior of the malware sample.
Lattice Structures of Precoders Maximizing the Minimum Distance in Linear Channels

This paper investigates linear precoding over nonsingular linear channels with additive white Gaussian noise, with lattice-type inputs. The aim is to maximize the minimum distance of the received lattice points, where the precoder is subject to an energy constraint. It is shown that the optimal precoder only produces a finite number of different lattices, namely perfect lattices, at the receiver. The well-known densest lattice packings are instances of perfect lattices, but are not always the solution. This is a counter-intuitive result as previous work in the area showed a tight connection between densest lattices and minimum distance. Since there are only finite many different perfect lattices, they can theoretically be enumerated offline. A new upper bound on the optimal minimum distance is derived, which significantly improves upon a previously reported bound, and is useful when actually constructing the precoders.
GMM-Free DNN Training

While deep neural networks (DNNs) have become the dominant acoustic model (AM) for speech recognition systems, they are still dependent on Gaussian mixture models (GMMs) for alignments both for supervised training and for context dependent (CD) tree building. Here we explore bootstrapping DNN AM training without GMM AMs and show that CD trees can be built with DNN alignments which are better matched to the DNN model and its features. We show that these trees and alignments result in better models than from the GMM alignments and trees. By removing the GMM acoustic model altogether we simplify the system required to train a DNN from scratch.
What Lies behind the 'Too-Small-To-Survive' Banks?

It is a common place that during financial crises, like the one started in 2007, authorities provide substantial financial support to some problem banks, whilst at the same time let several others to go bankrupt. Is this happening because some particular banks are considered important and big enough to save, whereas some others are perceived as being ‘Too-Small-To-Survive’? Is the size of banks the fundamental factor that makes authorities to treat them differently, or it is also that some banks perform poorly and are not capable of withstanding some considerable shocks whatsoever? Our study provides concrete answers to these questions thus filling part of the void in the existing literature. A short- and a long-run positive relationship between size and performance is documented regardless of the level of bank soundness (healthy vs. failed and assisted banks) under scrutiny. Importantly, we pose and lend support to the ‘Too-Small-To-Survive’ hypothesis according to which the impact of bank performance on failure probability strongly depends on size. Evidence shows that authorities tend not to save banks whose size is below some specific threshold.
Study of broad sensing system model based on the IoT with heterogeneous devices

The Internet of Things(IoT) composed of large-scale and heterogeneous devices can be considered as a broad sensing system. This paper presents a comparison, in terms of scalability, heterogeneity and query complexity, among three types of data management solutions in sensing systems - wireless sensor networks (WSN), Database Stream Management Systems (DSMS), and hybrid schemes. Based on the open source project Churkwa of Apache, a data accessing model is designed in this study. This model adopts the structure of hybrid schemes, integrates the various computing tasks with the concepts of data management, and distributes operations, such as data collection, data integration, data query, and data evaluation, to suitable layers for handling. Regarding to the inconsistency caused by the concurrency management and control of devices, FOCC algorithm is optimized in this paper, and centralized FOCC concurrency control mechanism is adopted in the model. This model provides references for the data management in the sensing system of the IoT with heterogeneous devices.
EM algorithm and varible Neighborhood Search for fitting Finite Mixture Model parameters

Finding maximum likelihood parameter values for Finite Mixture Model (FMM) is often done with the Expectation Maximization (EM) algorithm. However the choice of initial values can severely affect the time to attain convergence of the algorithm and its efficiency in finding global maxima. We alleviate this defect by embedding the EM algorithm within the variable Neighborhood Search (VNS) methaheurestic framework. Computational experiment in several problems in literature as well as some larger ones are reported.
Automatic topic identification for two-level call routing

This paper presents an approach to routing telephone calls automatically, based upon their speech content. The data consist of a set of calls collected from a customer-service center with a two-level menu, which allows jumping past the second level, and we view the routing of these calls as a topic identification problem. Our topic identifier employs a multinomial model for keyword occurrences. We describe the call routing task in detail, discuss the multinomial model, and present experiments which investigate several issues that arise from using the model for this task.
THE KNOWLEDGE OF WOOD GAS PURIFICATION BY ADSORPTION CARTRIDGE FILTERS FOLLOW UP WITH ITS APPLICATION IN COMBUSTION ENGINES. POZNATKY Z ČISTENIA DREVNÉHO PLYNU ADSORPČNÝMI NÁPLŇOVÝMI FILTRAMI V NÁVÄZNOSTI NA JEHO VYUŽITIE V SPAĽOVACÍCH MOTOROCH

The article deals with a highly actual theme of wood biomass exploitation for energy purposes, specifically problems of wood gas production and purification systems in relation to the requirements of combustion engines. Wood matter gasification represents one of the ways of alternative exploitation of wood matter in power engineering. The paper deals with filter efficiency and effectiveness evaluation by means of adsorption cartridge filters. The evaluation was carried out on a basis of determination of the ingoing and outgoing change of solid particles concentration in wood gas during a continuous 4 – hours loading. The evaluating criterion was an acceptable value of contaminants in wood gas for its utilization in combustion engines. Abstrakt Clanok sa venuje vysoko aktualnej teme vyuţivania drevnej biomasy pre energeticke ucely, konkretne problematike produkcie drevneho plynu a systemom jeho cistenia vzhľadom na poţiadavky spaľovacich motorov. Splynovanie drevnej hmoty predstavuje jednu z ciest alternativneho vyuţivania drevnej hmoty v energetike. Prispevok sa zaobera hodnotenim filtracnej ucinnosti a efektivnosti cistenia drevneho plynu pomocou adsorpcných naplňových filtrov. Hodnotenie prebiehalo na zaklade zisťovania zmien vstupnej a výstupnej koncentracie tuhých castic obsiahnutých v drevnom plyne pocas 4-hodinoveho zaťaţenia. Hodnotiacim kriteriom bol pripustný obsah kontaminantov v drevnom plyne pre jeho pouţitie v spaľovacom motore.
String-Based Semantic Web Data Management Using Ternary B-Trees

The Resource Description Framework (RDF) stems from the Semantic Web but can also be regarded simply as a data model, independent of its origins. Its simple structure is ideal for describing and merging heterogeneous data from different sources quickly, without having to design a complex schema first. The different nature of RDF requires new approaches for data management and query processing and entails new problems when it comes to efficiency and scalability. Current solutions rely on string mapping and extensive indexing, but concentrate on a subset of the RDF query language SPARQL. They also ignore inherent properties residing in RDF itself, particularly its string characteristics. In this work we propose the ternary B-tree as a new data structure for storing and accessing RDF. It is string-based, making use of the intrinsic features of RDF. Strings are decomposed into fixed-size elements and organized as tree nodes. This approach permits new ways of filtering RDF, joining query results, partitioning for different orderings, and for parallelization.
Asparagos96 and the traveling salesman problem

The paper describes a spatially structured evolutionary algorithm being applied to the symmetric and asymmetric traveling salesman problem (TSP). This approach shows that a genetic algorithm with high degree of isolation-by-distance in combination with a simple repairing mechanism is able to find high quality solutions for the TSP. The evolutionary part of the algorithm presented differs from the original version of Asparagos in the choice of the topological pattern being now a ring structure and the support of hierarchy. The application part in contrast has been revised in more depth. A new representation which the author calls the bi-directional array representation is used for the TSP. This representation is invariant concerning the starting point of a tour and allows the realization of a k-Opt move in O(1). The crossover operator MPX is slightly modified in the sense that if there are differing edges in the parent tours, it is now guaranteed that at least one differing edge will occur in the offspring's tour. The mutation operator has been exchanged by the double-bridge 4-Opt move. The complexity of Asparagos96 for the symmetric TSP is O(N/sup 1.1/); for the asymmetric TSP it is O(N).
Internet-Scale Storage Systems under Churn -- A Study of the Steady-State using Markov Models

Content storage in a distributed collaborative environment uses redundancy for better resilience and thus provides good availability and durability. In a peer-to-peer environment, where peers continuously leave and rejoin the network, various lazy strategies can be employed to maintain a minimal redundancy of stored content in the system. Existing static resilience analyses fail to capture in detail the system's behavior over time, particularly the probability mass function of the actual available redundancy, since it ignores the crucial interplay between churn and maintenance operations, and looks only at the average system property. We perform a Markovian time-evolution analysis of the system specified by probability mass function of each possible system state, and establish that given a fixed rate of churn and a specific maintenance strategy, the system operates in a corresponding steady-state (dynamic equilibrium). Understanding the behavior of the system under such a dynamic equilibrium is a fundamental ingredient to precisely evaluate analytically the system's performance and availability as well as to determine the required operational maintenance cost. We also propose a new randomized variant of a lazy-maintenance scheme which has significant performance benefits in comparison to the existing deterministic procrastination based maintenance. We demonstrate the use of our analysis methodology in comparing performance of maintenance schemes using the examples of the new maintenance scheme we propose and the erstwhile best known existing lazy maintenance scheme. The comparative study shows that our randomized lazy maintenance strategy has substantially better resilience at same maintenance cost
Low-cost multiple degrees-of-freedom optical tracking for 3D interaction in head-mounted display virtual reality

Interacting with the 3D content present in games and virtual environments generally involves some form of 3D interaction. As such, the design and development of 3D spatial interaction devices are important in creating more realistic and immersive user experiences in 3D virtual environment applications through natural and intuitive human expression. In general, current commercially available 3D input devices for virtual reality applications like data gloves, multiple DOF sensors and trackers, etc. typically come with a heavy price tag. The objective of this research is to investigate an approach to setting up an inexpensive 6-DOF optical tracking system using Wii Remotes, which is adequate for 3D interaction in an interactive Head-Mounted Display (HMD) virtual reality system. For the purpose of HMD virtual reality, a user should ideally be able to use a 3D interaction device in a space surrounding the user. This cannot be achieved when using this game controller in the conventional manner. Also, normal usage of the controller only allows for relative positioning and cannot reliably track 6-DOF. This paper outlines a method of using Wii Remotes for 3D spatial interaction in an area surrounding the user. This paper also presents experimental results conducted in order to benchmark the accuracy of the system, by comparing the system's position and orientation estimates with the readings obtained from a commercial 6-DOF magnetic tracker.
Detecting Outliers in the Estimator Bank-Based Direction Finding Techniques using the Likelihood Ratio Quality Assessment

Estimator bank-based direction finding techniques make use of a number of parallel randomly weighted MUSIC direction-of-arrival (DOA) estimates and obtain the final estimate by keeping only the "successful" candidates while sorting out the outlying estimates. In this paper, we develop a powerful approach to detect the outliers in the estimator bank-based direction finders using the likelihood ratio quality assessment. Computer simulations show substantial improvements of the proposed approach as compared to the earlier techniques used to sort out the outliers in the estimator bank-based direction finding methods.
Multivehicle Cooperative Local Mapping: A Methodology Based on Occupancy Grid Map Merging

Local mapping is valuable for many real-time applications of intelligent vehicle systems. Multivehicle cooperative local mapping can bring considerable benefits to vehicles operating in some challenging scenarios. In this paper, we introduce a method of occupancy grid map merging, dedicated to multivehicle cooperative local mapping purpose in outdoor environments. In a general map merging framework, we propose an objective function based on occupancy likelihood and provide some concrete procedures designed in the spirit of genetic algorithm to optimize the defined objective function. Based on the introduced method, we further describe a strategy of indirect vehicle-to-vehicle (V2V) relative pose (RP) estimation, which can serve as a general solution for multivehicle perception association. We present a variety of experiments that validate the effectiveness of the proposed occupancy grid map merging method. We also demonstrate several useful application examples of the indirect V2V RP estimation strategy.
Adolescents’ Relationship between Physical Education and Longitudinal Physical Activity Trends

With physical education (PE) being an avenue to be physically active and learn about health and wellbeing, it is important to understand enrollment trends and physical activity (PA) behaviors among adolescents. The purpose of this study is to examine adolescents’ health profiles and gain an understanding of adolescents’ perspective of PE. The current study used mixed methodology to examine adolescents’ health profiles and gain understanding of their perspectives of PE. Part 1 identified relationships over a two-year period between: PE rating, physical activity (PA), and health variables using data from the National Longitudinal Survey of Children and Youth (NLSCY). Part 2 examined adolescents’ perspectives of PE through four focus groups.Overall, PE was preferred over other subjects by 78% of participants; and preferring PE predicted higher frequencies of PA, lower BMI, and higher self-esteem. Enrollment in high school PE was influenced by the environment, gender, course conflicts, and teacher influence. In summary, the majority of adolescents prefer PE; it has an influence on health, and is an avenue for PA. Continued efforts need to be made to increase PE enrollment and participation to ensure the health of young people.