Miracle-dz/UniFAR-FTUs huggingface dataset:
Using memory to reduce the information overload in a university digital library

In the recent times the amount of information coming overwhelms us, and because of it we have serious problems to access to relevant information, that is, we suffer information overload problems. Recommender systems have been applied successfully to avoid the information overload in different scopes, but the number of electronic resources daily generated keeps growing and the problem still remain. Therefore, we find a persistent problem of information overload. In this paper we propose an improved recommender system to avoid the persistent information overload found in a University Digital Library. The idea is to include a memory to remember selected resources but not recommended to the user, and in such a way, the system could incorporate them in future recommendations to complete the set of filtered resources, for example, if there are a few resources to be recommended or if the user wishes output obtained by combination of resources selected in different recommendation rounds.
Direct Word Sense Matching for Lexical Substitution

This paper investigates conceptually and empirically the novel sense matching task, which requires to recognize whether the senses of two synonymous words match in context. We suggest direct approaches to the problem, which avoid the intermediate step of explicit word sense disambiguation, and demonstrate their appealing advantages and stimulating potential for future research.
Novel pathways for biosynthesis of nucleotide-activated glycero-manno-heptose precursors of bacterial glycoproteins and cell surface polysaccharides.

glycero-manno-Heptose is widely present in the lipopolysaccharide (LPS) of most Gram-negative bacteria. This carbohydrate and its derivatives are sometimes also found in capsules and O antigens as well as in the glycan moieties of bacterial cell surface (S-layer) glycoproteins. Gram-negative bacteria with a heptoseless LPS are highly sensitive to hydrophobic substances and they also display reduced virulence in animal infection models. Thus heptose biosynthetic genes and their products are potentially attractive targets for developing novel antimicrobial compounds. Recent work in our laboratories has elucidated the complete pathways for biosynthesis of the nucleotide-activated heptosyl precursors (Fig. 1), which are utilized in the assembly of the LPS inner-core oligosaccharide and a bacterial S-layer glycoprotein (Kneidinger et al., 2001, 2002). We review the state of the art of these biosynthesis pathways, particularly regarding the enzyme functions involved in the various steps of the biosynthesis of ADP--glycero--mannoheptose and GDP--glycero--manno-heptose. We also provide a comparative genetic analysis of the biosynthetic genes in sequenced bacterial genomes.
Faults and unbalance forces in the switched reluctance machine

The author identifies and analyzes a number of severe fault conditions that can arise in the switched reluctance machine from the electrical and mechanical points of view. It is shown how the currents, torques, and forces may be estimated, and examples are included showing the possibility of large lateral forces on the rotor. The methods used for analysis include finite element analysis, magnetic circuit models, and experiments on a small machine specially modified for the measurement of forces and magnetization characteristics when the rotor is off-centered. The author also describes a computer program (PC-SRD Dynamic) which is used for simulating operation under fault, as well as normal, conditions. The author discusses various electrical configurations of windings and controller circuits, along with methods of fault detection and protective relaying. <<ETX>>
FlowTrApp: An SDN based architecture for DDoS attack detection and mitigation in data centers

Distributed Denial of Service attack (DDoS) is one of the severe security problems in data centers. In present times, data center operators adopt several hardware based dedicated measures for detection and mitigation of such attacks. It is a challenging task always to detect and mitigate DDoS attacks completely. Software Defined Network (SDN) provides a central control over the network which helps in getting the global view of the network. In this paper, we propose an SDN framework for data centers named FlowTrApp which performs DDoS detection and mitigation using some bounds on two per flow based traffic parameters i.e., flow rate and flow duration of a flow. It attempts to detect attack traffic ranging from low rate to high rate and long lived to short lived attacks using an SDN engine consisting of sFlow based flow analytics engine sFlow-RT and an OpenFlow controller. The proposed framework of FlowTrApp has been implemented in mininet emulator which outperforms an OpenFlow based QoS approach for DoS attack mitigation.
Atmospheric Chemistry and Physics Technical Note: a New Comprehensive Scavenging Submodel for Global Atmospheric Chemistry Modelling

We present the new scavenging scheme SCAV, simulating the removal of trace gases and aerosol particles by clouds and precipitation in global atmospheric chemistry models. The scheme is quite flexible and can be used for various purposes, e.g. long term chemistry simulations as well as detailed cloud and precipitation chemistry calculations. The presence of clouds can substantially change the chemical composition of the atmosphere. We present a new method of mechanistically coupling gas phase, aerosol, cloud and precipitation chemistry, which enables studies of feedbacks between multiphase chemistry and transport processes.
Determining the operational limits of an anomaly-based intrusion detector

Anomaly-detection techniques have considerable promise for two difficult and critical problems in information security and intrusion detection: detecting novel attacks, and detecting masqueraders. One of the best-known anomaly detectors used in intrusion detection is stide. (Rather than STIDE or Stide or s-tide, we have chosen "stide" in keeping with the way the detector was referred to in the paper by Warrender et al., 1999.) Developed at the University of New Mexico, stide aims to detect attacks that exploit processes that run with root privileges. The original work on stide presented empirical results indicating that data sequences of length six and above were required for effective intrusion detection. This observation has given rise to the long-standing question, "why six? " accompanied by related questions regarding the conditions under which six may (not) be appropriate. This paper addresses the "why six" issue by presenting an evaluation framework for mapping out stide's effective operating space and by identifying conditions that contribute to detection capability, particularly detection blindness. A theoretical justification explains the effectiveness of sequence lengths of six and above, as well as the consequences of using other values. In addition, results of an investigation are presented, comparing stide's anomaly-detection capabilities with those of a competing detector.
Cytoskeleton Regulation of Glycine Receptor Number at Synapses and Diffusion in the Plasma Membrane

Lateral diffusion of neurotransmitter receptors in and out of synapses has been postulated as a core mechanism for rapid changes in receptor number at synapses during plastic processes. In this study, we have used single particle tracking to investigate how changes in glycine receptor (GlyR) lateral diffusion properties might account for changes in receptor number at synapses after disruption of the cytoskeleton in dissociated spinal cord neurons. We found that pharmacological disruption of F-actin and microtubules decreased the amount of GlyR and gephyrin, the backbone of the inhibitory postsynaptic scaffold, at synapses. F-actin and microtubule disruption increased GlyR exchanges between the synaptic and extrasynaptic membranes and decreased receptor dwell time at synapses. GlyR lateral diffusion was predominantly controlled by microtubules in the extrasynaptic membrane and by actin at synapses. Both diffusion coefficients and confinement at synapses were affected after F-actin disruption. Our results indicate that receptor exchanges between the synaptic and extrasynaptic compartments depend on the properties of both the postsynaptic differentiation and the extrasynaptic membrane. Consequently, GlyR number at synapses may be rapidly modulated by the cytoskeleton through the regulation of lateral diffusion in the plasma membrane and of receptor stabilization at synapses.
Digital sound synthesis of string vibrations with physical and psychoacoustic models

The digital synthesis of musical sounds is a field with many applications in digital audio, computer music, and virtual environments. One family of sound synthesis methods is physical modeling, which is based on digital implementations of vibrating bodies. The physical model is expressed by partial differential equations which describe the dynamic behavior of strings, membranes, plates, and other resonators. Various methods can be used to convert a partial differential equation into a discrete- time model. The so-called functional transformation method expands the differential operators into their eigenfunctions and expresses the effect of each eigenfunction by a simple digital model. The numerical expense grows linearly with the number of considered eigenfunctions and can be quite large for 2D or 3D vibrating bodies. This contribution describes a method to reduce the number of basic digital models by considering only a representative subset of eigenfunctions. The selection of the eigenfunctions is based on the well-known concept of critical bands. Audio examples show that the number of relevant eigenfunctions can be considerably reduced without impairing the timbral quality of the synthesized sounds.
Performance improvement of HEVC using Adaptive Quantization

High Efficiency Video Coding (HEVC) is the most recent video compression standard that achieves higher encoding efficiency over earlier popular standards like MPEG-2 and H.264/AVC. By adopting a variety of coding efficiency enhancement and parallel processing tools, HEVC is in a position to provide up to 50% more bit-rate reduction over its precursor H.264/AVC. In an HEVC encoder, transforms are applied to the residual signal resulting from inter or intra-frame prediction. The transformed coefficients in a Transform Unit (TU) are then equally quantized based on the value of Quantization Parameter (QP) to provide the input for the entropy encoder. The quantization process does not consider the directional bias on the energy distribution of transformed coefficients and the scanning pattern that follows the quantization stage. This paper presents an Adaptive Quantization technique that adjusts the level of quantization based on intra prediction mode, type of component(luma or chroma) and the block size. The proposed method provides an average BD rate reduction of -0.35% and -0.46% along with a BD PSNR improvement of 0.065dB and 0.038dB for Intra Main and Intra Main 10 configurations respectively, without Rate Distortion Optimized Quantizer (RDOQ).
Cross-layer based congestion control for WLANs

Congestion control is a fundamental mechanism for the stability of the Internet and is a central mechanism for TCP. However, this congestion control mechanism focuses mainly on the core network state and is blind to the characteristics of wireless and mobile access networks. Moreover, TCP window based congestion control ignores totally application layer QoS needs and entails throughput variations which are not compliant with application layer QoS constraints such as bandwidth, delay and jitter. The TCP-Friendly Rate Control protocol (TFRC) was originally designed in the context of wired networks. This protocol is one of the most convincing attempt to provide a congestion control mechanism adapted to multimedia flows, although limited in its capacity to fully address these issues. After an identification and evaluation of the subtle counterproductive interactions between the WLANs MAC layer and the transport layer, this paper shows a new approach towards congestion control for WLANs. This paper also introduces a specialization of TFRC (MTFRC: Mobile TFRC), which is adapted to wireless access networks. This TFRC specialization requires only slight changes to the standard TFRC protocol. Simulation results show substantial improvements for applications over TFRC in scenarios where the bottleneck situates on the MAC layer of the mobile nodes.
Contrived Depreciation in the Presence of Resale Markets 1 Sudipta Sarangi

We consider a model of heterogeneous consumers where a monopolist engages in price discrimination by inducing lavish consumers to repeat purchase of a good that depreciates over time. Depreciation here is contrived in the sense that the monopolist chooses both the price and the point in time when the good will lose value. Conditions under which such a discriminating monopolist will earn higher pro ̄ts are identi ̄ed. We then analyze the impact of a secondary market for this good on the monopolist. Welfare considerations under alternative scenarios are also dicussed. JEL Classi ̄cation: D4.
Boosting a Biologically Inspired Local Descriptor for Geometry-free Face and Full Multi-view 3D Object Recognition

Object recognition systems relying on local descriptors are increasingly used because of their perceived robustness with respect to occlusions and to global geometrical deformations. Descriptors of this type -based on a set of oriented Gaussian derivative filters -are used in our recognition system. In this paper, we explore a multiview 3D object recognition system that does not use explicit geometrical information. The basic idea is to find discriminant features to describe an object across different views. A boosting procedure is used to select features out of a large feature pool of local features collected from the positive training examples. We describe experiments on face images with excellent recognition rate.
Parent-Teacher Concordance for DSM V Diagnosis of Attention-Deficit/Hyperactivity Disorder in a Community Based Sample in Nigeria

Objective: To examine concordance between parent and teacher DBDRS in diagnosing ADHD among pupils in Ile Ife, Nigeria.Method: This study was carried out among 1385 children aged 5-12 years. Parents and teachers of the pupils were asked to complete the DBDRS checklist and children diagnosed for ADHD and their parents were interviewed for the symptoms and impaired function. Parent-teacher concordance of ADHD symptoms was evaluated.Results: Agreement between parents and teachers DBDRS was found to be good for ADHD diagnosis and its subtypes (r = 0.525, p < 0.001). There was a statistically significant positive correlation between parents’ and teachers’ DBDRS in ADHD diagnosis: Inattention, r = 0.522, p < 0.001, hyperactivity, r = 0.572, p < 0.001, Combined, r = 0.569, p < 0.001. Conclusions: The good parent-teacher agreement implies that ADHD symptoms were noted appreciably by both parents and teachers in different settings with little bias.
The impact of the employment insurance repayment policy: nonexperimental approaches

This research examines the impact of the strengthened repayment provision under the Employment Insurance (EI) system in Canada. The provision has introduced an increase in benefit repayment rates and a reduction in the repayment threshold. Without a randomized experiment, this article uses various nonexperimental approaches, particularly matching, to estimate the policy effect. Using data from the Survey of Changes in Employment (CIE) and the Survey of Labour and Income Dynamics (SLID), the results suggest that the new repayment policy has reduced the probabilities of filing a claim among workers whose annual income is equal to or greater than $48 750. The estimated decline in the claim rate ranges between 6 and 12 percentage points, depending on the datasets. The results appear to be robust regardless of the methods of estimation.
Physical layer specification of the L-band Digital Aeronautical Communications System (L-DACS1)

L-DACS1 is the broadband candidate technology for the future L-band Digital Aeronautical Communications System (L-DACS). The flexible design of L-DACS1 allows the deployment as an inlay system in the spectral gaps between two adjacent channels used by the Distance Measuring Equipment (DME) as well as the non-inlay deployment in unused parts of the L-band. In this paper, the specification of the L-DACS1 physical layer enabling the deployment as an inlay and as a non-inlay or as a mixed inlay and non-inlay system is presented. Apart from the transmitter design, the design of the L-DACS1 receiver is addressed including methods for mitigating interference from other L-band systems. Special emphasis is put on channel estimation which has to be robust towards interference. The different proposed algorithms for channel estimation are evaluated in simulations of the overall L-DACS1 physical layer performance. The results show that L-DACS1 is capable of operating even under severe interference conditions, hence confirming the feasibility of the inlay concept.
On integrating the lexicon with the language model

The goal of this work was to develop an algorithm for the integration of the lexicon with the language model which would be computationally efficient in terms of memory requirements, even in the case of large trigram models. Two specialized versions of the algorithm for transducer composition were implemented. The first one is basically a composition algorithm that uses the precomputed set of the output labels that can be reached from a particular epsilon edge of the lexicon; the second includes an ”on the fly” implementation of the pushing of weights and output labels. Very significant memory savings were obtained with the proposed algorithms compared with the general determinization algorithm for weighted transducers.
Adaptive event prediction strategy with dynamic time window for large-scale HPC systems

In this paper, we analyse messages generated by different HPC large-scale systems in order to extract sequences of correlated events which we lately use to predict the normal and faulty behaviour of the system. Our method uses a dynamic window strategy that is able to find frequent sequences of events regardless on the time delay between them. Most of the current related research narrows the correlation extraction to fixed and relatively small time windows that do not reflect the whole behaviour of the system. The generated events are in constant change during the lifetime of the machine. We consider that it is important to update the sequences at runtime by applying modifications after each prediction phase according to the forecast's accuracy and the difference between what was expected and what really happened. Our experiments show that our analysing system is able to predict around 60% of events with a precision of around 85% at a lower event granularity than before.
Evaluation of Programming Competency Using Student Error Patterns

Computer programming is a challenging skill that students in computer science and related disciplines are expected to learn. Computer science educators and students are concerned about the failures in programming competency. Programming errors reflect various details of student conceptual understanding and programming skills developed. This paper attempts to predict the failures in programming comprehension and debugging skills based on programming errors generated by the learner. We conduct a mixed method approach with pre-post test experimental design to evaluate the Java programming competency of the learner. We also compute the error metrics and supplement the course material to improve the competency through self-learning spoken tutorial workshops. The characterization of student programming patterns helps to identify at risk students and determine specific interventions. We analyze the compilation errors, computational time and computed Error Quotients to predict the programming behaviour. Results of the study show that students have improved their programming skills and benefited from the approach. Implications of this study is also helpful to computing education practitioners, workshop organizers, content developers and reviewers to improvise the course content.
FPGA implementation of a greedy scheme for Bioinformatics applications

(1) Universita' del Sannio Research Centre on Software Technology RCOST Benevento, Italy (2) Universita' di Napoli "Federico II" Dipartimento di Scienze Fisiche and INFN Napoli, Italy Motivation In the past decade there has been an explosive growth of biological data, including genome projects, proteomics, protein structure determination, cellular regulatory mechanisms, and the rapid expansion in digitization of patient biological data. Although raw computational power follows "Moore's Law", the genomic data at GenBank (the NIH genetic sequence database, an annotated collection of all publicly available DNA sequences) is doubling every six months. Proteomic and cellular imaging data appear to grow even faster. Post-genomic-era bioinformatics will require high-performance computing power of the order of several hundreds of teraflops or more. Moreover a growing number of related problems is posed as complex optimization. Methods In recent years, FPGAs, short for Field-Programmable Gate Arrays, logic programmable chips [1] [2] have emerged as high-performance computing accelerators capable of implementing finegrained, massively parallelized versions of computationally intensive algorithms [3]. In particular several problems arising in biomedical and bioinformatics research can be viewed as finding the optimal covering of a finite set [4] [5]. While the Set Covering problem is known to be NPcomplete [6] a number of approximation heuristics have been proposed. The most efficient schema remains the greedy one [7]. Recently, a new greedy algorithm for approximating minimum set cover has been presented [8]. The algorithm, while not randomized, is based on a probability distribution that leads the greedy choice. It shows very good empirical performances and it has successfully been applied in wireless network applications [9] [10]. While efficient implementations are given, the cost of probability distribution evaluation can still be unaffordable in massive realtime applications. In this paper we describe an implementation based on a FPGA of a tailored version of the algorithm. It makes the algorithm suitable for several real world bioinformatics problems. Results The test results show very good empirical performances on the used benchmarks. The speed up of our approach is also successfully tested.
Revisiting the Memory of Evolution

A new evolution scheme is presented, memorizing the extreme (best and worst) past individuals through distributions over the binary search space. These distributions are used to bias the mutation operator in a (μ + λ) Evolution Strategy, guiding the generation of newborn offspring: different mimetic strategies are defined, combining either attraction, indifference or repulsion with respect to the two distributions. These distributions are then updated from the best and the worse individuals in the current population. Experiments on large size binary problems allow one to delineate the niche of each of these mimetic strategies.